技术领域technical field
本发明涉及智能大厦空调系统的调度策略评估，尤其涉及一种不确定环境下智能大厦空调系统的调度策略评估方法。The invention relates to dispatching strategy evaluation of an air-conditioning system of an intelligent building, in particular to a dispatching strategy evaluation method of an air-conditioning system of an intelligent building under an uncertain environment.
背景技术Background technique
近年来，各国开始重视发展智能电网(SmartGrid)技术。作为智能电网的重要组成部分，智能大厦也受到了高度的关注，逐渐在国内外兴起。智能大厦依赖于现代建筑科技、计算机技术、控制技术以及通信技术的发展，内部系统高度集成、资源管理更加高效，同时也更加节能舒适，是未来建筑的发展趋势。在智能大厦的内部系统中，楼宇自动化系统负责给用户提供健康舒适的使用环境，同时保证大厦的经济运行和智能化管理，是智能大厦的核心支柱系统。作为楼宇自动化系统的子系统，除了使用环保的建筑材料以及先进的设备之外，空调系统通过对大厦内外环境的温湿度进行监测并根据适当的策略合理调度空调机组及通风设备，能够在保证用户舒适度的前提下最大限度地降低智能大厦的能耗。因此，调度策略在智能大厦的空调系统中占有十分重要的地位。In recent years, countries have begun to attach importance to the development of smart grid (SmartGrid) technology. As an important part of the smart grid, smart buildings have also received high attention and are gradually rising at home and abroad. Intelligent buildings rely on the development of modern building technology, computer technology, control technology and communication technology. The internal system is highly integrated, resource management is more efficient, and it is also more energy-saving and comfortable. This is the development trend of future buildings. In the internal system of the intelligent building, the building automation system is responsible for providing users with a healthy and comfortable environment, while ensuring the economic operation and intelligent management of the building. It is the core pillar system of the intelligent building. As a subsystem of the building automation system, in addition to using environmentally friendly building materials and advanced equipment, the air conditioning system monitors the temperature and humidity inside and outside the building and dispatches air conditioning units and ventilation equipment according to appropriate strategies to ensure that users Under the premise of comfort, the energy consumption of intelligent buildings can be minimized. Therefore, dispatching strategy occupies a very important position in the air-conditioning system of the intelligent building.
在理想条件下对控制系统中的调度策略进行分析评估时，通常会遇到难以定量分析、评价方法不够准确等问题。物理世界存在固有的动态性，因而智能大厦所处环境的不确定性大大增加了调度策略评估的难度，目前缺少可靠有效的方法。在设计系统时，如何考虑不确定环境对智能大厦的影响，制定出合理的调度策略以满足智能大厦在节能和舒适度方面的需求，是空调系统设计者面临的一大挑战。When analyzing and evaluating the scheduling strategy in the control system under ideal conditions, it usually encounters problems such as difficult quantitative analysis and inaccurate evaluation methods. The physical world is inherently dynamic, so the uncertainty of the environment where the smart building is located greatly increases the difficulty of evaluating scheduling strategies, and there is currently a lack of reliable and effective methods. When designing the system, how to consider the impact of the uncertain environment on the smart building and formulate a reasonable scheduling strategy to meet the needs of the smart building in terms of energy saving and comfort is a major challenge for the air conditioning system designer.
现有的评估方法更多的是侧重在系统的能耗和性能等方面，这些方法都没有脱离系统针对调度策略进行定量的评估，尤其是在不确定环境下。构建一套不确定环境下智能大厦空调系统的调度策略评估方法，能帮助空调系统的设计者在设计系统时进行分析和评估，作出正确的决策。Existing evaluation methods focus more on the energy consumption and performance of the system, and these methods do not deviate from the system for quantitative evaluation of scheduling strategies, especially in uncertain environments. Constructing a scheduling strategy evaluation method for intelligent building air-conditioning system in an uncertain environment can help air-conditioning system designers analyze and evaluate the system and make correct decisions.
发明内容Contents of the invention
本发明的目的是弥补当前在不确定环境下智能大厦空调系统的调度策略方面的空白，提供一种对不确定环境下智能大厦空调系统的调度策略进行定量分析评估的方法，实现了调度策略和控制器模型的分离，并可以给出对模型进行属性查询的结果，从而实现了对不确定环境下智能大厦空调系统调度策略的分析评估。The purpose of the present invention is to make up for the current gap in the dispatching strategy of the air-conditioning system of the intelligent building in an uncertain environment, provide a method for quantitative analysis and evaluation of the dispatching strategy of the air-conditioning system of the intelligent building in an uncertain environment, and realize the dispatching strategy and The controller model is separated, and the result of attribute query of the model can be given, so as to realize the analysis and evaluation of the scheduling strategy of the air-conditioning system of the intelligent building in an uncertain environment.
