Energy-saving system and method coupled with building automatic control and comprehensive energy management

Abstract

The invention discloses an energy-saving system coupling building self-control and comprehensive energy management, the system comprises a building self-control end and a comprehensive energy end, the building self-control end collects equipment operation data and system monitoring data and sends the data to the comprehensive energy end, the comprehensive energy end collects the equipment operation data and records energy consumption data during current equipment operation, and the energy consumption data is sent to the comprehensive energy end. Energy consumption abnormity reasons are analyzed and determined according to the equipment operation data and the corresponding energy consumption data, an operation optimization strategy is obtained, and the operation optimization strategy is sent to the building automatic control end to be executed. In addition, the cloud management platform also realizes intelligent management of the building based on an AI training module. The invention further discloses an energy-saving method coupling building automatic control and comprehensive energy management. The method has the advantages that the building self-control end and the comprehensive energy end are interconnected to form an energy-saving verification cycle, continuous optimization iteration of operation data of the comprehensive energy end and the building self-control end is achieved, and then continuous and maximum energy-saving and carbon-reducing operation of the building is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of building energy saving and carbon reduction, and in particular to an energy-saving system and method for coupling building automatic control and integrated energy management. Background Art

[0002] Integrated energy management is an energy management platform that integrates multiple functions and manages and analyzes various energy consumption in buildings. Building automation management is a comprehensive system for centralized control and monitoring of various electromechanical equipment in buildings. Managers adjust the electromechanical equipment in the building by adjusting operating parameters or control logic, as well as perform maintenance and management on the equipment. The operation mode of equipment in a building is directly related to energy consumption. However, integrated energy management and building automation systems are two independent systems that cannot form an effective closed-loop management and cannot evaluate the causes of abnormal energy consumption. There are also problems such as high manual participation and imperfect automated operation, which results in the inability to maximize energy-saving and carbon-reduction operations. Summary of the invention

[0003] To solve the above problems, the purpose of the present invention is to provide a comprehensive management and energy-saving system and method that realizes the coordinated interconnection of comprehensive energy management and building automation management, the coupling iteration between equipment operation management and energy consumption management, and maximizes the realization of sustainable energy-saving and carbon-reducing operation.

[0004] To achieve the above-mentioned object, the present invention provides an energy-saving system for coupling building automation and integrated energy management, the system comprising a building automation terminal and an integrated energy terminal, wherein the building automation terminal and the integrated energy terminal exchange data via a network communication unit;

[0005] The building automatic control terminal includes: collecting equipment operation data and system monitoring data of each equipment in the building at a set collection period, sending the collected equipment operation data and system monitoring data to the comprehensive energy terminal, and regulating the equipment operation data and / or system control scheme according to the operation optimization strategy;

[0006] The comprehensive energy terminal includes: using a sub-item metering method to record the energy consumption data of the equipment during operation, and collecting the operation data of the equipment, comparing the energy consumption data of the current equipment during operation with the historical energy consumption data, screening out the corresponding equipment operation data whose energy consumption data exceeds the preset energy consumption range, and determining the cause of abnormal energy consumption based on the screened equipment operation data and the corresponding energy consumption data analysis, and obtaining an operation optimization strategy based on the cause of abnormal energy consumption, the operation optimization strategy includes one or more operation optimization schemes, and the operation optimization strategy is sent to the building automatic control terminal.

[0007] As a further improvement of the present invention, the building automatic control terminal includes a data acquisition unit, a system control unit and an equipment management unit;

[0008] The data acquisition unit collects the equipment operation data and system monitoring data of each equipment in the building at a set collection cycle, and sends the collected equipment operation data and system monitoring data to the integrated energy terminal; the system control unit controls the equipment operation and / or system control scheme according to the operation optimization strategy; the equipment management unit locates and records the data of the monitored equipment; the equipment operation data includes but is not limited to: the start and stop, operation frequency, operation time and valve opening of the refrigerator, air conditioner, fan and water pump; the system monitoring data includes but is not limited to: temperature, humidity, pressure difference and flow.

[0009] The building automation terminal also includes a visualization unit involving equipment operating status and system parameter monitoring. The visualization unit visualizes the equipment operating data and system monitoring data recorded by the equipment controller and various sensors, and provides an interactive port for operating mode, parameter setting and adjustment.

[0010] As a further improvement of the present invention, the integrated energy terminal includes a data collection and storage unit and an energy consumption analysis unit; the data collection and storage unit adopts sub-item metering to record the energy consumption data of the equipment during operation, and collects the operation data of the equipment; the energy consumption analysis unit compares the energy consumption data of the current equipment during operation with the historical energy consumption data, screens out the corresponding equipment operation data whose energy consumption data exceeds a preset range, and determines the cause of the abnormal energy consumption based on the screened equipment operation data and the corresponding energy consumption data analysis, and obtains an operation optimization strategy based on the cause of the abnormal energy consumption, and sends the operation optimization strategy to the building automatic control terminal; the energy consumption data includes: electricity consumption, heat consumption and water consumption.

[0011] As a further improvement of the present invention, the integrated energy terminal also includes a data visualization unit and an energy consumption abnormality warning unit. The data visualization unit visualizes the data collected by the data collection and storage unit, and the energy consumption abnormality warning unit prompts the abnormal energy consumption operation conditions screened out by the energy consumption analysis unit.

[0012] As a further improvement of the present invention, the cause of abnormal energy consumption is determined based on the analysis of the screened equipment operation data and the corresponding energy consumption data, and the operation optimization strategy obtained based on the cause of abnormal energy consumption includes multiple operation optimization schemes, the multiple operation optimization schemes are prioritized, and the operation optimization scheme with the highest priority is sent to the building automatic control terminal.

[0013] As a further improvement of the present invention, the integrated energy end includes an optimized operation tracking and detection unit, which is used to track and detect the execution results of the operation optimization plan. The optimized operation tracking and detection unit obtains the energy consumption data before the execution of the operation optimization plan and the energy consumption data after the execution of the operation optimization plan, and judges the effectiveness of the execution of the operation optimization plan based on the comprehensive difference between the energy consumption data before the execution of the operation optimization plan and the energy consumption data after the execution of the operation optimization plan. If the comprehensive difference exceeds the preset difference range, it is determined that the operation optimization plan is invalid, and the operation optimization plan with the second highest priority is sent to the building automatic control end, and the building automatic control end adjusts the equipment operation data and / or system control plan according to the operation optimization plan with the second highest priority.

