Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an intelligent mine ore conveying management system, which realizes self-adaptive optimal dispatching, accurate state monitoring and fault prediction of the ore conveying system by introducing deep reinforcement learning dispatching, digital twin, edge computing and block chain data management technologies, provides an efficient safety protection and intelligent user interface, improves the running efficiency, reliability and safety of the system, and solves the problems of incapability of dynamic adjustment, high maintenance cost, poor data safety and complex operation of the traditional system.
In order to achieve the purpose, the intelligent mine ore conveying management system is realized by the following technical scheme that the intelligent mine ore conveying management system comprises:
The deep reinforcement learning scheduling module is used for realizing optimal scheduling through a deep reinforcement learning algorithm based on the state of ore conveying equipment, the conveying path condition and the ore queue information;
The digital twin system is used for creating a digital twin model of the conveying equipment and carrying out multi-physical-field simulation, real-time data synchronization and state prediction so as to manage the full life cycle of the equipment;
The edge calculation module is used for carrying out data acquisition, real-time processing and lightweight AI reasoning on the mine site, so as to realize equipment anomaly detection and early warning;
The block chain data management module is used for recording and storing equipment operation logs and maintaining records, and ensuring the safety and the non-tamper property of data through a block chain;
the network safety protection module is used for monitoring mine network safety in real time through an AI-driven network detection and response system and protecting potential network attack and data leakage;
The user interface and the control platform are used for operators to monitor and control the ore conveying system in real time and provide a state visualization, alarm management and operation interface.
Preferably, the deep reinforcement learning scheduling module includes:
modeling state space, namely converting equipment state, path state and ore queue information into multidimensional vectors and inputting the multidimensional vectors into a deep reinforcement learning model;
establishing a multi-objective optimized rewarding function based on conveying time, energy consumption, maintenance cost and safety;
strategy and value network training, namely performing strategy optimization by adopting a PPO algorithm, and training by using a loss function formed by strategy ratio, cost function loss and strategy entropy;
And (3) strategy execution and optimization, namely deploying a scheduling strategy by using a distributed architecture, and carrying out strategy adjustment and optimization through real-time feedback data.
Preferably, the digital twin system comprises:
establishing a simulation model, namely constructing dynamic, thermal and electrical characteristic models of conveying equipment by adopting a multi-physical field coupling method;
Real-time data synchronization, namely carrying out real-time synchronization on sensor data and a digital twin body through OPC UA protocol, and ensuring that a model reflects the real-time state of equipment;
State estimation and updating, namely performing state estimation and error correction on real-time data by using a Kalman filter;
full life cycle management, namely analyzing the running state of equipment through a Markov chain model, and carrying out fault prediction and maintenance optimization by combining an LSTM prediction model;
and the redundant design and fault management are to design a device redundant system, and automatically switch to standby equipment or a system when the equipment or a sensor fails, so that the continuous operation of the system is ensured.
Preferably, the edge calculation module includes:
Edge hardware configuration, namely performing edge calculation by using NVIDIA Jetson Nano, and deploying EdgeX Foundry platform for data management;
Collecting and preprocessing data, namely collecting sensor data through an equipment service layer, and performing standardization and denoising processing;
deployment of a lightweight AI model, namely using TensorFlow Lite to deploy a quantized convolutional neural network for real-time anomaly detection;
Real-time reasoning and feedback, namely realizing high-efficiency real-time reasoning by utilizing batch processing and parallel computing technology, and feeding back to a central control system;
And the benefit analysis is integrated with a benefit analysis module, and the performance of the equipment and the system efficiency are evaluated in real time, wherein the benefit analysis comprises response time, energy consumption and maintenance cost saving.
Preferably, the blockchain data management module includes:
Constructing a blockchain network based on HYPERLEDGER FABRIC, wherein the nodes comprise a device controller and a management server;
the intelligent contract is implemented by deploying the intelligent contract for recording and verifying equipment operation logs and maintaining records, and achieving consensus by using PBFT protocol;
Data storage and verification, namely performing hash calculation on each operation and storing the hash calculation into a blockchain to ensure the integrity and non-tamper property of the data;
The multi-copy data storage and access authority control are realized, and the fault tolerance and the data security of the system are ensured;
And the data tracing and analysis is realized by a chain structure of a block chain, so that data support is provided for equipment maintenance decision.
Preferably, the network security protection module includes:
Monitoring data flow by using an AI-based network detection system, and identifying and early warning abnormal flow;
An anomaly detection model, which is to adopt a self-encoder (Autoencoder) model to perform unsupervised learning on normal flow and identify anomaly based on reconstruction errors;
The dynamic safety threshold value is adjusted, namely the threshold value of abnormal detection is adaptively adjusted based on the real-time network state, so that the detection accuracy and response speed are improved;
The safety response mechanism is used for automatically triggering isolation, alarm and protection operations when abnormal traffic is detected;
and the redundant path design is to configure a redundant network path, ensure that the redundant network path is automatically switched to a standby path when the network fails or is attacked, and ensure the continuity of the system.