实现本发明目的的具体技术方案：The concrete technical scheme that realizes the object of the present invention:
一种不确定环境下智能大厦空调系统的调度策略评估方法，包括以下步骤：A scheduling strategy evaluation method for an air-conditioning system of an intelligent building in an uncertain environment, comprising the following steps:
步骤一：对天气、设备、用户、房间、房间温度监测器、控制器以及调度策略建模，生成空调系统的NPTA模板；Step 1: Model the weather, equipment, users, rooms, room temperature monitors, controllers, and scheduling strategies to generate an NPTA template for the air conditioning system;
步骤二：对空调系统的不确定环境参数以及可配置参数进行配置；Step 2: Configure the uncertain environmental parameters and configurable parameters of the air conditioning system;
步骤三：将空调系统的设计约束转化为查询属性，使用UPPAAL-SMC作为查询引擎对系统模型进行随机模拟运行，得到结果数据；Step 3: Transform the design constraints of the air conditioning system into query attributes, use UPPAAL-SMC as the query engine to run random simulations on the system model, and obtain the result data;
步骤四：根据结果数据绘制图形和表格，并以此作为依据对策略进行定量分析和评估。Step 4: Draw graphs and tables based on the resulting data, and use this as a basis for quantitative analysis and evaluation of the strategy.
本发明提出的不确定环境下智能大厦空调系统的调度策略评估方法中，系统建模分为NPTA模板构建和参数配置两部分，其中参数配置由不确定环境参数及可配置参数两部分构成，NPTA模板描述了各个模型的行为模式。In the scheduling strategy evaluation method of the intelligent building air-conditioning system under the uncertain environment proposed by the present invention, the system modeling is divided into two parts: NPTA template construction and parameter configuration, wherein the parameter configuration is composed of uncertain environment parameters and configurable parameters. NPTA Templates describe the behavioral patterns of individual models.
本发明提出的不确定环境下智能大厦空调系统的调度策略评估方法中，所述属性查询的形式如下：In the scheduling strategy evaluation method of the intelligent building air-conditioning system under the uncertain environment proposed by the present invention, the form of the attribute query is as follows:
Pr[<＝t](<>energy>＝E)；Pr[<=t](<>energy>=E);
Pr[<＝t](<>rangeout[rid]>＝tout)；Pr[<=t](<>rangeout [rid]>=tout );
式中，t为时间约束，E为能耗约束，energy>＝e表示消耗的能量超过e，rangeout[rid]>＝tout表示房间rid超出温度限制的时间超过tout。In the formula, t is the time constraint, E is the energy consumption constraint, energy>=e means that the energy consumed exceeds e, and rangeout [rid]>=tout means that the room rid exceeds the temperature limit for more than tout .
优选地，本发明提出的不确定环境下智能大厦空调系统的调度策略评估方法中，所述参数配置包括不确定环境参数及可配置参数。其中不确定环境参数为概率分布，即天气模型、用户模型以及加热器模型中所设定的概率分布函数，设定不同的概率分布函数会影响环境的不确定性。而可配置参数指房间温度需求、加热器功率输出等需要随具体情境的变化而修改但较为固定的参数。所述参数配置可以被NPTA模板访问。Preferably, in the scheduling strategy evaluation method for an air-conditioning system of an intelligent building under an uncertain environment proposed by the present invention, the parameter configuration includes uncertain environment parameters and configurable parameters. The uncertain environmental parameters are probability distributions, that is, the probability distribution functions set in the weather model, user model, and heater model. Setting different probability distribution functions will affect the uncertainty of the environment. The configurable parameters refer to room temperature requirements, heater power output and other parameters that need to be modified according to the specific situation, but are relatively fixed. The parameter configuration can be accessed by the NPTA template.
优选地，本发明提出的不确定环境下智能大厦空调系统的调度策略评估方法中，所述对用户建模，每个用户与所处的房间对应。用户行为服从不确定环境参数中的概率分布，在a时刻到b时刻间以概率分布函数生成的概率进入房间并通知控制器用户到达，在c时刻至d时刻间以概率分布函数生成的概率离开房间并通知控制器用户离开。Preferably, in the scheduling strategy evaluation method for an air-conditioning system of an intelligent building under an uncertain environment proposed by the present invention, in the user modeling, each user corresponds to a room where the user is located. User behavior obeys the probability distribution in uncertain environment parameters, enters the room with the probability generated by the probability distribution function between time a and b and notifies the controller of the user's arrival, and leaves with the probability generated by the probability distribution function between time c and d room and notifies the controller that the user is leaving.
