技术领域Technical Field
本发明涉及电力数据控制技术领域，尤其涉及一种基于深度学习的电力资源响应控制方法及系统。The present invention relates to the field of power data control technology, and in particular to a power resource response control method and system based on deep learning.
背景技术Background Art
电力资源响应（Demand Response，DR）是一种基于用户需求变化调整电力消耗的技术，旨在提高电网效率、降低成本、增加可再生能源利用比例。基于深度学习的DR控制方法随着深度学习技术的发展而逐步成熟。传统的电力系统难以应对日益复杂的电力供需关系，电力资源响应成为调节电力系统的一种有效手段。然而，传统的DR方法往往基于规则和简单的模型，难以适应复杂多变的电力系统需求。因此，基于深度学习的DR控制方法应运而生。这种方法利用深度学习算法，如循环神经网络（RNN）、长短期记忆网络（LSTM）、卷积神经网络（CNN）等，对大规模数据进行学习和建模，实现了对电力系统的智能化响应。通过深度学习的特征学习和模式识别能力，可以更准确地预测用户需求、识别电力系统状态，并作出相应调整。然而，目前的现有技术通常缺乏对响应策略的实际模拟和动态调节，同时往往局限于单一数据维度的分析，无法充分挖掘数据之间的关联，进而导致系统的安全性、可靠性和效率较低。Demand Response (DR) is a technology that adjusts power consumption based on changes in user demand, aiming to improve grid efficiency, reduce costs, and increase the proportion of renewable energy utilization. DR control methods based on deep learning have gradually matured with the development of deep learning technology. Traditional power systems are difficult to cope with the increasingly complex power supply and demand relationship, and power resource response has become an effective means to regulate power systems. However, traditional DR methods are often based on rules and simple models, which are difficult to adapt to the complex and changing power system needs. Therefore, DR control methods based on deep learning have emerged. This method uses deep learning algorithms, such as recurrent neural networks (RNN), long short-term memory networks (LSTM), convolutional neural networks (CNN), etc., to learn and model large-scale data, and realize intelligent response to power systems. Through the feature learning and pattern recognition capabilities of deep learning, user demand can be more accurately predicted, the power system status can be identified, and corresponding adjustments can be made. However, current existing technologies usually lack actual simulation and dynamic adjustment of response strategies, and are often limited to the analysis of a single data dimension, and cannot fully explore the relationship between data, which leads to low system security, reliability and efficiency.
发明内容Summary of the invention
基于此，有必要提供一种基于深度学习的电力资源响应控制方法及系统，以解决至少一个上述技术问题。Based on this, it is necessary to provide a power resource response control method and system based on deep learning to solve at least one of the above technical problems.
为实现上述目的，一种基于深度学习的电力资源响应控制方法，所述方法包括以下步骤：To achieve the above object, a power resource response control method based on deep learning is provided, the method comprising the following steps:
步骤S1：获取电力系统资源数据；对电力系统资源数据进行数据预处理，生成标准电力系统资源数据；对标准电力系统资源数据进行数据湮没化，生成电力系统资源脱敏数据；Step S1: acquiring power system resource data; performing data preprocessing on the power system resource data to generate standard power system resource data; performing data annihilation on the standard power system resource data to generate power system resource desensitized data;
步骤S2：对电力系统资源脱敏数据进行多维度数据融合，生成多维度电力资源数据；对多维度电力资源数据进行时空关联，生成多维度电力资源时空关联分析数据；对多维度电力资源时空关联分析数据进行电力资源响应供应制定，从而生成电力资源响应策略；Step S2: Perform multi-dimensional data fusion on the power system resource desensitized data to generate multi-dimensional power resource data; perform spatiotemporal correlation on the multi-dimensional power resource data to generate multi-dimensional power resource spatiotemporal correlation analysis data; formulate power resource response supply for the multi-dimensional power resource spatiotemporal correlation analysis data, thereby generating a power resource response strategy;
步骤S3：根据电力资源响应策略进行电力资源模拟分配，从而获取电力资源控制模拟数据；对电力资源控制模拟数据进行控制指令生成，得到实时控制指令；根据实时控制指令进行电力动态调节，生成电力资源动态控制数据；Step S3: simulate the allocation of power resources according to the power resource response strategy, thereby obtaining power resource control simulation data; generate control instructions for the power resource control simulation data to obtain real-time control instructions; dynamically adjust power according to the real-time control instructions to generate power resource dynamic control data;
步骤S4：对电力资源动态控制数据进行性能指标评估，得到电力资源控制性能指标；将电力资源控制性能指标和预设的标准控制性能阈值进行对比，当电力资源控制性能指标小于预设的标准控制性能阈值时，则对电力资源控制性能指标进行控制性能优化，生成电力资源响应控制报告。Step S4: Evaluate the performance index of the power resource dynamic control data to obtain the power resource control performance index; compare the power resource control performance index with the preset standard control performance threshold; when the power resource control performance index is less than the preset standard control performance threshold, optimize the control performance of the power resource control performance index and generate a power resource response control report.
本发明通过从各个电力系统组件（例如发电厂、输电网、配电网等）收集原始数据，包括电力负荷、发电量、设备状态、运行参数等。提供了实时、历史和实验性数据，为系统分析和优化提供基础。帮助系统运营者更好地了解系统状况。对获取的原始数据进行清洗、去噪、缺失值处理等预处理步骤，以生成标准格式的电力系统资源数据，提高数据的质量和一致性，减少因为不规范或不准确的数据而引起的错误。标准数据有助于建立系统模型和进行分析。数据湮没化是一种隐私保护技术，通过对敏感数据进行转换或屏蔽，以保护个体的隐私信息。通过融合多个维度的数据，可以提高数据的综合性和完整性，为后续的分析提供更丰富的信息基础。时空关联分析可以帮助识别出资源之间的潜在联系和影响因素，有助于更好地理解资源的分布情况和变化趋势。通过制定合适的电力资源响应策略，可以提高电力系统的灵活性、鲁棒性和效率，使其能够更好地适应不同的需求和变化情况，从而提高系统的可靠性和稳定性。通过电力资源的模拟分配，可以预测和规划资源的使用情况，确保资源的合理利用，提高系统的效率和可靠性。通过生成实时的控制指令，可以及时调整系统运行状态，应对突发情况或优化系统性能，确保电力系统的安全稳定运行。通过电力系统的动态调节，可以实现对系统运行状态的灵活控制和调整，确保系统能够适应不同的负载变化和外部环境影响，提高系统的鲁棒性和稳定性。通过对性能指标的评估，可以全面了解电力系统的运行状况，发现潜在的问题和改进空间，为后续的优化提供基础。通过与标准控制性能阈值的对比，可以确定当前系统的运行状态是否达到预期的要求，是否需要进行进一步的优化和改进。通过控制性能的优化，可以提高系统的运行效率和性能，降低系统的故障率和维护成本，提升用户体验和服务质量。通过生成控制报告，可以清晰地记录系统的改进历程和优化效果，为未来的决策和规划提供参考依据，同时也可以向相关利益相关者沟通系统的运行状况和改进成果。因此，本发明通过数据预处理、多维度融合、模拟动态调节和性能优化，提高了电力资源响应和控制的安全性、可靠性和效率。The present invention collects raw data from various power system components (such as power plants, transmission networks, distribution networks, etc.), including power load, power generation, equipment status, operating parameters, etc. Real-time, historical and experimental data are provided to provide a basis for system analysis and optimization. Help system operators better understand the system status. The acquired raw data is cleaned, denoised, missing value processed and other pre-processing steps to generate power system resource data in a standard format, improve the quality and consistency of the data, and reduce errors caused by non-standard or inaccurate data. Standard data helps to establish system models and conduct analysis. Data annihilation is a privacy protection technology that protects individual privacy information by converting or shielding sensitive data. By integrating data from multiple dimensions, the comprehensiveness and integrity of the data can be improved, providing a richer information basis for subsequent analysis. Spatiotemporal correlation analysis can help identify potential connections and influencing factors between resources, and help to better understand the distribution and change trends of resources. By formulating appropriate power resource response strategies, the flexibility, robustness and efficiency of the power system can be improved, so that it can better adapt to different needs and changes, thereby improving the reliability and stability of the system. Through the simulation allocation of power resources, the use of resources can be predicted and planned to ensure the rational use of resources and improve the efficiency and reliability of the system. By generating real-time control instructions, the system operation status can be adjusted in time to respond to emergencies or optimize system performance to ensure the safe and stable operation of the power system. Through the dynamic regulation of the power system, the flexible control and adjustment of the system operation status can be achieved to ensure that the system can adapt to different load changes and external environmental influences and improve the robustness and stability of the system. Through the evaluation of performance indicators, the operation status of the power system can be fully understood, potential problems and improvement space can be found, and a basis for subsequent optimization can be provided. By comparing with the standard control performance threshold, it can be determined whether the current system operation status meets the expected requirements and whether further optimization and improvement are needed. Through the optimization of control performance, the system operation efficiency and performance can be improved, the system failure rate and maintenance cost can be reduced, and the user experience and service quality can be improved. By generating a control report, the system improvement process and optimization effect can be clearly recorded to provide a reference for future decision-making and planning, and the system operation status and improvement results can also be communicated to relevant stakeholders. Therefore, the present invention improves the safety, reliability and efficiency of power resource response and control through data preprocessing, multi-dimensional fusion, simulated dynamic adjustment and performance optimization.
在本说明书中，提供了一种基于深度学习的电力资源响应控制系统，用于执行上述的基于深度学习的电力资源响应控制方法，该基于深度学习的电力资源响应控制系统包括：In this specification, a power resource response control system based on deep learning is provided, which is used to execute the above-mentioned power resource response control method based on deep learning. The power resource response control system based on deep learning includes:
数据预处理模块，用于获取电力系统资源数据；对电力系统资源数据进行数据预处理，生成标准电力系统资源数据；对标准电力系统资源数据进行数据湮没化，生成电力系统资源脱敏数据；The data preprocessing module is used to obtain power system resource data; perform data preprocessing on the power system resource data to generate standard power system resource data; perform data annihilation on the standard power system resource data to generate power system resource desensitized data;
时空关联模块，用于对电力系统资源脱敏数据进行多维度数据融合，生成多维度电力资源数据；对多维度电力资源数据进行时空关联，生成多维度电力资源时空关联分析数据；对多维度电力资源时空关联分析数据进行电力资源响应供应制定，从而生成电力资源响应策略；The spatiotemporal correlation module is used to perform multi-dimensional data fusion on the power system resource desensitized data to generate multi-dimensional power resource data; perform spatiotemporal correlation on the multi-dimensional power resource data to generate multi-dimensional power resource spatiotemporal correlation analysis data; formulate power resource response supply based on the multi-dimensional power resource spatiotemporal correlation analysis data, thereby generating a power resource response strategy;
响应调整模块，用于根据电力资源响应策略进行电力资源模拟分配，从而获取电力资源控制模拟数据；对电力资源控制模拟数据进行控制指令生成，得到实时控制指令；根据实时控制指令进行电力动态调节，生成电力资源动态控制数据；The response adjustment module is used to simulate the allocation of power resources according to the power resource response strategy, so as to obtain power resource control simulation data; generate control instructions for the power resource control simulation data to obtain real-time control instructions; perform power dynamic adjustment according to the real-time control instructions to generate power resource dynamic control data;
性能控制模块，用于对电力资源动态控制数据进行性能指标评估，得到电力资源控制性能指标；将电力资源控制性能指标和预设的标准控制性能阈值进行对比，当电力资源控制性能指标小于预设的标准控制性能阈值时，则对电力资源控制性能指标进行控制性能优化，生成电力资源响应控制报告。The performance control module is used to evaluate the performance indicators of the dynamic control data of the power resources to obtain the power resource control performance indicators; compare the power resource control performance indicators with the preset standard control performance thresholds, and when the power resource control performance indicators are less than the preset standard control performance thresholds, the control performance of the power resource control performance indicators is optimized to generate a power resource response control report.
本发明的有益效果在于通过对电力资源的精确模拟和响应控制，系统的整体性能可以得到优化，提高电力系统的效率和可靠性。采用脱敏技术可以确保在处理电力系统资源数据时保持数据的隐私和安全。通过实时控制指令和动态调节，系统能够更灵活地应对电力需求的变化，提高对突发事件的响应能力。生成的电力资源响应控制报告为决策制定提供了依据，使决策者能够基于实际数据做出明智的决策，进一步提高系统的稳定性和可管理性。因此，本发明通过数据预处理、多维度融合、模拟动态调节和性能优化，提高了电力资源响应和控制的安全性、可靠性和效率。The beneficial effect of the present invention is that through accurate simulation and response control of power resources, the overall performance of the system can be optimized, and the efficiency and reliability of the power system can be improved. The use of desensitization technology can ensure that the privacy and security of data are maintained when processing power system resource data. Through real-time control instructions and dynamic adjustment, the system can respond to changes in power demand more flexibly and improve the ability to respond to emergencies. The generated power resource response control report provides a basis for decision-making, enabling decision makers to make wise decisions based on actual data, and further improving the stability and manageability of the system. Therefore, the present invention improves the safety, reliability and efficiency of power resource response and control through data preprocessing, multi-dimensional fusion, simulated dynamic adjustment and performance optimization.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1为一种基于深度学习的电力资源响应控制方法的步骤流程示意图；FIG1 is a schematic diagram of a process flow of a power resource response control method based on deep learning;
图2为图1中步骤S2的详细实施步骤流程示意图；FIG2 is a schematic diagram of a detailed implementation process of step S2 in FIG1 ;
图3为图2中步骤S25的详细实施步骤流程示意图；FIG3 is a schematic diagram of a detailed implementation process of step S25 in FIG2 ;
图4为图3中步骤S251的详细实施步骤流程示意图；FIG4 is a schematic diagram of a detailed implementation process of step S251 in FIG3 ;
本发明目的的实现、功能特点及优点将结合实施例，参照附图做进一步说明。The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings.
