Control strategy determination method and device, storage medium and electronic device

Abstract

The invention discloses a control strategy determination method and device, a storage medium and an electronic device.The method comprises the steps that real-time operation data of a heat exchange station and real-time meteorological data of the position where the heat exchange station is located are obtained; inputting the real-time operation data and the real-time meteorological data into a pre-trained load prediction model to obtain a predicted load of the heat exchange station; and determining a control strategy of the heat exchange station through the predicted load. The problem that the control strategy of the heat exchange station is difficult to accurately determine due to the diversity of the external environment and the user behavior and the high time-varying property and uncertainty of the load demand of the heat exchange station is solved.

Description

Technical Field

[0001] The present application relates to the field of heating technology, and in particular to a method and device for determining a control strategy, a storage medium, and an electronic device. Background Art

[0002] The heating system, especially the central heating system, is a complex and large system, which includes multiple components such as heat source, heat network, heat exchange station, etc. As the core of the heat network, the heat exchange station is responsible for the transmission and distribution of heat energy, and its operation status directly affects the heating quality of the entire heating system.

[0003] Due to the diversity of external environment (such as temperature, wind speed, etc.) and user behavior (such as opening and closing windows, adjusting indoor temperature, etc.), the load demand of the heat exchange station is highly time-varying and uncertain. This complexity makes it difficult to accurately determine the control strategy for the heat exchange station, and thus makes it difficult to achieve efficient and accurate control effects for the heat exchange station.

[0004] Regarding the related technologies, due to the diversity of external environment and user behavior, the load demand of the heat exchange station is highly time-varying and uncertain, which makes it difficult to accurately determine the control strategy for the heat exchange station. No effective solution has yet been found. Summary of the invention

[0005] The embodiments of the present application provide a method and device for determining a control strategy, a storage medium, and an electronic device to at least solve the problem in the prior art that, due to the diversity of the external environment and user behavior, the load demand of the heat exchange station is highly time-varying and uncertain, making it difficult to accurately determine the control strategy for the heat exchange station.

[0006] According to one embodiment of the embodiments of the present application, a method for determining a control strategy is provided, comprising: obtaining real-time operating data of a heat exchange station and real-time meteorological data of the location of the heat exchange station; inputting the real-time operating data and the real-time meteorological data into a pre-trained load prediction model to obtain a predicted load of the heat exchange station; and determining the control strategy of the heat exchange station according to the predicted load.

[0007] In an exemplary embodiment, before inputting the real-time operating data and the real-time meteorological data into a pre-trained load forecasting model, the method further includes: obtaining historical operating data of the heat exchange station and historical meteorological data corresponding to the historical operating data; preprocessing the historical operating data and the historical meteorological data; and training a preset model with the preprocessed historical operating data and the preprocessed historical meteorological data to obtain the load forecasting model.

[0008] In an exemplary embodiment, the control strategy of the heat exchange station is determined by the predicted load, including: obtaining the real-time load corresponding to the real-time operation data; comparing the real-time load with the predicted load to obtain a comparison result; when the comparison result indicates that the predicted load is greater than the real-time load, determining that the control strategy is a first strategy, wherein the first strategy includes: increasing the flow rate of the target water pump of the heat exchange station, and increasing the secondary water supply temperature of the heat exchange station.

[0009] In an exemplary embodiment, after comparing the real-time load with the predicted load and obtaining a comparison result, the method further includes: when the comparison result indicates that the predicted load is less than the real-time load, determining that the control strategy is a second strategy, wherein the first strategy includes: reducing the flow rate of the target water pump of the heat exchange station, and reducing the secondary water supply temperature of the heat exchange station.

[0010] In an exemplary embodiment, after determining the control strategy of the heat exchange station by the predicted load, the method further includes: obtaining the pressure difference monitored by a pressure difference sensor deployed in the circulating water system of the heat exchange station; when the pressure difference exceeds a set range, adjusting the circulating pump in the circulating water system, wherein adjusting the circulating pump in the circulating water system includes one of the following: turning on a preset number of circulating pumps, turning off a preset number of circulating pumps, and adjusting the speed of the circulating pump.

