CN117704604A - Refrigerating system upper control method, device, equipment and storage medium - Google Patents

Abstract

This application relates to a refrigeration system upper control method, device, equipment and storage medium. The method includes: training multiple initial control strategies based on the control objectives of the refrigeration system and the operating status of each refrigeration equipment in the refrigeration system model, obtaining the target control strategy corresponding to each initial control strategy, and using the operating status and meteorological prediction data. , cooling load prediction data of the refrigeration system, determine the optimal target control strategy from multiple target control strategies, and obtain the target control instructions according to the optimal target control strategy; the target control instructions are used for upper-level control of the refrigeration equipment in the refrigeration system. The method provided by the embodiments of this application can solve the problem of low adaptability of a single control strategy under changing weather and building cooling load conditions.

Description

Refrigeration system upper control method, device, equipment and storage medium

Technical field

The present application relates to the technical field of building refrigeration system control, and in particular to a refrigeration system upper-level control method, device, equipment and storage medium.

Background technique

At present, public building operation energy consumption accounts for a large proportion of building operation energy consumption, and more than 40% of public building energy consumption comes from refrigeration systems. The refrigeration system generally includes chillers, water distribution pump units, cooling tower units, air conditioning units and other equipment, which can discharge the heat in the indoor air to the outdoor air through efficient heat exchange technology. The refrigeration system has complex hydraulic and thermal coupling characteristics, and the operating efficiency of different equipment components has complex interactions. Therefore, controlling the refrigeration system is an important method for efficient operation of the refrigeration system.

The control of the refrigeration system is divided into upper control and lower control. Upper level control refers to controlling the operating mode of the refrigeration system equipment, for example, setting the start and stop status of the chiller, the outlet water temperature of the chiller, etc. Lower level control refers to controlling the components of the refrigeration system equipment, for example, setting the speed of the compressor in the chiller, etc. The energy efficiency improvement of the refrigeration system mainly depends on upper-level control.

However, the current upper-level control method of the refrigeration system has a single strategy, resulting in low adaptability of the method.

Contents of the invention

Based on this, it is necessary to address the above technical problems and provide a refrigeration system upper-level control method, device, equipment and storage medium that improves the adaptability of the refrigeration system upper-level control method.

In the first aspect, this application provides an upper-level control method for a refrigeration system, including:

Based on the control objectives of the refrigeration system and the operating status of each refrigeration equipment in the refrigeration system model, multiple initial control strategies are trained to obtain the target control strategy corresponding to each initial control strategy;

Using the operating status, meteorological prediction data, and cooling load prediction data of the refrigeration system, determine the optimal target control strategy from multiple target control strategies;

A target control instruction is obtained according to the optimal target control strategy; the target control instruction is used for upper-level control of the refrigeration equipment in the refrigeration system.

In one embodiment, based on the control objectives of the refrigeration system and the operating status of each refrigeration equipment in the refrigeration system model, multiple initial control strategies are trained to obtain target control strategies corresponding to each initial control strategy, including:

For each initial control strategy, obtain the simulated control instructions output under the initial control strategy based on the control target, the operating status, cooling load measurement data and historical meteorological data;

According to the simulation control instructions corresponding to each initial control strategy, the target control strategy corresponding to each initial control strategy is determined.

In one embodiment, determining the target control strategy corresponding to each initial control strategy based on the simulation control instructions corresponding to each initial control strategy includes:

For the simulation control instructions corresponding to each initial control strategy, use the simulation control instructions to control the refrigeration equipment in the refrigeration system, and obtain the control results and new operating states corresponding to the simulation control instructions;

According to the control results and new operating states corresponding to the simulated control instructions under each initial control strategy, the target control strategy corresponding to each initial control strategy is determined.

In one embodiment, the target control strategy corresponding to each initial control strategy is determined based on the control results and new operating states corresponding to the simulated control instructions under each initial control strategy, including:

Regarding the control results corresponding to the simulated control instructions under each initial control strategy, if the control results meet the preset conditions, then the initial control strategy is used as the target control strategy;

If the control result does not meet the preset condition, the control result is used to modify the initial control strategy to obtain an intermediate control strategy, and execute the control strategy according to the control target, the new operating state, the cooling load measurement data and the historical weather. The data is used to obtain the new simulation control instruction output under the intermediate control strategy, and the new simulation control instruction is used to control the refrigeration equipment in the refrigeration system, and the new control result corresponding to the new simulation control instruction and the next new operating state are obtained. Steps, until the new control result meets the preset conditions, use the control strategy of the last output simulation control instruction as the target control strategy.

In one embodiment, the operating status, weather prediction data, and cooling load prediction data of the refrigeration system are used to determine the optimal target control strategy from multiple target control strategies, including:

According to the operating status, the cooling load prediction data and the weather prediction data, use each target control strategy to output simulation control instructions corresponding to each target control strategy;

For each simulation control instruction, obtain the control result corresponding to the simulation control instruction;

According to each control result, the optimal target control strategy is determined from multiple target control strategies.

In one embodiment, the method further includes:

According to the performance simulation equations of each refrigeration equipment in the refrigeration system, the initial refrigeration system model is obtained;

Using the cooling load, heat load and electricity data of the refrigeration system, the parameters in the initial refrigeration system model are calibrated to obtain the refrigeration system model.

In the second aspect, this application also provides a refrigeration system upper control device, including:

The first determination module is used to train multiple initial control strategies based on the control objectives of the refrigeration system and the operating status of each refrigeration equipment in the refrigeration system model, and obtain a target control strategy corresponding to each initial control strategy.