[0014] As a further improvement of the present invention, the analysis of determining the cause of abnormal energy consumption based on the screened equipment operation data and the corresponding energy consumption data, and obtaining an operation optimization strategy based on the cause of abnormal energy consumption includes:

[0015] The cause of abnormal energy consumption is determined through multiple embedded deeply customized expert energy consumption diagnosis and analysis processes, and an operation optimization strategy is obtained based on the one or more causes of abnormal energy consumption. The multiple expert energy consumption diagnosis and analysis processes are pre-set, and the integrated energy end selects one or more expert energy consumption diagnosis and analysis processes as target judgment processes based on the equipment operation data, and automatically performs energy consumption data analysis based on the target judgment processes to obtain one or more causes of abnormal energy consumption, and obtains one or more operation optimization solutions based on the one or more causes of abnormal energy consumption.

[0016] As a further improvement of the present invention, the integrated energy end includes a hybrid expert model unit, through which energy consumption analysis is performed and an operation optimization strategy is output, and the hybrid expert model unit includes:

[0017] Based on the operation optimization strategy and energy consumption data set, a deep learning model describing the relationship between the operation optimization strategy and energy consumption is trained to build an energy consumption anomaly analysis model; different energy consumption anomaly analysis models are built according to different operating conditions;

[0018] The operation optimization strategy, the equipment operation data after the optimization strategy is executed, and the corresponding energy consumption data are used as training data sets to train the energy consumption anomaly analysis model; and a plurality of trained energy consumption anomaly analysis models are obtained;

[0019] Based on the neural network gating mechanism, multiple trained energy consumption anomaly analysis models are integrated to obtain an energy consumption anomaly analysis expert model, and the optimal operation optimization strategy is obtained through the energy consumption anomaly analysis expert model.

[0020] As a further improvement of the present invention, the system also includes a cloud management platform, which includes an AI training module. The building automation terminal collects equipment operation data and system monitoring data of each device in the building and uploads them to the cloud management platform. The integrated energy terminal uploads the equipment energy consumption data and operation optimization strategy to the cloud management platform. The AI ​​training module obtains the optimal operation optimization strategy based on the equipment operation data, corresponding energy consumption data and operation optimization strategy training, and sends the optimal operation optimization strategy to the building automation terminal.

[0021] The present invention also provides an energy-saving method for coupling building automation and integrated energy management, the method comprising:

[0022] The building automatic control terminal collects the equipment operation data and system monitoring data of each equipment in the building at a set collection cycle, sends the collected equipment operation data and system monitoring data to the integrated energy terminal, and regulates the equipment operation data and / or system control scheme according to the operation optimization strategy;

[0023] The integrated energy terminal uses itemized metering to record the energy consumption data of the equipment during operation, collects the operation data of the equipment, compares the energy consumption data of the current equipment during operation with the historical energy consumption data, screens out the corresponding equipment operation data whose energy consumption data exceeds the preset energy consumption range, and determines the cause of abnormal energy consumption based on the screened equipment operation data and the corresponding energy consumption data analysis, and obtains an operation optimization strategy based on the cause of abnormal energy consumption, the operation optimization strategy includes one or more operation optimization schemes, and sends the operation optimization strategy to the building automatic control terminal;

[0024] The building automation terminal and the integrated energy terminal exchange data via a network communication unit.

[0025] As a further improvement of the present invention, the building automatic control terminal includes a data acquisition unit, a system control unit and an equipment management unit;

[0026] The data acquisition unit collects the equipment operation data and system monitoring data of each equipment in the building at a set collection cycle, and sends the collected equipment operation data and system monitoring data to the integrated energy terminal; the system control unit controls the equipment operation and / or system control scheme according to the operation optimization strategy; the equipment management unit locates and records the data of the monitored equipment; the equipment operation data includes but is not limited to: the start and stop, operation frequency, operation time and valve opening of the refrigerator, air conditioner, fan and water pump; the system monitoring data includes but is not limited to: temperature, humidity, pressure difference and flow.

[0027] The building automation terminal also includes a visualization unit involving equipment operating status and system parameter monitoring. The visualization unit visualizes the equipment controller, equipment operating data and system monitoring data recorded by various sensors, and provides an interactive port for operating mode, parameter setting and adjustment.

[0028] As a further improvement of the present invention, the integrated energy terminal includes a data collection and storage unit and an energy consumption analysis unit; the data collection and storage unit adopts sub-item metering to record the energy consumption data of the equipment during operation, and collects the operation data of the equipment; the energy consumption analysis unit compares the energy consumption data of the current equipment during operation with the historical energy consumption data, screens out the corresponding equipment operation data whose energy consumption data exceeds a preset range, and determines the cause of the abnormal energy consumption based on the screened equipment operation data and the corresponding energy consumption data analysis, and obtains an operation optimization strategy based on the cause of the abnormal energy consumption, and sends the operation optimization strategy to the building automatic control terminal; the energy consumption data includes: electricity consumption, heat consumption and water consumption.

[0029] As a further improvement of the present invention, the integrated energy terminal also includes a data visualization unit and an energy consumption abnormality warning unit. The data visualization unit visualizes the data collected by the data collection and storage unit, and the energy consumption abnormality warning unit prompts the abnormal energy consumption operation conditions screened out by the energy consumption analysis unit.

[0030] As a further improvement of the present invention, the cause of abnormal energy consumption is determined based on the analysis of the screened equipment operation data and the corresponding energy consumption data, and the operation optimization strategy obtained based on the cause of abnormal energy consumption includes multiple operation optimization schemes, the multiple operation optimization schemes are prioritized, and the operation optimization scheme with the highest priority is sent to the building automatic control terminal.

[0031] As a further improvement of the present invention, the integrated energy end includes an optimized operation tracking and detection unit, which is used to track and detect the execution results of the operation optimization plan. The optimized operation tracking and detection unit obtains the energy consumption data before the execution of the operation optimization plan and the energy consumption data after the execution of the operation optimization plan, and judges the effectiveness of the execution of the operation optimization plan based on the comprehensive difference between the energy consumption data before the execution of the operation optimization plan and the energy consumption data after the execution of the operation optimization plan. If the comprehensive difference exceeds the preset difference range, it is determined that the operation optimization plan is invalid, and the operation optimization plan with the second highest priority is sent to the building automatic control end, and the building automatic control end adjusts the equipment operation data and / or system control plan according to the operation optimization plan with the second highest priority.

