A control method and system for electromechanical equipment in building intelligent system integration

Abstract

The present invention discloses a control method and system for electromechanical equipment integrated in a building intelligent system, which relates to the technical field of monitoring systems. The control method for electromechanical equipment integrated in the building intelligent system includes the following steps: integrating all the electromechanical equipment interfaces in the building intelligent system to establish a communication and interaction connection between the operation data of each electromechanical equipment; predicting the energy consumption trend of each electromechanical equipment based on the time series analysis result; identifying the risk factors affecting the energy consumption of the electromechanical equipment based on the prediction result of the energy consumption trend, and performing collaborative optimization control on the electromechanical equipment based on the risk factors combined with the collaborative optimization technology; monitoring the abnormal energy consumption of the electromechanical equipment through an energy consumption over-warning monitoring model. The present invention predicts the energy consumption trend of each electromechanical equipment based on the time series analysis result, which helps to realize the energy consumption management of the electromechanical equipment, reduce the energy consumption cost, improve the energy utilization efficiency, and thus plays an important role in the control of the electromechanical equipment integrated in the building intelligent system.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring systems, and more specifically, to a control method and system for mechanical and electrical equipment integrated in a building intelligent system. Background Art

[0002] Mechanical and Electrical equipment generally refers to equipment that provides various basic services and functions in a building environment, including heating, ventilation, and air conditioning (HVAC) systems, power systems, lighting equipment, water supply and drainage systems, elevators, and escalators, etc. Such equipment plays a crucial role in modern buildings, not only providing comfort and functionality but also involving energy use and safety management.

[0003] Mechanical and Electrical equipment is usually the main source of building energy consumption. Through effective energy consumption management, energy waste can be significantly reduced, energy use efficiency can be optimized, and thus operating costs can be lowered. For example, by adjusting the operating time and parameters of the HVAC system, unnecessary energy consumption can be reduced, and appropriate energy consumption management can help the equipment operate in an optimal state, reducing wear caused by overuse or improper use.

[0004] In the control method of mechanical and electrical equipment, time series analysis is a key tool for understanding and predicting the energy consumption trends of mechanical and electrical equipment. If the operating data of mechanical and electrical equipment cannot be analyzed by time series, it is difficult to accurately predict the future energy consumption requirements and patterns of mechanical and electrical equipment, resulting in energy waste and unoptimized energy distribution, and further leading to unbalanced and excessive energy utilization.

[0005] Regarding the problems in the related art, no effective solutions have been proposed yet. Summary of the Invention

[0006] In view of the deficiencies of the prior art, the present invention provides a control method and system for mechanical and electrical equipment integrated in a building intelligent system, which has the advantage of accurately predicting the future energy consumption requirements and patterns of mechanical and electrical equipment, thereby solving the problem of difficult to accurately predict the future energy consumption requirements and patterns of mechanical and electrical equipment.

[0007] To achieve the above-mentioned advantage of accurately predicting the future energy consumption requirements and patterns of mechanical and electrical equipment, the specific technical solutions adopted by the present invention are as follows:

[0008] According to one aspect of the present invention, there is provided a control method for mechanical and electrical equipment integrated in a building intelligent system, and the control method for mechanical and electrical equipment integrated in a building intelligent system includes the following steps:

[0009] S1. Integrate all the mechanical and electrical equipment interfaces in the building intelligent system to establish a communication and interaction connection between the operating data of each mechanical and electrical equipment;

[0010] S2. Perform time series analysis on the operation data of the electromechanical equipment, and predict the energy consumption trends of each electromechanical equipment based on the results of the time series analysis;

[0011] S3. Identify the risk factors affecting the energy consumption of the electromechanical equipment based on the prediction results of the energy consumption trends, and perform collaborative optimization control on the electromechanical equipment based on the risk factors combined with the collaborative optimization technology;

[0012] S4. Establish an over - energy - consumption early - warning monitoring model, monitor the abnormal energy consumption of the electromechanical equipment through the over - energy - consumption early - warning monitoring model, and execute the early - warning response in a timely manner.

