Electromechanical equipment control method and system for building intelligent system integration

Abstract

The invention discloses an electromechanical equipment control method and system integrated with a building intelligent system, and relates to the technical field of monitoring systems.The electromechanical equipment control method integrated with the building intelligent system comprises the following steps that all electromechanical equipment interfaces in the building intelligent system are integrated, establishing communication interaction connection among the operation data of the electromechanical devices; predicting the energy consumption trend of each electromechanical device based on the time sequence analysis result; identifying risk factors influencing 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 in combination with a collaborative optimization technology; and monitoring energy consumption abnormity of the electromechanical equipment through the excessive energy consumption early-warning monitoring model. According to the method, the energy consumption trend of each electromechanical device is predicted based on the time sequence analysis result, the energy consumption management of the electromechanical devices can be realized, the energy consumption cost can be reduced, the energy utilization efficiency can be improved, and the method plays an important role in the control of the electromechanical devices integrated by a building intelligent system.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring systems, and in particular to a method and system for controlling electromechanical equipment integrated in an intelligent building system. Background Art

[0002] Mechanical and Electrical equipment generally refers to equipment that provides a variety of basic services and functions in the building environment, including heating, ventilation and air conditioning (HVAC) systems, power systems, lighting equipment, water supply and drainage systems, elevators and escalators, etc. This type of equipment plays a vital 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. Effective energy management can significantly reduce energy waste, optimize energy efficiency, and thus reduce operating costs. For example, by adjusting the operating hours and parameters of the HVAC system, unnecessary energy consumption can be reduced, and proper energy management can help equipment operate in the best condition and reduce wear and tear caused by excessive or improper use.

[0004] In the control method of electromechanical equipment, time series analysis is a key tool for understanding and predicting the energy consumption trend of electromechanical equipment. If the time series analysis of the operating data of electromechanical equipment cannot be performed, it will be difficult to accurately predict the future energy consumption demand and pattern of the electromechanical equipment, which will lead to energy waste and unoptimized energy distribution, and then lead to unbalanced energy utilization and excessive consumption.

[0005] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a control method and system for electromechanical equipment integrated with a building intelligent system, which has the advantage of accurately predicting the future energy consumption demands and patterns of electromechanical equipment, thereby solving the problem of difficulty in accurately predicting the future energy consumption demands and patterns of electromechanical equipment.

[0007] In order to achieve the above advantages of accurately predicting the future energy consumption demand and pattern of electromechanical equipment, the specific technical solutions adopted by the present invention are as follows: According to one aspect of the present invention, a method for controlling electromechanical equipment integrated with a building intelligent system is provided, and the method for controlling electromechanical equipment integrated with a building intelligent system comprises the following steps: S1. Integrate all mechanical and electrical equipment interfaces in the building intelligent system to establish communication and interactive connections between the operating data of each mechanical and electrical equipment; S2. Perform time series analysis on the operation data of electromechanical equipment, and predict the energy consumption trend of each electromechanical equipment based on the time series analysis results; S3. Based on the prediction results of energy consumption trends, identify the risk factors that affect the energy consumption of electromechanical equipment, and conduct collaborative optimization control of electromechanical equipment based on the risk factors and in combination with collaborative optimization technology; S4. Establish an excessive energy consumption early warning monitoring model, monitor the abnormal energy consumption of electromechanical equipment through the excessive energy consumption early warning monitoring model, and perform early warning response in a timely manner.

[0008] Furthermore, performing time series analysis on the operation data of the electromechanical equipment and predicting the energy consumption trend of each electromechanical equipment based on the time series analysis results includes the following steps: S21, extracting the operation data of each electromechanical device for preprocessing, converting the preprocessed operation data into a time series format, and ensuring that each data point corresponds to a timestamp; S22, detecting the stability and non-stationarity of the pre-processed operation data, and performing multi-order difference on the non-stationary operation data to obtain operation data in a stable state; S23. Perform time series analysis on the operating data under a steady state, and predict the energy consumption trend of each electromechanical equipment in a future time period based on the time series analysis results.

[0009] Furthermore, performing time series analysis on the operation data in a steady state and predicting the energy consumption trend of each electromechanical device in a future time period based on the time series analysis results includes the following steps: S231, forming time series data based on the operation data of each electromechanical device in a stable state, and clustering the associated time nodes of each electromechanical device from the time series data; S232, calculating the energy consumption index of each electromechanical device at each associated time node, and evaluating the energy consumption level of the electromechanical device at each associated time node based on the energy consumption index; S233, analyzing and counting energy consumption changes of the electromechanical equipment at each associated time node based on the energy consumption level of the electromechanical equipment at each associated time node; S234. The energy consumption index, energy consumption level and energy consumption change of the electromechanical equipment at each associated time node are integrated as the input of the pre-built seasonal autoregressive integrated moving average model, and the prediction result of the energy consumption trend of each electromechanical equipment is output through the seasonal autoregressive integrated moving average model.