[0032] As a further improvement of the present invention, the analysis of determining the cause of abnormal energy consumption based on the screened equipment operation data and the corresponding energy consumption data, and obtaining an operation optimization strategy based on the cause of abnormal energy consumption includes:

[0033] The cause of abnormal energy consumption is determined through multiple embedded deeply customized expert energy consumption diagnosis and analysis processes, and an operation optimization strategy is obtained based on the one or more causes of abnormal energy consumption. The multiple expert energy consumption diagnosis and analysis processes are pre-set, and the integrated energy end selects one or more expert energy consumption diagnosis and analysis processes as target judgment processes based on the equipment operation data, and automatically performs energy consumption data analysis based on the target judgment processes to obtain one or more causes of abnormal energy consumption, and obtains one or more operation optimization solutions based on the one or more causes of abnormal energy consumption.

[0034] As a further improvement of the present invention, the method of the present invention further includes a hybrid expert model, through which energy consumption analysis is performed and an operation optimization strategy is output, and the hybrid expert model includes:

[0035] Based on the operation optimization strategy and energy consumption data set, a deep learning model describing the relationship between the operation optimization strategy and energy consumption is trained to build an energy consumption anomaly analysis model; different energy consumption anomaly analysis models are built according to different operating conditions;

[0036] The operation optimization strategy, the equipment operation data after the optimization strategy is executed, and the corresponding energy consumption data are used as training data sets to train the energy consumption anomaly analysis model; and a plurality of trained energy consumption anomaly analysis models are obtained;

[0037] Based on the neural network gating mechanism, multiple trained energy consumption anomaly analysis models are integrated to obtain an energy consumption anomaly analysis expert model, and the optimal operation optimization strategy is obtained through the energy consumption anomaly analysis expert model.

[0038] As a further improvement of the present invention, the energy-saving method for coupling building automation and integrated energy management of the present invention also includes a cloud management platform, the cloud management platform includes an AI training module, the building automation terminal collects equipment operation data and system monitoring data of each device in the building and uploads them to the cloud management platform, the integrated energy terminal uploads the energy consumption data and operation optimization strategy to the cloud management platform, the AI ​​training module obtains the optimal operation optimization strategy based on the equipment operation data, the corresponding energy consumption data and the operation optimization strategy, and sends the optimal operation optimization strategy to the building automation terminal.

[0039] The beneficial effects of the present invention are as follows: by interconnecting the building automation end and the integrated energy end, based on the equipment operation data, system monitoring data collected by the building automation end and the equipment energy consumption data recorded by the integrated energy end, the integrated energy end analyzes the energy consumption abnormality problem, and obtains the operation optimization strategy based on the energy consumption abnormality problem, and sends the operation optimization strategy to the building automation end for execution, so that the energy-saving verification forms a closed loop, and the continuous optimization and iteration of the operation data of the integrated energy end and the building automation end is realized, thereby realizing the continuous and maximized energy-saving and carbon-reduction operation of the building. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A system schematic diagram of an energy-saving system coupling building automation and integrated energy management according to an embodiment of the present invention;

[0041] Figure 2 Another system schematic diagram of an energy-saving system coupling building automation and integrated energy management according to an embodiment of the present invention;

[0042] Figure 3 Another system schematic diagram of an energy-saving system coupling building automation and integrated energy management according to an embodiment of the present invention;

[0043] Figure 4 Another system schematic diagram of an energy-saving system coupling building automation and integrated energy management according to an embodiment of the present invention;

[0044] Figure 5 Another system schematic diagram of an energy-saving system coupling building automation and integrated energy management according to an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this disclosure.

[0046] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0047] In addition, in the description of the present invention, the terms used are only for illustrative purposes and are not intended to limit the scope of the present disclosure. The terms "include" and / or "comprise" are used to specify the existence of elements, steps, operations and / or components, but do not exclude the existence or addition of one or more other elements, steps, operations and / or components. The terms "first", "second", etc. may be used to describe various elements, do not represent order, and do not limit these elements. In addition, in the description of the present invention, unless otherwise specified, the meaning of "multiple" is two and more than two. These terms are only used to distinguish one element from another element. In conjunction with the following drawings, these and / or other aspects become apparent, and it is easier for those of ordinary skill in the art to understand the description of the embodiments of the present invention. The accompanying drawings are used to describe the embodiments of the present disclosure for illustrative purposes only. Those skilled in the art will easily recognize from the following description that, without departing from the principles of the present disclosure, alternative embodiments of the structures and methods shown in the present invention can be adopted.

[0048] An energy-saving system for coupling building automation and integrated energy management according to an embodiment of the present invention is as follows: Figure 1 As shown, the system includes a building automation terminal and an integrated energy terminal, and data is exchanged between the building automation terminal and the integrated energy terminal through a network communication unit; the network communication unit adopts a standard network protocol to realize communication between the building automation terminal and the integrated energy terminal, for example, one or more of a wired network protocol or a wireless network protocol is adopted, and when multiple network protocols are adopted, they can back up each other. The standard network protocol adopted by the present invention can be used as long as it can realize data communication between the building automation terminal and the integrated energy terminal, and no specific limitation is made here.

[0049] The building automation terminal includes: collecting the equipment operation data and system monitoring data of each device in the building at a set collection cycle, sending the collected equipment operation data and system monitoring data to the integrated energy terminal, and regulating the equipment operation data and / or system control plan according to the operation optimization strategy;

[0050] The comprehensive energy end includes: recording the energy consumption data of the equipment during operation through reasonable sub-item metering, and collecting the operation data of the equipment, such as the average value of multiple energy consumption data within the collection period, or the total energy consumption within the collection period; comparing the energy consumption data of the current equipment during operation with the historical energy consumption data of similar equipment, screening out the corresponding equipment operation data whose energy consumption data exceeds the preset energy consumption range, and determining the cause of abnormal energy consumption based on the screened equipment operation data and the corresponding energy consumption data analysis, and obtaining the operation optimization strategy based on the cause of abnormal energy consumption, the operation optimization strategy includes one or more operation optimization schemes, and sending the operation optimization strategy to the building automatic control end. According to different equipment operation data and corresponding energy consumption data, specific energy consumption abnormality problems can be analyzed and diagnosed, and different optimization strategies can be generated according to the energy consumption abnormality problems. Abnormal energy consumption problems include but are not limited to: control logic errors, control strategies to be optimized, equipment failures or aging, inaccuracies of metering instruments or sensors, and changes in usage requirements and scenarios. Operation optimization strategies include but are not limited to: changing control logic, optimizing control strategies, repairing or replacing equipment, and calibrating metering instruments or sensors.