[0013] Furthermore, performing time series analysis on the operation data of the electromechanical equipment and predicting the energy consumption trends of each electromechanical equipment based on the results of the time series analysis includes the following steps:

[0014] S21. Extract the operation data of each electromechanical equipment for pre - processing, and convert the pre - processed operation data into a time series format to ensure that each data point corresponds to a timestamp;

[0015] S22. Detect the stationarity and non - stationarity of the pre - processed operation data, and perform multiple - order differencing on the non - stationary operation data to obtain the operation data in a stationary state;

[0016] S23. Perform time series analysis on the operation data in a stationary state, and predict the energy consumption trends of each electromechanical equipment in the future time period based on the results of the time series analysis.

[0017] Furthermore, performing time series analysis on the operation data in a stationary state and predicting the energy consumption trends of each electromechanical equipment in the future time period based on the results of the time series analysis includes the following steps:

[0018] S231. Form time series data based on the operation data of each electromechanical equipment in a stationary state, and cluster the associated time nodes of each electromechanical equipment from the time series data;

[0019] S232. Calculate the energy consumption indicators of each electromechanical equipment at each associated time node, and evaluate the energy consumption levels of the electromechanical equipment at each associated time node based on the energy consumption indicators;

[0020] S233. Analyze and statistically calculate the energy consumption changes of the electromechanical equipment at each associated time node based on the energy consumption levels of the electromechanical equipment at each associated time node;

[0021] S234. Integrate the energy consumption indicators, energy consumption levels, and energy consumption changes of the electromechanical equipment at each associated time node as the input of a pre - constructed seasonal autoregressive integrated moving average model, and output the prediction results of the energy consumption trends of each electromechanical equipment through the seasonal autoregressive integrated moving average model.

[0022] Furthermore, based on the prediction results of the energy consumption trend, risk factors affecting the energy consumption of electromechanical equipment are identified, and the electromechanical equipment is subjected to collaborative optimization control based on the risk factors combined with collaborative optimization technology, including the following steps:

[0023] S31. Calculate the correlation coefficient between different variables in the prediction results of the energy consumption trend of each electromechanical equipment, and evaluate the risk factors affecting the energy consumption of the electromechanical equipment based on the correlation coefficient;

[0024] S32. Establish a collaborative optimization model according to the risk factors, and use the collaborative optimization model combined with the prediction results of the energy consumption trend of each electromechanical equipment to dynamically control the operating parameters of each electromechanical equipment;

[0025] S33. Integrate the collaborative optimization model into the building intelligent system to ensure that each electromechanical equipment can receive the dynamic adjustment instructions sent by the building intelligent system.

[0026] Furthermore, calculating the correlation coefficient between different variables in the prediction results of the energy consumption trend of each electromechanical equipment, and evaluating the risk factors affecting the energy consumption of the electromechanical equipment based on the correlation coefficient includes the following steps:

[0027] S311. Based on the prediction results of the energy consumption trend of each electromechanical equipment, identify the abnormal energy consumption intervals in the energy consumption trend of each electromechanical equipment, and establish a fitting correlation model based on the abnormal energy consumption intervals;

[0028] S312. Initially identify the variable factors affecting the energy consumption of the electromechanical equipment, and use the fitting correlation model to calculate the Pearson correlation coefficient between each variable factor and the energy consumption of the electromechanical equipment;

[0029] S313. Perform weighted summation on the calculation results of the Pearson correlation coefficient to obtain a comprehensive correlation matrix;

[0030] S314. Use the comprehensive correlation matrix to measure the correlation between each variable factor and the energy consumption of the electromechanical equipment, and conduct a comprehensive ranking of the correlations, and use the variable factors corresponding to the correlations within the preset range as risk factors.

[0031] Furthermore, the variable factors affecting the energy consumption of the electromechanical equipment include: equipment operating condition factors, personnel operation mode factors, environmental condition factors, external variable factors, and load condition factors.

[0032] Furthermore, establishing a collaborative optimization model according to the risk factors, and using the collaborative optimization model combined with the prediction results of the energy consumption trend of each electromechanical equipment to dynamically control the operating parameters of each electromechanical equipment includes the following steps:

[0033] S321. Establish a digital model of the electromechanical equipment, and generate an energy consumption impact matrix based on the digital model of the electromechanical equipment and the risk factors;

[0034] S322. Divide the mechanical and electrical equipment with similar energy consumption patterns into the same mechanical and electrical equipment group according to the energy consumption impact matrix, and calculate the ultimate load of the mechanical and electrical equipment group;

[0035] S323. Taking the minimum energy consumption of the mechanical and electrical equipment group as the optimization goal, the control parameters as the decision variables, and the ultimate load as the constraint condition, establish a collaborative optimization model based on the mechanical and electrical equipment group;

[0036] S324. Develop a collaborative operation optimization algorithm according to the collaborative optimization model, and use the collaborative operation optimization algorithm to dynamically control the operation parameters of each mechanical and electrical equipment.