[0011] In an exemplary embodiment, after determining the control strategy of the heat exchange station by the predicted load, the following steps are included: evaluating the control effect obtained by executing the control strategy to obtain an evaluation result, wherein the control effect includes at least one of the following values: heating effect, energy consumption, and system stability of the heating system where the heat exchange station is located; and optimizing the load prediction model by means of the evaluation result.

[0012] According to another embodiment of the embodiment of the present application, a device for determining a control strategy is also provided, including: an acquisition module, used to acquire real-time operating data of a heat exchange station and real-time meteorological data of the location where the heat exchange station is located; an input module, used to input the real-time operating data and the real-time meteorological data into a pre-trained load prediction model to obtain the predicted load of the heat exchange station; and a determination module, used to determine the control strategy of the heat exchange station through the predicted load.

[0013] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above method when running.

[0014] According to another aspect of the embodiments of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the method through the computer program.

[0015] According to another aspect of the embodiments of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0016] In an embodiment of the present application, the real-time operation data of the heat exchange station and the real-time meteorological data of the location of the heat exchange station are obtained; the real-time operation data and the real-time meteorological data are input into a pre-trained load prediction model to obtain the predicted load of the heat exchange station; the control strategy of the heat exchange station is determined by the predicted load. Through the above embodiment, the problem in the prior art that the load demand of the heat exchange station is highly time-varying and uncertain due to the diversity of the external environment and user behavior, resulting in difficulty in accurately determining the control strategy of the heat exchange station is solved, and the technical effect of accurately determining the control strategy of the heat exchange station is achieved, thereby achieving accurate and efficient control of the heat exchange station. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0018] Figure 1 It is a hardware structure block diagram of a computer terminal of a method for determining a control strategy in an embodiment of the present application;

[0019] Figure 2 is a flow chart of a method for determining a control strategy according to an embodiment of the present application;

[0020] Figure 3 is a heat exchange station adjustment schematic diagram according to a method for determining a control strategy according to an embodiment of the present application;

[0021] Figure 4 It is a structural block diagram of a device for determining a control strategy according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices; "plurality" means two or more.

[0024] The method embodiments provided in the embodiments of the present application can be executed in a computer terminal or similar computing device or cloud platform or independent physical server or software platform, wherein the above software platform is run through one or more servers. Taking running on a computer terminal as an example, Figure 1 FIG. 1 is a hardware structure block diagram of a computer terminal for a method for determining a control strategy according to an embodiment of the present application. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) processor 102 and memory 104 for storing data. In an exemplary embodiment, the computer terminal may also include a transmission device 106 for communication functions and an input and output device 108, wherein the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA. A person skilled in the art can understand that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Equivalent functions or comparisons shown Figure 1 A different configuration with more features is shown.

[0025] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as computer programs corresponding to the methods in the embodiments of the present application. The processor 102 executes various functional applications and data processing by running the computer programs stored in the memory 104, that is, the above-mentioned methods are implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0026] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0027] In this embodiment, a method for determining a control strategy is provided, which is applied to the above-mentioned computer terminal, which communicates with the heat exchange station; or is applied to the heat exchange station. Figure 2 : is a flow chart of a method for determining a control strategy according to an embodiment of the present application, the flow chart comprising the following steps:

[0028] Step S202, obtaining real-time operation data of the heat exchange station and real-time meteorological data of the location where the heat exchange station is located;

[0029] Step S204, inputting the real-time operation data and the real-time meteorological data into a pre-trained load prediction model to obtain a predicted load of the heat exchange station;

[0030] Step S206: determining a control strategy for the heat exchange station according to the predicted load.

[0031] The above steps can be executed by the control system deployed in the above computer terminal or the heat exchange station.

[0032] Through the above steps, the real-time operation data of the heat exchange station and the real-time meteorological data of the location of the heat exchange station are obtained; the real-time operation data and the real-time meteorological data are input into a pre-trained load prediction model to obtain the predicted load of the heat exchange station; the control strategy of the heat exchange station is determined by the predicted load. Through the above embodiments, the problem in the prior art that the load demand of the heat exchange station is highly time-varying and uncertain due to the diversity of the external environment and user behavior, resulting in difficulty in accurately determining the control strategy of the heat exchange station is solved, and the technical effect of accurately determining the control strategy of the heat exchange station is achieved, thereby achieving accurate and efficient control of the heat exchange station.