[0051] By interconnecting the building automation end and the integrated energy end, the integrated energy end analyzes the operation optimization strategy based on the equipment operation data, system monitoring data collected by the building automation end and the equipment energy consumption data recorded by the integrated energy end, and sends the operation optimization strategy to the building automation end for execution, so that the energy-saving verification forms a closed loop and realizes the continuous optimization and iteration of the operation data of the integrated energy end and the building automation end.

[0052] An optional implementation method, such as Figure 2 As shown, the building automatic control terminal includes a data acquisition unit, a system control unit and an equipment management unit; the data acquisition unit collects the equipment operation data and system monitoring data of each device in the building at a set collection period, and sends the collected equipment operation data and system monitoring data to the comprehensive energy terminal. The equipment operation data includes but is not limited to the start and stop, operation frequency, operation time and valve opening of the chiller, air conditioner, fan and water pump; the system monitoring data includes but is not limited to: temperature, humidity, pressure difference and flow rate. The system monitoring data is used to determine the same working conditions and determine the historical data of the same type of equipment under the same working conditions. The system control unit controls the equipment operation and / or system control plan according to the operation optimization strategy, and the equipment management unit locates the monitored equipment and records the data.

[0053] The building automation terminal also includes a visualization unit for equipment operation status and system parameter monitoring. The visualization unit visualizes the equipment controller, equipment operation data recorded by various sensors, and system monitoring data, and provides interactive ports for operation mode, parameter setting and adjustment, making it convenient for staff to monitor and adjust the system.

[0054] The comprehensive energy terminal includes a data collection and storage unit and an energy consumption analysis unit; the data collection and storage unit records the energy consumption data of the equipment during operation through reasonable sub-item metering, and achieves refined management of the entire system by independently measuring various indicators, accurately locates abnormal energy consumption equipment, and the energy consumption data includes but is not limited to electricity consumption, heat consumption and water consumption, and collects the operation data of the equipment. The energy consumption analysis unit compares the energy consumption data of the current equipment during operation with the historical energy consumption data, screens out the corresponding equipment operation data whose energy consumption data exceeds the preset range, and analyzes and diagnoses the cause of abnormal energy consumption based on the screened equipment operation data and the corresponding energy consumption data, obtains the corresponding operation optimization strategy based on the cause of abnormal energy consumption, and sends the operation optimization strategy to the building automatic control terminal for execution.

[0055] After executing a data collection cycle, the data collection unit at the building automation end collects a new round of equipment operation data and system monitoring data of each device in the building. The energy consumption analysis unit at the integrated energy end performs energy consumption diagnosis and analysis based on the new round of equipment operation data, system monitoring data and equipment energy consumption data, and obtains the optimized operation optimization strategy to achieve energy-saving verification cycle iteration.

[0056] An optional implementation method, such as Figure 2 As shown, the integrated energy terminal also includes a data visualization unit and an abnormal energy consumption warning unit. The data visualization unit visualizes the data collected by the data collection and storage unit. The visualized data includes equipment operation data, system monitoring data, and energy consumption data. Visual display methods include but are not limited to: visualization charts, statistical reports, and three-dimensional simulation displays, as well as the generation of monthly, quarterly, and annual energy consumption reports. By displaying system data in real time, it is convenient to view, monitor, and count.

[0057] The abnormal energy consumption warning unit prompts the abnormal energy consumption operation conditions selected by the energy consumption analysis unit. The abnormal energy consumption warning prompt methods include but are not limited to voice prompts, pop-up window prompts, sound and light prompts and remote information prompts. The abnormal energy consumption warning unit promptly informs relevant staff of abnormal conditions, thereby realizing efficient and convenient management of the system. The data visualization unit can also count and display the abnormal warnings of the abnormal energy consumption warning unit.

[0058] In an optional implementation mode, the integrated energy terminal compares the energy consumption data of the current equipment during operation with the historical energy consumption data of the same type of equipment, screens out the corresponding equipment operation data whose energy consumption data exceeds the preset energy consumption range, analyzes and diagnoses the cause of abnormal energy consumption based on the screened equipment operation data and the corresponding energy consumption data, and obtains an operation optimization strategy based on the cause of abnormal energy consumption, including multiple operation optimization schemes, and prioritizes the multiple operation optimization schemes, and sends the operation optimization scheme with the highest priority to the building automatic control terminal for execution. Prioritization of multiple operation optimization schemes is, for example, based on the difficulty of troubleshooting, or based on the complexity of abnormal problems, or based on the probability values ​​of abnormal situations, from high to low, and one is randomly selected when the probability values ​​of abnormal situations are the same; the priority sorting method of the embodiment of the present invention is not limited to the above method, and different priority sorting methods can be formulated according to different working conditions. The causes of abnormal energy consumption include but are not limited to: control logic errors, control strategies to be optimized, equipment failures or aging, inaccuracies of metering instruments or sensors, changes in usage requirements and scenarios, etc.; the corresponding operation optimization strategies include but are not limited to: changing control logic, optimizing control strategies, repairing or replacing equipment, calibrating metering instruments or sensors, etc.

[0059] An optional implementation method, such as Figure 3 As shown, the integrated energy end includes an optimized operation tracking and detection unit, which is used to track and detect the execution results of the operation optimization plan. The optimized operation tracking and detection unit obtains the energy consumption data before the execution of the operation optimization plan and the energy consumption data after the execution of the operation optimization plan, and judges the effectiveness of the execution of the operation optimization plan according to the comprehensive difference between the energy consumption data before the execution of the operation optimization plan and the energy consumption data after the execution of the operation optimization plan. If the comprehensive difference exceeds the preset difference range, it is determined that the operation optimization plan is invalid, and the operation optimization plan with the second highest priority is sent to the building automatic control end for execution. The building automatic control end adjusts the equipment operation data and / or the system control plan according to the operation optimization plan with the second highest priority.