[0037] Further, the calculation formula of the Pearson correlation coefficient is: ;

[0038] In the formula, r represents the Pearson correlation coefficient; represents the observed value of the variable factor of the i-th data point; represents the energy consumption value of the mechanical and electrical equipment of the i-th variable factor; represents the average value of the observed values of the variable factors; represents the average value of the energy consumption of the mechanical and electrical equipment.

[0039] Further, establish an energy consumption over-warning monitoring model, and monitor the energy consumption anomalies of the mechanical and electrical equipment through the energy consumption over-warning monitoring model, and timely execute the warning response, including the following steps:

[0040] S41. Extract the operation data of the mechanical and electrical equipment to formulate the normal energy consumption baseline of each mechanical and electrical equipment, and at the same time set the energy consumption anomaly threshold of each mechanical and electrical equipment;

[0041] S42. Use a deep learning network to establish an energy consumption over-warning monitoring model, and use the energy consumption over-warning monitoring model to monitor the energy consumption of the mechanical and electrical equipment, and at the same time generate the energy consumption baseline of the mechanical and electrical equipment;

[0042] S43. When it is monitored that the energy consumption baseline of the mechanical and electrical equipment deviates from the normal energy consumption baseline and the energy consumption of the mechanical and electrical equipment is greater than the preset threshold, it indicates that there is an energy consumption anomaly in the mechanical and electrical equipment, and execute the warning response.

[0043] According to another aspect of the present invention, there is also provided a control system for mechanical and electrical equipment integrated in a building intelligent system. The control system for mechanical and electrical equipment integrated in the building intelligent system includes a data interaction module, an energy consumption trend analysis module, a collaborative optimization control module, and an abnormal energy consumption monitoring module;

[0044] Among them, the data interaction module is used to integrate all the mechanical and electrical equipment interfaces in the building intelligent system and establish a communication interaction connection between the operation data of each mechanical and electrical equipment;

[0045] The energy consumption trend analysis module is used to perform time series analysis on the operation data of electromechanical equipment and predict the energy consumption trends of each electromechanical equipment based on the results of the time series analysis;

[0046] The collaborative optimization control module identifies risk factors affecting the energy consumption of electromechanical equipment based on the prediction results of the energy consumption trends, and performs collaborative optimization control on the electromechanical equipment based on the risk factors combined with collaborative optimization technology;

[0047] The abnormal energy consumption monitoring module is used to establish an over - energy - consumption early warning monitoring model, monitor the abnormal energy consumption of electromechanical equipment through the over - energy - consumption early warning monitoring model, and promptly execute the early warning response.

[0048] Compared with the prior art, the present invention provides a control method and system for electromechanical equipment integrated in a building intelligent system, having the following beneficial effects:

[0049] (1) By detecting the stationarity and non - stationarity of the pre - processed operation data and performing multi - order differencing on the non - stationary operation data, the present invention can ensure the accuracy of time series analysis, perform time series analysis on the operation data in the stationary state, which helps to deeply understand the energy consumption status at different time points. At the same time, predicting the energy consumption trends of each electromechanical equipment based on the results of the time series analysis helps to realize the energy consumption management of electromechanical equipment, reduce the energy consumption cost, improve the energy utilization efficiency, and thus plays an important role in the control of electromechanical equipment integrated in the building intelligent system.

[0050] (2) By analyzing the energy consumption trends of electromechanical equipment and calculating the correlation coefficients, the present invention can accurately identify the key variable factors affecting the energy consumption, and combined with collaborative optimization technology, adjust and optimize the operation parameters of electromechanical equipment according to the risk factors, which can minimize the energy consumption while ensuring the equipment efficiency, achieving double savings of energy and cost. This collaborative optimization control strategy based on energy consumption prediction and risk factors not only improves the energy efficiency and operation performance, but also enhances the overall reliability and economy of the intelligent building system. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0052] Figure 1 is a flowchart of the control method for electromechanical equipment integrated in a building intelligent system according to an embodiment of the present invention;

[0053] Figure 2It is a schematic block diagram of an electromechanical equipment control system integrated in a building intelligent system according to an embodiment of the present invention.