The second determination module is used to determine the optimal target control strategy from multiple target control strategies using the operating status, weather forecast data, and cooling load forecast data of the refrigeration system.

The third determination module is used to obtain a target control instruction according to the optimal target control strategy; the target control instruction is used to perform upper-level control of the refrigeration equipment in the refrigeration system.

In a third aspect, the present application also provides a computer device, including a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the steps of the above method are implemented.

In a fourth aspect, the present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

In a fifth aspect, the present application also provides a computer program product, including a computer program that implements the steps of the above method when executed by a processor.

The above-mentioned refrigeration system upper-level control methods, devices, equipment and storage media train multiple initial control strategies based on the control objectives of the refrigeration system and the operating status of each refrigeration equipment in the refrigeration system model, and obtain the target control corresponding to each initial control strategy. Strategy, using operating status, weather forecast data, and cooling load forecast data of the refrigeration system, determines the optimal target control strategy from multiple target control strategies, and obtains target control instructions based on the optimal target control strategy; the target control instructions are used to control refrigeration The refrigeration equipment in the system is controlled by upper level. Due to the changing characteristics of meteorology and building cooling loads, it is less adaptable to use only a single upper-level control strategy to control the refrigeration system. Therefore, in the embodiment of this application, an optimal control strategy is determined from multiple target control strategies for upper-level control. , that is, the method provided by the embodiment of the present application can be used to solve the problem of low adaptability of a single control strategy under changing weather and building cooling load conditions.

Description of the drawings

In order to more clearly explain the technical solutions in the embodiments of the present application or related technologies, the drawings needed to be used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings in the following description are only for the purpose of describing the embodiments or related technologies. For some embodiments of the application, those of ordinary skill in the art can also obtain other drawings based on these drawings without exerting creative efforts.

Figure 1 is an application environment diagram of a refrigeration system upper-level control method provided by an embodiment of the present application;

Figure 2 is a schematic flow chart of an upper-level control method for a refrigeration system provided by an embodiment of the present application;

Figure 3 is one of the flow diagrams of a method for determining a target control strategy provided by an embodiment of the present application;

Figure 4 is a schematic flowchart 2 of a method for determining a target control strategy provided by an embodiment of the present application;

Figure 5 is a schematic diagram of a control strategy training method provided by an embodiment of the present application;

Figure 6 is a schematic flowchart of an optimal target control strategy determination method provided by an embodiment of the present application;

Figure 7 is a schematic flow chart of calibrating a refrigeration system model provided by an embodiment of the present application;

Figure 8 is a structural diagram of an integrated intelligent control strategy provided by an embodiment of the present application;

Figure 9 is an engineering application rendering provided by the embodiment of the present application;

Figure 10 is a structural block diagram of a device for determining a target control strategy provided by an embodiment of the present application;

Figure 11 is an internal structure diagram of a computer device provided by an embodiment of the present application.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present application more clear, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the application and are not used to limit the application.

The upper-level control method of the refrigeration system provided by the embodiment of the present application can be applied in the application environment as shown in Figure 1. Figure 1 is an application environment diagram of the upper-level control method of the refrigeration system provided by the embodiment of the present application. The control device 102 can access weather data in the Internet through an application program interface (API). The control device 102 can also be connected to the device 104 hosting the building automation system through the TCP/IP protocol to collect cooling load data and send target control instructions to the device 104 hosting the building automation system. The target control instructions are passed through the building automation system. The system parses the lower control instructions of the refrigeration system equipment 106 and transmits them to the refrigeration system equipment 106 through the Modbus protocol to realize operation control of the refrigeration equipment. Among them, Modbus is a serial communication protocol. The control device 102 may be a personal computer, a microcomputer, a notebook computer, an Internet of Things device, etc.

In an exemplary embodiment, Figure 2 is a schematic flowchart of an upper-level control method for a refrigeration system provided by an embodiment of the present application. It provides an upper-level control method for a refrigeration system, and this method is applied to the control in Figure 1 Device 102 is used as an example for illustration, including the following steps:

S201. Based on the control objectives of the refrigeration system and the operating status of each refrigeration equipment in the refrigeration system model, train multiple initial control strategies to obtain the target control strategy corresponding to each initial control strategy.

Among them, refrigeration equipment can include refrigeration units, water pumps and cooling towers. Control targets such as system power consumption, system power cost, etc. The initial control strategy can be sequential least squares strategy, double simulation regression strategy, differential evolution strategy, reinforcement learning strategy, etc.

In the embodiment of this application, the hyperparameters of the initial control strategy can be initialized and the default hyperparameter values of the algorithm can be selected. According to the control objectives of the refrigeration system, the operating status of each refrigeration equipment, cooling load measurement data and historical meteorological data in the refrigeration system model can be input into the initial control strategy to obtain simulation control instructions, and the simulation control instructions can be used to control the refrigeration system model. , obtain the control results and new operating status, determine whether the control results meet the conditions, and obtain the target control strategy corresponding to each initial control strategy, so as to optimize the hyperparameter values based on the default hyperparameter values.

S202. Use the operating status, weather forecast data, and cooling load forecast data of the refrigeration system to determine the optimal target control strategy from multiple target control strategies.

Among them, the weather forecast data can be future weather forecast data obtained from the Internet, such as air dry bulb temperature, air relative humidity, solar radiation intensity, wind speed and other information. The cooling load prediction data may be predicted cooling load data of the building refrigeration system.