Preferably, the user interface and control platform comprises:
Data visualization, namely providing real-time visual display of conveying equipment state, path condition and ore queue information;
Scheduling control, namely controlling a scheduling module through an interface and adjusting a conveying strategy and parameters;
Alarm management, namely displaying system abnormality and fault information in real time and providing alarm management and emergency operation functions;
Providing system performance analysis and benefit report interface, displaying response time, energy consumption saving and maintenance cost index;
And configuring user authority, namely configuring access authorities of different user levels, and ensuring the operation safety and data confidentiality of the system.
Preferably, the bonus function construction includes:
calculating time efficiency rewards according to the ratio of the actual conveying time to the target conveying time;
energy consumption evaluation, namely calculating energy consumption rewards according to the ratio of the current energy consumption to the target energy consumption, and exciting energy efficiency optimization;
calculating the ratio of the current maintenance cost to the preset maintenance budget for optimizing the maintenance plan;
A safety evaluation, wherein a forward rewarding is given when the operation of the equipment meets the safety standard;
queue management, namely monitoring and controlling the length of an ore queue, and avoiding the reduction of conveying efficiency caused by overlong queue.
Preferably, the real-time data synchronization includes:
the data filtering processing, namely removing noise and abnormal values in the data by using a filtering algorithm, and improving the data quality;
timestamp correction, namely ensuring the synchronization of the sensor data and the digital twin body through the timestamp correction;
predicting state updating, namely predicting state based on short-term historical data to optimize real-time adjustment equipment parameters;
establishing a bidirectional data feedback mechanism of the digital twin body and the actual equipment, and ensuring that the model is consistent with the equipment state;
And (3) performing fault early warning and response, namely performing early warning on potential faults by using a prediction model and triggering corresponding maintenance response.
Preferably, the lightweight AI model deployment includes:
data preprocessing, namely normalizing the acquired sensor data to ensure the stability and accuracy of model input;
Model compression and optimization, namely reducing the size and the computational complexity of a model through pruning and quantization technologies;
real-time reasoning optimization, namely realizing high-efficiency real-time reasoning on the edge equipment by utilizing batch processing and parallel computing technologies;
Integrating AI reasoning results with a system control module to realize automatic decision;
And the benefit analysis is used for evaluating the contribution of the AI model to the system benefit in real time, wherein the contribution comprises equipment response time, energy consumption optimization and maintenance cost control.
The invention provides an intelligent mine ore conveying management system. The beneficial effects are as follows:
1. According to the invention, by introducing the deep reinforcement learning scheduling, the digital twin system and the edge computing module, the intellectualization and the high efficiency of ore conveying management are realized, meanwhile, the scheduling strategy can be optimized in real time, the equipment state can be accurately predicted, the abnormality can be rapidly responded, the ore conveying efficiency is obviously improved, the energy consumption and the maintenance cost are reduced, and the safe operation of the equipment is ensured.
2. According to the invention, by adopting the blockchain data management module and the network safety protection module, the non-tamperable storage and the real-time network safety monitoring of the equipment operation data are realized, the data integrity and the safety are ensured through intelligent contracts and multi-copy storage, and the data safety and the operation stability of a mine conveying system are effectively improved by combining an AI-driven abnormality detection and automatic protection mechanism.
3. According to the invention, through combining the functions of data visualization, automatic control and benefit analysis by a user interface and a control platform, visual state monitoring, flexible scheduling control and accurate benefit evaluation are provided for operators, and through real-time data driving decision and control optimization, the transparency and the operation efficiency of mine management are improved, and the continuous improvement and development of intelligent mines are supported.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, an embodiment of the present invention provides an intelligent mine ore conveying management system, including:
The deep reinforcement learning scheduling module is used for realizing optimal scheduling through a deep reinforcement learning algorithm based on the state of ore conveying equipment, the condition of a conveying path and the information of an ore queue, and the system can adaptively adjust a conveying strategy, maximize the utilization rate of the equipment, reduce energy consumption and maintenance cost and improve the overall efficiency and safety of ore conveying through the introduction of the deep reinforcement learning scheduling module;
The digital twin system is used for creating a digital twin model of the conveying equipment, carrying out multi-physical-field simulation, real-time data synchronization and state prediction so as to manage the whole life cycle of the equipment, not only improving the monitoring precision of the equipment state, but also carrying out fault early warning in advance, optimizing the equipment maintenance strategy, reducing the unplanned downtime, prolonging the service life of the equipment, and improving the reliability and the operation efficiency of the whole system;
The system can rapidly respond to the data generation place through the edge calculation module, thereby improving the instantaneity and the accuracy of the anomaly detection, reducing the delay of data processing and the cost of network transmission, and further improving the intellectualization and the efficiency of mine operation;
the block chain data management module is used for recording and storing the operation log of the equipment and maintaining the record, ensures the safety and the non-tamper property of the data through the block chain, provides a safe and transparent solution for the operation record and the maintenance management of the mining equipment, ensures that the data is not tampered, and improves the credibility of the equipment management and the accuracy of decision making;
The network safety protection module is used for monitoring mine network safety in real time through an AI-driven network detection and response system, protecting potential network attack and data leakage, improving the safety of the mine network, preventing potential network attack and data leakage, and guaranteeing the safety of stable operation and data transmission of the system;
The user interface and the control platform are used for operators to monitor and control the ore conveying system in real time, provide state visualization, alarm management and an operation interface, integrate system monitoring, control, analysis and management functions, and provide omnibearing support and convenience for the operators, so that the control capability of the operators on the system and the transparency of the operators on the running state of equipment are improved, the management and maintenance of the ore conveying process are optimized, the manual misoperation is reduced, and the overall running efficiency and the safety of the system are improved.