[0054] In the figure:

[0055] 1. Data interaction module; 2. Energy consumption trend analysis module; 3. Collaborative optimization control module; 4. Abnormal energy consumption monitoring module. Detailed implementation manners

[0056] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention. They are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operation principle of the embodiments. With reference to these contents, those of ordinary skill in the art should be able to understand other possible implementation manners and the advantages of the present invention.

[0057] According to an embodiment of the present invention, a method and system for controlling electromechanical equipment integrated in a building intelligent system are provided.

[0058] Now, the present invention will be further described in conjunction with the drawings and specific implementation manners. As Figure 1 shown, a method for controlling electromechanical equipment integrated in a building intelligent system according to an embodiment of the present invention, the method for controlling electromechanical equipment integrated in the building intelligent system includes the following steps:

[0059] S1. Integrate all the electromechanical equipment interfaces in the building intelligent system to establish a communication and interaction connection between the operation data of each electromechanical equipment.

[0060] It should be noted that in the integration of the building intelligent system, the integration of all electromechanical equipment interfaces and the establishment of a communication and interaction connection between the operation data of each equipment are key technical challenges, which involve multiple steps, including hardware interface integration, software platform development, and implementation of data communication protocols. Specifically, it includes:

[0061] 1. Hardware interface integration

[0062] It is necessary to conduct a compatibility assessment of the existing electromechanical equipment to determine the interface type and communication requirements of each equipment.

[0063] Communication gateway and adapter: Install a communication gateway or adapter for non-standard or old equipment so that they can be connected to a modern intelligent building management system.

[0064] Unified interface design: Develop or adopt a unified hardware interface standard to ensure seamless integration of newly installed and existing equipment.

[0065] 2. Software platform development

[0066] Centralized Control System: Develop or deploy a centralized control system (such as a Building Automation System, BAS) that can communicate with all devices and control their operations.

[0067] User Interface: Design an intuitive user interface that enables operators to easily monitor and control connected devices.

[0068] Data Processing and Analysis: Integrate data processing and analysis tools to extract insights from operational data and support decisions on maintenance and energy consumption optimization.

[0069] 3. Data Communication Protocol

[0070] Adoption of Standardized Protocols: Adopt standardized communication protocols (such as BACnet, Modbus, KNX, etc.) that support data exchange and interoperability between different devices.

[0071] Security Measures: Implement data encryption and security protocols to protect device data from unauthorized access and cyberattacks.

[0072] Network Topology Design: Design an efficient network topology to ensure the stability and real-time nature of data transmission and reduce latency.

[0073] 4. Testing and Verification

[0074] System Integration Testing: Perform comprehensive system integration testing to verify that the communication and functions of all devices meet expectations.

[0075] Performance Evaluation: Regularly evaluate system performance to ensure that communication is without delay and data is transmitted accurately.

[0076] S2. Conduct time series analysis on the operation data of electromechanical equipment and predict the energy consumption trends of each electromechanical equipment based on the results of the time series analysis.

[0077] Among them, conducting time series analysis on the operation data of electromechanical equipment and predicting the energy consumption trends of each electromechanical equipment based on the results of the time series analysis includes the following steps:

[0078] S21. Extract the operation data of each electromechanical equipment for preprocessing, and convert the preprocessed operation data into a time series format to ensure that each data point corresponds to a timestamp;

[0079] S22. Detect the stationarity and non-stationarity of the preprocessed operation data, and perform multiple-order differencing on the non-stationary operation data to obtain the operation data in a stationary state;

[0080] S23. Conduct time series analysis on the operation data in a stationary state and predict the energy consumption trends of each electromechanical equipment in the future time period based on the results of the time series analysis.

[0081] Among them, performing time series analysis on the operation data in the steady state and predicting the energy consumption trends of each electromechanical device in the future time period based on the results of the time series analysis includes the following steps:

[0082] S231. Form time series data based on the operation data of each electromechanical device in the steady state, and cluster the associated time nodes of each electromechanical device from the time series data;

[0083] S232. Calculate the energy consumption indicators of each electromechanical device at each associated time node, and evaluate the energy consumption level of the electromechanical device at each associated time node based on the energy consumption indicators;

[0084] S233. Analyze and statistically calculate the energy consumption changes of the electromechanical device at each associated time node based on the energy consumption levels of the electromechanical device at each associated time node;

[0085] S234. Integrate the energy consumption indicators, energy consumption levels, and energy consumption changes of the electromechanical device at each associated time node as the input of a pre-constructed seasonal autoregressive integrated moving average model, and output the prediction results of the energy consumption trends of each electromechanical device through the seasonal autoregressive integrated moving average model.