优选地，本发明提出的不确定环境下智能大厦空调系统的调度策略评估方法中，所述对天气建模模拟了智能大厦外界天气的温度变化。空调系统开始运行后，室外天气从“初始”状态按照概率分布函数生成的概率跳转到不同的天气即“雨天”或“晴天”状态，跳转的同时设置这两种天气的初始温度。外界环境温度服从温度变化函数，模型中采用正弦函数模拟一天的温度变化。Preferably, in the scheduling strategy evaluation method for the air-conditioning system of an intelligent building under an uncertain environment proposed by the present invention, the weather modeling simulates the temperature change of the external weather of the intelligent building. After the air-conditioning system starts to operate, the outdoor weather jumps from the "initial" state to a different weather state, namely "rainy day" or "sunny day" according to the probability generated by the probability distribution function, and the initial temperature of these two kinds of weather is set at the same time as the jump. The external ambient temperature obeys the temperature change function, and the sine function is used in the model to simulate the temperature change of a day.
优选地，本发明提出的不确定环境下智能大厦空调系统的调度策略评估方法中，所述对加热器建模用于统计大厦各个房间的总能耗。控制器根据房间温度状态控制加热器打开或关闭。在“开”状态下，加热器根据当前加热器的实际功率使用公式统计各个房间的累积能耗。Preferably, in the scheduling strategy evaluation method for an air-conditioning system of an intelligent building under an uncertain environment proposed by the present invention, the modeling of the heater is used to count the total energy consumption of each room of the building. The controller controls the heater to turn on or off according to the room temperature state. In the "on" state, the heater uses the formula according to the actual power of the current heater Calculate the cumulative energy consumption of each room.
优选地，本发明提出的不确定环境下智能大厦空调系统的调度策略评估方法中，所述对房间建模描述了房间的温度变化。智能大厦中的每个房间根据用户的特点有不同的温度配置，温度配置即温度上限和下限以及舒适温度需求。房间内的温度变化受相邻房间的影响。在“降温”状态和“加热”状态下，房间温度会根据相应状态下的函数进行变化。Preferably, in the scheduling strategy evaluation method for an air-conditioning system of an intelligent building under an uncertain environment proposed by the present invention, the room modeling describes the temperature change of the room. Each room in an intelligent building has different temperature configurations according to the characteristics of users. The temperature configuration refers to the upper and lower limits of temperature and the comfort temperature requirements. Temperature changes in a room are influenced by adjacent rooms. In the "Cooling" state and "Heating" state, the room temperature changes as a function of the corresponding state.
优选地，本发明提出的不确定环境下智能大厦空调系统的调度策略评估方法中，所述对温度监测器建模用于统计各房间温度超出其要求的温度范围的时间，每个房间对应一个温度监测器。若某房间内的温度不在其需求的温度范围内，房间内用户会感觉“过冷”或“过热”，这种情况下用户的舒适度较差，因此房间温度监测器模型反映出了房间内用户的舒适度。Preferably, in the scheduling strategy evaluation method of the intelligent building air-conditioning system under the uncertain environment proposed by the present invention, the modeling of the temperature monitor is used to count the time when the temperature of each room exceeds its required temperature range, and each room corresponds to a temperature monitor. If the temperature in a room is not within the required temperature range, the user in the room will feel "too cold" or "too hot". In this case, the user's comfort level is poor, so the room temperature monitor model reflects the User comfort.