[0060] The effectiveness of the operation optimization plan is judged based on the comprehensive difference between the energy consumption data before the operation optimization plan is executed and the energy consumption data after the operation optimization plan is executed. The following method is used:

[0061]

[0062] Among them, σ represents the comprehensive difference of energy consumption data, xi represents the corresponding energy consumption data after the operation optimization scheme is executed, It represents the energy consumption data corresponding to the operation optimization plan before execution, and N represents the number of types of energy consumption data.

[0063] If the comprehensive difference is less than or equal to the set value, the operation optimization plan is determined to be effective. If the comprehensive difference is greater than the set value, the operation optimization plan is determined to be invalid.

[0064] By tracking the execution results of the operation optimization plan and adjusting the operation optimization plan in time when the operation optimization plan is ineffective, the accuracy, timeliness and effectiveness of building automatic control management are guaranteed.

[0065] Optionally, if the system determines that the operation optimization plan is invalid, the energy consumption abnormality cause diagnosed by the system analysis and the corresponding operation optimization plan can be manually reconfirmed, and the operation optimization plan can be manually adjusted to ensure the accuracy of the system operation. In addition, the manually adjusted data can be used as historical data for system abnormality diagnosis model training to improve the accuracy of the model.

[0066] In an optional implementation, the integrated energy terminal screens out the corresponding equipment operation data of equipment energy consumption data that exceeds the preset energy consumption range, and determines the cause of abnormal energy consumption based on the screened equipment operation data and the corresponding energy consumption data analysis, and obtains the corresponding operation optimization strategy based on the cause of abnormal energy consumption. The energy consumption data analysis includes multiple embedded deeply customized expert energy consumption diagnosis and analysis processes, and multiple expert energy consumption diagnosis and analysis processes are pre-set and embedded in the system. The expert energy consumption diagnosis and analysis process is, for example, a summary based on the technical capabilities and practical experience of relevant professionals, or an energy consumption anomaly prediction based on historical data learning, or an equipment anomaly troubleshooting process, etc. The expert energy consumption diagnosis and analysis process includes but is not limited to the above-mentioned methods.

[0067] The comprehensive energy end selects one or more expert energy consumption diagnosis and analysis processes as the target judgment process based on the equipment operation data. For example, if the equipment operation data includes the air-conditioning unit operation data and the variable air volume terminal equipment operation data, then the expert energy consumption diagnosis and analysis process for judging abnormal air-conditioning energy consumption and the expert energy consumption diagnosis and analysis process for judging abnormal variable air volume terminal equipment energy consumption are selected as the target judgment process respectively; and / or the expert energy consumption diagnosis and analysis process that includes both air-conditioning unit and variable air volume terminal equipment energy consumption abnormality judgment is selected as the target judgment process. The selection of the target judgment process can be adjusted according to actual conditions, such as water pumps, fans, fan coil units and other equipment, and no specific limitations are made here.

[0068] The integrated energy end automatically analyzes energy consumption data according to the selected target judgment process, and finally determines the cause of abnormal energy consumption through multiple rounds of diagnosis and elimination, and obtains one or more operation optimization strategies. If different target judgment processes obtain the same operation optimization strategy, the operation optimization strategy is set as the operation optimization strategy with higher priority. The operation optimization strategy to be executed this time is selected and sent to the building automation end. The building automation end adjusts the equipment operation data and / or system control plan according to the operation optimization strategy to achieve a cycle of energy consumption abnormality optimization.

[0069] An optional implementation method, such as Figure 4 As shown, the integrated energy end includes a hybrid expert model unit, through which energy consumption analysis is performed and an operation optimization strategy is output. The hybrid expert model unit includes: based on the operation optimization strategy and the energy consumption data set, a deep learning model describing the relationship between the operation optimization strategy and energy consumption is trained to construct an energy consumption anomaly analysis model; different energy consumption anomaly analysis models are constructed according to different operating conditions. Equipment operation data and energy consumption data are used as model inputs, operation optimization strategies are used as model outputs, equipment operation data-energy consumption relationship is used as the target optimization function, and energy consumption minimization is used as the optimization goal. The neural network model can use, for example, Transformer, CNN, RNN or LSTM, preferably Transformer.

[0070] The operation optimization strategy, the equipment operation data after the optimization strategy is executed, and the corresponding energy consumption data are used as training data sets to train the energy consumption anomaly analysis model; multiple trained energy consumption anomaly analysis models are obtained. Multiple trained energy consumption anomaly analysis models are integrated to obtain an energy consumption anomaly analysis expert model, and the optimal operation optimization strategy is obtained through the energy consumption anomaly analysis expert model.

[0071] In this embodiment, multiple trained energy consumption anomaly analysis models are integrated and the model integration can be performed by stacking. Optionally, multiple trained energy consumption anomaly analysis models can be fused by using a prediction fusion method, and the fused model can be used as an energy consumption anomaly analysis expert model. The energy consumption anomaly analysis expert model can improve the performance of the model through multi-model integration or fusion, as well as the accuracy and efficiency of model prediction. The energy consumption anomaly analysis expert model is used to generate the optimal operation optimization strategy, which improves the accuracy and efficiency of energy consumption anomaly analysis and realizes the refinement and precision of building energy consumption optimization management.

[0072] An optional implementation method, such as Figure 5As shown in the figure, the system also includes a cloud management platform, which includes an AI training module. The building automation terminal collects the equipment operation data and system monitoring data of each device in the building and uploads them to the cloud management platform. The integrated energy terminal uploads the equipment energy consumption data and operation optimization strategy to the cloud management platform. The AI ​​training module obtains the optimal operation optimization strategy based on the equipment operation data, the corresponding energy consumption data and the operation optimization strategy training, and sends the optimal operation optimization strategy to the building automation terminal for execution. The cloud management platform can process multiple energy consumption anomaly analysis tasks at the same time, improve the system operation efficiency, accurately locate the cause of energy consumption anomalies and propose the optimal operation optimization plan, so as to better achieve sustainable energy conservation and carbon reduction; through continuous iteration of data and continuous training of the model, it can finally realize the automatic and continuous improvement of the equipment operation strategy and energy efficiency management of each building.