[0086] It should be noted that the seasonal autoregressive integrated moving average model (SARIMA) is a model for time series data analysis, especially suitable for data with seasonal variations. The main steps for constructing the SARIMA model include the following:

[0087] Observe the overall trend and seasonal pattern of the data, and decompose the time series into trend, seasonal, and random components through seasonal decomposition techniques; perform first-order or multi-order differencing to eliminate the trend component of the time series and make the series reach a stationary state, and perform seasonal differencing to eliminate seasonal effects, especially patterns that appear at fixed periods; assist in determining the orders of AR (autoregressive) and MA (moving average) through the autocorrelation function (ACF) and partial autocorrelation function (PACF) plots, and select the parameters of seasonal autoregression and moving average according to the seasonal ACF and PACF plots; use maximum likelihood estimation or other suitable methods to estimate the model parameters, fit the SARIMA model on the training data, and evaluate the goodness of fit of the model and the white noise characteristics of the residuals.

[0088] S3. Identify the risk factors affecting the energy consumption of the electromechanical device based on the prediction results of the energy consumption trends, and perform collaborative optimization control on the electromechanical device based on the risk factors combined with collaborative optimization techniques.

[0089] Among them, identifying risk factors affecting the energy consumption of electromechanical equipment based on the prediction results of energy consumption trends, and performing collaborative optimization control on electromechanical equipment based on risk factors combined with collaborative optimization technology includes the following steps:

[0090] S31. Calculate the correlation coefficient between different variables in the prediction results of the energy consumption trends of each electromechanical equipment, and evaluate the risk factors affecting the energy consumption of electromechanical equipment based on the correlation coefficient.

[0091] Among them, calculating the correlation coefficient between different variables in the prediction results of the energy consumption trends of each electromechanical equipment, and evaluating the risk factors affecting the energy consumption of electromechanical equipment includes the following steps:

[0092] S311. Based on the prediction results of the energy consumption trends of each electromechanical equipment, identify the abnormal energy consumption intervals in the energy consumption trends of each electromechanical equipment, and establish a fitting correlation model based on the abnormal energy consumption intervals;

[0093] S312. Initially identify the variable factors affecting the energy consumption of electromechanical equipment, and use the fitting correlation model to calculate the Pearson correlation coefficient between each variable factor and the energy consumption of electromechanical equipment.

[0094] Among them, the variable factors affecting the energy consumption of electromechanical equipment include: equipment operation status factors, personnel operation mode factors, environmental condition factors, external variable factors, and load condition factors.

[0095] S313. Perform weighted summation on the calculation results of the Pearson correlation coefficient to obtain a comprehensive correlation matrix.

[0096] It should be noted that the Pearson correlation coefficient, also known as the Pearson product-moment correlation coefficient, is a statistic used to measure the linear correlation degree between two variables. The Pearson correlation coefficient is widely used in data analysis to evaluate the linear dependence relationship between two quantitative variables. In the energy consumption management of electromechanical equipment, by calculating the correlation coefficient between different equipment parameters or environmental factors and energy consumption, the factors with the greatest impact on energy consumption can be identified, so as to carry out effective energy management and control.

[0097] Among them, the calculation formula of the Pearson correlation coefficient is: ;

[0098] In the formula, r represents the Pearson correlation coefficient; represents the observed value of the variable factor of the i-th data point; represents the energy consumption value of the electromechanical equipment of the i-th variable factor; represents the average value of the observed values of the variable factor; represents the average value of the energy consumption of the electromechanical equipment.

[0099] S314. Measure the correlation between each variable factor and the energy consumption of the electromechanical equipment using the comprehensive correlation matrix, comprehensively sort the correlations, and take the variable factors corresponding to the correlations within the preset range as risk factors.

[0100] S32. Establish a collaborative optimization model based on the risk factors, and use the collaborative optimization model to dynamically control the operating parameters of each electromechanical equipment in combination with the prediction results of the energy consumption trends of each electromechanical equipment.