优选地，本发明提出的不确定环境下智能大厦空调系统的调度策略评估方法中，所述对控制器建模，设定了空调系统中控制器的工作方式，根据各个房间的温度调度加热器对房间供暖。该模型负责根据某种调度策略选择房间供暖。控制器中包含一个由各个房间形成的优先级队列，该队列隐含了各个房间的优先级，同时根据房间是否在队列中也能说明该房间中是否有用户。当接收到用户模型发出的用户到达的消息时，控制器将该房间号加入到队列中，同时向其他模型广播用户到达的消息。当控制器接收到房间发出的加热请求或降温请求后，会根据当前的策略选择出一个合适的房间进行加热。当控制器接收到用户模型发出的离开消息时，先将该房间从队列中移除，再判断当前队列情况：若当前队列不为空，则选择房间并通知该房间加热请求被允许，同时禁止其他房间加热，最后通知加热器打开并设置当前功率；若队列为空，则通知所有房间停止加热，并通知加热器关闭。Preferably, in the scheduling strategy evaluation method of the intelligent building air-conditioning system under the uncertain environment proposed by the present invention, the controller is modeled, the working mode of the controller in the air-conditioning system is set, and the heater is scheduled according to the temperature of each room Heating the room. The model is responsible for choosing room heating according to some scheduling strategy. The controller contains a priority queue formed by each room, which implies the priority of each room, and also indicates whether there are users in the room according to whether the room is in the queue. When receiving the user arrival message sent by the user model, the controller adds the room number to the queue, and broadcasts the user arrival message to other models at the same time. When the controller receives the heating request or cooling request from the room, it will select a suitable room for heating according to the current strategy. When the controller receives the leave message sent by the user model, it first removes the room from the queue, and then judges the current queue situation: if the current queue is not empty, select the room and notify the room that the heating request is allowed, and at the same time prohibit Other rooms are heated, and finally the heater is notified to turn on and the current power is set; if the queue is empty, all rooms are notified to stop heating, and the heater is notified to turn off.
优选地，本发明提出的不确定环境下智能大厦空调系统的调度策略评估方法中，所述策略模型是对控制器中的调度策略进行建模。调度策略被写入到控制器中，同时又独立于控制器，这是因为控制器建模仅仅规定了控制器的行为模式，具体的策略决定了控制器的实际行为动作，控制器根据策略调度加热器对各个房间供暖。调度策略需要房间队列信息以及各个房间的温度数据。多个策略可以被同时写入，控制器根据当前采用的策略号调用具体策略进行房间的选择。Preferably, in the dispatching strategy evaluation method of an air-conditioning system of an intelligent building under an uncertain environment proposed by the present invention, the strategy model is a modeling of the dispatching strategy in the controller. The scheduling strategy is written into the controller and is independent of the controller at the same time. This is because the controller modeling only specifies the behavior mode of the controller, and the specific strategy determines the actual behavior of the controller. The controller schedules according to the strategy Heaters heat the individual rooms. The scheduling policy requires room queue information and temperature data for each room. Multiple strategies can be written at the same time, and the controller invokes a specific strategy to select a room according to the currently adopted strategy number.
本发明的有益效果：本发明可以准确地反映不同调度策略在能耗和用户舒适度方面的表现，帮助智能大厦空调系统的设计者对调度策略进行定量分析评估，进行选取和修改，使智能大厦更加节能舒适。Beneficial effects of the present invention: the present invention can accurately reflect the performance of different scheduling strategies in terms of energy consumption and user comfort, and help the designers of air-conditioning systems of intelligent buildings to quantitatively analyze and evaluate the scheduling strategies, select and modify them, and make intelligent buildings More energy-saving and comfortable.
附图说明Description of drawings
图1为本发明框架图；Fig. 1 is a frame diagram of the present invention;
图2为本发明流程图；Fig. 2 is a flowchart of the present invention;
图3为本发明中对用户建模的示意图；Fig. 3 is a schematic diagram of user modeling in the present invention;
图4为本发明中对天气建模的示意图；Fig. 4 is a schematic diagram of weather modeling in the present invention;
图5为本发明中对加热器建模的示意图；Fig. 5 is the schematic diagram of heater modeling in the present invention;
图6为本发明中对房间建模的示意图；Fig. 6 is a schematic diagram of room modeling in the present invention;
图7为本发明中对房间温度监测器建模的示意图；Fig. 7 is a schematic diagram of modeling a room temperature monitor in the present invention;
图8为本发明中对控制器建模的示意图；Fig. 8 is a schematic diagram of controller modeling in the present invention;
图9为本发明中对策略建模的示意图。Fig. 9 is a schematic diagram of policy modeling in the present invention.
具体实施方式detailed description
结合以下具体实施例和附图，对本发明作进一步的详细说明。实施本发明的过程、条件、试验方法等，除以下专门提及的内容之外，均为本领域的普遍知识和公知常识，本发明没有特别限制内容。The present invention will be further described in detail in conjunction with the following specific embodiments and accompanying drawings. The process, conditions, test methods, etc. for implementing the present invention, except for the content specifically mentioned below, are common knowledge and common knowledge in this field, and the present invention has no special limitation content.
本发明提出了一种不确定环境下智能大厦空调系统的调度策略评估方法，图1是本发明中调度策略评估方法的框架图。The present invention proposes a scheduling strategy evaluation method for an air-conditioning system of an intelligent building in an uncertain environment. FIG. 1 is a frame diagram of the scheduling strategy evaluation method in the present invention.