[0073] An energy-saving method for coupling building automation and integrated energy management according to an embodiment of the present invention comprises:

[0074] The building automation terminal collects the equipment operation data and system monitoring data of each device in the building at a set collection cycle. The equipment operation data includes but is not limited to the start and stop, operation frequency, operation time and valve opening of the chiller, air conditioner, fan and water pump; the system monitoring data includes but is not limited to: temperature, humidity, pressure difference and flow rate. The collected equipment operation data and system monitoring data are sent to the integrated energy terminal, and the equipment operation data and / or system control plan are adjusted according to the operation optimization strategy;

[0075] The building automation terminal also includes a visualization unit for equipment operation status and system parameter monitoring. The visualization unit visualizes the equipment controller, equipment operation data recorded by various sensors, and system monitoring data, and provides interactive ports for operation mode, parameter setting and adjustment, making it convenient for staff to monitor and adjust the system.

[0076] The comprehensive energy end records the energy consumption data of the equipment during operation through reasonable sub-item metering, including but not limited to electricity consumption, heat consumption and water consumption, and collects the operation data of the equipment. The energy consumption data of the current equipment during operation is compared with the historical energy consumption data, and the corresponding equipment operation data whose energy consumption data exceeds the preset energy consumption range is screened out, and the cause of abnormal energy consumption is analyzed and diagnosed based on the screened equipment operation data and the corresponding energy consumption data. The corresponding operation optimization strategy is obtained based on the cause of abnormal energy consumption, and the operation optimization strategy is sent to the building automatic control end; the cause of abnormal energy consumption includes but is not limited to: control logic error, control strategy to be optimized, equipment failure or aging, inaccuracy of metering instruments or sensors, changes in usage requirements and scenarios, etc.; the corresponding operation optimization strategy includes but is not limited to: changing control logic, optimizing control strategy, repairing or replacing equipment, calibrating metering instruments or sensors, etc.

[0077] The building automation terminal and the integrated energy terminal exchange data via a network communication unit, and the network communication unit uses a standard network protocol to achieve communication between the building automation terminal and the integrated energy terminal.

[0078] In an optional implementation, the integrated energy terminal further includes a data visualization unit and an energy consumption abnormality warning unit. The data visualization unit visualizes the data collected by the data collection and storage unit. The visualized data includes equipment operation data, system monitoring data, and energy consumption data. Visual display methods include but are not limited to: visualized charts, statistical reports, and three-dimensional simulation displays, as well as the generation of monthly, quarterly, and annual energy consumption reports. By displaying system data in real time, it is convenient to view, monitor, and collect statistics.

[0079] The abnormal energy consumption warning unit prompts the abnormal energy consumption operation conditions selected by the energy consumption analysis unit. The abnormal energy consumption warning prompt methods include but are not limited to voice prompts, pop-up window prompts, sound and light prompts and remote information prompts. The abnormal energy consumption warning unit promptly informs relevant staff of abnormal conditions, thereby realizing efficient and convenient management of the system. The data visualization unit can also count and display the abnormal warnings of the abnormal energy consumption warning unit.

[0080] In an optional implementation mode, the integrated energy terminal compares the energy consumption data of the current equipment during operation with the historical energy consumption data of the same type of equipment, screens out the corresponding equipment operation data whose energy consumption data exceeds the preset energy consumption range, analyzes and diagnoses the cause of abnormal energy consumption based on the screened equipment operation data and the corresponding energy consumption data, and obtains an operation optimization strategy based on the cause of abnormal energy consumption, including multiple operation optimization schemes, and prioritizes the multiple operation optimization schemes, and sends the operation optimization scheme with the highest priority to the building automatic control terminal for execution. Prioritization of multiple operation optimization schemes is, for example, based on the difficulty of troubleshooting, or based on the complexity of abnormal problems, or based on the probability values ​​of abnormal situations, from high to low, and one is randomly selected when the probability values ​​of abnormal situations are the same; the priority sorting method of the embodiment of the present invention is not limited to the above method, and different priority sorting methods can be formulated according to different working conditions. The causes of abnormal energy consumption include but are not limited to: control logic errors, control strategies to be optimized, equipment failures or aging, inaccuracies of metering instruments or sensors, changes in usage requirements and scenarios, etc.; the corresponding operation optimization strategies include but are not limited to: changing control logic, optimizing control strategies, repairing or replacing equipment, calibrating metering instruments or sensors, etc.

[0081] In an optional implementation, the integrated energy end includes an optimized operation tracking and detection unit, which is used to track and detect the execution results of the operation optimization plan. The optimized operation tracking and detection unit obtains the energy consumption data before the execution of the operation optimization plan and the energy consumption data after the execution of the operation optimization plan, and judges the effectiveness of the execution of the operation optimization plan based on the comprehensive difference between the energy consumption data before the execution of the operation optimization plan and the energy consumption data after the execution of the operation optimization plan. If the comprehensive difference exceeds a preset difference range, it is determined that the operation optimization plan is invalid, and the operation optimization plan with the next highest priority is sent to the building automatic control end for execution. The building automatic control end adjusts the equipment operation data and / or system control plan according to the operation optimization plan with the next highest priority.

[0082] The effectiveness of the operation optimization plan is judged based on the comprehensive difference between the energy consumption data before the operation optimization plan is executed and the energy consumption data after the operation optimization plan is executed. The following method is used:

[0083]

[0084] Among them, σ represents the comprehensive difference of energy consumption data, xi represents the corresponding energy consumption data after the operation optimization scheme is executed, It indicates the energy consumption data corresponding to the operation optimization plan before execution, and N indicates the type of energy consumption data.

[0085] If the comprehensive difference is less than or equal to the set value, the operation optimization plan is determined to be effective. If the comprehensive difference is greater than the set value, the operation optimization plan is determined to be invalid.

[0086] By tracking the execution results of the operation optimization plan and adjusting the operation optimization plan in time when the operation optimization plan is ineffective, the accuracy, timeliness and effectiveness of building automatic control management are guaranteed.

[0087] Optionally, if the system determines that the operation optimization plan is invalid, the energy consumption abnormality cause diagnosed by the system analysis and the corresponding operation optimization plan can be manually reconfirmed, and the operation optimization plan can be manually adjusted to ensure the accuracy of the system operation. In addition, the manually adjusted data can be used as historical data for system abnormality diagnosis model training to improve the accuracy of the model.