[0101] Among them, establishing a collaborative optimization model based on the risk factors and using the collaborative optimization model to dynamically control the operating parameters of each electromechanical equipment in combination with the prediction results of the energy consumption trends of each electromechanical equipment includes the following steps:

[0102] S321. Establish a digital model of the electromechanical equipment, and generate an energy consumption impact matrix based on the digital model of the electromechanical equipment and the risk factors;

[0103] S322. Divide the electromechanical equipment with similar energy consumption patterns into the same electromechanical equipment group according to the energy consumption impact matrix, and calculate the ultimate load of the electromechanical equipment group;

[0104] S323. Establish a collaborative optimization model based on the electromechanical equipment group with the minimum energy consumption of the electromechanical equipment group as the optimization goal, the control parameters as decision variables, and the ultimate load as the constraint condition;

[0105] S324. Develop a collaborative operation optimization algorithm according to the collaborative optimization model, and use the collaborative operation optimization algorithm to dynamically control the operating parameters of each electromechanical equipment.

[0106] S33. Integrate the collaborative optimization model into the building intelligent system to ensure that each electromechanical equipment can receive the dynamic adjustment instructions sent by the building intelligent system.

[0107] S4. Establish an energy consumption over - warning monitoring model, monitor the energy consumption anomalies of the electromechanical equipment through the energy consumption over - warning monitoring model, and execute the warning response in a timely manner.

[0108] Among them, establishing an energy consumption over - warning monitoring model, monitoring the energy consumption anomalies of the electromechanical equipment through the energy consumption over - warning monitoring model, and executing the warning response in a timely manner includes the following steps:

[0109] S41. Extract the operation data of the electromechanical equipment to formulate the normal energy consumption baseline of each electromechanical equipment, and at the same time set the energy consumption anomaly threshold of each electromechanical equipment;

[0110] S42. Use a deep learning network to establish an energy consumption over - warning monitoring model, and use the energy consumption over - warning monitoring model to monitor the energy consumption of the electromechanical equipment, and at the same time generate the energy consumption baseline of the electromechanical equipment.

[0111] S43. When it is detected that the energy consumption baseline of the electromechanical equipment deviates from the normal energy consumption baseline and the energy consumption of the electromechanical equipment is greater than the preset threshold, it indicates that there is an abnormal energy consumption in the electromechanical equipment, and a warning response is executed.

[0112] As Figure 2 shown, according to another embodiment of the present invention, there is also provided a control system for electromechanical equipment integrated in a building intelligent system. The control system for electromechanical equipment integrated in the building intelligent system includes a data interaction module 1, an energy consumption trend analysis module 2, a collaborative optimization control module 3, and an abnormal energy consumption monitoring module 4.

[0113] Among them, the data interaction module 1 is used to integrate all the electromechanical equipment interfaces in the building intelligent system and establish a communication interaction connection between the operation data of each electromechanical equipment.

[0114] The energy consumption trend analysis module 2 is used to perform time series analysis on the operation data of the electromechanical equipment and predict the energy consumption trend of each electromechanical equipment based on the time series analysis results.

[0115] The collaborative optimization control module 3 identifies the risk factors affecting the energy consumption of the electromechanical equipment based on the prediction results of the energy consumption trend, and performs collaborative optimization control on the electromechanical equipment based on the risk factors combined with the collaborative optimization technology.

[0116] The abnormal energy consumption monitoring module 4 is used to establish an over - energy - consumption warning monitoring model, monitor the abnormal energy consumption of the electromechanical equipment through the over - energy - consumption warning monitoring model, and execute a warning response in a timely manner.

[0117] In summary, by means of the above - mentioned technical solutions of the present invention, the present invention can ensure the accuracy of time series analysis by detecting the stationarity and non - stationarity of the pre - processed operation data and performing multi - order differencing on the non - stationary operation data, and perform time series analysis on the operation data in the stationary state, which helps to deeply understand the energy consumption situation at different time points. At the same time, predicting the energy consumption trend of each electromechanical equipment based on the time series analysis results helps to realize the energy consumption management of the electromechanical equipment, reduce the energy consumption cost, and improve the energy utilization efficiency. Furthermore, it has an important role in the control of electromechanical equipment integrated in the building intelligent system. The present invention can accurately identify the key variable factors affecting energy consumption by analyzing the energy consumption trend of electromechanical equipment and calculating the correlation coefficient, and combined with the collaborative optimization technology, adjust and optimize the operation parameters of the electromechanical equipment according to the risk factors, which can minimize the energy consumption while ensuring the equipment efficiency, realizing the double savings of energy and cost. This collaborative optimization control strategy based on energy consumption prediction and risk factors not only improves the energy efficiency and operation performance, but also enhances the overall reliability and economy of the intelligent building system.