参阅图1，该图反映了整个策略评估的过程。在对天气、设备、用户、房间、房间温度监测器、控制器以及调度策略建模之后，可以得到该空调系统的NPTA模板，对于相同的软件环境该NPTA模板通用。其中，天气、设备以及用户属于智能大厦的不确定环境，房间及房间温度监测器属于可配置环境。控制器建模设定了系统中控制器的工作方式。控制器中写有房间的调度策略，系统设计者可以自行设计，调度策略建模即对写入控制器的策略进行建模。除NPTA模板以外还需要进行参数的配置，参数配置包括不确定环境参数及可配置参数。其中不确定环境参数主要为可配置的概率分布，包括天气模型、用户模型以及加热器模型中所设定的概率分布函数，设定不同的概率分布函数会影响环境的不确定性。而可配置参数指房间温度需求、加热器功率输出等随着具体情境的变化而修改但较为固定的参数，如房间的温度需求和加热器的输出功率等。在生成NPTA模板并配置好相应参数后，系统建模完成。在进行策略评估之前需要将设计者的需求即设计约束转化为具体的查询属性。得到查询属性后，使用UPPAAL-SMC作为查询引擎即可对所建的模型进行随机模拟运行。运行生成的结果数据可以被用于绘制直观的图形和表格，以进行定量分析和策略评估。See Figure 1, which shows the entire strategy evaluation process. After modeling the weather, equipment, users, rooms, room temperature monitors, controllers and scheduling strategies, the NPTA template of the air conditioning system can be obtained, which is common to the same software environment. Among them, weather, equipment, and users belong to the uncertain environment of intelligent buildings, and rooms and room temperature monitors belong to the configurable environment. Controller modeling sets out how the controllers in the system work. The scheduling strategy of the room is written in the controller, and the system designer can design it by himself. The scheduling strategy modeling is to model the strategy written in the controller. In addition to the NPTA template, parameter configuration is also required. The parameter configuration includes uncertain environment parameters and configurable parameters. The uncertain environmental parameters are mainly configurable probability distributions, including the probability distribution functions set in the weather model, user model, and heater model. Setting different probability distribution functions will affect the uncertainty of the environment. The configurable parameters refer to room temperature requirements, heater power output and other parameters that are modified with specific situations but relatively fixed, such as room temperature requirements and heater output power. After generating the NPTA template and configuring the corresponding parameters, the system modeling is completed. Before strategy evaluation, the designer's requirements, that is, design constraints, need to be transformed into specific query attributes. After obtaining the query attributes, use UPPAAL-SMC as the query engine to run random simulations on the built model. The result data generated by running can be used to draw intuitive graphs and tables for quantitative analysis and strategy evaluation.
参阅图3，房间中的用户对象对应于所处的房间。用户模型从“开始”状态，不消耗时间跳转到“等待”状态，同时调用函数将用户到达和离开房间的时间以及用户所在房间的设备信息等配置进行初始化。用户行为服从均匀分布或正态分布。假设当用户在a时刻到b时刻间以概率分布函数生成的概率进入房间时，模型从“等待”状态跳转到“到达”状态，同时向控制器发送到达消息。当用户在c时刻至d时刻间离开房间时，模型向控制器发送用户离开的消息并从“到达”状态进入“离开”状态。Referring to FIG. 3, a user object in a room corresponds to the room in which it is located. The user model jumps from the "start" state to the "waiting" state without consuming time, and at the same time calls functions to initialize the time when the user arrives and leaves the room and the device information of the room where the user is located. User behavior obeys uniform distribution or normal distribution. Assume that when the user enters the room with the probability generated by the probability distribution function between time a and time b, the model jumps from the "waiting" state to the "arrival" state, and at the same time sends an arrival message to the controller. When the user leaves the room from time c to time d, the model sends a user leaving message to the controller and enters the "leave" state from the "arrival" state.