Referring to fig. 1, in a preferred embodiment of the present invention, the deep reinforcement learning scheduling module includes:
The state space modeling is carried out, namely, the equipment state, the path state and the ore queue information are converted into multidimensional vectors to be input into a deep reinforcement learning model, the operation state (such as current speed, temperature, vibration and the like) of the ore conveying equipment, the state (such as path load, congestion condition and abrasion degree) of the conveying path and the information (such as ore type, quantity and priority) of the ore queue are subjected to data acquisition, and are integrated into a multidimensional vector form, the state space modeling provides accurate environment description for a deep reinforcement learning algorithm by comprehensively capturing and modeling key factors of an ore conveying system, can help the system to accurately reflect dynamic changes in the ore conveying process, provides high-quality input data for a dispatching algorithm, and accordingly improves the accuracy and adaptability of a dispatching strategy, and realizes more efficient ore conveying dispatching;
Establishing a multi-objective optimized rewarding function based on the conveying time, the energy consumption, the maintenance cost and the safety, integrating a plurality of optimized targets such as the conveying time, the energy consumption, the maintenance cost and the safety into a comprehensive rewarding function by a system in the rewarding function construction stage, wherein the constructed rewarding function adopts the following form:
The system comprises a machine body, a control system and a control system, wherein Tactual and Toptimal respectively represent actual conveying time and optimal conveying time, Econsumed and Et arget are actual and target energy consumption, Cmaint is current maintenance cost, Cm ax is maximum allowable maintenance cost, Isafety is a safety index, when operation meets safety standards, rewards are given, parameters alpha, beta, gamma and delta are used for weighing importance of each optimization target, and the rewarding function guides a dispatching strategy to develop towards the directions of high conveying efficiency, low energy consumption, low maintenance cost and high safety by integrating a plurality of optimization targets, so that optimal balance points can be found among different operation targets, comprehensive dispatching optimization is realized, the ore conveying efficiency is improved, the running cost and risk of the system are also reduced, and the overall benefit and safety of the system are improved;
Strategy and value network training, namely performing strategy optimization by adopting a PPO algorithm, and training by adopting a loss function formed by strategy ratio, cost function loss and strategy entropy, so that the ore conveying efficiency can be improved, the system running cost and risk can be reduced, the overall benefit and safety of the system are improved, and the loss function is used during training:
Wherein: is the policy ratio; As a dominance function, the method is used for measuring superiority of the current action relative to an average strategy; the E is a cutting range, so that unstable training caused by overlarge strategy updating amplitude is prevented, instability in a strategy gradient method is avoided by a PPO algorithm through limiting the strategy updating amplitude, meanwhile, higher training efficiency is kept, and the common training of a strategy network and a value network enables a system to continuously optimize a scheduling strategy in a complex environment and rapidly adapt to environment changes, thereby ensuring continuous optimization of the scheduling strategy in the complex and changeable mine environment, improving accuracy and robustness of scheduling decisions and improving intelligent level and operation efficiency of an ore conveying system;
The system deploys the trained scheduling strategy in the distributed architecture so as to be executed in actual mine operation, the distributed architecture supports multi-node parallel calculation, the scheduling strategy can be ensured to be fast responded to the change of field data, the strategy execution and optimization realizes dynamic scheduling through closed loop control, and the system can automatically adjust the scheduling strategy and ensure the optimal conveying path and equipment utilization rate even under the condition that the mine environment is continuously changed, thereby improving the ore conveying efficiency, reducing the operation cost and effectively coping with the uncertainty and complexity in mine operation.