[0118] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. All within the spirit and principles of the present invention.

Claims

1. A control method for electromechanical equipment in building intelligent system integration, characterized in that, The control method for electromechanical equipment integrated in the building intelligent system includes the following steps: S1. Integrate all the electromechanical equipment interfaces in the building intelligent system to establish a communication and interaction connection between the operation data of each electromechanical equipment; S2. Conduct time series analysis on the operation data of the electromechanical equipment, and predict the energy consumption trends of each electromechanical equipment based on the results of the time series analysis; S3. Identify the risk factors affecting the energy consumption of the electromechanical equipment based on the prediction results of the energy consumption trends, and conduct collaborative optimization control on the electromechanical equipment based on the risk factors combined with the collaborative optimization technology; Among them, step S3 includes the following steps: S31. Calculate the correlation coefficient between different variables in the prediction results of the energy consumption trends of each electromechanical equipment, and evaluate the risk factors affecting the energy consumption of the electromechanical equipment based on the correlation coefficient; S32. Establish a collaborative optimization model according to the risk factors, and use the collaborative optimization model combined with the prediction results of the energy consumption trends of each electromechanical equipment to dynamically control the operation parameters of each electromechanical equipment; Among them, step S32 includes the following steps S321, S322, S323, S324: S321. Establish a digital model of the electromechanical equipment, and generate an energy consumption impact matrix based on the digital model of the electromechanical equipment and the risk factors; S322. Divide the electromechanical equipment with similar energy consumption patterns into the same electromechanical equipment group according to the energy consumption impact matrix, and calculate the ultimate load of the electromechanical equipment group; S323. Take the minimum energy consumption of the electromechanical equipment group as the optimization goal, the control parameters as the decision variables, and the ultimate load as the constraint condition to establish a collaborative optimization model based on the electromechanical equipment group; S324. Develop a collaborative operation optimization algorithm according to the collaborative optimization model, and use the collaborative operation optimization algorithm to dynamically control the operation parameters of each electromechanical equipment; S33. Integrate the collaborative optimization model into the building intelligent system to ensure that each electromechanical equipment can receive the dynamic adjustment instructions sent by the building intelligent system; S4. Establish an energy consumption over-warning monitoring model, monitor the energy consumption anomalies of the electromechanical equipment through the energy consumption over-warning monitoring model, and execute the warning response in a timely manner.

2. The control method of the mechanical and electrical equipment for the integration of the building intelligent system according to claim 1, characterized in that, The conduct of time series analysis on the operation data of the electromechanical equipment and the prediction of the energy consumption trends of each electromechanical equipment based on the results of the time series analysis include the following steps: S21. Extract the operation data of each electromechanical equipment for preprocessing, and convert the preprocessed operation data into a time series format to ensure that each data point corresponds to a time stamp; S22. Detect the stationarity and non-stationarity of the preprocessed operation data, and perform multi-order differencing on the non-stationary operation data to obtain the operation data in a stationary state; S23. Conduct time series analysis on the operation data in a stationary state, and predict the energy consumption trends of each electromechanical equipment in the future time period based on the results of the time series analysis.

3. The electromechanical equipment control method for building intelligent system integration according to claim 2, characterized in that The conduct of time series analysis on the operation data in a stationary state and the prediction of the energy consumption trends of each electromechanical equipment in the future time period based on the results of the time series analysis include the following steps: S231. Form time series data based on the operation data of each electromechanical equipment in a stationary state, and cluster the associated time nodes of each electromechanical equipment from the time series data; S232. Calculate the energy consumption indicators of each mechanical and electrical equipment at each associated time node, and evaluate the energy consumption level of the mechanical and electrical equipment at each associated time node based on the energy consumption indicators; S233. Analyze and statistically calculate the energy consumption changes of the mechanical and electrical equipment at each associated time node based on the energy consumption levels of the mechanical and electrical equipment at each associated time node; S234. Integrate the energy consumption indicators, energy consumption levels, and energy consumption changes of the mechanical and electrical equipment at each associated time node as the input of a pre-constructed seasonal autoregressive integrated moving average model, and output the prediction results of the energy consumption trends of each mechanical and electrical equipment through the seasonal autoregressive integrated moving average model.