参阅图4，天气模型中带“U”的状态为urgent状态，这种状态不消耗时间，即模型在该状态下不作停留，立刻跳转。智能大厦模型中考虑的天气包括晴天和雨天两种。系统开始运行后，室外天气从“初始”状态按照概率分布函数生成的概率跳转到不同的天气即“雨天”或“晴天”状态，模型中设置的晴雨天比例为7︰3，即晴天的概率为70％，雨天的概率为30％，跳转的同时设置这两种天气的初始温度。外界环境温度服从温度变化函数，模型中采用正弦函数模拟一天的温度变化，具体为每天0点时温度最低，12点温度最高。参数配置中包含记录房间及室外温度的温度矩阵，矩阵元素为时钟类型。在“晴天”状态或“雨天”状态下，室外温度变量按照设置的函数变化。Referring to Figure 4, the state with "U" in the weather model is the urgent state, which does not consume time, that is, the model does not stay in this state and jumps immediately. The weather considered in the intelligent building model includes sunny and rainy days. After the system starts running, the outdoor weather jumps from the "initial" state to different weather states according to the probability generated by the probability distribution function, that is, "rainy" or "sunny". The ratio of sunny and rainy days set in the model is 7:3, that is, the The probability is 70%, the probability of rainy days is 30%, and the initial temperature of these two kinds of weather is set while jumping. The external environment temperature obeys the temperature change function, and the sine function is used in the model to simulate the temperature change of a day, specifically, the temperature is the lowest at 0:00 and the highest at 12:00 every day. The parameter configuration includes a temperature matrix for recording room and outdoor temperatures, and the matrix elements are clock types. In "Sunny" state or "Rainy" state, the outdoor temperature variable changes according to the set function.
参阅图5，空调系统为大厦内的各个房间供暖，本发明中的加热器模型用于统计大厦各个房间的总能耗。加热器初始时处于“关”状态，当接收到控制器发出打开命令时跳转到“开”状态，根据当前加热器的实际功率统计累积能耗。当接收到控制器发出的关闭命令时，加热器跳转到“关”状态停止工作。加热器消耗的能量由公式计算得出。Referring to Fig. 5, the air conditioning system heats each room in the building, and the heater model in the present invention is used to count the total energy consumption of each room in the building. The heater is initially in the "off" state, and jumps to the "on" state when it receives an open command from the controller, and the cumulative energy consumption is calculated according to the current actual power of the heater. When the shutdown command from the controller is received, the heater jumps to the "off" state and stops working. The energy consumed by the heater is given by the formula Calculated.
参阅图6，智能大厦中的每个房间根据房间内用户的特点有不同的温度配置，温度配置即温度上限和下限以及舒适温度需求。房间内的温度变化受相邻房间的影响。空调系统开始运行后，房间在接收到控制器发出的用户到达的消息后从“开始”状态跳转到“降温”状态，同时将加热标志位need置为true，并将房间内温度初始化为与外界温度相等。当房间内温度低于该房间温度上限temp_upper时，该房间向控制器按固定的时间间隔发出加热请求，并将need置为true。当加热请求被控制器允许后，该房间接收到允许消息，从“降温”状态跳转到“加热”状态，房间开始加热。当房间处于“加热”状态时：若温度超过了其温度上限temp_upper，则房间向控制器发出消息要求停止加热，并将need置为false；若房间接收到停止加热的消息，则跳转到“降温”状态停止加热。在“降温”状态和“加热”状态下，房间温度会根据相应状态下的变化函数。当房间内用户下班离开时，该房间接收到控制器发出的用户离开的消息，模型从“降温”或“加热”状态跳转到“结束”状态，同时将加热标志位need设置为false。Referring to Figure 6, each room in an intelligent building has a different temperature configuration according to the characteristics of the users in the room. The temperature configuration refers to the upper and lower limits of temperature and the comfort temperature requirements. Temperature changes in a room are influenced by adjacent rooms. After the air-conditioning system starts to run, the room will jump from the "Start" state to the "Cooling" state after receiving the user arrival message from the controller, and at the same time set the heating flag bit need to true, and initialize the temperature in the room to be the same as The outside temperature is equal. When the temperature in the room is lower than the upper limit temp_upper of the room temperature, the room sends a heating request to the controller at a fixed time interval, and sets need as true. When the heating request is allowed by the controller, the room receives the permission message, jumps from the "cooling down" state to the "heating" state, and the room starts to heat up. When the room is in the "heating" state: if the temperature exceeds its temperature upper limit temp_upper, the room will send a message to the controller to stop heating, and set need to false; if the room receives a message to stop heating, it will jump to " Cool down" state to stop heating. In the "Cooling" state and "Heating" state, the room temperature will be changed according to the change function in the corresponding state. When the user in the room leaves after get off work, the room receives a message from the controller that the user has left, and the model jumps from the "cooling down" or "heating" state to the "end" state, and at the same time sets the heating flag bit need to false.