In the embodiment of the present application, the operating status, weather forecast data, and cooling load forecast data of the refrigeration system can be input into each target control strategy to obtain the simulation control instructions corresponding to each target control strategy, and then input each simulation control instruction into In the refrigeration system model, the control results corresponding to each simulation control instruction are obtained. The target control strategy corresponding to the best control result is regarded as the optimal target control strategy.

S203. Obtain the target control instruction according to the optimal target control strategy; the target control instruction is used for upper-level control of the refrigeration equipment in the refrigeration system.

In the embodiment of the present application, the cooling load prediction data and the weather prediction data can be input into the optimal target control strategy to obtain upper-level control of the refrigeration equipment in the refrigeration system.

In the above-mentioned upper-level control method of the refrigeration system, multiple initial control strategies are trained based on the control objectives of the refrigeration system and the operating status of each refrigeration equipment in the refrigeration system model, and the target control strategy corresponding to each initial control strategy is obtained. Using the operating status, Meteorological forecast data and cooling load forecast data of the refrigeration system determine the optimal target control strategy from multiple target control strategies, and obtain target control instructions based on the optimal target control strategy; the target control instructions are used to control the refrigeration equipment in the refrigeration system. Superior control. The current upper-level control method of the refrigeration system has a single strategy, resulting in low adaptability of the method. Due to the variable characteristics of meteorology and building cooling loads, the adaptability of only using a single strategy for upper-level control of the refrigeration system is low. In the embodiments of this application, an optimal control strategy is determined from multiple target control strategies for upper-level control. That is, the method provided by the embodiments of this application can solve the problem of a single control strategy being more adaptable under changing weather and building cooling load conditions. low question.

In an exemplary embodiment, Figure 3 is one of the flow diagrams of a method for determining a target control strategy provided by an embodiment of the present application. As shown in Figure 3, this embodiment relates to how to control the target based on the refrigeration system. and the operating status of each refrigeration equipment in the refrigeration system model, train multiple initial control strategies, and obtain a possible implementation manner of the target control strategy corresponding to each initial control strategy. Based on the above embodiment, the above S201 includes :

S301. For each initial control strategy, obtain the simulation control instructions output under the initial control strategy based on the control target, operating status, cooling load measurement data and historical meteorological data.

Among them, the initial control strategy algorithm can use sequential least squares programming algorithm, double simulated annealing algorithm, differential evolution control algorithm, reinforcement learning algorithm, etc. The cooling load measurement data is actual measured data.

In the embodiment of the present application, for each initial control strategy, the operating status of each equipment of the current refrigeration system simulated by the refrigeration system model, cooling load measurement data and historical meteorological data can be input into the initial control strategy. Each initial control strategy Output the simulation control instructions corresponding to the initial control strategy.

S302. Determine the target control strategy corresponding to each initial control strategy according to the simulation control instructions corresponding to each initial control strategy.

Exemplarily, Figure 4 is a schematic flowchart 2 of a method for determining a target control strategy provided by an embodiment of the present application. As shown in Figure 4, this embodiment involves how to simulate control instructions corresponding to each initial control strategy, A possible implementation manner of determining the target control strategy corresponding to each initial control strategy. Based on the above embodiment, the above S302 includes:

S401. According to the simulation control instructions corresponding to each initial control strategy, use the simulation control instructions to control the refrigeration equipment in the refrigeration system, and obtain the control results and new operating states corresponding to the simulation control instructions.

In the embodiment of the present application, the simulation control instructions corresponding to each initial control strategy can be input into the refrigeration system model to simulate the control of the refrigeration equipment, and the control results and new operating states corresponding to each simulation control instruction can be obtained. .

S402. Determine the target control strategy corresponding to each initial control strategy based on the control results and new operating states corresponding to the simulation control instructions under each initial control strategy.

Illustratively, this embodiment involves a possible implementation of how to determine the target control strategy corresponding to each initial control strategy based on the control results and new operating states corresponding to the simulated control instructions under each initial control strategy. In the above implementation On the basis of examples, the above S402 includes:

Case 1: Regarding the control results corresponding to the simulated control instructions under each initial control strategy, if the control results meet the preset conditions, the initial control strategy will be used as the target control strategy.

In the embodiment of the present application, it can be determined whether the control results corresponding to the simulated control instructions under each initial control strategy meet the preset conditions. If so, the initial control strategy can be used as the target control strategy.

Specifically, the preset condition may be whether the control result is stable, for example, starting from the second training, whether the change value of the control result compared to the previous time is less than a preset threshold, and the preset threshold may be 0.001%. If this is the first training, the control result value is retained and used to judge the change value of the control result in the next training.

Case 2: If the control results do not meet the preset conditions, the control results are used to modify the initial control strategy to obtain an intermediate control strategy, and the intermediate control strategy is obtained based on the control objectives, new operating status, cooling load measurement data and historical meteorological data. Use the new simulation control instructions output under the strategy to control the refrigeration equipment in the refrigeration system, and obtain the new control results corresponding to the new simulation control instructions and the steps for the next new operating state, until the new control results meet the preset Condition, the control strategy of the last output simulation control instruction is used as the target control strategy.