具体实施方式DETAILED DESCRIPTION
下面结合附图对本发明专利的技术方法进行清楚、完整的描述，显然，所描述的实施例是本发明的一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域所属的技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.
此外，附图仅为本发明的示意性图解，并非一定是按比例绘制。图中相同的附图标记表示相同或类似的部分，因而将省略对它们的重复描述。附图中所示的一些方框图是功能实体，不一定必须与物理或逻辑上独立的实体相对应。可以采用软件形式来实现功能实体，或在一个或多个硬件模块或集成电路中实现这些功能实体，或在不同网络和/或处理器方法和/或微控制器方法中实现这些功能实体。In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and/or processor methods and/or microcontroller methods.
应当理解的是，虽然在这里可能使用了术语“第一”、“第二”等等来描述各个单元，但是这些单元不应当受这些术语限制。使用这些术语仅仅是为了将一个单元与另一个单元进行区分。举例来说，在不背离示例性实施例的范围的情况下，第一单元可以被称为第二单元，并且类似地第二单元可以被称为第一单元。这里所使用的术语“和/或”包括其中一个或更多所列出的相关联项目的任意和所有组合。It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and/or" used herein includes any and all combinations of one or more of the listed associated items.
请参阅图1至图4，一种基于深度学习的电力资源响应控制方法，所述方法包括以下步骤：Referring to FIG. 1 to FIG. 4 , a power resource response control method based on deep learning is shown, and the method comprises the following steps:
步骤S1：获取电力系统资源数据；对电力系统资源数据进行数据预处理，生成标准电力系统资源数据；对标准电力系统资源数据进行数据湮没化，生成电力系统资源脱敏数据；Step S1: acquiring power system resource data; performing data preprocessing on the power system resource data to generate standard power system resource data; performing data annihilation on the standard power system resource data to generate power system resource desensitized data;
步骤S2：对电力系统资源脱敏数据进行多维度数据融合，生成多维度电力资源数据；对多维度电力资源数据进行时空关联，生成多维度电力资源时空关联分析数据；对多维度电力资源时空关联分析数据进行电力资源响应供应制定，从而生成电力资源响应策略；Step S2: Perform multi-dimensional data fusion on the power system resource desensitized data to generate multi-dimensional power resource data; perform spatiotemporal correlation on the multi-dimensional power resource data to generate multi-dimensional power resource spatiotemporal correlation analysis data; formulate power resource response supply for the multi-dimensional power resource spatiotemporal correlation analysis data, thereby generating a power resource response strategy;
步骤S3：根据电力资源响应策略进行电力资源模拟分配，从而获取电力资源控制模拟数据；对电力资源控制模拟数据进行控制指令生成，得到实时控制指令；根据实时控制指令进行电力动态调节，生成电力资源动态控制数据；Step S3: simulate the allocation of power resources according to the power resource response strategy, thereby obtaining power resource control simulation data; generate control instructions for the power resource control simulation data to obtain real-time control instructions; dynamically adjust power according to the real-time control instructions to generate power resource dynamic control data;
步骤S4：对电力资源动态控制数据进行性能指标评估，得到电力资源控制性能指标；将电力资源控制性能指标和预设的标准控制性能阈值进行对比，当电力资源控制性能指标小于预设的标准控制性能阈值时，则对电力资源控制性能指标进行控制性能优化，生成电力资源响应控制报告。Step S4: Evaluate the performance index of the power resource dynamic control data to obtain the power resource control performance index; compare the power resource control performance index with the preset standard control performance threshold; when the power resource control performance index is less than the preset standard control performance threshold, optimize the control performance of the power resource control performance index and generate a power resource response control report.
本发明通过从各个电力系统组件（例如发电厂、输电网、配电网等）收集原始数据，包括电力负荷、发电量、设备状态、运行参数等。提供了实时、历史和实验性数据，为系统分析和优化提供基础。帮助系统运营者更好地了解系统状况。对获取的原始数据进行清洗、去噪、缺失值处理等预处理步骤，以生成标准格式的电力系统资源数据，提高数据的质量和一致性，减少因为不规范或不准确的数据而引起的错误。标准数据有助于建立系统模型和进行分析。数据湮没化是一种隐私保护技术，通过对敏感数据进行转换或屏蔽，以保护个体的隐私信息。通过融合多个维度的数据，可以提高数据的综合性和完整性，为后续的分析提供更丰富的信息基础。时空关联分析可以帮助识别出资源之间的潜在联系和影响因素，有助于更好地理解资源的分布情况和变化趋势。通过制定合适的电力资源响应策略，可以提高电力系统的灵活性、鲁棒性和效率，使其能够更好地适应不同的需求和变化情况，从而提高系统的可靠性和稳定性。通过电力资源的模拟分配，可以预测和规划资源的使用情况，确保资源的合理利用，提高系统的效率和可靠性。通过生成实时的控制指令，可以及时调整系统运行状态，应对突发情况或优化系统性能，确保电力系统的安全稳定运行。通过电力系统的动态调节，可以实现对系统运行状态的灵活控制和调整，确保系统能够适应不同的负载变化和外部环境影响，提高系统的鲁棒性和稳定性。通过对性能指标的评估，可以全面了解电力系统的运行状况，发现潜在的问题和改进空间，为后续的优化提供基础。通过与标准控制性能阈值的对比，可以确定当前系统的运行状态是否达到预期的要求，是否需要进行进一步的优化和改进。通过控制性能的优化，可以提高系统的运行效率和性能，降低系统的故障率和维护成本，提升用户体验和服务质量。通过生成控制报告，可以清晰地记录系统的改进历程和优化效果，为未来的决策和规划提供参考依据，同时也可以向相关利益相关者沟通系统的运行状况和改进成果。因此，本发明通过数据预处理、多维度融合、模拟动态调节和性能优化，提高了电力资源响应和控制的安全性、可靠性和效率。The present invention collects raw data from various power system components (such as power plants, transmission networks, distribution networks, etc.), including power load, power generation, equipment status, operating parameters, etc. Real-time, historical and experimental data are provided to provide a basis for system analysis and optimization. Help system operators better understand the system status. The acquired raw data is cleaned, denoised, missing value processed and other pre-processing steps to generate power system resource data in a standard format, improve the quality and consistency of the data, and reduce errors caused by non-standard or inaccurate data. Standard data helps to establish system models and conduct analysis. Data annihilation is a privacy protection technology that protects individual privacy information by converting or shielding sensitive data. By integrating data from multiple dimensions, the comprehensiveness and integrity of the data can be improved, providing a richer information basis for subsequent analysis. Spatiotemporal correlation analysis can help identify potential connections and influencing factors between resources, and help to better understand the distribution and change trends of resources. By formulating appropriate power resource response strategies, the flexibility, robustness and efficiency of the power system can be improved, so that it can better adapt to different needs and changes, thereby improving the reliability and stability of the system. Through the simulation allocation of power resources, the use of resources can be predicted and planned to ensure the rational use of resources and improve the efficiency and reliability of the system. By generating real-time control instructions, the system operation status can be adjusted in time to respond to emergencies or optimize system performance to ensure the safe and stable operation of the power system. Through the dynamic regulation of the power system, the flexible control and adjustment of the system operation status can be achieved to ensure that the system can adapt to different load changes and external environmental influences and improve the robustness and stability of the system. Through the evaluation of performance indicators, the operation status of the power system can be fully understood, potential problems and improvement space can be found, and a basis for subsequent optimization can be provided. By comparing with the standard control performance threshold, it can be determined whether the current system operation status meets the expected requirements and whether further optimization and improvement are needed. Through the optimization of control performance, the system operation efficiency and performance can be improved, the system failure rate and maintenance cost can be reduced, and the user experience and service quality can be improved. By generating a control report, the system improvement process and optimization effect can be clearly recorded to provide a reference for future decision-making and planning, and the system operation status and improvement results can also be communicated to relevant stakeholders. Therefore, the present invention improves the safety, reliability and efficiency of power resource response and control through data preprocessing, multi-dimensional fusion, simulated dynamic adjustment and performance optimization.
本发明实施例中，参考图1所述，为本发明一种基于深度学习的电力资源响应控制方法的步骤流程示意图，在本实例中，所述一种基于深度学习的电力资源响应控制方法包括以下步骤：In an embodiment of the present invention, referring to FIG1 , which is a schematic flow chart of a method for controlling power resource response based on deep learning of the present invention, in this example, the method for controlling power resource response based on deep learning includes the following steps:
步骤S1：获取电力系统资源数据；对电力系统资源数据进行数据预处理，生成标准电力系统资源数据；对标准电力系统资源数据进行数据湮没化，生成电力系统资源脱敏数据；Step S1: acquiring power system resource data; performing data preprocessing on the power system resource data to generate standard power system resource data; performing data annihilation on the standard power system resource data to generate power system resource desensitized data;
本发明实施例中，通过定义需要的数据，包括发电量、负载需求、电网状态等。确保数据的来源可靠，可以来自传感器、监控设备、历史记录等。使用合适的通信协议和接口来实时或定期地获取数据。处理缺失值、异常值和错误数据，确保数据的一致性和准确性。将不同来源的数据进行格式标准化，以确保数据的一致性和可比性。对时间戳进行同步，以确保数据在时序上的一致性，进行单位转换或其他数据变换以满足后续分析的需要。对于涉及个体或敏感信息的数据，采用匿名化方法，例如用唯一标识符替代实际用户或设备标识。通过引入噪声或随机变化，使得具体数值难以还原，以保护隐私。使用可逆或不可逆的加密技术，如哈希函数，以保障数据隐私，生成电力系统资源脱敏数据。In an embodiment of the present invention, the required data is defined, including power generation, load demand, grid status, etc. Ensure that the source of the data is reliable, which can come from sensors, monitoring equipment, historical records, etc. Use appropriate communication protocols and interfaces to obtain data in real time or periodically. Process missing values, outliers, and erroneous data to ensure data consistency and accuracy. Standardize the formats of data from different sources to ensure data consistency and comparability. Synchronize timestamps to ensure data consistency in time series, perform unit conversion or other data transformations to meet the needs of subsequent analysis. For data involving individual or sensitive information, anonymization methods are used, such as replacing actual user or device identifiers with unique identifiers. By introducing noise or random changes, it is difficult to restore specific values to protect privacy. Use reversible or irreversible encryption techniques, such as hash functions, to protect data privacy and generate desensitized data of power system resources.
步骤S2：对电力系统资源脱敏数据进行多维度数据融合，生成多维度电力资源数据；对多维度电力资源数据进行时空关联，生成多维度电力资源时空关联分析数据；对多维度电力资源时空关联分析数据进行电力资源响应供应制定，从而生成电力资源响应策略；Step S2: Perform multi-dimensional data fusion on the power system resource desensitized data to generate multi-dimensional power resource data; perform spatiotemporal correlation on the multi-dimensional power resource data to generate multi-dimensional power resource spatiotemporal correlation analysis data; formulate power resource response supply for the multi-dimensional power resource spatiotemporal correlation analysis data, thereby generating a power resource response strategy;
本发明实施例中，通过将来自不同源头的数据整合到一个统一的数据集中，确保数据的一致性和完整性。在脱敏数据的基础上，添加额外的维度信息，例如天气数据、季节信息、区域特征等，以丰富数据的多样性。对不同维度的数据进行标准化处理，确保数据具有相似的量纲和范围。对数据进行时间序列分析，探索数据随时间变化的趋势和周期性。分析不同地区或不同资源之间的关联性，例如发电厂的产能与消费地区的负载需求之间的关系。使用数据挖掘算法和技术，如聚类、关联规则挖掘等，发现数据中隐藏的关联规律和模式。基于时空关联分析的结果，建立电力资源响应的数学模型，以预测未来的电力需求和供应情况。根据需求响应模型的输出，制定相应的电力资源响应策略，包括调整发电计划、优化能源分配、调节负载需求等。对制定的策略进行风险评估，考虑不同因素对策略实施的影响，确保策略的可行性和稳健性。In an embodiment of the present invention, data consistency and integrity are ensured by integrating data from different sources into a unified data set. On the basis of desensitized data, additional dimensional information, such as weather data, seasonal information, regional characteristics, etc., is added to enrich the diversity of data. Data of different dimensions are standardized to ensure that the data has similar dimensions and ranges. Time series analysis is performed on the data to explore the trend and periodicity of data changes over time. The correlation between different regions or different resources is analyzed, such as the relationship between the production capacity of a power plant and the load demand of a consumption area. Data mining algorithms and techniques, such as clustering and association rule mining, are used to discover the hidden association laws and patterns in the data. Based on the results of spatiotemporal association analysis, a mathematical model of power resource response is established to predict future power demand and supply. According to the output of the demand response model, a corresponding power resource response strategy is formulated, including adjusting the power generation plan, optimizing energy distribution, adjusting load demand, etc. Risk assessment is performed on the formulated strategy, considering the impact of different factors on the implementation of the strategy, and ensuring the feasibility and robustness of the strategy.