[0033] In an exemplary embodiment, before inputting the real-time operating data and the real-time meteorological data into a pre-trained load forecasting model, the method further includes: obtaining historical operating data of the heat exchange station and historical meteorological data corresponding to the historical operating data; preprocessing the historical operating data and the historical meteorological data; and training a preset model with the preprocessed historical operating data and the preprocessed historical meteorological data to obtain the load forecasting model.

[0034] Optionally, historical operation data can be obtained from the SCADA system (data acquisition and monitoring system) of the heat exchange station. Historical operation data usually includes but is not limited to water supply temperature, return water temperature, water supply pressure, return water pressure, water pump speed, valve opening, heat exchange efficiency, heat energy consumption, etc. At the same time, historical meteorological data corresponding to the historical operation data is obtained from a meteorological station or a third-party meteorological service provider, including environmental parameters such as outdoor temperature, humidity, wind speed, wind direction, and sunshine intensity.

[0035] Furthermore, data preprocessing is the key to improving the performance of the prediction model. It mainly includes the following steps: 1) Data cleaning, removing invalid data, filling missing values, detecting and processing outliers, and ensuring the integrity and consistency of the data. 2) Data denoising, using filtering technology to remove random noise in the data and improve the signal quality of the data. 3) Data normalization, converting data of different magnitudes to the same magnitude (such as the 0-1 interval) to avoid certain features from having too much impact on the prediction results, while simplifying the model training process. 4) Feature selection, based on correlation analysis, principal component analysis and other technologies, screen out the features that have the greatest impact on load forecasting, reduce the complexity of the model, and improve prediction efficiency. 5) Time series analysis, for data with time series characteristics (such as meteorological data, operation data), time series preprocessing is performed, such as decomposing seasonal and trend components, performing differential stabilization processing, etc.

[0036] Through the above preprocessing steps, we can obtain the preprocessed historical operation data and preprocessed historical meteorological data. Next, use these data to train the preset model. The preset model can be a variety of machine learning or deep learning models, such as: ARIMA, autoregressive integrated moving average model, suitable for time series data prediction. LSTM, long short-term memory network, is particularly suitable for processing time series data with long-term dependencies. GRU model, gated recurrent unit, is a simplified version of LSTM and is also suitable for time series prediction. XGBoost model, extreme gradient boosting, is suitable for processing large-scale data sets and can automatically handle interactions between features.

[0037] The model training process includes: dividing the data set into training set, validation set and test set, usually in a ratio of 60%, 20% and 20%. Use the training set data to train the model and optimize the model parameters through continuous iteration. Use the validation set data to preliminarily verify the model and adjust the model parameters to avoid overfitting. Use the test set data to conduct a final test on the model to evaluate the generalization ability and prediction accuracy of the model. According to the test results, adjust and optimize the model structure, parameters, etc. to improve the prediction performance.

[0038] Through the above steps, the final load forecasting model can more accurately predict the future load demand of the heat exchange station, providing a solid data basis for the formulation of feedforward control strategies, thereby achieving more efficient and precise control of the heating system.

[0039] After the load forecasting model is trained, the real-time operation data and real-time meteorological data are input into the trained load forecasting model to obtain the load forecasting results of the heat exchange station in the future. Based on these forecasting results, the operating parameters of the heating system can be adjusted in advance to achieve heating feedforward control, improve the stability and efficiency of the system operation, and reduce energy consumption, thereby achieving energy conservation and emission reduction.

[0040] In some optional embodiments, the control strategy of the heat exchange station is determined by the predicted load, including: obtaining the real-time load corresponding to the real-time operation data; comparing the real-time load with the predicted load to obtain a comparison result; when the comparison result indicates that the predicted load is greater than the real-time load, determining that the control strategy is the first strategy, wherein the first strategy includes: increasing the flow rate of the target water pump of the heat exchange station, and increasing the secondary water supply temperature of the heat exchange station.

[0041] Furthermore, after comparing the real-time load with the predicted load and obtaining a comparison result, the method also includes: when the comparison result indicates that the predicted load is less than the real-time load, determining that the control strategy is a second strategy, wherein the first strategy includes: reducing the flow rate of the target water pump of the heat exchange station, and reducing the secondary water supply temperature of the heat exchange station.