In the embodiment of the present application, if the control result does not meet the preset conditions, the control result is used to modify the initial control strategy, that is, the hyperparameters in the initial control strategy are adjusted to obtain an intermediate control strategy. Then continue to input the control target, new operating status, cooling load measurement data and historical meteorological data into the intermediate control strategy to obtain a new simulation control instruction output under the intermediate control strategy, and input the new simulation control instruction into the refrigeration system model. The refrigeration equipment in the refrigeration system performs simulated control to obtain new control results and the next new operating state corresponding to the new simulated control instructions. Then determine whether the new control result meets the preset conditions, and if so, use the intermediate control strategy as the target control strategy. If it is not satisfied, use the new control results to modify the intermediate control strategy, and continue to obtain the next new simulation control instruction output under the next intermediate control strategy based on the next new operating status, cooling load measurement data and historical meteorological data. Use the next new simulation control instruction to control the refrigeration equipment in the refrigeration system, and obtain the new control result corresponding to the next new simulation control instruction and the steps for the next new operating state, until the next new control result meets the preset conditions. The control strategy of the last output simulation control instruction is used as the target control strategy.

Figure 5 is a schematic diagram of a control strategy training method provided by an embodiment of the present application. As shown in Figure 5, the operating status, cooling load measurement data and historical meteorological data can be input into the control strategy (this is the initial control strategy) to obtain simulation control instructions. The simulation control instructions are used to control the refrigeration system model, and we get The control result and new operating state. If the control result meets the preset conditions, the initial control strategy will be used as the target control strategy. If the control results do not meet the preset conditions, the control results are used to modify the initial control strategy to obtain an intermediate control strategy, and a new simulation output under the intermediate control strategy is obtained based on the new operating status, cooling load measurement data and historical meteorological data. Control instructions, use the new simulation control instructions to control the refrigeration equipment in the refrigeration system, and obtain the new control results corresponding to the new simulation control instructions and the steps for the next new operating state. Until the new control results meet the preset conditions, the last output The control strategy of the simulated control instruction is used as the target control strategy.

Usually, each target control strategy is updated and trained every one month. First, use the refrigeration system data collected within this month (the operating status of each refrigeration equipment in the refrigeration system model) to recalibrate and update the refrigeration system model. Subsequently, the building measured cooling load data and historical meteorological data collected within this month were used to update the hyperparameters of the control strategy. It can ensure that the control strategy can adapt and give corresponding refrigeration system control instructions under changes in seasons, building usage behavior, and refrigeration system performance.

In this embodiment, the target control strategy is obtained by training the initial control strategy, which can improve the adaptability of the controller to changes in seasons, building usage behavior, and refrigeration system performance, and ensure the reliability of the upper-level control strategy of the refrigeration system in actual projects. .

In an exemplary embodiment, Figure 6 is a schematic flow chart of an optimal target control strategy determination method provided by an embodiment of the present application. As shown in Figure 6, this embodiment involves how to use operating status, weather forecast Data and cooling load prediction data of the refrigeration system are used to determine the optimal target control strategy from multiple target control strategies, including:

S601. According to the operating status, cooling load prediction data and meteorological prediction data, use each target control strategy to output simulation control instructions corresponding to each target control strategy.

In the embodiment of the present application, before each implementation of higher-level control, the control results of each target control strategy can be simulated. For each target control strategy, the operating status of each refrigeration equipment, cooling load prediction data and weather forecast data in the refrigeration system model can be input into the target control strategy, and the simulation control instructions corresponding to each target control strategy can be output.

For example, in the day-ahead control of the ice storage system, for each target control strategy, the building cooling load forecast data, weather forecast data (outdoor temperature forecast, outdoor relative humidity forecast) and operating status for the next 24 hours can be input to the target control strategy, and obtain the simulation control instructions corresponding to each target control strategy. This simulation control instruction is to give instructions for the system to melt and store ice in the next 24 hours. That is to say, according to the time step and prediction window of the input data, the target control instruction under the same time step and prediction window can be given through the target control strategy. It helps to automatically adjust the appropriate control method according to specific boundary conditions, thereby improving the robustness of the control results and improving the refrigeration system control results.

S602. For each simulation control instruction, obtain the control result corresponding to the simulation control instruction.

In the embodiment of the present application, for each simulation control instruction, the simulation control instruction can be input into the refrigeration system model to obtain the control result corresponding to the simulation control instruction.

S603. According to each control result, determine the optimal target control strategy from multiple target control strategies.

In the embodiment of the present application, the target control strategy with the best control results can be determined from multiple target control strategies, and the target control strategy with the best control results can be used as the optimal target control strategy.

In this embodiment, before each implementation of upper-level control, the control results of each target control strategy can be simulated to find the optimal target control strategy. The optimal target control strategy can be used to output target control instructions to perform upper-level control of the refrigeration system. . Due to the variability of meteorological and building cooling loads, only using a single strategy has low adaptability. The method provided by the embodiments of this application can solve the problem of low adaptability of a single control strategy under changing meteorological and building cooling load conditions, thereby improving the robustness of the control results and improving the refrigeration system control results. In addition, it solves the problem that the effects of existing intelligent upper-level control technologies for refrigeration systems are mostly evaluated using simulation analysis methods and are rarely used in actual projects.

Figure 7 is a schematic flow chart of calibrating a refrigeration system model provided by an embodiment of the present application. As shown in Figure 7, the method includes:

S701. Obtain the initial refrigeration system model based on the performance simulation equations of each refrigeration equipment in the refrigeration system.

Among them, the performance of the refrigeration unit in the refrigeration equipment can be simulated using the internal efficiency DCOP/external efficiency ICOP method to obtain the performance simulation equation of the refrigeration unit. The performance of the water pump in the refrigeration equipment is described by the flow-head-power curve method, and the performance simulation equation of the water pump is obtained. The performance of the cooling tower in the refrigeration equipment can be simulated using the number of heat transfer units ε-NTU method to obtain the performance simulation equation of the cooling tower. The pipeline characteristics in the refrigeration equipment are described by the resistance equation, and the performance simulation equation of the pipeline is obtained.