Referring to fig. 1, in a preferred embodiment of the present invention, a digital twin system comprises:
The method comprises the steps of establishing a dynamic, thermal and electrical characteristic model of the conveying equipment by adopting a multi-physical field coupling method, firstly, carrying out detailed modeling on physical characteristics of the conveying equipment, such as rigidity, vibration mode, heat conduction path and the like of the conveying equipment by a system through CAD modeling and Finite Element Analysis (FEA), wherein the dynamic simulation simulates the motion characteristics of the conveying equipment, including belt tension, speed change and load distribution, the thermal model is used for predicting the temperature distribution of the equipment under different loads and environmental conditions, ensuring that the equipment cannot lose efficacy due to overheat in operation, and the electrical characteristic model focuses on the working states of a motor and a control system, such as current, voltage fluctuation, power consumption and the like, and through the establishment of the simulation model, the digital twin system can accurately simulate and predict the operation of the equipment in a virtual environment, so that data support is provided for the optimal design and operation decision of the equipment, the actual error testing cost is effectively reduced, and the design and operation efficiency of the equipment are improved;
Real-time data synchronization, namely carrying out real-time synchronization on sensor data and a digital twin body through an OPC UA protocol, ensuring that a model reflects the real-time state of equipment, providing a standardized data exchange mode, supporting data transmission and interoperability among various equipment, acquiring the latest operation data of the equipment, such as vibration, temperature, speed and other parameters in time through the real-time data synchronization, updating the state in a simulation model, ensuring the synchronous operation of the digital twin body and the actual equipment through the real-time data synchronization, enabling a digital twin system to reflect the real-time state and operation trend of the equipment, enhancing the accuracy of equipment state monitoring, and providing a reliable data base for predictive maintenance and intelligent decision of the equipment;
State estimation and updating, namely carrying out state estimation and error correction on real-time data by using a Kalman filter, wherein the Kalman filter can effectively filter noise and abnormal values in measured data by iteration between state prediction and observation updating, improves the accuracy of state estimation, improves the prediction precision and response speed of a digital twin body on equipment state, enables a system to rapidly detect the abnormality and deviation in equipment operation, and provides timely and reliable support for safe operation and maintenance of equipment;
Analyzing the running state of the equipment through a Markov chain model, carrying out fault prediction and maintenance optimization by combining an LSTM prediction model, estimating the transition probability among different states by using the historical running data of the equipment by the Markov chain model so as to predict the future state distribution of the equipment, training the historical data of the equipment by the LSTM model through a deep learning algorithm, capturing the long-term and short-term dependence of the running state of the equipment on time change, accurately predicting the future state and potential fault risk of the equipment, optimizing the maintenance plan of the equipment through dynamic analysis and prediction of the running state of the equipment, reducing the number of unplanned shutdown times, improving the availability and running efficiency of the equipment, prolonging the service life of the equipment and reducing the running and maintenance cost;
The redundancy design and fault management comprises the steps of designing a device redundancy system, automatically switching to standby devices or systems when devices or sensors fail, ensuring continuous operation of the systems, forming a multi-level protection mechanism by the aid of the standby devices and the redundancy sensors, improving the recovery capability of the systems when the devices or the sensors fail, enabling a fault management module to rapidly identify the faults and perform corresponding switching and recovery operation through monitoring the operating states of the devices and the sensors in real time, reducing the influence of the faults on the operation of the systems, improving the reliability and the robustness of the systems, ensuring that the systems can be quickly recovered to operate when the devices or the sensors fail, reducing downtime and production loss, and ensuring the continuity and the high efficiency of ore conveying management systems.