4. A control method for electromechanical equipment in building intelligent system integration according to claim 1, characterized in that, The steps of calculating the correlation coefficient between different variables in the prediction results of the energy consumption trends of each mechanical and electrical equipment and evaluating the risk factors affecting the energy consumption of the mechanical and electrical equipment based on the correlation coefficient include the following: S311. Based on the prediction results of the energy consumption trends of each mechanical and electrical equipment, identify the abnormal energy consumption intervals in the energy consumption trends of each mechanical and electrical equipment, and establish a fitting correlation model based on the abnormal energy consumption intervals; S312. Initially identify the variable factors affecting the energy consumption of the mechanical and electrical equipment, and calculate the Pearson correlation coefficient between each variable factor and the energy consumption of the mechanical and electrical equipment using the fitting correlation model; S313. Perform weighted summation on the calculation results of the Pearson correlation coefficient to obtain a comprehensive correlation matrix; S314. Use the comprehensive correlation matrix to measure the correlation between each variable factor and the energy consumption of the mechanical and electrical equipment, and perform a comprehensive ranking on the correlation. The variable factors corresponding to the correlation within a preset range are used as risk factors.

5. A control method for electromechanical equipment in the integration of building intelligent systems according to claim 4, characterized in that, The variable factors affecting the energy consumption of the mechanical and electrical equipment include: equipment operating condition factors, personnel operation mode factors, environmental condition factors, external variable factors, and load condition factors.

6. A method for controlling electromechanical equipment in the integration of a building intelligent system according to claim 4, characterized in that, The calculation formula for the Pearson correlation coefficient is: ； In the formula, r represents the Pearson correlation coefficient; Indicates the observed value of the variable factor for the i th data point; Indicates the energy consumption value of the electromechanical equipment for i the th variable factor; represents the average value of the observed values of the variable factors; Represents the average energy consumption of electromechanical equipment.

7. A control method for electromechanical equipment in the integration of building intelligent systems according to claim 6, characterized in that, The steps of establishing an energy consumption over-warning monitoring model, monitoring the energy consumption anomalies of the mechanical and electrical equipment through the energy consumption over-warning monitoring model, and promptly executing a warning response include the following: S41. Extract the operation data of the mechanical and electrical equipment to formulate the normal energy consumption baseline of each mechanical and electrical equipment, and at the same time set the energy consumption anomaly threshold of each mechanical and electrical equipment; S42. Use a deep learning network to establish an energy consumption over-warning monitoring model, and use the energy consumption over-warning monitoring model to monitor the energy consumption of the mechanical and electrical equipment, and at the same time generate the energy consumption baseline of the mechanical and electrical equipment; S43. When it is monitored that the energy consumption baseline of the mechanical and electrical equipment deviates from the normal energy consumption baseline and the energy consumption of the mechanical and electrical equipment is greater than the preset threshold, it indicates that there is an energy consumption anomaly in the mechanical and electrical equipment, and a warning response is executed.

8. An electromechanical equipment control system for building intelligent system integration, which is used to implement the electromechanical equipment control method for building intelligent system integration according to any one of claims 1-7, and is characterized in that, The mechanical and electrical equipment control system integrated in this building intelligent system includes a data interaction module, an energy consumption trend analysis module, a collaborative optimization control module, and an abnormal energy consumption monitoring module; Among them, the data interaction module is used to integrate all the mechanical and electrical equipment interfaces in the building intelligent system and establish a communication interaction connection between the operation data of each mechanical and electrical equipment; The energy consumption trend analysis module is used to perform time series analysis on the operation data of the mechanical and electrical equipment and predict the energy consumption trends of each mechanical and electrical equipment based on the time series analysis results; The collaborative optimization control module identifies risk factors affecting the energy consumption of electromechanical equipment based on the prediction results of the energy consumption trend, and performs collaborative optimization control on the electromechanical equipment by combining the risk factors with collaborative optimization technology; The abnormal energy consumption monitoring module is used to establish an over - energy - consumption early - warning monitoring model, monitor the abnormal energy consumption of electromechanical equipment through the over - energy - consumption early - warning monitoring model, and promptly execute the early - warning response.

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