In the embodiment of this application, the DCOP/ICOP method can be used to simulate the performance simulation equation of the refrigeration unit, the flow-lift-power curve method can be used to obtain the performance simulation equation of the water pump, and the ε-NTU method can be used to simulate the performance simulation equation of the cooling tower. , using the resistance equation to obtain the performance simulation equation of the pipeline. The above performance simulation equation can be used as the initial refrigeration system model to simulate the refrigeration system.

S702. Calibrate the parameters in the initial refrigeration system model using the cooling load, heat load and power data of the refrigeration system to obtain the refrigeration system model.

Among them, the cooling load, heating load and power data of the refrigeration system are the cold, hot and electric data, and this data can be collected in real time. It should be noted that in the implementation of this application, non-real-time collected cooling load, heat load and power data of the refrigeration system can also be used to calibrate the parameters in the initial refrigeration system model, that is, using the actual measured cooling load, The heat load and electricity data are used to calibrate the simulation data in the initial refrigeration system model to obtain the refrigeration system model.

In this embodiment, each refrigeration equipment in the refrigeration system is simulated, and the actual measured data is used to calibrate the simulation data in the initial refrigeration system model, which can improve the accuracy of the refrigeration system model. Furthermore, the results of subsequent upper-level control of the refrigeration system using the refrigeration system model can be improved.

Figure 8 is a structural diagram of an integrated intelligent control strategy provided by an embodiment of the present application. As shown in Figure 8, the performance simulation equations of each refrigeration equipment in the refrigeration system can first be used to complete the model initialization, that is, the initial refrigeration system model is obtained, and then the cooling load, heat load and power data of the refrigeration system are used to calculate the initial refrigeration system model. The parameters in are calibrated to obtain the refrigeration system model. According to the refrigeration system model and control objectives, multiple initial control strategies are trained to obtain the target control strategy corresponding to each initial control strategy. Control strategies such as sequential least squares programming control strategy, double simulated annealing control strategy, differential evolution control strategy, reinforcement learning control strategy, etc. Operating status, weather forecast data, and cooling load forecast data of the refrigeration system are used to determine the optimal target control strategy from multiple target control strategies; the target control instructions are obtained based on the optimal target control strategy; the target control instructions are used to control the refrigeration system. Refrigeration equipment is subject to upper-level control.

Figure 9 is an engineering application rendering provided by the embodiment of the present application. The comparison of electricity costs from May to September between the optimal target control strategy provided by this application and the control strategy in traditional technology is demonstrated. It shows the refrigeration system of a large commercial complex. It can save 170,000 yuan in electricity costs for the refrigeration system during the cooling season throughout the year, saving 16% compared to the original strategy.

It should be noted that the upper-level control method of the refrigeration system provided by the embodiment of the present application mainly involves access to the Internet and building automation systems during on-site deployment. The Raspberry Pi equipped with the (target control strategy) controller is powered by an independent power supply. Its connection to the Internet can be achieved through network communication cables or WIFI transceivers. Its access to the building automation system is achieved through network communication cables. After the connection is completed, the network communication address of the Raspberry Pi needs to be configured according to the on-site environment to achieve normal data exchange between the Raspberry Pi and the Internet and the building automation system, thereby realizing the deployment and application of this method. A control strategy hardware deployment device and communication method with a microcomputer as the core. The Raspberry Pi microcomputer is equipped with an integrated control strategy for the refrigeration system. The physical connection is realized through network cables, WIFI transceivers, etc., through TCP/IP, API, Modbus and other methods are used to realize data exchange and realize the deployment and application of integrated intelligent upper-level control strategies for building refrigeration systems. It solves the problem that most of the existing intelligent upper-level control technologies of refrigeration systems are in the methodology stage, and there is little research on hardware deployment devices involving control strategies. It also provides a technical implementation solution for hardware system communication methods.

It should be understood that although the steps in the flowcharts involved in the above embodiments are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated in this article, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but may be executed at different times. The execution order of these steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

Based on the same inventive concept, embodiments of the present application also provide a refrigeration system upper-level control device for implementing the above-mentioned upper-level control method of the refrigeration system. The solution to the problem provided by this device is similar to the solution recorded in the above method. Therefore, the specific limitations in the embodiments of one or more upper-level refrigeration system control devices provided below can be found in the above-mentioned upper-level control of the refrigeration system. The limitations of the method will not be repeated here.

In an exemplary embodiment, Figure 10 is a structural block diagram of a target control strategy determination device provided by an embodiment of the present application. As shown in Figure 10, a refrigeration system upper control device 1000 is provided, including: a first Determining module 1001, second determining module 1002 and third determining module 1003, wherein:

The first determination module 1001 is used to train multiple initial control strategies based on the control objectives of the refrigeration system and the operating status of each refrigeration equipment in the refrigeration system model, and obtain the target control strategy corresponding to each initial control strategy.

The second determination module 1002 is used to determine the optimal target control strategy from multiple target control strategies using operating status, weather forecast data, and cooling load forecast data of the refrigeration system.

The third determination module 1003 is used to obtain target control instructions according to the optimal target control strategy; the target control instructions are used to perform upper-level control of the refrigeration equipment in the refrigeration system.

In an exemplary embodiment, the first determination module 1001 includes:

The first determination sub-module is used for each initial control strategy to obtain the simulated control instructions output under the initial control strategy based on the control target, operating status, cooling load measurement data and historical meteorological data.