[0042] It is understandable that the operation data of the heat exchange station is collected in real time, including parameters such as supply water temperature, return water temperature, flow rate, pressure, etc., and the current real-time load is obtained by calculation or measurement. At the same time, the predicted load generated by the prediction model is compared with the current real-time load to obtain a comparison result, so as to judge the relationship between the future load change trend and the current operation status.

[0043] According to the comparison results, the operating parameters of the heat exchange station can be adjusted automatically or semi-automatically to adapt to the load changes: when the predicted load is greater than the real-time load, it means that the load demand of the heat exchange station will increase in the future. In order to ensure that sufficient heat energy can be provided and the heating quality is maintained when the load increases, the control system (deployed in the heat exchange station or in a computer terminal that can communicate with the heat exchange station) will take the following measures: Increase the flow rate of the target water pump: Increasing the flow rate of circulating water helps to improve the heat exchange efficiency and ensure that when the load increases, the heat energy can be quickly and evenly delivered to the user end. Increase the secondary water supply temperature: By adjusting the primary side electric regulating valve of the heat exchange station, the temperature of the secondary water supply is increased to increase the heating amount to meet the upcoming higher load demand.

[0044] On the contrary, when the predicted load is less than the real-time load, it means that the load demand of the heat exchange station will decrease in the future. In order to avoid energy waste and improve energy efficiency, the control system will take the following measures: Reduce the flow of the target water pump: reduce the flow of circulating water, reduce the operating energy consumption of the heat exchange station, and avoid temperature fluctuations caused by excess heat to maintain the quality of heating. Low secondary water supply temperature: by adjusting the primary side electric regulating valve of the heat exchange station, reduce the temperature of the secondary water supply, reduce unnecessary heat supply, and avoid energy waste.

[0045] Through the above steps, the embodiment of the present application adjusts the system parameters in advance based on the predicted load to achieve a rapid response to the upcoming load change, improve the operating efficiency and economy of the heating system, reduce unnecessary energy consumption, and achieve the goal of energy conservation and emission reduction. At the same time, by comparing with the real-time operating status, the rationality of the control strategy is ensured, the overreaction or lag problems that may occur in traditional feedback control are avoided, and the control accuracy of the heat exchange station is improved.

[0046] In an exemplary embodiment, after determining the control strategy of the heat exchange station by the predicted load, the method further includes: obtaining the pressure difference monitored by a pressure difference sensor deployed in the circulating water system of the heat exchange station; when the pressure difference exceeds a set range, adjusting the circulating pump in the circulating water system, wherein adjusting the circulating pump in the circulating water system includes one of the following: turning on a preset number of circulating pumps, turning off a preset number of circulating pumps, and adjusting the speed of the circulating pump.

[0047] It should be noted that in the circulating water system, differential pressure sensors are deployed in key locations, such as the pipeline between the heat source and the heat exchange station, inside the heat exchange station, and between the heat exchange station and the user end. Its main function is to monitor the pressure difference in the system in real time, that is, the difference between the water supply pressure and the return water pressure. The pressure difference is an important indicator for measuring the flow resistance in the circulating water system, which is directly related to the working efficiency and energy consumption of the circulating pump.

[0048] When the pressure difference detected by the pressure difference sensor exceeds the set range, the control system will automatically trigger the adjustment mechanism of the circulation pump to ensure that the circulating water system operates within a safe and efficient pressure range. Specific adjustment strategies include:

[0049] 1. Start the preset number of circulation pumps: When the monitored pressure difference is lower than the set lower limit, it means that the water volume or pressure in the system is insufficient to meet the current heating demand. At this time, the control system will automatically start the preset number of circulation pumps to increase the circulating water volume of the system to increase the pressure and meet the load demand.

[0050] 2. Turn off the preset number of circulation pumps: On the contrary, when the monitored pressure difference exceeds the set upper limit, it means that there is excess water or pressure in the system, which may cause energy waste or system overload. At this time, the control system will automatically turn off the preset number of circulation pumps to reduce the circulating water volume of the system to reduce pressure and optimize energy consumption.

[0051] 3. Adjust the speed of the circulation pump: In addition to turning the circulation pump on or off, the control system can also achieve more precise pressure difference control by adjusting the speed of the circulation pump. When the monitored pressure difference is close to the lower limit of the set range, the control system can appropriately increase the speed of the circulation pump to increase the water volume; when it is close to the upper limit, the speed is reduced to reduce the water volume. This control strategy based on speed adjustment can achieve smoother and more energy-saving operation, avoiding the impact and energy consumption caused by frequent starting or stopping of the circulation pump.