参阅图7，大厦的每个房间对应一个温度监测器，温度监测器模型用于统计各房间温度超出其要求的温度范围的时间，若某房间内的温度不在其需求的温度范围内，房间内用户会感觉“过冷”或“过热”，这种情况下用户的舒适度较差，因此房间温度监测器模型反映出了房间内用户的舒适度。接收到用户到达的消息后，模型从“开始”状态跳转到“等待”状态。当房间温度上升首次超过要求温度范围的下限时，模型跳转到“范围内”状态，表明房间温度在所要求的范围之内。当房间温度在限制范围外即低于温度下限temp_lower或超出温度上限temp_upper后，模型从“范围内”状态跳转到“范围外”状态并开始计时；当房间温度再次回到温度限制范围内时，模型从“范围外”状态跳转到“范围内”状态。当温度监测器模型接收到控制器发出的用户离开的消息时，模型跳转到“结束”状态停止工作。Referring to Figure 7, each room in the building corresponds to a temperature monitor. The temperature monitor model is used to count the time when the temperature of each room exceeds its required temperature range. If the temperature in a certain room is not within its required temperature range, the temperature in the room The user will feel "too cold" or "too hot", in which case the user's comfort level is poor, so the room temperature monitor model reflects the comfort level of the user in the room. After receiving the message that the user has arrived, the model jumps from the "Start" state to the "Waiting" state. When the room temperature rises above the lower limit of the required temperature range for the first time, the model jumps to the "in range" state, indicating that the room temperature is within the required range. When the room temperature is outside the limit range, that is, lower than the temperature lower limit temp_lower or exceeds the temperature upper limit temp_upper, the model jumps from the "in-range" state to the "out-of-range" state and starts timing; when the room temperature returns to the temperature limit range again , the model jumps from the "out of range" state to the "in range" state. When the temperature monitor model receives the message from the controller that the user has left, the model jumps to the "end" state and stops working.
参阅图8，控制器模型负责根据某种调度策略选择房间供暖。控制器中包含一个由各个房间形成的优先级队列。控制器初始处于“接收”状态，在该状态下控制器可以接收各个模型发来的消息。当接收到用户模型发出的用户到达的消息时，控制器将该房间号加入到队列中，同时向其他模型广播用户到达的消息。这里的队列是一个优先级队列，隐含了各个房间的优先级，同时也能根据房间是否在队列中说明该房间中是否有用户。当控制器接收到房间发出的加热请求或降温请求后，会根据当前的策略调用choose函数选择出一个合适的房间(选择结果保存到变量ch中)进行加热，并跳转到“发送”状态。或者当有用户下班离开房间时，控制器接收到用户模型发出的离开消息，先将该房间从队列中移除，再判断当前队列情况：若当前队列不为空，则调用choose函数选择房间；若队列为空，则将选择结果ch置为-1。选择出合适的待加热房间后，控制器向各个房间发出控制信息。若有房间需要供暖，则控制器通知选出的房间的加热请求被允许，同时通知其他房间禁止加热，最后通知加热器打开并调用power_set()函数根据概率分布函数设置当前加热器的实际功率；若没有房间需要供暖，控制器则通知所有房间停止加热，并通知加热器关闭并调用power_set()函数进行功率设置。Referring to Figure 8, the controller model is responsible for selecting room heating according to some scheduling strategy. The controller contains a priority queue formed by individual rooms. The controller is initially in the "receiving" state, in which the controller can receive messages from various models. When receiving the user arrival message sent by the user model, the controller adds the room number to the queue, and broadcasts the user arrival message to other models at the same time. The queue here is a priority queue, which implies the priority of each room, and can also indicate whether there are users in the room according to whether the room is in the queue. When the controller receives the heating request or cooling request from the room, it will call the choose function according to the current strategy to select a suitable room (the selection result is saved in the variable ch) for heating, and jump to the "sending" state. Or when a user leaves the room after get off work, the controller receives the leave message from the user model, first removes the room from the queue, and then judges the current queue situation: if the current queue is not empty, call the choose function to select the room; If the queue is empty, set the selection result ch to -1. After selecting a suitable room to be heated, the controller sends control information to each room. If there is a room that needs to be heated, the controller notifies the selected room that the heating request is allowed, and at the same time notifies other rooms to prohibit heating, and finally notifies the heater to turn on and calls the power_set() function to set the actual power of the current heater according to the probability distribution function; If no room needs heating, the controller will notify all rooms to stop heating, and notify the heater to turn off and call the power_set() function to set the power.