The second determination sub-module is used to determine the target control strategy corresponding to each initial control strategy according to the simulation control instructions corresponding to each initial control strategy.

In an exemplary embodiment, the second determination sub-module includes:

The first determination unit is configured to use the simulation control instructions to control the refrigeration equipment in the refrigeration system according to the simulation control instructions corresponding to each initial control strategy, and obtain the control results and new operating states corresponding to the simulation control instructions.

The second determination unit is used to determine the target control strategy corresponding to each initial control strategy based on the control results and new operating states corresponding to the simulated control instructions under each initial control strategy.

In an exemplary embodiment, the second determination unit is specifically configured to simulate the control results corresponding to the control instructions under each initial control strategy. If the control results meet the preset conditions, the initial control strategy is used as the target control strategy; if the control If the result does not meet the preset conditions, the control results will be used to modify the initial control strategy to obtain an intermediate control strategy. Based on the control objectives, new operating status, cooling load measurement data and historical meteorological data, a new output under the intermediate control strategy will be obtained. Simulate control instructions, use the new simulation control instructions to control the refrigeration equipment in the refrigeration system, and obtain the new control results corresponding to the new simulation control instructions and the steps for the next new operating state. Until the new control results meet the preset conditions, the last time The control strategy of the output simulation control instruction is used as the target control strategy.

In an exemplary embodiment, the second determination module 1002 is specifically configured to use each target control strategy according to the operating status, cooling load prediction data and weather forecast data to output simulation control instructions corresponding to each target control strategy. For each simulated control instruction, the control results corresponding to the simulated control instructions are obtained; based on each control result, the optimal target control strategy is determined from multiple target control strategies.

In an exemplary embodiment, the refrigeration system upper control device 1000 also includes:

The third determination submodule is used to obtain the initial refrigeration system model based on the performance simulation equations of each refrigeration equipment in the refrigeration system.

The calibration submodule is used to calibrate the parameters in the initial refrigeration system model using the cooling load, heat load and power data of the refrigeration system to obtain the refrigeration system model.

Each module in the above-mentioned upper control device of the refrigeration system can be realized in whole or in part through software, hardware and combinations thereof. Each of the above modules may be embedded in or independent of the processor of the computer device in the form of hardware, or may be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

In an exemplary embodiment, a computer device is provided. The computer device is a control device carrying the above-mentioned upper-level control method of the refrigeration system. It may be a terminal, and its internal structure diagram may be as shown in Figure 11. The computer device includes a processor, memory, input/output interface, communication interface, display unit and input device. Among them, the processor, memory and input/output interface are connected through the system bus, and the communication interface, display unit and input device are connected to the system bus through the input/output interface. Wherein, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes non-volatile storage media and internal memory. The non-volatile storage medium stores operating systems and computer programs. This internal memory provides an environment for the execution of operating systems and computer programs in non-volatile storage media. The input/output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used for wired or wireless communication with external terminals. The wireless mode can be implemented through WIFI, mobile cellular network or other technologies. When the computer program is executed by the processor, a method for upper-level control of the refrigeration system is implemented. The display unit of the computer equipment is used to form a visually visible picture, and may be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display or an electronic ink display. The input device of the computer device can be a touch layer covered on the display screen, or it can be a button, trackball or touch pad provided on the computer device casing, or it can be External keyboard, trackpad or mouse, etc.

Those skilled in the art can understand that the structure shown in Figure 11 is only a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer equipment to which the solution of the present application is applied. Specific computer equipment can May include more or fewer parts than shown, or combine certain parts, or have a different arrangement of parts.

In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, it implements the following steps:

Based on the control objectives of the refrigeration system and the operating status of each refrigeration equipment in the refrigeration system model, multiple initial control strategies are trained to obtain the target control strategy corresponding to each initial control strategy;

Use operating status, weather forecast data, and cooling load forecast data of the refrigeration system to determine the optimal target control strategy from multiple target control strategies;

The target control instructions are obtained according to the optimal target control strategy; the target control instructions are used for upper-level control of the refrigeration equipment in the refrigeration system.

In one embodiment, the processor also implements the following steps when executing the computer program:

For each initial control strategy, based on the control objectives, operating status, cooling load measurement data and historical meteorological data, the simulated control instructions output under the initial control strategy are obtained;

According to the simulation control instructions corresponding to each initial control strategy, the target control strategy corresponding to each initial control strategy is determined.

In one embodiment, the processor also implements the following steps when executing the computer program:

According to the simulation control instructions corresponding to each initial control strategy, use the simulation control instructions to control the refrigeration equipment in the refrigeration system, and obtain the control results and new operating status corresponding to the simulation control instructions;

According to the control results and new operating states corresponding to the simulated control instructions under each initial control strategy, the target control strategy corresponding to each initial control strategy is determined.

In one embodiment, the processor also implements the following steps when executing the computer program:

For the control results corresponding to the simulated control instructions under each initial control strategy, if the control results meet the preset conditions, the initial control strategy will be used as the target control strategy;

If the control results do not meet the preset conditions, the control results are used to modify the initial control strategy to obtain an intermediate control strategy, and the output under the intermediate control strategy is obtained based on the control objectives, new operating status, cooling load measurement data and historical meteorological data. The new simulation control instructions are used to control the refrigeration equipment in the refrigeration system, and the new control results corresponding to the new simulation control instructions and the steps of the next new operating state are obtained until the new control results meet the preset conditions. The control strategy of the last output simulation control instruction is used as the target control strategy.