Referring to fig. 1, in a preferred embodiment of the present invention, the edge calculation module includes:
Edge hardware configuration, namely carrying out edge calculation by using NVIDIA Jetson Nano, deploying EdgeX Foundry platform for data management, deploying EdgeX Foundry platform as a data management and service integration framework, supporting data access of various sensors and devices, wherein EdgeX Foundry is an open and modularized edge calculation platform, providing a device service layer, middleware and an application service layer, being capable of effectively managing complete processes from data acquisition and processing to reasoning, jetson Nano providing strong parallel computing capacity by virtue of an embedded GPU thereof, being capable of accelerating the reasoning process of an AI model, edgeX Foundry integrating data of various sensors and devices through a loosely coupled micro-service architecture, providing functions of data management, a rule engine, edge analysis and the like, and providing a flexible and efficient solution for on-site data processing of an intelligent mine, thereby reducing data transmission delay and dependence on cloud computing resources, and enhancing response speed and autonomous decision making capability of the system;
The data acquisition and preprocessing, namely acquiring sensor data through an equipment service layer, and performing standardization and denoising processing, wherein the denoising processing is used for improving the quality and the reliability of the data by filtering irrelevant noise and abnormal data, ensuring the accuracy of subsequent AI reasoning, improving the abnormality detection accuracy of the system, reducing misjudgment and delay caused by a data problem, and improving the reliability and the instantaneity of mine site decision;
the lightweight AI model deployment, namely a TensorFlow Lite-deployed quantized convolutional neural network is used for real-time anomaly detection, tensorFlowLite provides a lightweight inference engine, is specially optimized for edge equipment, can realize efficient model reasoning under limited computing resources, greatly reduces the volume and the computation complexity of the model through quantization, enables the convolutional neural network to smoothly run on edge computing equipment such as Jetson Nano, realizes real-time anomaly detection, and can timely take corrective measures through quick response to anomaly conditions so as to avoid shutdown and loss caused by equipment faults;
The system utilizes batch processing and parallel computing technology to realize efficient real-time reasoning, feeds back to a central control system, utilizes batch processing and parallel computing technology to rapidly input collected pretreatment data into an AI model for reasoning, timely transmits a reasoning result to the central control system to provide real-time decision support for mine management, and in addition, the system can automatically generate a feedback report according to the reasoning result and trigger corresponding alarming and processing measures to ensure safe operation of mine equipment, and the parallel computing utilizes a Jetson Nano multi-core framework to further accelerate the data processing and model reasoning process, and can rapidly respond to the occurrence of abnormality by feeding back the reasoning result to the central control system in real time, thereby reducing risks and losses, enabling the system to respond at the moment of the occurrence of the abnormality, providing effective decision support, improving continuity and reliability of mine operation and reducing unplanned shutdown and production interruption caused by the abnormality of the equipment;
The system uses big data analysis technology to process historical data, identifies potential optimization opportunities and efficiency improvement points, enables a management layer to see the benefit change trend of the system more clearly through regular benefit reports, adjusts strategies and optimization measures in time, enables the system to find weak links and optimization space in operation of the equipment through accurate benefit assessment and trend analysis, continuously improves the equipment performance, reduces operation cost and improves the overall benefit of mine operation.
Referring to fig. 1, in a preferred embodiment of the present invention, a blockchain data management module includes:
constructing a blockchain network based on HYPERLEDGER FABRIC, wherein nodes comprise equipment controllers and management servers, HYPERLEDGER FABRIC is an open-source enterprise-level blockchain platform, a modularized architecture and a flexible authority management mechanism are provided, the blockchain network of HYPERLEDGER FABRIC realizes data sharing and cooperation among a plurality of nodes through a distributed account book technology, each node stores a complete copy of the account book, when new equipment operation data is written into the account book, each node confirms and synchronizes through a consensus protocol, the consistency and the integrity of all node account books are ensured, safe and reliable data sharing and cooperation between each equipment controller and the management server of a mine are realized, the transparency and the credibility of the data are enhanced, thereby effectively preventing data tampering and loss, and providing a solid foundation for operation management of mine equipment;
The intelligent contract is used for recording and verifying equipment operation logs and maintaining the records, and the PBFT protocol is used for achieving consensus, the intelligent contract automatically processes equipment operation and maintenance events through predefined logic, the possibility of manual intervention is avoided, and human errors are reduced, wherein the PBFT protocol is an efficient consensus algorithm which is suitable for enterprise-level blockchain application, and can still ensure normal operation and data consensus of the blockchain network when part of nodes have unreliable behaviors, so that the blockchain network can efficiently record the equipment operation logs and maintain the records, ensure real-time property and integrity of data, simultaneously improve the anti-interference capability and data consistency of the system under a complex mine environment, reduce errors and omission of operation data, and provide reliable data support for decision-making of equipment maintenance;
The hash calculation is carried out on each operation and stored in a blockchain, so that the integrity and the non-tamper property of the data are ensured, the operation data are converted into unique digital fingerprints through the hash calculation, and even tiny data change can cause the change of hash values, so that the tampering behavior of the data can be rapidly detected, the non-tamper property of the data is ensured, and the integrity of the whole chain can be damaged through any modification of the historical data;
The multi-copy data storage and the access authority management are realized, the fault tolerance and the data security of the system are ensured, the multi-copy storage utilizes the distributed characteristic of a blockchain, and the redundancy backup of the data is realized by storing a complete ledger copy at each node, so that the fault tolerance and the data recovery capability of the system are improved, the safety of the data and the operation compliance of the system are ensured, and meanwhile, the reliability and the risk resistance of the system are enhanced by the multi-copy storage, and the integrity and the availability of the data can be ensured by the system even under the condition that part of nodes fail;
And the data tracing and analysis is realized by a chain structure of a block chain, so that data support is provided for equipment maintenance decision.