步骤S3：根据电力资源响应策略进行电力资源模拟分配，从而获取电力资源控制模拟数据；对电力资源控制模拟数据进行控制指令生成，得到实时控制指令；根据实时控制指令进行电力动态调节，生成电力资源动态控制数据；Step S3: simulate the allocation of power resources according to the power resource response strategy, thereby obtaining power resource control simulation data; generate control instructions for the power resource control simulation data to obtain real-time control instructions; dynamically adjust power according to the real-time control instructions to generate power resource dynamic control data;
本发明实施例中，通过基于电力系统的特性和响应策略，建立电力资源模拟器，模拟电力资源的产生、传输和消费过程。设定模拟过程中的参数，包括发电设备的容量、传输线路的损耗、负载需求的模式等。运行电力资源模拟器，模拟不同条件下的电力系统运行情况，生成电力资源的模拟分配数据。对电力资源控制模拟数据进行分析，了解电力系统的运行状况和资源分配情况。根据当前的系统状态和预设的控制策略，匹配相应的控制指令。根据匹配的控制策略，生成相应的实时控制指令，包括发电设备的启停、负载的调节等。将生成的实时控制指令传输到相应的设备或控制中心。设备或系统根据接收到的控制指令进行相应的调节，例如启动、停止发电机、调整输电线路负载等。记录电力系统在动态调节过程中的各项参数和状态，生成电力资源动态控制数据，用于后续分析和优化。In an embodiment of the present invention, a power resource simulator is established based on the characteristics and response strategies of the power system to simulate the generation, transmission and consumption of power resources. Parameters in the simulation process are set, including the capacity of the power generation equipment, the loss of the transmission line, the mode of load demand, etc. The power resource simulator is run to simulate the operation of the power system under different conditions and generate simulated allocation data of power resources. The power resource control simulation data is analyzed to understand the operation status and resource allocation of the power system. According to the current system state and the preset control strategy, the corresponding control instructions are matched. According to the matched control strategy, the corresponding real-time control instructions are generated, including the start and stop of the power generation equipment, the adjustment of the load, etc. The generated real-time control instructions are transmitted to the corresponding equipment or control center. The equipment or system makes corresponding adjustments according to the received control instructions, such as starting and stopping the generator, adjusting the transmission line load, etc. The various parameters and states of the power system in the dynamic adjustment process are recorded, and the dynamic control data of power resources are generated for subsequent analysis and optimization.
步骤S4：对电力资源动态控制数据进行性能指标评估，得到电力资源控制性能指标；将电力资源控制性能指标和预设的标准控制性能阈值进行对比，当电力资源控制性能指标小于预设的标准控制性能阈值时，则对电力资源控制性能指标进行控制性能优化，生成电力资源响应控制报告。Step S4: Evaluate the performance index of the power resource dynamic control data to obtain the power resource control performance index; compare the power resource control performance index with the preset standard control performance threshold; when the power resource control performance index is less than the preset standard control performance threshold, optimize the control performance of the power resource control performance index and generate a power resource response control report.
本发明实施例中，通过确定用于评估电力资源控制性能的指标，例如系统频率稳定性、负载供需平衡、传输线损耗等。从电力资源动态控制数据中提取相关参数和数据，用于计算性能指标。根据采集到的数据，计算出各项性能指标的数值。根据电力系统的要求和相关标准，设定各项性能指标的标准控制性能阈值，作为评价指标是否达标的依据。确定每个性能指标的合理范围或具体数值，超出该范围则表示性能存在问题。将计算得到的性能指标与预设的标准控制性能阈值进行对比分析。检测哪些性能指标的数值低于预设阈值，表明系统存在性能问题。分析导致性能指标低于阈值的原因，确定需要改进或优化的方面。提出针对性能问题的优化方案，涉及调整控制策略、优化设备配置、改进调度算法等。根据优化方案，对电力资源控制系统进行相应调整和优化。汇总控制性能评估结果、优化方案和实施情况，生成电力资源响应控制报告。In an embodiment of the present invention, an indicator for evaluating the control performance of power resources is determined, such as system frequency stability, load supply and demand balance, transmission line loss, etc. Relevant parameters and data are extracted from the dynamic control data of power resources for calculating performance indicators. According to the collected data, the values of various performance indicators are calculated. According to the requirements of the power system and relevant standards, the standard control performance thresholds of various performance indicators are set as the basis for evaluating whether the indicators meet the standards. The reasonable range or specific value of each performance indicator is determined, and exceeding the range indicates that there is a performance problem. The calculated performance indicators are compared and analyzed with the preset standard control performance thresholds. It is detected which performance indicators have values lower than the preset threshold, indicating that there is a performance problem in the system. The reasons for the performance indicators being lower than the threshold are analyzed, and the aspects that need to be improved or optimized are determined. An optimization plan for performance problems is proposed, involving adjusting the control strategy, optimizing the equipment configuration, improving the scheduling algorithm, etc. According to the optimization plan, the power resource control system is adjusted and optimized accordingly. The control performance evaluation results, optimization plans and implementation status are summarized to generate a power resource response control report.
优选的，步骤S1包括以下步骤：Preferably, step S1 comprises the following steps:
步骤S11：利用通信协议解析获取电力系统资源数据；Step S11: Obtaining power system resource data by using communication protocol analysis;
步骤S12：对电力系统资源数据进行数据校验，生成电力系统资源校验数据；根据电力系统资源校验数据对电力系统资源数据进行重复数据剔除，生成电力系统资源去重数据；Step S12: performing data verification on the power system resource data to generate power system resource verification data; performing duplicate data removal on the power system resource data according to the power system resource verification data to generate power system resource deduplication data;
步骤S13：对电力系统资源去重数据进行统一数据格式转换，生成电力系统资源格式转换数据；对电力系统资源格式转换数据进行数据时序对齐，生成电力系统资源对齐数据；对电力系统资源对齐数据进行数据标准化，生成标准电力系统资源数据；Step S13: performing unified data format conversion on the power system resource deduplication data to generate power system resource format conversion data; performing data timing alignment on the power system resource format conversion data to generate power system resource alignment data; performing data standardization on the power system resource alignment data to generate standard power system resource data;
步骤S14：对标准电力系统资源数据进行敏感数据提取，生成电力系统资源敏感数据；对电力系统资源敏感数据进行匿名化，生成电力系统资源敏感匿名数据；对电力系统资源敏感匿名数据进行数据加噪，生成电力系统资源加噪数据；对电力系统资源加噪数据进行数据脱敏，从而生成电力系统资源脱敏数据。Step S14: extract sensitive data from standard power system resource data to generate power system resource sensitive data; anonymize the power system resource sensitive data to generate power system resource sensitive anonymous data; perform data noise addition on the power system resource sensitive anonymous data to generate power system resource noisy data; perform data desensitization on the power system resource noisy data to generate power system resource desensitized data.
本发明通过使用通信协议对电力系统进行解析，从中提取相关的资源数据，可以准确地获取电力系统的实时数据，为后续的处理提供基础。从电力系统中获得的数据进行校验，确保其准确性和完整性。生成校验数据有助于追踪数据的来源和变更，提高数据可信度。去重数据有益于消除存在的重复数据，确保数据一致性，有助于避免对后续分析和应用造成混淆。将去重后的数据进行格式转换，使其符合统一的数据格式，有助于简化数据处理流程，提高数据的一致性和可操作性。数据时序对齐有助于将不同时间点的数据进行对齐，使其能够更好地用于时间序列分析，在电力系统中对于了解系统运行趋势和事件发生的时序关系至关重要。数据标准化使得数据在不同来源之间具有一致的度量和单位，提高了数据的比较和分析的准确性。敏感数据提取有助于识别和隔离电力系统中的敏感信息，如个人身份、关键设备等，是保护隐私和关键信息的重要一步。匿名化操作有助于隐藏数据中的个体身份，以满足隐私保护的需求，处理后的数据更适合在非敏感场景中分享和分析。数据加噪是为了增加数据的不确定性，提高数据难以被还原的难度，同时保护数据的隐私性。数据脱敏则是最终的隐私保护步骤，确保最终输出的数据不再包含任何敏感信息。The present invention uses a communication protocol to parse the power system and extract relevant resource data from it, so as to accurately obtain the real-time data of the power system and provide a basis for subsequent processing. The data obtained from the power system is verified to ensure its accuracy and integrity. Generating verification data helps to track the source and change of data and improve the credibility of data. Deduplication data is beneficial to eliminate existing duplicate data, ensure data consistency, and help avoid confusion in subsequent analysis and application. Format conversion of deduplication data to conform to a unified data format helps to simplify the data processing process and improve data consistency and operability. Data time series alignment helps to align data at different time points so that it can be better used for time series analysis, which is crucial in understanding the system operation trend and the time series relationship of event occurrence in the power system. Data standardization enables data to have consistent measurements and units between different sources, improving the accuracy of data comparison and analysis. Sensitive data extraction helps to identify and isolate sensitive information in the power system, such as personal identity, key equipment, etc., and is an important step in protecting privacy and key information. Anonymization operation helps to hide individual identities in data to meet the needs of privacy protection, and the processed data is more suitable for sharing and analysis in non-sensitive scenarios. Data noise is used to increase the uncertainty of data, making it more difficult to restore data, while protecting the privacy of data. Data desensitization is the final privacy protection step, ensuring that the final output data no longer contains any sensitive information.
本发明实施例中，通过使用特定的通信协议与电力系统进行通信，例如Modbus、DNP3、IEC 61850等。通过使用相应的通信协议，可以与电力系统的监控设备、传感器或控制器进行通信，获取实时数据流。采用各种数据校验算法，如CRC校验、哈希算法等，对从电力系统获取的数据进行完整性和准确性检查，涉及开发自定义的数据校验程序或使用现有的数据校验工具。设计数据转换和时序对齐的算法或工具，确保数据在处理过程中保持一致的格式和时间序列，可以使用编程语言（如Python、Java等）编写脚本或程序来实现数据格式转换和时序对齐的功能。敏感数据提取可以通过数据挖掘、关键词匹配等技术来实现，以识别出电力系统中包含的个人身份信息、敏感设备信息等。匿名化和加噪可以使用加密算法、哈希函数、数据脱敏工具等来实现，以保护识别出的敏感数据并降低数据的可逆性，常见的方法包括对个人身份信息进行加密或替换，对数据进行扰动以增加噪音等。In an embodiment of the present invention, a specific communication protocol is used to communicate with the power system, such as Modbus, DNP3, IEC 61850, etc. By using the corresponding communication protocol, it is possible to communicate with the monitoring equipment, sensors or controllers of the power system to obtain real-time data streams. Various data verification algorithms, such as CRC verification, hash algorithms, etc., are used to check the integrity and accuracy of the data obtained from the power system, involving the development of a custom data verification program or the use of existing data verification tools. Design algorithms or tools for data conversion and timing alignment to ensure that the data maintains a consistent format and time series during processing. Programming languages (such as Python, Java, etc.) can be used to write scripts or programs to implement the functions of data format conversion and timing alignment. Sensitive data extraction can be achieved through data mining, keyword matching and other technologies to identify personal identity information, sensitive equipment information, etc. contained in the power system. Anonymization and noise addition can be achieved using encryption algorithms, hash functions, data desensitization tools, etc. to protect the identified sensitive data and reduce the reversibility of the data. Common methods include encrypting or replacing personal identity information, perturbing the data to increase noise, etc.
优选的，步骤S2包括以下步骤：Preferably, step S2 comprises the following steps:
步骤S21：对电力系统资源脱敏数据进行电力消耗模式特征提取，得到电力消耗模式特征数据；通过快速傅里叶变换方法对电力消耗模式特征数据进行电力消耗周期分析，生成电力资源消耗周期数据；Step S21: extracting power consumption pattern features from the power system resource desensitized data to obtain power consumption pattern feature data; performing power consumption cycle analysis on the power consumption pattern feature data using a fast Fourier transform method to generate power resource consumption cycle data;
步骤S22：对电力系统资源脱敏数据进行外部电力设备天气变化特征提取，得到电力设备天气变化特征数据；利用电力设备天气变化特征数据对电力系统资源脱敏数据进行设备状态特征提取，得到设备状态特征数据；将电力资源消耗周期数据、电力设备天气变化特征数据和设备状态特征数据进行多维度数据融合，生成多维度电力资源数据；Step S22: extracting the weather change characteristics of external power equipment from the power system resource desensitized data to obtain the weather change characteristic data of the power equipment; extracting the equipment status characteristics from the power system resource desensitized data using the weather change characteristic data of the power equipment to obtain the equipment status characteristic data; performing multi-dimensional data fusion on the power resource consumption cycle data, the weather change characteristic data of the power equipment and the equipment status characteristic data to generate multi-dimensional power resource data;
步骤S23：对多维度电力资源数据进行时序分析，生成多维度时序分析数据；对多维度电力资源数据进行空间分析，生成多维度空间分析数据；将多维度时序分析数据和多维度空间分析数据进行时空关联，生成多维度电力资源时空关联分析数据；Step S23: performing time series analysis on the multi-dimensional power resource data to generate multi-dimensional time series analysis data; performing spatial analysis on the multi-dimensional power resource data to generate multi-dimensional spatial analysis data; performing spatiotemporal association between the multi-dimensional time series analysis data and the multi-dimensional spatial analysis data to generate multi-dimensional power resource spatiotemporal association analysis data;
步骤S24：基于外部电力传输损耗分析公式对多维度电力资源时空关联分析数据进行外部电力传输损耗计算，得到外部电力传输损耗值；通过外部电力传输损耗值对多维度电力资源时空关联分析公式进行外部电力响应调整，生成外部电力资源响应调整数据；Step S24: Calculate the external power transmission loss of the multi-dimensional power resource spatiotemporal correlation analysis data based on the external power transmission loss analysis formula to obtain the external power transmission loss value; perform external power response adjustment on the multi-dimensional power resource spatiotemporal correlation analysis formula according to the external power transmission loss value to generate external power resource response adjustment data;
步骤S25：利用深度学习模型对外部电力资源响应调整数据进行电力资源响应供应策略制定，从而生成电力资源响应策略。Step S25: Utilize the deep learning model to formulate a power resource response supply strategy for the external power resource response adjustment data, thereby generating a power resource response strategy.