In one embodiment, the processor also implements the following steps when executing the computer program:

According to the operating status, cooling load forecast data and meteorological forecast data, each target control strategy is used to output the simulation control instructions corresponding to each target control strategy;

For each simulation control instruction, the control result corresponding to the simulation control instruction is obtained;

According to each control result, the optimal target control strategy is determined from multiple target control strategies.

In one embodiment, the processor also implements the following steps when executing the computer program:

According to the performance simulation equations of each refrigeration equipment in the refrigeration system, the initial refrigeration system model is obtained;

Using the cooling load, heat load and power data of the refrigeration system, the parameters in the initial refrigeration system model are calibrated to obtain the refrigeration system model.

In one embodiment, a computer-readable storage medium is provided with a computer program stored thereon. When the computer program is executed by a processor, the following steps are implemented:

Based on the control objectives of the refrigeration system and the operating status of each refrigeration equipment in the refrigeration system model, multiple initial control strategies are trained to obtain the target control strategy corresponding to each initial control strategy;

Use operating status, weather forecast data, and cooling load forecast data of the refrigeration system to determine the optimal target control strategy from multiple target control strategies;

The target control instructions are obtained according to the optimal target control strategy; the target control instructions are used for upper-level control of the refrigeration equipment in the refrigeration system.

In one embodiment, the computer program, when executed by the processor, also implements the following steps:

For each initial control strategy, based on the control objectives, operating status, cooling load measurement data and historical meteorological data, the simulated control instructions output under the initial control strategy are obtained;

According to the simulation control instructions corresponding to each initial control strategy, the target control strategy corresponding to each initial control strategy is determined.

In one embodiment, the computer program, when executed by the processor, also implements the following steps:

According to the simulation control instructions corresponding to each initial control strategy, use the simulation control instructions to control the refrigeration equipment in the refrigeration system, and obtain the control results and new operating status corresponding to the simulation control instructions;

According to the control results and new operating states corresponding to the simulated control instructions under each initial control strategy, the target control strategy corresponding to each initial control strategy is determined.

In one embodiment, the computer program, when executed by the processor, also implements the following steps:

For the control results corresponding to the simulated control instructions under each initial control strategy, if the control results meet the preset conditions, the initial control strategy will be used as the target control strategy;

If the control results do not meet the preset conditions, the control results are used to modify the initial control strategy to obtain an intermediate control strategy, and the output under the intermediate control strategy is obtained based on the control objectives, new operating status, cooling load measurement data and historical meteorological data. The new simulation control instructions are used to control the refrigeration equipment in the refrigeration system, and the new control results corresponding to the new simulation control instructions and the steps of the next new operating state are obtained until the new control results meet the preset conditions. The control strategy of the last output simulation control instruction is used as the target control strategy.

In one embodiment, the computer program, when executed by the processor, also implements the following steps:

According to the operating status, cooling load forecast data and meteorological forecast data, each target control strategy is used to output the simulation control instructions corresponding to each target control strategy;

For each simulation control instruction, the control result corresponding to the simulation control instruction is obtained;

According to each control result, the optimal target control strategy is determined from multiple target control strategies.

In one embodiment, the computer program, when executed by the processor, also implements the following steps:

According to the performance simulation equations of each refrigeration equipment in the refrigeration system, the initial refrigeration system model is obtained;

Using the cooling load, heat load and power data of the refrigeration system, the parameters in the initial refrigeration system model are calibrated to obtain the refrigeration system model.

In one embodiment, a computer program product is provided, comprising a computer program that when executed by a processor implements the following steps:

Based on the control objectives of the refrigeration system and the operating status of each refrigeration equipment in the refrigeration system model, multiple initial control strategies are trained to obtain the target control strategy corresponding to each initial control strategy;

Use operating status, weather forecast data, and cooling load forecast data of the refrigeration system to determine the optimal target control strategy from multiple target control strategies;

The target control instructions are obtained according to the optimal target control strategy; the target control instructions are used for upper-level control of the refrigeration equipment in the refrigeration system.

In one embodiment, the computer program, when executed by the processor, also implements the following steps:

For each initial control strategy, based on the control objectives, operating status, cooling load measurement data and historical meteorological data, the simulated control instructions output under the initial control strategy are obtained;

According to the simulation control instructions corresponding to each initial control strategy, the target control strategy corresponding to each initial control strategy is determined.

In one embodiment, the computer program, when executed by the processor, also implements the following steps:

According to the simulation control instructions corresponding to each initial control strategy, use the simulation control instructions to control the refrigeration equipment in the refrigeration system, and obtain the control results and new operating status corresponding to the simulation control instructions;

According to the control results and new operating states corresponding to the simulated control instructions under each initial control strategy, the target control strategy corresponding to each initial control strategy is determined.

In one embodiment, the computer program, when executed by the processor, also implements the following steps:

For the control results corresponding to the simulated control instructions under each initial control strategy, if the control results meet the preset conditions, the initial control strategy will be used as the target control strategy;

If the control results do not meet the preset conditions, the control results are used to modify the initial control strategy to obtain an intermediate control strategy, and the output under the intermediate control strategy is obtained based on the control objectives, new operating status, cooling load measurement data and historical meteorological data. The new simulation control instructions are used to control the refrigeration equipment in the refrigeration system, and the new control results corresponding to the new simulation control instructions and the steps of the next new operating state are obtained until the new control results meet the preset conditions. The control strategy of the last output simulation control instruction is used as the target control strategy.