[0088] In an optional implementation, the integrated energy terminal screens out the corresponding equipment operation data of equipment energy consumption data that exceeds the preset energy consumption range, and determines the cause of abnormal energy consumption based on the screened equipment operation data and the corresponding energy consumption data analysis, and obtains an operation optimization strategy based on the cause of abnormal energy consumption. The energy consumption data analysis includes multiple embedded deeply customized expert energy consumption diagnosis and analysis processes, and multiple expert energy consumption diagnosis and analysis processes are pre-set and embedded in the system. The expert energy consumption diagnosis and analysis process is, for example, a summary based on the technical capabilities and practical experience of relevant professionals, or an energy consumption anomaly prediction based on historical data learning, or an equipment anomaly troubleshooting process, etc. The expert energy consumption diagnosis and analysis process includes but is not limited to the above-mentioned methods.

[0089] The comprehensive energy end selects one or more expert energy consumption diagnosis and analysis processes as the target judgment process based on the equipment operation data. For example, if the equipment operation data includes the air-conditioning unit operation data and the fan operation data, then the expert energy consumption diagnosis and analysis process for judging abnormal air-conditioning energy consumption and the expert energy consumption diagnosis and analysis process for judging abnormal fan energy consumption are selected as the target judgment processes respectively; and / or a standard judgment process that includes both air-conditioning and fan energy consumption abnormality judgments is selected as the target judgment process. The selection of the target judgment process can be adjusted according to actual conditions, and no specific limitation is made here.

[0090] The integrated energy end automatically analyzes energy consumption data according to the selected target judgment process, and finally determines the cause of abnormal energy consumption through multiple rounds of diagnosis and elimination, and obtains one or more operation optimization strategies. If different target judgment processes obtain the same operation optimization strategy, the operation optimization strategy is set as the operation optimization strategy with higher priority. The operation optimization strategy to be executed this time is selected and sent to the building automation end. The building automation end adjusts the equipment operation data and / or system control plan according to the operation optimization strategy to achieve a cycle of energy consumption abnormality optimization.

[0091] In an optional implementation, the integrated energy end includes a hybrid expert model unit, through which energy consumption analysis is performed and an operation optimization strategy is output. The hybrid expert model unit includes: training a deep learning model that describes the relationship between the operation optimization strategy and energy consumption based on the operation optimization strategy and the energy consumption data set, and constructing an energy consumption anomaly analysis model; constructing different energy consumption anomaly analysis models according to different operating conditions. The equipment operation data and energy consumption data are used as model inputs, the operation optimization strategy is used as the model output, the equipment operation data-energy consumption relationship is used as the target optimization function, and energy consumption minimization is used as the optimization goal. The neural network model can use, for example, Transformer, CNN, RNN or LSTM, etc., preferably Transformer.

[0092] The operation optimization strategy, the equipment operation data after the optimization strategy is executed, and the corresponding energy consumption data are used as training data sets to train the energy consumption anomaly analysis model; multiple trained energy consumption anomaly analysis models are obtained. Multiple trained energy consumption anomaly analysis models are integrated to obtain an energy consumption anomaly analysis expert model, and the optimal operation optimization strategy is obtained through the energy consumption anomaly analysis expert model.

[0093] In this embodiment, multiple trained energy consumption anomaly analysis models are integrated and the model integration can be performed by stacking. Optionally, multiple trained energy consumption anomaly analysis models can be fused by using a prediction fusion method, and the fused model can be used as an energy consumption anomaly analysis expert model. The energy consumption anomaly analysis expert model can improve the performance of the model through multi-model integration or fusion, as well as the accuracy and efficiency of model prediction. The energy consumption anomaly analysis expert model is used to generate the optimal operation optimization strategy, which improves the accuracy and efficiency of energy consumption anomaly analysis and realizes the refinement and precision of building energy consumption optimization management.

[0094] In an optional implementation, the method also includes a cloud management platform. The building automation terminal collects equipment operation data and system monitoring data of each device in the building and uploads it to the cloud management platform. The cloud management platform includes an AI training module. The integrated energy terminal uploads the equipment energy consumption data and operation optimization strategy to the cloud management platform. The AI ​​training module obtains the optimal operation optimization strategy based on the equipment operation data, the corresponding energy consumption data and the operation optimization strategy training, and sends the optimal operation optimization strategy to the building automation terminal for execution. The cloud management platform can process multiple energy consumption anomaly analysis tasks simultaneously, improve the system operation efficiency, accurately locate the cause of energy consumption anomalies and propose the optimal operation optimization plan, so as to better achieve sustainable energy conservation and carbon reduction. Through continuous iteration of data and continuous training of the model, it is ultimately possible to achieve automated and continuous improvement of the equipment operation strategy and energy efficiency management of each building.

[0095] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0096] In addition, it will be understood by those skilled in the art that although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present invention and form different embodiments. For example, in the claims, any one of the claimed embodiments can be used in any combination.

[0097] It will be appreciated by those skilled in the art that, although the present invention has been described with reference to exemplary embodiments, various changes may be made and equivalents may be substituted for its elements without departing from the scope of the present invention. In addition, many modifications may be made to adapt specific circumstances or materials to the teachings of the present invention without departing from the essential scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed, but the present invention will include all embodiments falling within the scope of the appended claims.

Claims

1. An energy-saving system coupling building automation and integrated energy management, characterized in that: The system includes a building automation terminal and an integrated energy terminal, and the building automation terminal and the integrated energy terminal exchange data through a network communication unit; The building automatic control terminal includes: collecting equipment operation data and system monitoring data of each equipment in the building at a set collection period, sending the collected equipment operation data and system monitoring data to the comprehensive energy terminal, and regulating the equipment operation data and / or system control scheme according to the operation optimization strategy; The comprehensive energy terminal includes: using a sub-item metering method to record the energy consumption data of the equipment during operation, and collecting the operation data of the equipment, comparing the energy consumption data of the current equipment during operation with the historical energy consumption data, screening out the corresponding equipment operation data whose energy consumption data exceeds the preset energy consumption range, and determining the cause of abnormal energy consumption based on the screened equipment operation data and the corresponding energy consumption data analysis, and obtaining an operation optimization strategy based on the cause of abnormal energy consumption, the operation optimization strategy includes one or more operation optimization schemes, and the operation optimization strategy is sent to the building automatic control terminal.