本发明通过电力消耗模式特征提取和周期分析，可以更准确地理解和预测电力消耗模式，从而为电力资源分配和调度提供科学依据。通过分析电力设备与天气变化之间的关系，能够更好地理解环境因素对电力系统的影响，提高电力系统对恶劣天气和环境变化的适应能力和韧性。多维度数据融合和时空关联分析提供了一个全面的视角来理解电力资源的使用和需求模式，帮助优化资源分配，减少浪费，提高能源使用效率。通过外部电力传输损耗的计算和响应调整，能够更有效地管理电力传输，减少在传输过程中的能量损耗，降低成本。利用深度学习模型对电力资源响应调整数据进行分析，可以制定更为精准和有效的电力资源响应策略，提高应对突发事件的能力，保障电力供应的稳定性。通过优化电力资源的管理和使用，有助于促进能源的可持续使用，减少环境污染，支持绿色能源和可持续发展的目标。The present invention can more accurately understand and predict the power consumption pattern through power consumption pattern feature extraction and cycle analysis, thereby providing a scientific basis for power resource allocation and scheduling. By analyzing the relationship between power equipment and weather changes, the impact of environmental factors on the power system can be better understood, and the adaptability and resilience of the power system to severe weather and environmental changes can be improved. Multi-dimensional data fusion and spatiotemporal correlation analysis provide a comprehensive perspective to understand the use and demand patterns of power resources, help optimize resource allocation, reduce waste, and improve energy efficiency. Through the calculation and response adjustment of external power transmission losses, power transmission can be managed more effectively, energy loss during transmission can be reduced, and costs can be reduced. By analyzing the power resource response adjustment data using a deep learning model, a more accurate and effective power resource response strategy can be formulated, the ability to respond to emergencies can be improved, and the stability of power supply can be ensured. By optimizing the management and use of power resources, it helps to promote the sustainable use of energy, reduce environmental pollution, and support the goals of green energy and sustainable development.
作为本发明的一个实例，参考图2所示，在本实例中所述步骤S2包括：As an example of the present invention, referring to FIG. 2 , in this example, step S2 includes:
步骤S21：对电力系统资源脱敏数据进行电力消耗模式特征提取，得到电力消耗模式特征数据；通过快速傅里叶变换方法对电力消耗模式特征数据进行电力消耗周期分析，生成电力资源消耗周期数据；Step S21: extracting power consumption pattern features from the power system resource desensitized data to obtain power consumption pattern feature data; performing power consumption cycle analysis on the power consumption pattern feature data using a fast Fourier transform method to generate power resource consumption cycle data;
本发明实施例中，通过获取电力系统资源的脱敏数据，包括电力消耗记录、时间戳以及其他相关数据。使用适当的特征提取方法，例如统计特征、频域特征或时域特征，从脱敏数据中提取电力消耗模式的相关特征，包括平均值、方差、峰度、偏度等。对电力消耗模式特征数据应用快速傅里叶变换。FFT是一种用于将信号从时域转换为频域的方法，它能够揭示信号中存在的周期性成分。通过分析FFT的结果，识别主要的频率分量，这对应于电力消耗的周期，确定峰值频率可以帮助确定电力资源的消耗周期。根据傅里叶变换的结果，生成电力资源的消耗周期数据，包括主要频率的振幅、相位等信息，以及对应的周期。In an embodiment of the present invention, desensitized data of power system resources are obtained, including power consumption records, timestamps, and other relevant data. Use appropriate feature extraction methods, such as statistical features, frequency domain features, or time domain features, to extract relevant features of the power consumption pattern from the desensitized data, including mean, variance, kurtosis, skewness, etc. Fast Fourier transform is applied to the power consumption pattern feature data. FFT is a method for converting a signal from the time domain to the frequency domain, which can reveal the periodic components present in the signal. By analyzing the results of FFT, the main frequency components are identified, which correspond to the cycle of power consumption, and determining the peak frequency can help determine the consumption cycle of the power resource. Based on the results of Fourier transform, the consumption cycle data of the power resource is generated, including information such as the amplitude and phase of the main frequency, and the corresponding cycle.
步骤S22：对电力系统资源脱敏数据进行外部电力设备天气变化特征提取，得到电力设备天气变化特征数据；利用电力设备天气变化特征数据对电力系统资源脱敏数据进行设备状态特征提取，得到设备状态特征数据；将电力资源消耗周期数据、电力设备天气变化特征数据和设备状态特征数据进行多维度数据融合，生成多维度电力资源数据；Step S22: extracting the weather change characteristics of external power equipment from the power system resource desensitized data to obtain the weather change characteristic data of the power equipment; extracting the equipment status characteristics from the power system resource desensitized data using the weather change characteristic data of the power equipment to obtain the equipment status characteristic data; performing multi-dimensional data fusion on the power resource consumption cycle data, the weather change characteristic data of the power equipment and the equipment status characteristic data to generate multi-dimensional power resource data;
本发明实施例中，通过获取与电力系统相关的外部天气数据，例如温度、湿度、风速等。利用这些天气数据，通过合适的特征提取方法（如统计特征、频域特征等），提取与电力设备运行状态相关的特征。例如，会发现某些电力设备在高温下运行负荷更高。使用电力设备天气变化特征数据，结合电力系统资源脱敏数据，对设备状态进行特征提取，包括设备的运行时间、运行负荷、温度变化率等方面的特征。将电力资源消耗周期数据、电力设备天气变化特征数据和设备状态特征数据进行融合，生成多维度电力资源数据，可以使用各种融合技术，如拼接、叠加或者模型集成等，将不同来源的数据整合在一起，确保在融合过程中保持数据的一致性和准确性。对生成的多维度电力资源数据进行验证，确保融合后的数据能够准确反映电力系统的状态和特征。In an embodiment of the present invention, external weather data related to the power system, such as temperature, humidity, wind speed, etc., is obtained. Using these weather data, features related to the operating status of the power equipment are extracted through appropriate feature extraction methods (such as statistical features, frequency domain features, etc.). For example, it will be found that some power equipment has a higher operating load at high temperatures. Using the weather change feature data of the power equipment, combined with the desensitized data of the power system resources, feature extraction is performed on the equipment status, including the characteristics of the equipment's operating time, operating load, temperature change rate, etc. The power resource consumption cycle data, the weather change feature data of the power equipment, and the equipment status feature data are fused to generate multi-dimensional power resource data. Various fusion technologies, such as splicing, superposition, or model integration, can be used to integrate data from different sources to ensure that the consistency and accuracy of the data are maintained during the fusion process. The generated multi-dimensional power resource data is verified to ensure that the fused data can accurately reflect the status and characteristics of the power system.
步骤S23：对多维度电力资源数据进行时序分析，生成多维度时序分析数据；对多维度电力资源数据进行空间分析，生成多维度空间分析数据；将多维度时序分析数据和多维度空间分析数据进行时空关联，生成多维度电力资源时空关联分析数据；Step S23: performing time series analysis on the multi-dimensional power resource data to generate multi-dimensional time series analysis data; performing spatial analysis on the multi-dimensional power resource data to generate multi-dimensional spatial analysis data; performing spatiotemporal association between the multi-dimensional time series analysis data and the multi-dimensional spatial analysis data to generate multi-dimensional power resource spatiotemporal association analysis data;
本发明实施例中，通过对多维度电力资源数据进行时间序列化，确保数据具有时间顺序。利用时序数据提取与时间相关的特征，例如趋势、季节性、周期性等。运用统计方法对时序数据进行分析，如均值、方差、相关性等。使用时间序列分析方法，如ARIMA（自回归集成移动平均）、Prophet等，建立时序模型。将多维度电力资源数据进行空间化，确保有关空间位置的信息。利用GIS工具进行空间分析，考虑电力资源在不同地理位置的分布、变化等。使用空间统计方法，例如克里金插值、空间自相关分析等，探索空间上的模式和关联。将时序分析得到的特征与空间分析得到的特征进行整合。使用适当的时空关联分析方法，如时空数据挖掘、时空回归分析等，探索多维度电力资源数据在时间和空间上的关联。考虑时序和空间因素之间的相互影响，确保关联分析能够准确反映电力资源的时空特征。对生成的多维度电力资源时空关联分析数据进行验证，确保分析结果的合理性和可靠性。In the embodiment of the present invention, the multi-dimensional power resource data is time-seriesified to ensure that the data has a time sequence. Time-series data is used to extract time-related features, such as trends, seasonality, and periodicity. Time-series data is analyzed using statistical methods, such as mean, variance, correlation, and the like. Time series analysis methods, such as ARIMA (autoregressive integrated moving average), Prophet, and the like, are used to establish a time series model. Multi-dimensional power resource data is spatialized to ensure information about spatial locations. GIS tools are used for spatial analysis, considering the distribution and changes of power resources in different geographical locations. Spatial statistical methods, such as Kriging interpolation and spatial autocorrelation analysis, are used to explore spatial patterns and associations. The features obtained from the time series analysis are integrated with the features obtained from the spatial analysis. Appropriate spatiotemporal association analysis methods, such as spatiotemporal data mining and spatiotemporal regression analysis, are used to explore the association of multi-dimensional power resource data in time and space. Consider the mutual influence between time series and spatial factors to ensure that the association analysis can accurately reflect the spatiotemporal characteristics of power resources. The generated multi-dimensional spatiotemporal association analysis data of power resources is verified to ensure the rationality and reliability of the analysis results.
步骤S24：基于外部电力传输损耗分析公式对多维度电力资源时空关联分析数据进行外部电力传输损耗计算，得到外部电力传输损耗值；通过外部电力传输损耗值对多维度电力资源时空关联分析公式进行外部电力响应调整，生成外部电力资源响应调整数据；Step S24: Calculate the external power transmission loss of the multi-dimensional power resource spatiotemporal correlation analysis data based on the external power transmission loss analysis formula to obtain the external power transmission loss value; perform external power response adjustment on the multi-dimensional power resource spatiotemporal correlation analysis formula according to the external power transmission loss value to generate external power resource response adjustment data;
本发明实施例中，通过获取与电力传输相关的数据，包括传输距离、线路类型、负载情况等。确定适合当前电力传输网络的损耗分析公式，涉及到基于电流、电压、线路材料和长度等因素的损耗模型。根据选定的损耗分析公式，对每个传输路径进行损耗计算，得到外部电力传输损耗值，需要使用数学建模或专业软件进行计算。将外部电力传输损耗值整合到多维度电力资源时空关联分析中，作为外部影响因素的一部分。设计一个调整公式，将外部电力传输损耗值与多维度电力资源的时空关联分析公式进行结合，以调整电力资源的响应。确定调整公式中的参数，需要根据实际数据或模拟结果进行参数估计。对每个时空点进行响应调整计算，根据外部电力传输损耗值和调整公式，生成外部电力资源的响应调整数据。In an embodiment of the present invention, data related to power transmission is obtained, including transmission distance, line type, load conditions, etc. Determining a loss analysis formula suitable for the current power transmission network involves a loss model based on factors such as current, voltage, line material and length. According to the selected loss analysis formula, the loss of each transmission path is calculated to obtain the external power transmission loss value, which requires the use of mathematical modeling or professional software for calculation. The external power transmission loss value is integrated into the multi-dimensional power resource spatiotemporal correlation analysis as part of the external influencing factors. An adjustment formula is designed to combine the external power transmission loss value with the spatiotemporal correlation analysis formula of the multi-dimensional power resources to adjust the response of the power resources. To determine the parameters in the adjustment formula, it is necessary to estimate the parameters based on actual data or simulation results. A response adjustment calculation is performed for each spatiotemporal point, and the response adjustment data of the external power resources is generated based on the external power transmission loss value and the adjustment formula.
步骤S25：利用深度学习模型对外部电力资源响应调整数据进行电力资源响应供应策略制定，从而生成电力资源响应策略。Step S25: Utilize the deep learning model to formulate a power resource response supply strategy for the external power resource response adjustment data, thereby generating a power resource response strategy.
本发明实施例中，通过确保外部电力资源响应调整数据已经准备好，包括各时空点的响应调整值。如果有历史数据可用，也应该收集并整理，作为模型训练和验证的一部分。选择适合处理时空数据的深度学习模型，如循环神经网络（RNN）、长短期记忆网络（LSTM）、卷积神经网络（CNN）等。设计深度学习模型的架构，包括网络层数、隐藏单元数、激活函数等。对外部电力资源响应调整数据进行标准化处理，确保数据在相同的尺度范围内，有利于模型训练的稳定性和收敛速度。将数据划分为训练集、验证集和测试集，通常采用时间序列划分方法，确保模型在未来数据上的泛化能力。初始化深度学习模型的参数，选择适合任务的损失函数，例如均方误差（MSE）。选择合适的优化器，如随机梯度下降（SGD）、Adam等。使用训练集对模型进行训练，并在验证集上进行验证，根据验证集的表现调整模型超参数。使用测试集评估模型的性能，通常采用各种指标如均方根误差（RMSE）、平均绝对误差（MAE）等进行评估。根据评估结果对模型进行调优，包括调整模型架构、修改超参数等。利用训练好的深度学习模型对未来外部电力资源响应进行预测，得到各个时空点的响应值。根据预测结果制定电力资源响应策略，包括调整供电计划、调整设备运行状态等，以应对外部电力资源的变化。In the embodiment of the present invention, by ensuring that the external power resource response adjustment data is ready, including the response adjustment value of each spatiotemporal point. If historical data is available, it should also be collected and organized as part of model training and verification. Select a deep learning model suitable for processing spatiotemporal data, such as a recurrent neural network (RNN), a long short-term memory network (LSTM), a convolutional neural network (CNN), etc. Design the architecture of the deep learning model, including the number of network layers, the number of hidden units, the activation function, etc. Standardize the external power resource response adjustment data to ensure that the data is within the same scale range, which is conducive to the stability and convergence speed of model training. Divide the data into a training set, a validation set, and a test set, usually using a time series partitioning method to ensure the generalization ability of the model on future data. Initialize the parameters of the deep learning model and select a loss function suitable for the task, such as mean square error (MSE). Select a suitable optimizer, such as stochastic gradient descent (SGD), Adam, etc. Use the training set to train the model and verify it on the validation set, and adjust the model hyperparameters according to the performance of the validation set. The performance of the model is evaluated using a test set, usually using various indicators such as root mean square error (RMSE) and mean absolute error (MAE). The model is tuned based on the evaluation results, including adjusting the model architecture and modifying hyperparameters. The trained deep learning model is used to predict the future response of external power resources and obtain the response values at each time and space point. Based on the prediction results, a power resource response strategy is formulated, including adjusting the power supply plan and the operating status of the equipment, to cope with changes in external power resources.