Referring to fig. 1, in a preferred embodiment of the present invention, a network security protection module includes:
Monitoring data flow by using an AI-based network detection system, identifying and early warning abnormal flow, deploying a flow monitoring sensor at a key network node by the system, acquiring data flow information of a network layer and an application layer by a Deep Packet Inspection (DPI) technology, analyzing the data flow by using an AI algorithm, identifying common network behavior characteristics and flow modes such as the size of a data packet, the transmission frequency, a communication protocol and the like, and once the abnormal flow characteristics are found, immediately generating an alarm by the system and recording an abnormal event, thereby ensuring the visibility and controllability of mine network safety, timely identifying and early warning at the early stage of attack occurrence, reducing the potential threat of network attack to a mine intelligent conveying system, and improving the safety and stability of the system;
The abnormal detection model comprises the steps of performing unsupervised learning on normal flow by adopting a self-encoder (Autoencoder) model, identifying abnormality based on reconstruction errors, collecting normal network flow data by a system, performing model training by utilizing the self-encoder, wherein the self-encoder consists of an encoder and a decoder, the encoder compresses input data into a low-dimensional characteristic space, the decoder tries to reconstruct original data, after training is finished, the self-encoder can accurately reconstruct the normal flow data, but when abnormal flow is encountered, reconstruction errors are increased, the abnormal is detected by the reconstruction errors, the system can effectively cope with various unknown or novel network threats, the limitations of the traditional rule and signature detection method are avoided, the model can adaptively update the normal flow mode by unsupervised learning, the high sensitivity to a dynamic change network environment is maintained, and the overall detection accuracy and reaction speed are improved;
the dynamic safety threshold value adjustment, namely, based on the real-time network state, the threshold value of the abnormal detection is adaptively adjusted to improve the detection accuracy and response speed, the system continuously evaluates the current network flow mode and the reconstruction error distribution of the self-encoder, the detection threshold value is dynamically adjusted according to the error change trend to avoid false alarm and missing report, the dynamic safety threshold value adjustment improves the accuracy and response speed of the abnormal detection, reduces the false alarm and missing report phenomena caused by the fixed threshold value, so that the system can stably and reliably operate under different network loads and environmental conditions, and the protection capability of mine network safety is enhanced;
The system firstly carries out quick isolation on the abnormal flow, prevents the abnormal flow from further spreading, prevents the abnormal flow from affecting other parts of the system, then sends alarm information to a network manager in a mode of interface or short message, mail and the like to describe the nature and the influence range of an abnormal event in detail, shortens the response time from threat detection to taking protective measures, reduces the loss possibly caused by network attack, and simultaneously improves the efficiency of processing the safety event by the quick isolation and alarm function of the system, thereby providing stronger guarantee for mine network safety management;
The redundant path design also combines the load balancing technology, disperses the flow under normal conditions, improves the transmission efficiency and stability of the whole network, judges the health state of the main path by utilizing the real-time monitoring technology, and rapidly switches to the standby path when detecting the path failure or abnormality, avoids data interruption, effectively improves the reliability and the anti-risk capability of the mine network, ensures the continuity and the stability of data transmission, and can still keep normal operation even under the condition of network failure or attack.
Referring to fig. 1, in a preferred embodiment of the present invention, the user interface and control platform comprises:
The data visualization is realized by converting complex operation data into visual elements which are easy to understand, helping a user to quickly master the operation state of the system, combining a real-time data flow and a graphic rendering algorithm, realizing dynamic updating and accurate display of the data, and improving the management level and the operation efficiency of an ore conveying system by the user through real-time state display;
The dispatching control is realized by controlling a dispatching module through an interface, adjusting a conveying strategy and parameters, allowing a user to directly interfere and adjust the conveying strategy and related parameters through the interface, adjusting dispatching parameters such as equipment operation mode, priority setting, path selection and the like through a control panel on the interface, controlling the conveying flow of ores in real time, and enabling a system to quickly execute dispatching adjustment through the direct operation of the user interface, optimizing the conveying path and equipment utilization rate, and improving the efficiency and safety of mine conveying;
The alarm management, namely displaying system abnormality and fault information in real time and providing alarm management and emergency operation functions, wherein the system receives abnormal events and fault alarms from various modules (such as a network safety protection module and an edge calculation module), the alarm information can be displayed in the forms of red highlighting, popup windows and the like on an interface, so that the alarm management attracts the attention of a user, the user can check detailed information, occurrence time and influence range of the alarm through the interface, and execute corresponding measures such as remote restarting equipment, adjusting operation parameters or notifying field personnel to process according to a preset emergency operation guideline, the alarm management function improves the response speed and processing efficiency of the system to the abnormal events, and the user can quickly take measures through real-time alarm prompt and emergency operation guideline, so that the loss caused by equipment faults or abnormal operation is reduced;
providing a system performance analysis and benefit report interface, displaying indexes of response time, energy consumption saving and maintenance cost, processing and analyzing system operation data by a performance analysis module by utilizing a big data analysis and machine learning algorithm, comparing and analyzing real-time data and historical data by the system according to preset performance indexes, identifying performance bottlenecks and improvement opportunities, quantifying economic benefits brought by equipment and strategy adjustment by a user through detailed benefit report, supporting decision optimization and continuous improvement, and improving the overall efficiency and profitability of a mine conveying system;
Configuring access rights of different user levels, ensuring system operation safety and data confidentiality, realizing user access behavior strict control through a role-based access control (RBAC) model, improving operation safety and data protection level of a mine transportation management system, preventing unauthorized access and operation, ensuring confidentiality and integrity of sensitive information, and simultaneously ensuring safe operation of the mine transportation system by a strict rights management and audit mechanism, wherein the system can quickly identify and respond to abnormal operation.