In one embodiment, the computer program, when executed by the processor, also implements the following steps:

According to the operating status, cooling load forecast data and meteorological forecast data, each target control strategy is used to output the simulation control instructions corresponding to each target control strategy;

For each simulation control instruction, the control result corresponding to the simulation control instruction is obtained;

According to each control result, the optimal target control strategy is determined from multiple target control strategies.

In one embodiment, the computer program, when executed by the processor, also implements the following steps:

According to the performance simulation equations of each refrigeration equipment in the refrigeration system, the initial refrigeration system model is obtained;

Using the cooling load, heat load and power data of the refrigeration system, the parameters in the initial refrigeration system model are calibrated to obtain the refrigeration system model.

Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. , when executed, the computer program may include the processes of the above method embodiments. Any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetic variable memory (Magnetoresistive Random) Access Memory (MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (PCM), graphene memory, etc. Volatile memory may include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration but not limitation, RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM). The databases involved in the various embodiments provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include blockchain-based distributed databases, etc., but are not limited thereto. The processors involved in the various embodiments provided in this application may be general-purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to this.

The technical features of the above embodiments can be combined in any way. To simplify the description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, all possible combinations should be used. It is considered to be within the scope of this manual.

The above-described embodiments only express several implementation modes of the present application, and their descriptions are relatively specific and detailed, but should not be construed as limiting the patent scope of the present application. It should be noted that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all fall within the protection scope of the present application. Therefore, the scope of protection of this application should be determined by the appended claims.

Claims (10)

1. A method for upper-level control of a refrigeration system, characterized in that the method includes: Based on the control objectives of the refrigeration system and the operating status of each refrigeration equipment in the refrigeration system model, train multiple initial control strategies to obtain the target control strategy corresponding to each of the initial control strategies; Using the operating status, weather prediction data, and cooling load prediction data of the refrigeration system, determine an optimal target control strategy from a plurality of the target control strategies; A target control instruction is obtained according to the optimal target control strategy; the target control instruction is used for upper-level control of the refrigeration equipment in the refrigeration system. 2. The method according to claim 1, characterized in that, based on the control objectives of the refrigeration system and the operating status of each refrigeration equipment in the refrigeration system model, a plurality of initial control strategies are trained to obtain each of the initial control strategies. The target control strategy corresponding to the strategy includes: For each of the initial control strategies, obtain the simulated control instructions output under the initial control strategy based on the control target, the operating status, cooling load measurement data and historical meteorological data; According to the simulation control instructions corresponding to each of the initial control strategies, a target control strategy corresponding to each of the initial control strategies is determined. 3. The method of claim 2, wherein determining the target control strategy corresponding to each initial control strategy based on the simulation control instructions corresponding to each initial control strategy includes: For the simulation control instructions corresponding to each of the initial control strategies, use the simulation control instructions to control the refrigeration equipment in the refrigeration system, and obtain the control results and new operating states corresponding to the simulation control instructions; According to the control results and new operating states corresponding to the simulated control instructions under each of the initial control strategies, the target control strategy corresponding to each of the initial control strategies is determined. 4. The method according to claim 3, characterized in that the target control corresponding to each of the initial control strategies is determined based on the control results and new operating states corresponding to the simulated control instructions under each of the initial control strategies. strategies, including: Regarding the control results corresponding to the simulated control instructions under each of the initial control strategies, if the control results meet the preset conditions, then the initial control strategy is used as the target control strategy; If the control result does not meet the preset condition, the control result is used to modify the initial control strategy to obtain an intermediate control strategy, and execute the control strategy according to the control target, the new operating state, and the cooling Load measurement data and the historical meteorological data are used to obtain new simulation control instructions output under the intermediate control strategy, and the new simulation control instructions are used to control the refrigeration equipment in the refrigeration system to obtain the new simulation control instructions. The steps corresponding to the new control result and the next new operating state, until the new control result meets the preset conditions, use the control strategy of the last output simulated control instruction as the target control strategy. 5. The method of claim 1, wherein the optimal target is determined from a plurality of target control strategies using the operating status, weather forecast data, and cooling load forecast data of the refrigeration system. Control strategies, including: According to the operating status, the cooling load prediction data and the weather prediction data, use each of the target control strategies to output simulation control instructions corresponding to each of the target control strategies; For each of the simulated control instructions, obtain the control result corresponding to the simulated control instruction; According to each of the control results, an optimal target control strategy is determined from a plurality of the target control strategies. 6. The method according to any one of claims 1-5, characterized in that the method further includes: Obtain an initial refrigeration system model according to the performance simulation equations of each refrigeration equipment in the refrigeration system; Using the cooling load, heating load and power data of the refrigeration system, the parameters in the initial refrigeration system model are calibrated to obtain the refrigeration system model. 7. A refrigeration system upper-level control device, characterized in that the device includes: The first determination module is used to train multiple initial control strategies based on the control objectives of the refrigeration system and the operating status of each refrigeration equipment in the refrigeration system model, and obtain the target control strategy corresponding to each of the initial control strategies; a second determination module, configured to determine an optimal target control strategy from a plurality of the target control strategies using the operating status, weather forecast data, and cooling load forecast data of the refrigeration system; The third determination module is used to obtain target control instructions according to the optimal target control strategy; the target control instructions are used to perform upper-level control of the refrigeration equipment in the refrigeration system. 8. A computer device, comprising a memory and a processor, the memory stores a computer program, wherein the method of any one of claims 1 to 6 is implemented when the processor executes the computer program. step. 9. A computer-readable storage medium with a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented. 10. A computer program product, comprising a computer program, characterized in that, when executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 6.