[0052] In an exemplary embodiment, after determining the control strategy of the heat exchange station by the predicted load, the following steps are included: evaluating the control effect obtained by executing the control strategy to obtain an evaluation result, wherein the control effect includes at least one of the following values: heating effect, energy consumption, and system stability of the heating system where the heat exchange station is located; and optimizing the load prediction model by means of the evaluation result.

[0053] After implementing the control strategy, the control effect needs to be evaluated to verify whether the feedforward control under the guidance of the predicted load has achieved the expected goal. The evaluation results will guide the subsequent model optimization. When evaluating the control effect, the following indicators are mainly concerned:

[0054] Heating effect: Monitor heating parameters such as temperature, pressure, flow, etc. to evaluate whether it is stable, whether it meets user needs, as well as the heating quality and comfort.

[0055] Energy consumption: By monitoring energy use (such as electricity and heat consumption), the impact of control strategies on energy consumption can be evaluated to analyze whether energy conservation and emission reduction have been achieved.

[0056] System stability: Monitor the operating status of the heat exchange station and the entire heating system, and evaluate the impact of the control strategy on system fluctuations, including but not limited to frequent start and stop of the circulation pump, rapid switching of the electric control valve, etc., to ensure the long-term stable operation of the system.

[0057] The evaluation process is usually carried out by comparing the system operation data before and after the implementation of feedforward control. Various methods such as statistical analysis, time series analysis, and machine learning models can be used to quantify the improvement of control effects. When evaluating, factors such as changes in the external environment and user behavior must also be considered to ensure the comprehensiveness and accuracy of the evaluation results.

[0058] Furthermore, based on the evaluation results of the control effect evaluation, the load forecasting model is optimized to improve the forecasting accuracy and system control effect. The optimization model can be carried out in the following aspects:

[0059] Feature selection: Evaluate the impact of input features in the existing model on the prediction results, screen out more effective features, or introduce new features, such as user behavior data, building structure parameters, etc., to improve the predictive ability and robustness of the model.

[0060] Model parameter adjustment: Based on the evaluation results, adjust the model parameter settings, such as the number of neural network layers, number of nodes, learning rate, etc., to optimize the performance and generalization ability of the model.

[0061] Algorithm improvement: Explore and try different algorithms or model structures, such as ensemble learning, deep learning, etc., to cope with complex data distribution and nonlinear relationships and improve prediction accuracy.

[0062] Data update: Regularly collect and update training data, including real-time operation data, meteorological data, user feedback, etc., to adapt to environmental changes and system evolution, and maintain the timeliness and accuracy of the model.

[0063] Effect verification: The optimized model needs to be verified through a new test data set to ensure that its performance on unseen data remains stable and accurate to avoid overfitting.

[0064] Continuous iteration: Control effect evaluation and model optimization is a continuous iterative process. With the accumulation of system operation data and changes in environmental conditions, regular evaluation and optimization are required to continuously improve prediction capabilities and system control efficiency.

[0065] Through evaluation and optimization, the heating feedforward control method based on heat exchanger station load prediction can predict load changes more accurately and adjust the operating parameters of the heating system more precisely. It not only improves the heating effect and reduces energy consumption, but also ensures the long-term stability and economy of the system, reflecting the advancement and practicality of the intelligent heating control system.

[0066] In order to better understand the process of the method for determining the above control strategy, the method for determining the above control strategy is described below in combination with an optional embodiment, but is not intended to limit the technical solution of the embodiment of the present application.

[0067] Modern control theory emphasizes the dynamic performance and stability of the system, and provides a theoretical basis for designing efficient and accurate control algorithms. The optional embodiments of the present application are based on prediction technology, through analysis and learning of historical data, and can predict future load change trends, providing a reliable basis for feedforward control of the heat exchange station. In terms of load prediction of heat exchange stations, the prediction methods that can be used in the embodiments of the present application include statistical methods, artificial intelligence methods, etc. Statistical methods such as time series analysis and regression analysis predict future load changes by modeling the statistical characteristics of historical data. Artificial intelligence methods such as neural networks and deep learning can automatically extract feature information from data and establish more complex prediction models. These methods perform well in terms of prediction accuracy and robustness, and provide reliable data support for feedforward control of heating.