参阅图9，为了使智能大厦节能、舒适，空调系统的控制器中需要写入适当的策略，控制器根据其中的策略调度加热器对各个房间供暖。调度策略被写入到控制器中，同时又独立于控制器，这是因为控制器建模仅仅规定了控制器的行为模式，具体的调度策略决定了控制器的实际行为动作。调度策略需要房间队列信息以及各个房间的温度数据等，而模型中的控制器可以从其他模型轻松获取到这些信息。系统设计者可以根据具体环境设计出适当的策略并将其写入控制器中。多个策略可以被同时写入，控制器根据策略号sn调用choose函数中的具体策略进行房间的选择。在进行调度时，控制器根据当前策略调整队列，然后选择房间。选择房间时取队首元素，被选中的房间将收到控制器发出的加热允许的消息，而其他房间则收到加热停止的消息。当有房间被选出进行加热时，控制器向加热器发送消息打开加热器；当队列为空时，所有房间将收到加热停止的消息，同时控制器通知加热器关闭。Referring to Figure 9, in order to make the smart building energy-saving and comfortable, the controller of the air-conditioning system needs to write an appropriate strategy, and the controller schedules the heater to heat each room according to the strategy. The dispatching strategy is written into the controller and is independent of the controller at the same time, because the modeling of the controller only specifies the behavior mode of the controller, and the specific scheduling strategy determines the actual behavior of the controller. The scheduling policy requires room queue information and temperature data for each room, etc., and the controller in the model can easily obtain this information from other models. System designers can design appropriate strategies according to the specific environment and write them into the controller. Multiple strategies can be written at the same time, and the controller calls the specific strategy in the choose function according to the strategy number sn to select the room. When scheduling, the controller adjusts the queue according to the current policy and then selects the room. When selecting a room, take the first element of the queue, the selected room will receive the message of heating permission from the controller, while other rooms will receive the message of heating stop. When a room is selected for heating, the controller sends a message to the heater to turn on the heater; when the queue is empty, all rooms will receive a message to stop heating, and the controller notifies the heater to turn off.
在系统建模完成之后，执行策略评估步骤。首先将能耗和用户舒适度的设计约束转化为属性查询语句，使用UPPAAL-SMC作为查询引擎对系统模型进行随机模拟运行，生成结果数据。为了比较不同调度策略在能耗及用户舒适度方面的表现，采用了如下形式的属性模板：After the system modeling is complete, a policy evaluation step is performed. Firstly, the design constraints of energy consumption and user comfort are transformed into attribute query statements, and UPPAAL-SMC is used as the query engine to run random simulations on the system model to generate result data. In order to compare the performance of different scheduling strategies in terms of energy consumption and user comfort, an attribute template of the following form is used:
Pr[<＝t](<>energy>＝E)；Pr[<=t](<>energy>=E);
Pr[<＝t](<>rangeout[rid]>＝tout)；Pr[<=t](<>rangeout [rid]>=tout );
式中，t为时间约束，E为能耗约束，energy>＝e表示消耗的能量超过e，rangeout[rid]>＝tout表示房间rid超出温度限制的时间超过tout。In the formula, t is the time constraint, E is the energy consumption constraint, energy>=e means that the energy consumed exceeds e, and rangeout [rid]>=tout means that the room rid exceeds the temperature limit for more than tout .
在大量的随机模拟运行后，UPPAAL-SMC将报告运行结果即成功运行的概率分布，生成统计数据。根据结果可以进行定量分析，同时绘制出直观的图表，便于系统设计者对空调系统的调度策略进行选取或改进。After a large number of random simulation runs, UPPAAL-SMC will report the results of the runs, i.e. the probability distribution of successful runs, generating statistics. Quantitative analysis can be carried out according to the results, and intuitive charts can be drawn at the same time, which is convenient for system designers to select or improve the scheduling strategy of the air conditioning system.
本发明提出的不确定环境下智能大厦空调系统的调度策略评估方法，根据价格时间自动机理论对不确定环境下的智能大厦进行建模，并使用UPPAAL-SMC对模型进行随机模拟运行，分析空调系统采用不同调度策略时大厦在能耗及舒适度方面的表现。智能大厦空调系统的设计者可以使用这种评估方法对不同策略进行定量分析，根据具体的需求采用适当的策略使智能大厦更加节能、舒适。The scheduling strategy evaluation method of the air-conditioning system of the intelligent building under the uncertain environment proposed by the present invention models the intelligent building under the uncertain environment according to the price-time automaton theory, and uses UPPAAL-SMC to carry out random simulation operation of the model, and analyzes the air-conditioning The performance of the building in terms of energy consumption and comfort when the system adopts different scheduling strategies. Designers of air-conditioning systems in intelligent buildings can use this evaluation method to quantitatively analyze different strategies, and adopt appropriate strategies to make intelligent buildings more energy-efficient and comfortable according to specific needs.
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