优选的，步骤S24中的外部电力传输损耗分析公式如下所示：Preferably, the external power transmission loss analysis formula in step S24 is as follows:
， ,
式中，表示为外部电力传输损耗值，表示为电力资源的多维度时空关联分析范围，表示为电压损耗系数，表示为发送端电压，表示为接收端电压，表示为电流损耗系数，表示为发送端电流，表示为接收端电流，表示为空间区域内的密度函数，表示为空间区域内的位置向量。In the formula, Expressed as the external power transmission loss value, It is expressed as the multi-dimensional spatiotemporal correlation analysis scope of power resources. Expressed as the voltage loss coefficient, It is represented as the voltage at the sending end. Expressed as the receiving end voltage, Expressed as the current loss coefficient, Expressed as the sending end current, Expressed as the receiving end current, Expressed as a density function in a spatial region, Represented as a position vector within a region of space.
本发明通过分析并整合了一种外部电力传输损耗分析公式，公式涵盖了电压损失项和电流损失项，并考虑了空间区域内电力资源分布的影响。公式中的电压损失和电流损失被乘以相应的系数，这是因为不同的系统中，电压和电流的影响程度不同。通过对电压和电流的损失项进行平方，可以更好地描述非线性的损失特性，并使得大的损失更加显著，这在损耗分析中是常见的处理方式。空间区域内的密度函数描述了空间区域内电力资源分布的情况。它可以考虑到不同位置的电力资源密度不同，从而对传输损耗产生影响。例如，在某些地方电力资源更加密集，而在其他地方更加稀疏，这会影响到传输过程中的损耗情况。通过这个密度函数，可以将不同位置的损耗加权起来，得到总体的传输损耗。积分对整个空间区域内的电力资源分布进行考虑。通过积分，可以将空间区域内的电力资源分布情况纳入损耗的计算，而不仅仅是局限于特定位置或情况。和控制着电压损失和电流损失在总损耗中的权重。若某系统更关注电压损失，则可以调整的值；若更关注电流损失，则可以调整的值。和表示传输端和接收端的电压，影响损耗的大小。若传输端电压高于接收端，则会增加损耗，反之则减少。和是传输端和接收端的电流，同样影响损耗。高电流会产生更大的损耗。在使用本领域常规的外部电力传输损耗分析公式时，可以得到外部电力传输损耗值，通过应用本发明提供的外部电力传输损耗分析公式，可以更加精确的计算出外部电力传输损耗值。公式的设计考虑了电压、电流损失的非线性特性，以及空间区域内电力资源分布的差异性。通过调整参数，可以更好地模拟不同系统的传输损耗情况，有助于在实际应用中优化电力传输系统，减少能源损耗。The present invention analyzes and integrates an external power transmission loss analysis formula, which covers voltage loss terms and current loss terms, and takes into account the influence of power resource distribution in the spatial region. and current loss is multiplied by the corresponding coefficients because the voltage and current have different effects in different systems. By squaring the loss terms of voltage and current, nonlinear loss characteristics can be better described and large losses can be made more significant, which is a common approach in loss analysis. Density function in a spatial region Describes the distribution of power resources in a spatial region. It can take into account the different densities of power resources at different locations, which affects the transmission losses. For example, power resources are more dense in some places and more sparse in other places, which affects the losses during transmission. Using this density function, the losses at different locations can be weighted to get the overall transmission loss. The integral is calculated for the entire spatial region. By integrating, the distribution of power resources in a spatial region can be included in the calculation of losses, rather than just being limited to a specific location or situation. and Controls the weight of voltage loss and current loss in the total loss. If a system is more concerned about voltage loss, it can be adjusted If you are more concerned about current loss, you can adjust The value of . and Indicates the voltage at the transmission end and the receiving end, which affects the magnitude of the loss. If the voltage at the transmission end is higher than that at the receiving end, the loss will increase, otherwise it will decrease. and It is the current at the transmission end and the receiving end, which also affects the loss. High current will produce greater loss. When using the conventional external power transmission loss analysis formula in the field, the external power transmission loss value can be obtained. By applying the external power transmission loss analysis formula provided by the present invention, the external power transmission loss value can be calculated more accurately. The design of the formula takes into account the nonlinear characteristics of voltage and current losses, as well as the differences in the distribution of power resources in spatial areas. By adjusting the parameters, the transmission loss conditions of different systems can be better simulated, which helps to optimize the power transmission system and reduce energy loss in practical applications.
优选的，步骤S25包括以下步骤：Preferably, step S25 includes the following steps:
步骤S251：基于深度学习技术对多维度电力资源时空关联分析数据进行多头注意力机制构建，得到电力响应空间注意力机制；Step S251: constructing a multi-head attention mechanism for multi-dimensional power resource spatiotemporal correlation analysis data based on deep learning technology to obtain a power response spatial attention mechanism;
步骤S252：根据电力响应空间注意力机制进行历史多维度电力资源时空关联分析数据收集，得到历史多维度电力资源时空关联分析数据；将历史多维度电力资源时空关联分析数据进行数据集划分，生成模型训练集和模型测试集；Step S252: collecting historical multi-dimensional power resource spatiotemporal correlation analysis data according to the power response space attention mechanism to obtain historical multi-dimensional power resource spatiotemporal correlation analysis data; dividing the historical multi-dimensional power resource spatiotemporal correlation analysis data into data sets to generate a model training set and a model test set;
步骤S253：利用残差神经网络算法对模型训练集进行模型训练，生成电力响应训练模型；通过模型测试集对电力响应训练模型进行模型优化及迭代，从而生成电力响应预测模型；将多维度电力资源时空关联分析数据导入至电力响应预测模型中进行电子资源响应预测，生成电力资源响应预测数据；Step S253: Perform model training on the model training set using the residual neural network algorithm to generate a power response training model; optimize and iterate the power response training model using the model test set to generate a power response prediction model; import the multi-dimensional power resource spatiotemporal correlation analysis data into the power response prediction model to perform electronic resource response prediction and generate power resource response prediction data;
步骤S254：通过外部电力资源响应调整数据对电力资源响应预测数据进行实际响应调整，生成实际电力资源响应数据；根据实际电力资源响应数据进行电力供应方案制定，生成电力资源响应策略。Step S254: Perform actual response adjustment on the power resource response prediction data through external power resource response adjustment data to generate actual power resource response data; formulate a power supply plan based on the actual power resource response data to generate a power resource response strategy.
本发明通过利用多头注意力机制构建电力响应空间注意力机制，有助于模型更好地捕捉电力资源之间的时空关联关系，从而提高电力资源响应预测的准确性和精度。历史多维度电力资源时空关联分析数据被收集和划分为模型训练集和模型测试集，确保了模型在训练和测试过程中具有足够的数据来学习和评估电力资源之间的关系，并能够在未知数据上进行准确的预测。利用残差神经网络算法对模型训练集进行训练，并通过模型测试集进行模型优化和迭代，确保了模型能够适应历史数据中的模式，并能够在新数据上进行准确的预测，生成的电力响应预测模型可以用于预测未来的电力资源响应情况。通过外部电力资源响应调整数据对电力资源响应预测数据进行实际响应调整，有助于将模型预测的结果与实际情况进行对比，并根据实际情况进行必要的调整，从而提高电力资源响应预测的准确性和实用性。根据实际电力资源响应数据制定电力供应方案和电力资源响应策略，以应对不同情况下的电力需求和供应变化。The present invention constructs a power response space attention mechanism by using a multi-head attention mechanism, which helps the model better capture the spatiotemporal correlation between power resources, thereby improving the accuracy and precision of power resource response prediction. The historical multi-dimensional power resource spatiotemporal correlation analysis data is collected and divided into a model training set and a model test set, ensuring that the model has sufficient data to learn and evaluate the relationship between power resources during training and testing, and can make accurate predictions on unknown data. The model training set is trained using a residual neural network algorithm, and the model is optimized and iterated through the model test set, ensuring that the model can adapt to the patterns in historical data and can make accurate predictions on new data. The generated power response prediction model can be used to predict future power resource responses. The actual response adjustment of the power resource response prediction data through external power resource response adjustment data helps to compare the results of the model prediction with the actual situation, and make necessary adjustments according to the actual situation, thereby improving the accuracy and practicality of the power resource response prediction. According to the actual power resource response data, a power supply plan and a power resource response strategy are formulated to cope with changes in power demand and supply under different circumstances.
作为本发明的一个实例，参考图3所示，在本实例中所述步骤S25包括：As an example of the present invention, referring to FIG. 3 , in this example, step S25 includes:
步骤S251：基于深度学习技术对多维度电力资源时空关联分析数据进行多头注意力机制构建，得到电力响应空间注意力机制；Step S251: constructing a multi-head attention mechanism for multi-dimensional power resource spatiotemporal correlation analysis data based on deep learning technology to obtain a power response spatial attention mechanism;
本发明实施例中，通过收集和准备多维度电力资源时空关联分析数据，包括历史电力使用情况、天气数据、电力设备状态等，确保数据格式一致且可供深度学习模型使用。对数据进行预处理，包括归一化、标准化和处理缺失值等，有助于提高深度学习模型的稳定性和性能。使用深度学习框架（如TensorFlow或PyTorch）构建多头注意力模型，多头注意力机制允许模型同时关注输入的不同部分，有助于捕捉时空关联关系。将每个输入特征嵌入到低维空间，实现多头注意力机制，使模型能够同时关注不同的注意力头，可以通过并行计算多个注意力头的方式来实现。将多头注意力的输出整合起来，得到电力响应空间注意力机制。将构建的多头注意力模型连接到适当的输出层，例如全连接层，以进行电力响应的预测，使用历史数据集进行模型的训练。通过反向传播算法和优化器对模型进行训练，同时使用验证集来监测模型的性能，需要调整模型结构、学习率等参数以获得更好的效果。使用测试集来评估模型的性能，包括准确性、精确度、召回率等指标。In an embodiment of the present invention, by collecting and preparing multi-dimensional power resource spatiotemporal correlation analysis data, including historical power usage, weather data, power equipment status, etc., ensure that the data format is consistent and can be used by the deep learning model. Preprocessing the data, including normalization, standardization, and processing missing values, helps to improve the stability and performance of the deep learning model. Use a deep learning framework (such as TensorFlow or PyTorch) to build a multi-head attention model. The multi-head attention mechanism allows the model to focus on different parts of the input at the same time, which helps to capture the spatiotemporal correlation relationship. Each input feature is embedded in a low-dimensional space to implement a multi-head attention mechanism, so that the model can focus on different attention heads at the same time, which can be achieved by parallel calculation of multiple attention heads. The outputs of the multi-head attention are integrated to obtain the power response space attention mechanism. The constructed multi-head attention model is connected to an appropriate output layer, such as a fully connected layer, to predict the power response, and the model is trained using a historical data set. The model is trained by a backpropagation algorithm and an optimizer, and the performance of the model is monitored using a validation set. Parameters such as the model structure and learning rate need to be adjusted to obtain better results. Use the test set to evaluate the performance of the model, including indicators such as accuracy, precision, and recall.
步骤S252：根据电力响应空间注意力机制进行历史多维度电力资源时空关联分析数据收集，得到历史多维度电力资源时空关联分析数据；将历史多维度电力资源时空关联分析数据进行数据集划分，生成模型训练集和模型测试集；Step S252: collecting historical multi-dimensional power resource spatiotemporal correlation analysis data according to the power response space attention mechanism to obtain historical multi-dimensional power resource spatiotemporal correlation analysis data; dividing the historical multi-dimensional power resource spatiotemporal correlation analysis data into data sets to generate a model training set and a model test set;
本发明实施例中，通过根据项目需求和可用资源，收集历史多维度电力资源时空关联分析数据，包括历史电力使用数据、天气数据、电力设备状态数据等，确保数据的准确性和完整性，以支持模型的训练和评估。对收集到的历史数据进行预处理，包括数据清洗、去除异常值、缺失值处理等，确保数据的质量和一致性，以提高模型的训练效果。将历史数据划分为模型训练集和模型测试集。常见的划分比例是将数据的大部分用于训练，少部分用于测试，通常是80%的数据用于训练，20%的数据用于测试，可以通过随机抽样或按时间顺序划分来完成，确保训练集和测试集的数据分布相似。如果历史数据中的电力响应空间注意力机制已知，可以将其作为标签添加到数据集中，有助于监督学习模型的训练。如果电力响应空间注意力机制未知，则需要进行无监督学习或半监督学习。根据模型的输入要求，将数据集转换为适当的格式，包括特征和标签（如果有）。确保数据集可以直接用于模型的训练和测试。将划分好的数据集保存在适当的位置，并建立数据管理机制，确保数据的安全性和可访问性。In an embodiment of the present invention, historical multi-dimensional spatiotemporal correlation analysis data of power resources, including historical power usage data, weather data, power equipment status data, etc., are collected according to project requirements and available resources to ensure the accuracy and completeness of the data to support model training and evaluation. The collected historical data is preprocessed, including data cleaning, removal of outliers, missing value processing, etc., to ensure the quality and consistency of the data to improve the training effect of the model. The historical data is divided into a model training set and a model test set. A common division ratio is to use most of the data for training and a small part for testing, usually 80% of the data for training and 20% of the data for testing, which can be done by random sampling or chronological division to ensure that the data distribution of the training set and the test set are similar. If the power response spatial attention mechanism in the historical data is known, it can be added to the data set as a label to help supervise the training of the learning model. If the power response spatial attention mechanism is unknown, unsupervised learning or semi-supervised learning is required. According to the input requirements of the model, the data set is converted into an appropriate format, including features and labels (if any). Ensure that the data set can be directly used for model training and testing. Store the divided data sets in appropriate locations and establish data management mechanisms to ensure data security and accessibility.