Referring to fig. 1, in a preferred embodiment of the present invention, the bonus function construction includes:
Calculating time efficiency rewards by calculating the ratio of the actual delivery time to the target delivery time, the time efficiency rewards being calculated by the following formula:
where α is a weight coefficient for adjusting the impact of time efficiency in the overall prize.
According to the formula, the system can excite the scheduling strategy to minimize the gap between the actual conveying time and the target time, the time efficiency calculation method quantifies the time performance of the conveying task by comparing the actual conveying time with the target time, and the rewarding function takes the shortened actual conveying time as a target so as to encourage the scheduling system to optimize the use and path selection of equipment, thereby improving the overall conveying efficiency, improving the response speed and the operation efficiency of the ore conveying system, accelerating the turnover and processing speed of ores by reducing the conveying time, optimizing the utilization rate of the equipment and improving the overall operation efficiency of mines;
energy consumption evaluation, namely calculating energy consumption rewards according to the ratio of current energy consumption to target energy consumption, exciting energy efficiency optimization, and calculating the energy consumption rewards through the following formula:
Where β is a weight coefficient for adjusting the impact of energy consumption on rewards.
The formula stimulates the dispatching system to reduce the energy consumption, and controls the actual energy consumption to be even lower in the target energy consumption range, so that energy conservation optimization is realized, the operation cost of the ore conveying system is effectively reduced by the energy consumption evaluation function, the system can reduce the power consumption and the energy cost by optimizing the energy consumption management, the economic benefit of a mine is improved, and meanwhile, the system is also beneficial to reducing the carbon emission and supporting the construction of a green mine;
Calculating the ratio of the current maintenance cost to the preset maintenance budget for optimizing the maintenance plan, wherein the maintenance cost rewards are calculated by the following formula:
Where γ is a weight coefficient, and the influence of maintenance costs on rewards is adjusted.
Through the formula, the system can encourage the scheduling strategy to control the maintenance cost within the budget range while meeting the conveying requirement, the maintenance cost evaluation function helps mines reduce unplanned shutdown and high maintenance cost, and through intelligent scheduling and maintenance optimization, the system can prolong the service life of equipment, reduce maintenance frequency and cost and realize economic and efficient management of equipment maintenance;
The safety evaluation comprises the steps of giving forward rewards when the operation of equipment meets the safety standard, ensuring that all the operations are carried out in a safety range by continuously monitoring the operation state of the equipment, wherein the forward rewards encourage a system to preferentially execute an operation mode with high safety, reduce risks in the operation of the equipment, improve the safety management level of an ore conveying system, reduce accidents and injuries through forward excitation of the safety operation, and ensure the safety of mine operation and the life safety of staff;
Monitoring and controlling the length of an ore queue, avoiding the reduction of conveying efficiency caused by overlong of the queue, wherein the queue management is used for monitoring and controlling the length of the ore queue, avoiding the reduction of conveying efficiency caused by overlong of the queue, and calculating the queue rewards through the following formula:
The E is a weight coefficient for adjusting the influence of the queue length on rewards, the formula stimulates the scheduling system to keep the ore queue within the optimal length range, so that resource waste caused by queue backlog or discussion short is avoided, the scheduling efficiency of the ore conveying system is improved by the queue management function, the equipment utilization rate and the conveying path selection can be optimized by reasonably controlling the ore queue length, efficiency reduction caused by queue problems is avoided, and stable and efficient operation of conveying operation is realized.