[0068] It should be noted that feedforward control is a predictive control method that can adjust the operating parameters of the heating system in advance according to the load forecast results to compensate for the upcoming load changes. This method avoids system fluctuations caused by response lag in traditional feedback control and improves the stability and efficiency of the heating system. In the heating system, feedforward control can be applied to multiple links, such as water pump flow regulation and heat exchange station temperature control. Taking the heat exchange station as an example, feedforward control can adjust the inlet water temperature, flow rate and other parameters of the heat exchange station in advance according to the load forecast results to ensure that the heating effect can be maintained constant when the load changes.

[0069] In an embodiment of the present application, the implementation method of controlling the heat exchange station includes: collecting historical operating data, historical meteorological data, etc. of the heat exchange station, and performing preprocessing work such as cleaning, denoising, and normalization to provide a reliable data basis for the load forecasting model. The load forecasting model is used to predict the load of the heat exchange station in the future. Select a suitable forecasting method according to actual needs, such as an artificial neural network method. According to the load forecast results and the actual situation of the heating system, a feedforward control strategy is formulated. When it is predicted that the load will increase, the flow rate of the water pump (i.e., the target water pump) can be increased in advance, and the secondary water supply temperature can be increased at the same time; when it is predicted that the load will decrease, the flow rate of the water pump can be reduced accordingly, and the secondary water supply temperature can be reduced at the same time. According to the feedforward control strategy, the operating parameters of the heating system are adjusted.

[0070] This process can be achieved through an automated control system or by manual intervention. The effect of the feedforward control (equivalent to the control effect in the above embodiment) is evaluated, including heating effect, energy consumption, system stability and other aspects. The load forecasting model and feedforward control strategy are optimized according to the evaluation results to improve the overall performance of the system. The heating feedforward control method based on the load forecast of the heat exchange station can improve the stability and efficiency of the heating system and reduce system fluctuations caused by load changes. Optimize energy consumption and achieve the goal of energy conservation and emission reduction. Improve the intelligence level of the heating system and reduce the cost of manual intervention.

[0071] Further, such as Figure 3 As shown in the figure, the circulating pump is controlled by constant differential pressure. A differential pressure sensor is installed in the circulating water system to monitor the differential pressure changes of the system in real time. The most unfavorable loop is set as the lower limit, and the safe differential pressure is set as the upper limit. A reasonable differential pressure is set. When the monitored differential pressure exceeds the set range, the speed of the circulating pump is automatically adjusted or a certain number of circulating pumps are turned on / off. A flow meter is installed in the main pipeline of the secondary network to find the linear relationship between differential pressure and flow.

[0072] The secondary water supply is controlled by constant temperature difference. The target value of the secondary water supply and return water is the predicted heat load / flow. The upper limit of the water supply temperature is set for protection. When the monitored water supply temperature deviates from the target value, the primary electric regulating valve of the heat exchange station is automatically adjusted. When the water supply temperature reaches the upper limit and the actual heat load is less than the predicted heat load, the pressure increase difference setting value is automatically adjusted after a delay of 5 minutes.

[0073] Ultimately, the automatic control of heating feedforward control based on heat exchange station load prediction is achieved, with predicted heat load as feedforward, combined with pressure difference control and temperature difference control double closed loop.

[0074] With the intensification of the global energy crisis and the improvement of environmental awareness, improving energy efficiency has become an important goal for all companies. In the field of heating, the above-mentioned scheme of the optional embodiment of the present application reduces unnecessary energy consumption and achieves energy conservation and emission reduction by accurately controlling the operating parameters of the heat exchange station. In addition, the heating feedforward control method based on load forecasting in the optional embodiment of the present application adjusts the system parameters in advance to ensure that a constant heating effect can be maintained when the load changes, thereby avoiding energy waste.

[0075] In summary, the heat supply feedforward control method based on heat exchange station load prediction in the embodiment of the present application (equivalent to the method for determining the control strategy in the above embodiment) has the following advantages:

[0076] Early response: Through load forecasting, the system can sense load changes in advance and adjust operating parameters in advance, thus avoiding the lag problem in traditional feedback control.