步骤S253：利用残差神经网络算法对模型训练集进行模型训练，生成电力响应训练模型；通过模型测试集对电力响应训练模型进行模型优化及迭代，从而生成电力响应预测模型；将多维度电力资源时空关联分析数据导入至电力响应预测模型中进行电子资源响应预测，生成电力资源响应预测数据；Step S253: using the residual neural network algorithm to perform model training on the model training set to generate a power response training model; optimizing and iterating the power response training model through the model test set to generate a power response prediction model; importing the multi-dimensional power resource spatiotemporal correlation analysis data into the power response prediction model to perform electronic resource response prediction and generate power resource response prediction data;
本发明实施例中，通过使用残差神经网络（Residual Neural Network，ResNet）算法对模型训练集进行模型训练。ResNet是一种深度神经网络架构，通过引入残差学习机制来解决深层网络训练中的梯度消失和梯度爆炸问题，有助于训练更深的神经网络。在训练过程中，使用适当的损失函数（如均方误差）和优化算法（如随机梯度下降）来最小化模型的预测误差。使用模型测试集对训练得到的电力响应训练模型进行评估，并根据评估结果进行模型优化和迭代。可以通过调整模型的超参数、网络结构或训练策略来改进模型性能。迭代过程可以通过交叉验证等方法进行，以确保模型的泛化能力和稳健性。在模型经过优化和迭代后，得到最终的电力响应预测模型。该模型能够根据输入的多维度电力资源时空关联分析数据，预测未来的电力资源响应情况。将多维度电力资源时空关联分析数据导入到生成的电力响应预测模型中。模型将基于输入数据进行预测，生成电力资源响应预测数据。In an embodiment of the present invention, a model training set is trained by using a residual neural network (Residual Neural Network, ResNet) algorithm. ResNet is a deep neural network architecture that introduces a residual learning mechanism to solve the gradient vanishing and gradient explosion problems in deep network training, which helps to train deeper neural networks. During the training process, an appropriate loss function (such as mean square error) and an optimization algorithm (such as stochastic gradient descent) are used to minimize the prediction error of the model. The trained power response training model is evaluated using a model test set, and the model is optimized and iterated according to the evaluation results. The model performance can be improved by adjusting the hyperparameters, network structure or training strategy of the model. The iterative process can be performed by methods such as cross-validation to ensure the generalization ability and robustness of the model. After the model is optimized and iterated, the final power response prediction model is obtained. The model can predict the future power resource response based on the input multi-dimensional power resource spatiotemporal correlation analysis data. The multi-dimensional power resource spatiotemporal correlation analysis data is imported into the generated power response prediction model. The model will make predictions based on the input data to generate power resource response prediction data.
步骤S254：通过外部电力资源响应调整数据对电力资源响应预测数据进行实际响应调整，生成实际电力资源响应数据；根据实际电力资源响应数据进行电力供应方案制定，生成电力资源响应策略。Step S254: Perform actual response adjustment on the power resource response prediction data through external power resource response adjustment data to generate actual power resource response data; formulate a power supply plan based on the actual power resource response data to generate a power resource response strategy.
本发明实施例中，通过收集外部数据，包括天气预报、能源市场价格、系统负荷预测等信息，这些数据可以影响电力资源的实际响应情况。确定外部数据收集频率和来源，确保数据的及时性和准确性。建立一个实际响应调整模型，该模型可以根据外部数据对电力资源响应预测数据进行调整。模型可以采用机器学习算法、时间序列分析、回归分析等方法，以预测数据与实际数据之间的关系为基础。利用建立的实际响应调整模型，对电力资源响应预测数据进行调整，以反映外部因素对电力资源响应的影响。调整过程涉及数据插值、趋势预测、误差修正等方法，以确保调整后的数据准确反映实际情况。根据经过调整的预测数据，生成实际的电力资源响应数据，数据反映了外部因素对电力资源响应的影响。In an embodiment of the present invention, by collecting external data, including weather forecasts, energy market prices, system load forecasts and other information, these data can affect the actual response of power resources. Determine the frequency and source of external data collection to ensure the timeliness and accuracy of the data. Establish an actual response adjustment model, which can adjust the power resource response prediction data based on external data. The model can use machine learning algorithms, time series analysis, regression analysis and other methods based on the relationship between predicted data and actual data. Using the established actual response adjustment model, the power resource response prediction data is adjusted to reflect the impact of external factors on the power resource response. The adjustment process involves methods such as data interpolation, trend prediction, and error correction to ensure that the adjusted data accurately reflects the actual situation. Based on the adjusted prediction data, the actual power resource response data is generated, and the data reflects the impact of external factors on the power resource response.
优选的，步骤S251包括以下步骤：Preferably, step S251 includes the following steps:
步骤S2511：对多维度电力资源时空关联分析数据进行电力供应地域分布分析，得到电力资源供应地区数据；对电力资源供应地区数据进行电力响应中心服务器位置确认，生成电力资源响应中心服务器位置数据；Step S2511: Performing power supply regional distribution analysis on the multi-dimensional power resource spatiotemporal correlation analysis data to obtain power resource supply area data; confirming the power response center server location on the power resource supply area data to generate power resource response center server location data;
步骤S2512：对电力资源响应中心服务器位置数据和电力资源供应地区数据进行响应路径分析，生成电力资源外部响应路径数据；对电力资源外部响应路径数据进行响应需求时间戳分析，得到响应路径需求时间戳；Step S2512: performing response path analysis on the power resource response center server location data and the power resource supply area data to generate power resource external response path data; performing response requirement timestamp analysis on the power resource external response path data to obtain a response path requirement timestamp;
步骤S2513：通过响应路径需求时间戳对电力资源外部响应路径数据进行同一时间戳内的电力供应需求划分，生成地区电力供应需求划分数据；对地区电力供应需求划分数据和电力资源外部响应路径数据进行数据整合，生成电力响应路径供需数据；Step S2513: dividing the power supply demand within the same timestamp of the power resource external response path data by the response path demand timestamp to generate regional power supply demand division data; integrating the regional power supply demand division data and the power resource external response path data to generate power response path supply and demand data;
步骤S2514：利用熵权法对电力响应路径供需数据进行权重赋予，生成电力响应空间路径权重数据；基于深度学习技术对进行多头注意力电力传输通道构建，从而得到电力响应空间注意力机制。Step S2514: Use the entropy weight method to assign weights to the power response path supply and demand data to generate power response space path weight data; construct a multi-head attention power transmission channel based on deep learning technology to obtain a power response space attention mechanism.
本发明通过对多维度电力资源时空关联分析数据进行分析，能够更好地理解电力资源的分布情况，从而优化电力资源在地域上的分布，提高供电效率和可靠性。确定电力响应中心服务器的位置可以使得响应更加及时和有效，减少响应路径的延迟，提高响应速度和准确性。通过对响应路径和需求时间戳的分析，可以更好地理解响应路径的需求和特点，为后续的响应策略制定提供数据支持。将地区电力供应需求划分数据与响应路径数据进行整合，可以更全面地考虑地区供需情况，为电力资源响应策略的制定提供更准确的基础数据。利用熵权法赋予电力响应路径权重，结合深度学习技术构建多头注意力电力传输通道，能够实现对电力资源响应的精准调控和分配，提高电力系统的运行效率和稳定性。By analyzing the multi-dimensional spatiotemporal correlation analysis data of electric power resources, the present invention can better understand the distribution of electric power resources, thereby optimizing the geographical distribution of electric power resources and improving power supply efficiency and reliability. Determining the location of the power response center server can make the response more timely and effective, reduce the delay of the response path, and improve the response speed and accuracy. By analyzing the response path and demand timestamps, the needs and characteristics of the response path can be better understood, providing data support for the subsequent response strategy formulation. Integrating the regional power supply demand division data with the response path data can more comprehensively consider the regional supply and demand situation and provide more accurate basic data for the formulation of power resource response strategies. Using the entropy weight method to assign weights to the power response path and combining deep learning technology to construct a multi-head attention power transmission channel can achieve precise regulation and allocation of power resource responses and improve the operating efficiency and stability of the power system.
作为本发明的一个实例，参考图4所示，在本实例中所述步骤S251包括：As an example of the present invention, referring to FIG. 4 , in this example, step S251 includes:
步骤S2511：对多维度电力资源时空关联分析数据进行电力供应地域分布分析，得到电力资源供应地区数据；对电力资源供应地区数据进行电力响应中心服务器位置确认，生成电力资源响应中心服务器位置数据；Step S2511: Performing power supply regional distribution analysis on the multi-dimensional power resource spatiotemporal correlation analysis data to obtain power resource supply area data; confirming the power response center server location on the power resource supply area data to generate power resource response center server location data;
本发明实施例中，通过收集并准备电力资源时空关联分析数据，数据包括不同地区的电力供应量、需求量、消耗情况、历史数据等，以及供应地区和需求地区的时空信息，这些数据可以通过电力供应商、地方电力管理机构、历史记录、传感器数据等来源获得。对收集的数据进行预处理和清洗，以保证数据质量，包括处理数据缺失、异常值、重复值等。对清洗后的数据进行地域分布分析，探究不同地域的电力供应情况，包括统计各地区的电力供应量、发电厂的地理位置和产能、地区用电量等信息。结合分析的地域分布数据，确定响应中心服务器的位置，需要考虑地理信息、电力网络连接情况、供电需求、服务器配置等因素。根据上一步的确认结果，生成电力资源响应中心服务器位置数据，包括响应服务器的地理坐标、相关信息、电力网络连接图等。In an embodiment of the present invention, by collecting and preparing the spatiotemporal correlation analysis data of power resources, the data includes the power supply, demand, consumption, historical data, etc. of different regions, as well as the spatiotemporal information of the supply area and the demand area, which can be obtained from sources such as power suppliers, local power management agencies, historical records, sensor data, etc. The collected data is preprocessed and cleaned to ensure data quality, including processing data missing, abnormal values, duplicate values, etc. The cleaned data is subjected to regional distribution analysis to explore the power supply situation in different regions, including statistics on the power supply of each region, the geographical location and production capacity of power plants, regional power consumption, and other information. Combined with the analyzed regional distribution data, the location of the response center server is determined, and factors such as geographic information, power network connection, power supply demand, and server configuration need to be considered. According to the confirmation result of the previous step, the power resource response center server location data is generated, including the geographical coordinates of the response server, relevant information, and a power network connection diagram.
步骤S2512：对电力资源响应中心服务器位置数据和电力资源供应地区数据进行响应路径分析，生成电力资源外部响应路径数据；对电力资源外部响应路径数据进行响应需求时间戳分析，得到响应路径需求时间戳；Step S2512: performing response path analysis on the power resource response center server location data and the power resource supply area data to generate power resource external response path data; performing response requirement timestamp analysis on the power resource external response path data to obtain a response path requirement timestamp;
本发明实施例中，通过准备电力资源响应中心服务器位置数据和电力资源供应地区数据，数据包括响应中心服务器位置信息、各地区的电力供应情况、地理信息等。使用路径规划算法（如Dijkstra算法、A*算法等），结合响应中心服务器位置和各地区的供电情况，计算生成电力资源的外部响应路径数据，路径是服务器到各个供电地区的最优路径，考虑到供电能力、距离、网络拓扑等因素。生成电力资源外部响应路径数据，包括响应路径的起始点、终点、路径长度、经过的地点等信息。准备响应路径数据以及相关的响应需求时间戳数据。响应需求时间戳是指各个地区对电力资源的需求时间点，可以是历史数据或者实时数据。将响应路径数据与需求时间戳数据结合，分析每条响应路径上的需求时间戳，涉及到时间序列分析、路径匹配等技术，以确定每条路径上的电力需求时间点。提取出响应路径上的电力需求时间戳，以便后续的响应调度和优化。In an embodiment of the present invention, by preparing the power resource response center server location data and the power resource supply area data, the data includes the response center server location information, the power supply situation of each region, geographic information, etc. Use a path planning algorithm (such as Dijkstra algorithm, A* algorithm, etc.), combined with the response center server location and the power supply situation of each region, calculate and generate the external response path data of the power resource, the path is the optimal path from the server to each power supply area, taking into account the power supply capacity, distance, network topology and other factors. Generate the external response path data of the power resource, including the starting point, end point, path length, and places passed by the response path. Prepare the response path data and the related response demand timestamp data. The response demand timestamp refers to the time point of the demand for power resources in each region, which can be historical data or real-time data. Combine the response path data with the demand timestamp data, analyze the demand timestamp on each response path, and involve time series analysis, path matching and other technologies to determine the power demand time point on each path. Extract the power demand timestamp on the response path for subsequent response scheduling and optimization.