Referring to fig. 1, in a preferred embodiment of the present invention, real-time data synchronization includes:
The data filtering processing, namely removing noise and abnormal values in the data by using a filtering algorithm, improving the data quality, wherein the Kalman filtering algorithm is an optimal estimator based on a state space model, and by combining the current measured value with the predicted state, the influence of noise is minimized, so that the accurate estimation of the real equipment state is realized, and by the data filtering processing, the system can effectively remove random noise and abnormal values in the sensor data, ensure that the real-time data transmitted to a digital twin body has high quality and high reliability, thereby enhancing the accuracy of equipment state monitoring and providing a solid data base for the subsequent state prediction and fault early warning;
The system adjusts the unified time reference of all the sensor data through a time stamp correction mechanism, and the time synchronization protocol (NTP) and PTP provide high-precision time alignment functions, so that each sensor data can be seamlessly integrated into a digital twin model, the synchronization of the model and the running state of the actual equipment is maintained, and the functions promote the reflection precision of the digital twin body to the actual state of the equipment, so that the linkage of the simulation model and the actual equipment is more compact and reliable;
the state prediction is updated, state prediction is carried out based on short-term historical data so as to optimize real-time adjustment of equipment parameters, a time sequence prediction model such as ARIMA (autoregressive integral moving average) or LSTM (long-short-term memory network) is adopted by the system to predict the state parameters of the equipment, the model predicts the state change such as temperature, vibration or load change and the like in the next moment or in a short term by using equipment operation data of a past period, the system can adjust the operation parameters of the equipment such as adjusting speed or load in advance according to the prediction result, possible abnormal or overload conditions are avoided, the system can adjust the equipment parameters in advance through accurate prediction of future states, potential abnormal and overload conditions are avoided, the stability and the operation efficiency of the equipment are improved, and the service life of the equipment is prolonged;
The two-way feedback mechanism is used for establishing a two-way data feedback mechanism of the digital twin body and the actual equipment, ensuring that the model is consistent with the state of the equipment, mutually transmitting and comparing the simulation result of the digital twin body and the state data of the actual equipment through an edge computing node or an industrial Internet of things gateway, timely feeding back the prediction and suggestion of the digital twin body to the equipment control system, simultaneously feeding back the actual operation data of the equipment to the digital twin body for model correction and updating, forming a closed-loop control system through real-time state comparison and data exchange by the two-way feedback mechanism, enhancing the control force and response speed of the digital twin body on the operation of the equipment, rapidly identifying and correcting the deviation through real-time data interaction, ensuring the operation of the equipment in an optimal state, and improving the accuracy and reliability of an ore conveying system;
The method comprises the steps of carrying out early warning on potential faults by utilizing a prediction model and triggering corresponding maintenance response, carrying out complex pattern recognition and trend analysis on equipment operation data by utilizing a deep learning model by utilizing the fault early warning and response, and capturing potential fault signals of equipment in advance, so that the occurrence of equipment faults and unplanned shutdown is reduced, the availability and operation efficiency of the equipment can be improved, the maintenance cost and equipment loss are reduced by adopting the system through the early recognition and treatment of potential problems, and the stability and the sustainable operation capability of an ore conveying system are enhanced.
Referring to FIG. 1, in a preferred embodiment of the present invention, a lightweight AI model deployment includes:
The data preprocessing function improves the robustness of the model to the input data, so that the model can stably work under different data sources and different working conditions, the influence of the data quality problem on the reasoning result is reduced, and the preprocessed data provides high-quality input for the AI model, thereby improving the accuracy and reliability of reasoning and the intelligent level of an ore conveying system;
Model compression and optimization, namely, model size and computational complexity are reduced through pruning and quantization technology, a system compresses the model through pruning and quantization technology, redundant calculation amount of the model is reduced through removing neuron connection with small contribution to reasoning, model parameters are converted into 8-bit integers from 32-bit floating point numbers through quantization technology, storage and calculation requirements of the model are reduced, and by reducing model size and computational complexity, calculation load and energy consumption of edge equipment are reduced, service life of the equipment is prolonged, and stability of physical performance is guaranteed;
The real-time reasoning optimization, namely realizing high-efficiency real-time reasoning on the edge equipment by utilizing batch processing and parallel computing technology, dividing the data acquired in real time into small batches by utilizing a multi-core processor or a GPU of the equipment for parallel reasoning, improving the reasoning speed by a batch processing mode, reducing the calculation cost of each reasoning process, ensuring that the system can still keep stable real-time reasoning capacity under the condition of high load by combining asynchronous task scheduling by the parallel computing technology, greatly improving the response speed of the system to the state change of the equipment by a real-time reasoning optimization function, enabling ore conveying management to make intelligent decisions in a short time, timely adjusting conveying strategies and parameters, and providing powerful support for efficient operation and intelligent decisions of mines;
Integrating an AI reasoning result with a system control module to realize automatic decision, realizing data-driven automatic decision by seamlessly butting the AI reasoning result with the control module, directly influencing the control strategy of equipment by the AI model on the analysis result of real-time data to form a self-adaptive and self-optimized intelligent control system, thereby reducing the risk of human intervention and misoperation, continuously optimizing the running state and the conveying strategy of the equipment by the real-time AI reasoning and automatic control, improving the efficiency and reliability of ore conveying, and improving the productivity and economic benefit of mines;
And the benefit analysis provides quantitative evaluation of the actual value of the AI model in the mine conveying system, helps a management layer to comprehensively know the economic benefit and operation improvement effect of AI deployment, and meanwhile, through detailed benefit report, the system can identify optimized key points, further enhance the intelligence and economy of mine operation and provide data support and decision support for continuous improvement.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.