2. The system according to claim 1, characterized in that The building automatic control terminal includes a data acquisition unit, a system control unit and an equipment management unit; The data collection unit collects the equipment operation data and system monitoring data of each equipment in the building at a set collection period, and sends the collected equipment operation data and system monitoring data to the integrated energy terminal; the system control unit controls the equipment operation and / or system control scheme according to the operation optimization strategy; The device management unit locates and records data of the monitored device; The equipment operation data includes: start and stop, operation frequency, operation time and valve opening of the chiller, air conditioner, fan and water pump; system monitoring data includes: temperature, humidity, pressure difference and flow; The building automation terminal also includes a visualization unit involving equipment operating status and system parameter monitoring. The visualization unit visualizes the equipment operating data and system monitoring data recorded by the equipment controller and various sensors, and provides an interactive port for operating mode, parameter setting and adjustment.

3. The system according to claim 1, characterized in that The integrated energy terminal includes a data collection and storage unit and an energy consumption analysis unit; the data collection and storage unit uses a sub-item metering method to record the energy consumption data of the equipment during operation, and collects the operation data of the equipment; the energy consumption analysis unit compares the energy consumption data of the current equipment during operation with the historical energy consumption data, screens out the corresponding equipment operation data whose energy consumption data exceeds a preset range, and determines the cause of abnormal energy consumption based on the screened equipment operation data and the corresponding energy consumption data analysis, and obtains an operation optimization strategy based on the cause of abnormal energy consumption, and sends the operation optimization strategy to the building automatic control terminal; The energy consumption data includes: electricity consumption, heat consumption and water consumption.

4. The system according to claim 3, characterized in that The integrated energy terminal also includes a data visualization unit and an energy consumption abnormality warning unit. The data visualization unit visualizes the data collected by the data collection and storage unit, and the energy consumption abnormality warning unit prompts the abnormal energy consumption operation conditions screened out by the energy consumption analysis unit.

5. The system according to claim 1, characterized in that The cause of abnormal energy consumption is determined based on the analysis of the screened equipment operation data and the corresponding energy consumption data, and the operation optimization strategy obtained based on the cause of abnormal energy consumption includes multiple operation optimization schemes, the multiple operation optimization schemes are prioritized, and the operation optimization scheme with the highest priority is sent to the building automatic control terminal.

6. The system according to claim 5, characterized in that The integrated energy terminal includes an optimized operation tracking and detection unit, which is used to track and detect the execution results of the operation optimization plan. The optimized operation tracking and detection unit obtains energy consumption data before the execution of the operation optimization plan and the energy consumption data after the execution of the operation optimization plan, and judges the effectiveness of the execution of the operation optimization plan based on the comprehensive difference between the energy consumption data before the execution of the operation optimization plan and the energy consumption data after the execution of the operation optimization plan. If the comprehensive difference exceeds the preset difference range, it is determined that the operation optimization plan is invalid, and the operation optimization plan with the next highest priority is sent to the building automatic control terminal. The building automatic control terminal adjusts the equipment operation data and / or system control plan according to the operation optimization plan with the next highest priority.

7. The system according to claim 1, characterized in that The analyzing and determining the cause of abnormal energy consumption according to the screened equipment operation data and the corresponding energy consumption data, and obtaining the operation optimization strategy based on the cause of abnormal energy consumption includes: The cause of abnormal energy consumption is determined through multiple embedded deeply customized expert energy consumption diagnosis and analysis processes, and an operation optimization strategy is obtained based on the one or more causes of abnormal energy consumption. The multiple expert energy consumption diagnosis and analysis processes are pre-set, and the integrated energy end selects one or more expert energy consumption diagnosis and analysis processes as target judgment processes based on the equipment operation data, and automatically performs energy consumption data analysis based on the target judgment process to obtain one or more causes of abnormal energy consumption, and obtains one or more operation optimization solutions based on the one or more causes of abnormal energy consumption.

8. The system according to claim 1, characterized in that The integrated energy terminal includes a hybrid expert model unit, through which energy consumption analysis is performed and an operation optimization strategy is output. The hybrid expert model unit includes: Based on the operation optimization strategy and energy consumption data set, a deep learning model describing the relationship between the operation optimization strategy and energy consumption is trained to build an energy consumption anomaly analysis model; different energy consumption anomaly analysis models are built according to different operating conditions; The operation optimization strategy, the equipment operation data after the optimization strategy is executed, and the corresponding energy consumption data are used as training data sets to train the energy consumption anomaly analysis model; and a plurality of trained energy consumption anomaly analysis models are obtained; A plurality of trained energy consumption anomaly analysis models are integrated to obtain an energy consumption anomaly analysis expert model, and an optimal operation optimization strategy is obtained through the energy consumption anomaly analysis expert model.

9. The system according to claim 1, characterized in that The system also includes a cloud management platform, which includes an AI training module. The building automation terminal collects equipment operation data and system monitoring data of each device in the building and uploads them to the cloud management platform. The integrated energy terminal uploads the equipment energy consumption data and operation optimization strategy to the cloud management platform. The AI ​​training module obtains the optimal operation optimization strategy based on the equipment operation data, corresponding energy consumption data and operation optimization strategy training, and sends the optimal operation optimization strategy to the building automation terminal.

10. An energy-saving method for coupling building automation and integrated energy management, characterized in that: The method includes: The building automatic control terminal collects the equipment operation data and system monitoring data of each equipment in the building at a set collection cycle, sends the collected equipment operation data and system monitoring data to the integrated energy terminal, and regulates the equipment operation data and / or system control scheme according to the operation optimization strategy; The integrated energy terminal uses a sub-item metering method to record the energy consumption data of the equipment during operation, collects the operation data of the equipment, compares the energy consumption data of the current equipment during operation with the historical energy consumption data, screens out the corresponding equipment operation data whose energy consumption data exceeds the preset energy consumption range, and determines the cause of abnormal energy consumption based on the screened equipment operation data and the corresponding energy consumption data analysis, and obtains an operation optimization strategy based on the cause of abnormal energy consumption, the operation optimization strategy includes one or more operation optimization schemes, and sends the operation optimization strategy to the building automatic control terminal; The building automation terminal and the integrated energy terminal exchange data via a network communication unit.

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