步骤S2513：通过响应路径需求时间戳对电力资源外部响应路径数据进行同一时间戳内的电力供应需求划分，生成地区电力供应需求划分数据；对地区电力供应需求划分数据和电力资源外部响应路径数据进行数据整合，生成电力响应路径供需数据；Step S2513: dividing the power supply demand within the same timestamp of the power resource external response path data by the response path demand timestamp to generate regional power supply demand division data; integrating the regional power supply demand division data and the power resource external response path data to generate power response path supply and demand data;
本发明实施例中，通过针对每条电力资源外部响应路径，将其对应的需求时间戳与路径上各地区的电力供应情况进行匹配。对于同一时间戳内的电力供应需求，将其划分到相应的地区，涉及到判断响应路径经过的地区以及各地区的电力需求量，以确定每个地区的电力供需情况。生成地区电力供应需求划分数据，包括每个地区在特定时间戳内的电力供需情况，包括电力需求量、电力供应量、电力短缺情况等信息。将电力资源外部响应路径数据与地区电力供应需求划分数据进行匹配，确保每条响应路径能够与相应的地区电力供需情况对应。将匹配后的响应路径数据和地区电力供应需求划分数据进行合并，形成电力响应路径供需数据，包括整合各地区的电力供需情况、响应路径的路线信息、时间戳信息等。In an embodiment of the present invention, for each external response path of electric power resources, the corresponding demand timestamp is matched with the power supply situation of each region on the path. For the power supply demand within the same timestamp, it is divided into corresponding regions, which involves judging the regions through which the response path passes and the power demand of each region to determine the power supply and demand situation of each region. Generate regional power supply demand division data, including the power supply and demand situation of each region within a specific timestamp, including information such as power demand, power supply, and power shortage. Match the external response path data of electric power resources with the regional power supply demand division data to ensure that each response path can correspond to the corresponding regional power supply and demand situation. Merge the matched response path data and the regional power supply demand division data to form power response path supply and demand data, including integrating the power supply and demand situation of each region, the route information of the response path, and the timestamp information.
步骤S2514：利用熵权法对电力响应路径供需数据进行权重赋予，生成电力响应空间路径权重数据；基于深度学习技术对进行多头注意力电力传输通道构建，从而得到电力响应空间注意力机制。Step S2514: Use the entropy weight method to assign weights to the power response path supply and demand data to generate power response space path weight data; construct a multi-head attention power transmission channel based on deep learning technology to obtain a power response space attention mechanism.
本发明实施例中，通过将电力响应路径供需数据准备为可供熵权法计算的格式，通常是一个矩阵，其中行表示不同的响应路径，列表示不同的特征或指标（如电力供应量、电力需求量等）。使用熵权法计算每个指标的权重。熵权法是一种多指标决策方法，通过计算各指标的熵值和权重来确定各指标的重要性。通常，熵值越大，代表该指标对最终结果的影响越大，因此其权重也越高。将计算得到的各项指标的权重赋予到对应的电力响应路径供需数据上，以得到电力响应空间路径权重数据，权重反映了不同指标在电力响应路径中的重要性，可以用于后续的路径选择和优化。将电力响应路径供需数据准备为深度学习模型可接受的输入格式，通常是一个张量，其中包含响应路径的特征表示。使用深度学习技术中的多头注意力机制构建电力传输通道。多头注意力允许模型同时关注不同的部分，并在不同的空间位置上聚焦于不同的特征，机制能够有效地捕捉到电力响应路径中不同部分之间的关系和重要性。基于准备好的电力响应路径供需数据和多头注意力机制，构建深度学习模型，并对其进行训练。训练过程中，模型会学习如何根据输入的电力响应路径数据，自动地构建出具有有效传输通道的注意力机制。训练完成后，深度学习模型会得到一个能够根据输入的电力响应路径数据自动构建出的电力响应空间注意力机制，注意力机制可以指导电力传输通道的选择和优化，以更好地满足电力供需的要求。In an embodiment of the present invention, the power response path supply and demand data is prepared in a format that can be calculated by the entropy weight method, usually a matrix, in which rows represent different response paths and columns represent different features or indicators (such as power supply, power demand, etc.). The entropy weight method is used to calculate the weight of each indicator. The entropy weight method is a multi-indicator decision-making method that determines the importance of each indicator by calculating the entropy value and weight of each indicator. Generally, the larger the entropy value, the greater the impact of the indicator on the final result, so its weight is also higher. The weights of each indicator calculated are assigned to the corresponding power response path supply and demand data to obtain the power response space path weight data. The weight reflects the importance of different indicators in the power response path, which can be used for subsequent path selection and optimization. The power response path supply and demand data is prepared in an input format acceptable to the deep learning model, usually a tensor containing the feature representation of the response path. The multi-head attention mechanism in deep learning technology is used to construct a power transmission channel. Multi-head attention allows the model to focus on different parts at the same time and focus on different features at different spatial positions. The mechanism can effectively capture the relationship and importance between different parts in the power response path. Based on the prepared power response path supply and demand data and multi-head attention mechanism, a deep learning model is constructed and trained. During the training process, the model will learn how to automatically construct an attention mechanism with an effective transmission channel based on the input power response path data. After the training is completed, the deep learning model will obtain a power response space attention mechanism that can be automatically constructed based on the input power response path data. The attention mechanism can guide the selection and optimization of the power transmission channel to better meet the requirements of power supply and demand.
优选的，步骤S3包括以下步骤：Preferably, step S3 comprises the following steps:
步骤S31：根据电力资源响应策略进行电力资源模拟分配，从而获取电力资源控制模拟数据；Step S31: performing power resource simulation allocation according to the power resource response strategy, thereby obtaining power resource control simulation data;
步骤S32：对电力资源控制模拟数据进行控制器选择，得到电力资源控制器类型数据；基于电力资源控制器类型数据进行控制反馈架构设计，生成电力资源控制架构；对电力资源控制架构进行通信协议确定，生成电力控制通信协议；Step S32: selecting a controller for the power resource control simulation data to obtain power resource controller type data; designing a control feedback architecture based on the power resource controller type data to generate a power resource control architecture; determining a communication protocol for the power resource control architecture to generate a power control communication protocol;
步骤S33：对电力资源控制架构进行数据流监控处理，生成电力资源实时控制数据流；对电力资源实时控制数据流进行电力传感器数据筛选，生成电力资源传感器内部控制数据；Step S33: performing data flow monitoring processing on the power resource control architecture to generate a power resource real-time control data flow; performing power sensor data screening on the power resource real-time control data flow to generate power resource sensor internal control data;
步骤S34：通过电力资源传感器内部控制数据进行控制指令生成，得到实时控制指令，其中实时控制指令包括电力资源设备开关控制指令和电力调度控制指令；根据电力控制通信协议对电力资源设备开关控制指令和电力调度控制指令进行电力动态调节，生成电力资源动态控制数据。Step S34: Generate control instructions through the internal control data of the power resource sensor to obtain real-time control instructions, where the real-time control instructions include power resource equipment switch control instructions and power dispatch control instructions; dynamically adjust the power resource equipment switch control instructions and the power dispatch control instructions according to the power control communication protocol to generate power resource dynamic control data.
本发明通过电力资源响应策略进行电力资源模拟分配，可以更好地了解电力资源的分布情况和供需关系，为后续的电力资源控制提供数据基础。根据电力资源控制模拟数据进行控制器选择，以及基于电力资源控制器类型数据进行控制反馈架构设计，可以根据实际情况选择合适的控制器类型，并设计出适合的控制架构，从而更好地实现对电力资源的控制。对电力资源控制架构进行通信协议确定，可以确保不同部分之间的通信顺畅，从而更好地实现电力资源的协同控制。对电力资源控制架构进行数据流监控处理，可以生成电力资源实时控制数据流，为后续的控制指令生成提供数据基础。对电力资源实时控制数据流进行电力传感器数据筛选，可以从海量数据中筛选出有用的信息，为后续的控制指令生成提供精确的数据支持。通过电力资源传感器内部控制数据进行控制指令生成，可以根据实时数据生成实时控制指令，从而更好地实现对电力资源的控制。根据电力控制通信协议对电力资源设备开关控制指令和电力调度控制指令进行电力动态调节，可以根据实际情况对电力资源进行动态调节，从而更好地满足电力需求。The present invention simulates the allocation of power resources through the power resource response strategy, which can better understand the distribution of power resources and the supply and demand relationship, and provide a data basis for subsequent power resource control. According to the power resource control simulation data, the controller is selected, and the control feedback architecture is designed based on the power resource controller type data. The appropriate controller type can be selected according to the actual situation, and a suitable control architecture can be designed, so as to better realize the control of power resources. The communication protocol is determined for the power resource control architecture, which can ensure smooth communication between different parts, so as to better realize the coordinated control of power resources. The data flow monitoring and processing of the power resource control architecture can generate a real-time control data flow of power resources, which provides a data basis for the subsequent control instruction generation. The power sensor data is screened for the real-time control data flow of power resources, which can filter out useful information from the massive data and provide accurate data support for the subsequent control instruction generation. The control instruction is generated through the internal control data of the power resource sensor, and the real-time control instruction can be generated according to the real-time data, so as to better realize the control of power resources. According to the power control communication protocol, the power resource equipment switch control instruction and the power dispatching control instruction are dynamically adjusted, and the power resources can be dynamically adjusted according to the actual situation, so as to better meet the power demand.
本发明实施例中，通过根据需求和实际情况确定电力资源响应策略，如基于需求响应、能源效率等。利用模拟工具或软件，根据电力资源响应策略进行电力资源模拟分配，生成模拟数据，包括各个电力资源的分配情况、供需关系等。根据电力资源控制模拟数据，选择适合的控制器类型，涉及PID控制器、模糊逻辑控制器等。根据选择的控制器类型和电力资源的特性，设计控制反馈架构，确保控制系统稳定性和性能。确定控制架构之间的通信协议，以便各部分之间能够有效地通信和交换数据。对电力资源控制架构进行数据流监控处理，确保实时控制数据流的准确性和完整性。利用数据处理算法对实时控制数据流进行筛选和处理，提取有用信息，去除噪声和冗余数据，生成传感器内部控制数据。根据传感器内部控制数据，生成实时控制指令，包括电力资源设备开关控制指令和电力调度控制指令。根据电力控制通信协议，将生成的控制指令发送到相应的设备或系统，实现电力资源的动态调节，确保电力系统的稳定运行和高效利用。In the embodiment of the present invention, the power resource response strategy is determined according to the demand and actual situation, such as based on demand response, energy efficiency, etc. The power resource simulation allocation is performed according to the power resource response strategy by using simulation tools or software, and simulation data is generated, including the allocation of each power resource, the supply and demand relationship, etc. According to the power resource control simulation data, a suitable controller type is selected, involving a PID controller, a fuzzy logic controller, etc. According to the selected controller type and the characteristics of the power resource, a control feedback architecture is designed to ensure the stability and performance of the control system. The communication protocol between the control architectures is determined so that each part can communicate and exchange data effectively. The power resource control architecture is subjected to data flow monitoring and processing to ensure the accuracy and integrity of the real-time control data flow. The real-time control data flow is screened and processed by using a data processing algorithm, useful information is extracted, noise and redundant data are removed, and sensor internal control data is generated. According to the sensor internal control data, real-time control instructions are generated, including power resource equipment switch control instructions and power dispatch control instructions. According to the power control communication protocol, the generated control instructions are sent to the corresponding equipment or system to realize the dynamic adjustment of power resources and ensure the stable operation and efficient utilization of the power system.
优选的，步骤S41包括以下步骤：Preferably, step S41 includes the following steps:
步骤S41：对电力资源动态控制数据进行性能指标选取，生成电力资源控制性能指标数据，其中电力资源控制性能指标数据包括能源利用数据、电力资源环境影响数据和电力资源响应成本数据；Step S41: selecting performance indicators for the power resource dynamic control data to generate power resource control performance indicator data, wherein the power resource control performance indicator data includes energy utilization data, power resource environmental impact data and power resource response cost data;
步骤S42：利用电力资源控制性能评估公式对能源利用数据、电力资源环境影响数据和电力资源响应成本数据进行性能指标评估，得到电力资源控制性能指标；将电力资源控制性能指标和预设的标准控制性能阈值进行对比，当电力资源控制性能指标小于预设的标准控制性能阈值时，则基于ALO算法对电力资源控制性能指标进行控制性能优化，从而生成正常电力资源控制数据；Step S42: using the power resource control performance evaluation formula to evaluate the performance indicators of energy utilization data, power resource environmental impact data and power resource response cost data, and obtaining the power resource control performance indicator; comparing the power resource control performance indicator with a preset standard control performance threshold, and when the power resource control performance indicator is less than the preset standard control performance threshold, optimizing the control performance of the power resource control performance indicator based on the ALO algorithm, thereby generating normal power resource control data;
步骤S43：基于可视化方法对正常电力资源控制数据进行数据可视化，生成电力资源响应控制报告。Step S43: Visualize the normal power resource control data based on the visualization method to generate a power resource response control report.