[0077] Precise control: Feedforward control combined with forecast data can adjust system parameters more accurately to ensure constant heating effect when load changes.

[0078] Energy saving and emission reduction: Through precise control, unnecessary energy consumption is reduced and the goal of energy saving and emission reduction is achieved.

[0079] Improve stability: Feedforward control can reduce system fluctuations and improve the stability and reliability of the heating system.

[0080] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0081] The present application also provides a structural block diagram of a device for determining a control strategy. Figure 4 is a structural block diagram of a device for determining a control strategy according to an embodiment of the present application; Figure 4 As shown, including:

[0082] An acquisition module 42 is used to acquire real-time operation data of the heat exchange station and real-time meteorological data of the location where the heat exchange station is located;

[0083] An input module 44, used to input the real-time operation data and the real-time meteorological data into a pre-trained load prediction model to obtain a predicted load of the heat exchange station;

[0084] The determination module 46 is used to determine the control strategy of the heat exchange station according to the predicted load.

[0085] Through the above-mentioned device, the real-time operation data of the heat exchange station and the real-time meteorological data of the location of the heat exchange station are obtained; the real-time operation data and the real-time meteorological data are input into a pre-trained load prediction model to obtain the predicted load of the heat exchange station; the control strategy of the heat exchange station is determined by the predicted load. Through the above-mentioned embodiment, the problem in the prior art that the load demand of the heat exchange station is highly time-varying and uncertain due to the diversity of the external environment and user behavior, resulting in difficulty in accurately determining the control strategy of the heat exchange station is solved, and the technical effect of accurately determining the control strategy of the heat exchange station is achieved, thereby achieving accurate and efficient control of the heat exchange station.

[0086] In an exemplary embodiment, the device also includes a training module, which is used to: obtain historical operation data of the heat exchange station and historical meteorological data corresponding to the historical operation data before inputting the real-time operation data and the real-time meteorological data into a pre-trained load forecasting model; pre-process the historical operation data and the historical meteorological data; and train a preset model with the pre-processed historical operation data and the pre-processed historical meteorological data to obtain the load forecasting model.

[0087] In an exemplary embodiment, the determination module 46 is also used to: obtain the real-time load corresponding to the real-time operating data; compare the real-time load with the predicted load to obtain a comparison result; when the comparison result indicates that the predicted load is greater than the real-time load, determine that the control strategy is the first strategy, wherein the first strategy includes: increasing the flow rate of the target water pump of the heat exchange station, and increasing the secondary water supply temperature of the heat exchange station.

[0088] In an exemplary embodiment, the determination module 46 is also used to: when the comparison result indicates that the predicted load is less than the real-time load, determine that the control strategy is a second strategy, wherein the first strategy includes: reducing the flow rate of the target water pump of the heat exchange station, and reducing the secondary water supply temperature of the heat exchange station.

[0089] In an exemplary embodiment, the device also includes an adjustment module, which is used to: obtain the pressure difference monitored by the pressure difference sensor deployed in the circulating water system of the heat exchange station after determining the control strategy of the heat exchange station through the predicted load; when the pressure difference exceeds a set range, adjust the circulating pump in the circulating water system, wherein the adjustment of the circulating pump in the circulating water system includes one of the following: turning on a preset number of circulating pumps, turning off a preset number of circulating pumps, and adjusting the speed of the circulating pump.

[0090] In an exemplary embodiment, the device also includes an evaluation and optimization module, which is used to evaluate the control effect obtained by executing the control strategy to obtain an evaluation result after determining the control strategy of the heat exchange station through the predicted load, wherein the control effect includes at least one of the following values: heating effect, energy consumption, and system stability of the heating system where the heat exchange station is located; and optimize the load prediction model through the evaluation result.

[0091] An embodiment of the present application further provides a storage medium, which includes a stored program, wherein the program executes any of the above methods when it is run.

[0092] Optionally, in this embodiment, the storage medium may be configured to store program codes for executing the following steps:

[0093] S1, obtaining real-time operation data of a heat exchange station and real-time meteorological data of a location where the heat exchange station is located;

[0094] S2, inputting the real-time operation data and the real-time meteorological data into a pre-trained load prediction model to obtain a predicted load of the heat exchange station;

[0095] S3, determining a control strategy of the heat exchange station according to the predicted load.