本发明通过对电力资源的各个方面进行全面性能评估，包括能源利用、环境影响和响应成本，有助于全面了解电力系统的运行情况，从而为后续的优化提供详尽的数据支持。实时监测电力资源的控制性能指标。利用评估公式和设定的标准控制性能阈值，系统可以及时识别潜在问题或异常情况。一旦性能指标低于阈值，系统将启动ALO算法进行控制性能优化。ALO算法为电力资源控制性能提供了智能优化机制，有助于系统根据实时情况调整控制策略，提高电力资源的利用效率，减少环境影响，降低响应成本，从而使整个电力系统更加智能和可持续。通过可视化方法生成电力资源响应控制报告，使复杂的性能数据以直观的方式呈现，使得系统运营人员、决策者等可以更容易理解系统的运行状况，并且可以及时采取必要的措施，进一步提高电力资源的管理效能。The present invention helps to fully understand the operation of the power system by conducting a comprehensive performance evaluation of various aspects of power resources, including energy utilization, environmental impact and response cost, thereby providing detailed data support for subsequent optimization. Real-time monitoring of the control performance indicators of power resources. Using the evaluation formula and the set standard control performance threshold, the system can promptly identify potential problems or abnormal situations. Once the performance indicator is lower than the threshold, the system will start the ALO algorithm to optimize the control performance. The ALO algorithm provides an intelligent optimization mechanism for the control performance of power resources, which helps the system adjust the control strategy according to the real-time situation, improve the utilization efficiency of power resources, reduce environmental impact, and reduce response costs, thereby making the entire power system more intelligent and sustainable. The power resource response control report is generated by a visualization method, so that complex performance data can be presented in an intuitive way, so that system operators, decision makers, etc. can more easily understand the operating status of the system, and can take necessary measures in time to further improve the management efficiency of power resources.
本发明实施例中，通过收集电力资源的动态控制数据，包括能源利用、环境影响和响应成本等方面的数据。根据系统需求和优化目标，选择合适的性能指标。例如，能源利用率、环境影响因子和成本响应时间等。利用采集到的数据，应用相应的计算公式生成电力资源控制性能指标数据。例如，能源利用率可以通过能源输入与产出的比率计算得到。制定电力资源控制性能评估的公式，考虑到能源利用、环境影响和响应成本等多个因素，可以是一个综合的加权评估公式，反映电力系统的整体性能。将采集到的电力资源控制性能指标数据带入评估公式中，得到具体的性能评估值。将评估值与预设的标准控制性能阈值进行比较。当性能指标低于阈值时，触发ALO算法进行控制性能优化。根据具体的ALO算法（人工鲁棒优化算法）的特性，调整电力资源的控制策略，以提高性能指标，达到系统的优化。选择适当的可视化工具，如数据可视化库、图表生成工具等，以便将电力资源控制数据以直观的方式呈现。将正常的电力资源控制数据映射到可视化工具上，通过图表、图形等方式展示能源利用、环境影响和成本等方面的数据。利用可视化工具生成电力资源响应控制报告。报告应包括系统的性能指标、优化结果、趋势分析等，以便决策者和操作人员能够快速理解系统的运行状况。In an embodiment of the present invention, dynamic control data of power resources are collected, including data on energy utilization, environmental impact, and response cost. According to system requirements and optimization goals, appropriate performance indicators are selected. For example, energy utilization rate, environmental impact factor, and cost response time. Using the collected data, the corresponding calculation formula is applied to generate power resource control performance indicator data. For example, energy utilization rate can be calculated by the ratio of energy input to output. Formulating a formula for evaluating power resource control performance, taking into account multiple factors such as energy utilization, environmental impact, and response cost, can be a comprehensive weighted evaluation formula that reflects the overall performance of the power system. The collected power resource control performance indicator data is brought into the evaluation formula to obtain a specific performance evaluation value. The evaluation value is compared with a preset standard control performance threshold. When the performance indicator is lower than the threshold, the ALO algorithm is triggered to optimize the control performance. According to the characteristics of the specific ALO algorithm (artificial robust optimization algorithm), the control strategy of the power resource is adjusted to improve the performance indicator and achieve system optimization. Select appropriate visualization tools, such as data visualization libraries, chart generation tools, etc., so that the power resource control data can be presented in an intuitive way. Map normal power resource control data to visualization tools, and display data on energy utilization, environmental impact, and cost through charts and graphs. Generate power resource response control reports using visualization tools. The reports should include system performance indicators, optimization results, trend analysis, etc., so that decision makers and operators can quickly understand the system's operating status.
优选的，步骤S42中的电力资源控制性能评估公式如下所示：Preferably, the power resource control performance evaluation formula in step S42 is as follows:
； ;
式中，表示为电力资源控制性能指标，表示为评估的总时间长度，表示为能源利用数据的样本数量，表示为电力资源环境影响数据的样本数量，表示为第个能源利用数据，表示为第个电力资源环境影响数据，表示为电力资源响应成本随时间的变化函数，表示为能源利用数据的权重系数，表示为电力资源环境影响数据的权重系数，表示为电力资源响应成本数据的权重系数，表示为能源利用数据的曲线特征参数，表示为电力资源环境影响数据的曲线特征参数，表示为电力资源响应成本数据的曲线特征参数，表示为评估时间点。In the formula, Expressed as power resource control performance index, Expressed as the total length of time for the evaluation, is represented by the number of samples of energy utilization data, It is expressed as the number of samples of power resource environmental impact data, Expressed as Energy utilization data, Expressed as Data on the environmental impact of power resources, Expressed as the power resource response cost over time The change function of Expressed as the weight coefficient of energy utilization data, Expressed as the weight coefficient of power resource environmental impact data, Expressed as the weight coefficient of the power resource response cost data, Expressed as the curve characteristic parameters of energy utilization data, It is expressed as the curve characteristic parameters of the power resource environmental impact data, The curve characteristic parameters expressed as the power resource response cost data, Indicated as the evaluation time point.
本发明通过分析并整合了一种电力资源控制性能评估公式，公式的原理是通过能源利用数据项考虑了能源利用的平均情况，指数控制着其曲线特征。增大会更加突出能源利用数据的平均值，对性能评估的贡献更大。减小则会减少对平均值的依赖，更注重样本的分布情况。环境影响数据项这一项考虑了电力资源的环境影响情况，指数控制着其曲线特征。增大会更加突出环境影响数据的平均值，对性能评估的贡献更大。减小会减少对平均值的依赖，更注重样本的分布情况。电力资源响应成本数据项这一项考虑了电力资源的响应成本，通过负号使其成为惩罚项。控制了这个惩罚项的权重，控制了其曲线特征。增大会更加突出响应成本对性能评估的影响，鼓励减少成本。增大使得成本的平均值对性能评估的贡献更大。权重系数、和调节了各项指标的重要性，能够根据实际情况赋予不同指标不同的权重。可以根据具体需求调整不同因素的重要性，使评估更符合实际情况。比如，在环保需求较高时增加来提高环境影响的权重。曲线特征参数、和影响了不同指标的曲线特征，即对平均值的依赖程度。可以调整不同指标的敏感度，使得评估更贴合实际情况。例如，增加可以使得评估更加敏感于能源利用的波动情况。在使用本领域常规的电力资源控制性能评估公式时，可以得到电力资源控制性能指标，通过应用本发明提供的电力资源控制性能评估公式，可以更加精确的计算出电力资源控制性能指标。公式的设计考虑了电压、电流损失的非线性特性，以及空间区域内电力资源分布的差异性。通过调整参数，可以更好地模拟不同系统的传输损耗情况，有助于在实际应用中优化电力传输系统，减少能源损耗。通过综合考虑多个因素，能够更全面地评估电力资源的控制性能，从而指导决策者制定更有效的资源管理策略。The present invention analyzes and integrates a power resource control performance evaluation formula, the principle of which is to use energy utilization data items Taking into account the average energy use, the index Controls its curve characteristics. Increasing it will highlight the average value of energy utilization data more and contribute more to performance evaluation. Decreasing it will reduce the reliance on the average value and pay more attention to the distribution of samples. This item takes into account the environmental impact of electricity resources. Controls its curve characteristics. Increase The average value of environmental impact data will be more prominent and contribute more to performance evaluation. It will reduce the reliance on the average value and pay more attention to the distribution of samples. This term takes into account the response cost of power resources and becomes a penalty term through the negative sign. Controls the weight of this penalty term, Controls its curve characteristics. It will highlight the impact of response costs on performance evaluation and encourage cost reduction. This makes the average cost contribute more to the performance evaluation. , and The importance of each indicator is adjusted, and different indicators can be given different weights according to actual conditions. The importance of different factors can be adjusted according to specific needs to make the evaluation more in line with actual conditions. For example, when the environmental protection demand is high, increase To increase the weight of environmental impact. Curve characteristic parameters , and This affects the curve characteristics of different indicators, that is, the degree of dependence on the average value. The sensitivity of different indicators can be adjusted to make the evaluation more in line with the actual situation. For example, adding The evaluation can be made more sensitive to fluctuations in energy utilization. When using the conventional power resource control performance evaluation formula in the field, the power resource control performance index can be obtained. By applying the power resource control performance evaluation formula provided by the present invention, the power resource control performance index can be calculated more accurately. The design of the formula takes into account the nonlinear characteristics of voltage and current losses, as well as the differences in the distribution of power resources in spatial areas. By adjusting the parameters, the transmission loss of different systems can be better simulated, which helps to optimize the power transmission system and reduce energy loss in practical applications. By comprehensively considering multiple factors, the control performance of power resources can be more comprehensively evaluated, thereby guiding decision makers to formulate more effective resource management strategies.
在本说明书中，提供了一种基于深度学习的电力资源响应控制系统，用于执行上述的基于深度学习的电力资源响应控制方法，该基于深度学习的电力资源响应控制系统包括：In this specification, a power resource response control system based on deep learning is provided, which is used to execute the above-mentioned power resource response control method based on deep learning. The power resource response control system based on deep learning includes:
数据预处理模块，用于获取电力系统资源数据；对电力系统资源数据进行数据预处理，生成标准电力系统资源数据；对标准电力系统资源数据进行数据湮没化，生成电力系统资源脱敏数据；The data preprocessing module is used to obtain power system resource data; perform data preprocessing on the power system resource data to generate standard power system resource data; perform data annihilation on the standard power system resource data to generate power system resource desensitized data;
时空关联模块，用于对电力系统资源脱敏数据进行多维度数据融合，生成多维度电力资源数据；对多维度电力资源数据进行时空关联，生成多维度电力资源时空关联分析数据；对多维度电力资源时空关联分析数据进行电力资源响应供应制定，从而生成电力资源响应策略；The spatiotemporal correlation module is used to perform multi-dimensional data fusion on the power system resource desensitized data to generate multi-dimensional power resource data; perform spatiotemporal correlation on the multi-dimensional power resource data to generate multi-dimensional power resource spatiotemporal correlation analysis data; formulate power resource response supply based on the multi-dimensional power resource spatiotemporal correlation analysis data, thereby generating a power resource response strategy;
响应调整模块，用于根据电力资源响应策略进行电力资源模拟分配，从而获取电力资源控制模拟数据；对电力资源控制模拟数据进行控制指令生成，得到实时控制指令；根据实时控制指令进行电力动态调节，生成电力资源动态控制数据；The response adjustment module is used to simulate the allocation of power resources according to the power resource response strategy, so as to obtain power resource control simulation data; generate control instructions for the power resource control simulation data to obtain real-time control instructions; perform power dynamic adjustment according to the real-time control instructions to generate power resource dynamic control data;
性能控制模块，用于对电力资源动态控制数据进行性能指标评估，得到电力资源控制性能指标；将电力资源控制性能指标和预设的标准控制性能阈值进行对比，当电力资源控制性能指标小于预设的标准控制性能阈值时，则对电力资源控制性能指标进行控制性能优化，生成电力资源响应控制报告。The performance control module is used to evaluate the performance indicators of the dynamic control data of the power resources to obtain the power resource control performance indicators; compare the power resource control performance indicators with the preset standard control performance thresholds, and when the power resource control performance indicators are less than the preset standard control performance thresholds, the control performance of the power resource control performance indicators is optimized to generate a power resource response control report.
本发明的有益效果在于通过对电力资源的精确模拟和响应控制，系统的整体性能可以得到优化，提高电力系统的效率和可靠性。采用脱敏技术可以确保在处理电力系统资源数据时保持数据的隐私和安全。通过实时控制指令和动态调节，系统能够更灵活地应对电力需求的变化，提高对突发事件的响应能力。生成的电力资源响应控制报告为决策制定提供了依据，使决策者能够基于实际数据做出明智的决策，进一步提高系统的稳定性和可管理性。因此，本发明通过数据预处理、多维度融合、模拟动态调节和性能优化，提高了电力资源响应和控制的安全性、可靠性和效率。The beneficial effect of the present invention is that through accurate simulation and response control of power resources, the overall performance of the system can be optimized, and the efficiency and reliability of the power system can be improved. The use of desensitization technology can ensure that the privacy and security of data are maintained when processing power system resource data. Through real-time control instructions and dynamic adjustment, the system can respond to changes in power demand more flexibly and improve the ability to respond to emergencies. The generated power resource response control report provides a basis for decision-making, enabling decision makers to make wise decisions based on actual data, and further improving the stability and manageability of the system. Therefore, the present invention improves the safety, reliability and efficiency of power resource response and control through data preprocessing, multi-dimensional fusion, simulated dynamic adjustment and performance optimization.
因此，无论从哪一点来看，均应将实施例看作是示范性的，而且是非限制性的，本发明的范围由所附权利要求而不是上述说明限定，因此旨在将落在申请文件的等同要件的含义和范围内的所有变化涵括在本发明内。Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.
以上所述仅是本发明的具体实施方式，使本领域技术人员能够理解或实现本发明。对这些实施例的多种修改对本领域的技术人员来说将是显而易见的，本文中所定义的一般原理可以在不脱离本发明的精神或范围的情况下，在其它实施例中实现。因此，本发明将不会被限制于本文所示的这些实施例，而是要符合与本文所发明的原理和新颖特点相一致的最宽的范围。The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.
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