[0096] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0097] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0098] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0099] S1, obtaining real-time operation data of a heat exchange station and real-time meteorological data of a location where the heat exchange station is located;

[0100] S2, inputting the real-time operation data and the real-time meteorological data into a pre-trained load prediction model to obtain a predicted load of the heat exchange station;

[0101] S3, determining a control strategy of the heat exchange station according to the predicted load.

[0102] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store program codes.

[0103] An embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0104] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0105] S1, obtaining real-time operation data of a heat exchange station and real-time meteorological data of a location where the heat exchange station is located;

[0106] S2, inputting the real-time operation data and the real-time meteorological data into a pre-trained load prediction model to obtain a predicted load of the heat exchange station;

[0107] S3, determining a control strategy of the heat exchange station according to the predicted load.

[0108] An embodiment of the present application further provides another computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0109] An embodiment of the present application also provides a computer program, which includes computer instructions stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in any one of the above method embodiments.

[0110] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0111] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0112] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for determining a control strategy, characterized in that: include: Acquiring real-time operating data of the heat exchange station and real-time meteorological data of the location where the heat exchange station is located; Inputting the real-time operation data and the real-time meteorological data into a pre-trained load prediction model to obtain a predicted load of the heat exchange station; The control strategy of the heat exchange station is determined according to the predicted load.

2. The method for determining a control strategy according to claim 1, characterized in that: Before inputting the real-time operation data and the real-time meteorological data into a pre-trained load forecasting model, the method further includes: Acquire historical operation data of the heat exchange station and historical meteorological data corresponding to the historical operation data; Preprocessing the historical operation data and the historical meteorological data; The load forecasting model is obtained by training a preset model with preprocessed historical operation data and preprocessed historical meteorological data.

3. The method for determining a control strategy according to claim 1, characterized in that: Determining the control strategy of the heat exchange station according to the predicted load includes: Obtaining the real-time load corresponding to the real-time operation data; Comparing the real-time load with the predicted load to obtain a comparison result; When the comparison result indicates that the predicted load is greater than the real-time load, the control strategy is determined to be a first strategy, wherein the first strategy includes: increasing the flow rate of the target water pump of the heat exchange station, and increasing the secondary water supply temperature of the heat exchange station.

4. The method for determining a control strategy according to claim 3, characterized in that: After comparing the real-time load with the predicted load and obtaining a comparison result, the method further includes: When the comparison result indicates that the predicted load is less than the real-time load, the control strategy is determined to be the second strategy, wherein the first strategy includes: reducing the flow rate of the target water pump of the heat exchange station, and reducing the secondary water supply temperature of the heat exchange station.

5. The method for determining a control strategy according to claim 3, characterized in that: After determining the control strategy of the heat exchange station by the predicted load, the method further includes: Acquiring a pressure difference monitored by a pressure difference sensor deployed in a circulating water system of the heat exchange station; When the pressure difference exceeds the set range, the circulation pump in the circulating water system is adjusted, wherein the adjustment of the circulation pump in the circulating water system includes one of the following: turning on a preset number of circulation pumps, turning off a preset number of circulation pumps, and adjusting the rotation speed of the circulation pump.

6. The method for determining a control strategy according to claim 1, characterized in that: After determining the control strategy of the heat exchange station by the predicted load, the method further includes: Evaluate the control effect obtained by executing the control strategy to obtain an evaluation result, wherein the control effect includes at least one of the following values: heating effect, energy consumption, and system stability of the heating system where the heat exchange station is located; The load forecasting model is optimized based on the evaluation results.

7. A device for determining a control strategy, characterized in that: include: An acquisition module, used to acquire real-time operation data of the heat exchange station and real-time meteorological data of the location where the heat exchange station is located; An input module, used for inputting the real-time operation data and the real-time meteorological data into a pre-trained load prediction model to obtain a predicted load of the heat exchange station; A determination module is used to determine the control strategy of the heat exchange station according to the predicted load.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method described in any one of claims 1 to 6 when executed.

9. An electronic device comprising a memory and a processor, characterized in that: The memory stores a computer program, and the processor is configured to execute the method according to any one of claims 1 to 6 through the computer program.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 6 are implemented.

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