[0010] Furthermore, based on the prediction results of energy consumption trends, the risk factors affecting the energy consumption of electromechanical equipment are identified, and based on the risk factors and in combination with collaborative optimization technology, the electromechanical equipment is collaboratively optimized and controlled, including the following steps: S31. Calculate the correlation coefficients between different variables in the prediction results of the energy consumption trends of various electromechanical equipment, and evaluate the risk factors affecting the energy consumption of electromechanical equipment based on the correlation coefficients; S32. Establish a collaborative optimization model based on risk factors, and use the collaborative optimization model in combination with the prediction results of the energy consumption trend of each electromechanical equipment to dynamically control the operating parameters of each electromechanical equipment; S33. Integrate the collaborative optimization model into the building intelligent system to ensure that all mechanical and electrical equipment can receive dynamic adjustment instructions sent by the building intelligent system.

[0011] Furthermore, calculating the correlation coefficients between different variables in the prediction results of the energy consumption trends of various electromechanical equipment, and evaluating the risk factors affecting the energy consumption of electromechanical equipment based on the correlation coefficients includes the following steps: S311, based on the prediction results of the energy consumption trends of each electromechanical device, identifying the abnormal energy consumption interval in the energy consumption trend of each electromechanical device, and establishing a fitting association model based on the abnormal energy consumption interval; S312. Preliminarily identify the variable factors that affect the energy consumption of electromechanical equipment, and use the fitting association model to calculate the Pearson correlation coefficient between each variable factor and the energy consumption of electromechanical equipment; S313, performing 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 electromechanical equipment, and comprehensively sort the correlations, and use the variable factors corresponding to the correlations within a preset range as risk factors.

[0012] Furthermore, the variable factors that affect the energy consumption of electromechanical equipment include: equipment operating status factors, personnel operating mode factors, environmental conditions factors, external variable factors and load conditions factors.

[0013] Furthermore, establishing a collaborative optimization model based on risk factors, and dynamically controlling the operating parameters of each electromechanical device by using the collaborative optimization model combined with the prediction results of the energy consumption trend of each electromechanical device includes the following steps: S321. Establish a digital model of electromechanical equipment, and generate an energy consumption impact matrix based on the digital model of electromechanical equipment and risk factors; S322, classifying electromechanical equipment with similar energy consumption patterns into the same electromechanical equipment group according to the energy consumption impact matrix, and calculating the limit load of the electromechanical equipment group; S323, taking the minimum energy consumption of the electromechanical equipment group as the optimization goal, the control parameter as the decision variable, and the limit load as the constraint condition, establishing a collaborative optimization model based on the electromechanical equipment group; S324. Formulate a collaborative operation optimization algorithm based on the collaborative optimization model, and use the collaborative operation optimization algorithm to dynamically control the operating parameters of each electromechanical equipment.

[0014] Furthermore, the calculation formula of the Pearson correlation coefficient is: ; In the formula, r represents the Pearson correlation coefficient; represents the observed value of the variable factor for the i-th data point; Represents the energy consumption value of electromechanical equipment of the i-th variable factor; It represents the average value of the observed values ​​of the variable factors; Indicates the average value of energy consumption of electromechanical equipment.

[0015] Furthermore, an excessive energy consumption early warning monitoring model is established, and abnormal energy consumption of electromechanical equipment is monitored through the excessive energy consumption early warning monitoring model, and early warning response is promptly executed, including the following steps: S41, extracting the operation data of electromechanical equipment to formulate a normal energy consumption baseline for each electromechanical equipment, and setting an abnormal energy consumption threshold for each electromechanical equipment; S42. Using a deep learning network to establish an excessive energy consumption early warning monitoring model, and using the excessive energy consumption early warning monitoring model to monitor the energy consumption of electromechanical equipment, and at the same time generate an energy consumption baseline for the electromechanical equipment; S43. When it is monitored 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 a preset threshold, it indicates that the electromechanical equipment has abnormal energy consumption, and an early warning response is executed.

[0016] According to another aspect of the present invention, there is also provided a mechanical and electrical equipment control system integrated with a building intelligent system, the mechanical and electrical equipment control system integrated with a building intelligent system comprising 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 electromechanical equipment interfaces in the building intelligent system and establish communication and interaction connections between the operating data of each electromechanical equipment; Energy consumption trend analysis module, used to perform time series analysis on the operation data of electromechanical equipment and predict the energy consumption trend of each electromechanical equipment based on the time series analysis results; The collaborative optimization control module identifies the risk factors that affect the energy consumption of electromechanical equipment based on the prediction results of energy consumption trends, and performs collaborative optimization control on electromechanical equipment based on the risk factors and collaborative optimization technology; The abnormal energy consumption monitoring module is used to establish an excessive energy consumption early warning monitoring model, monitor the abnormal energy consumption of electromechanical equipment through the excessive energy consumption early warning monitoring model, and execute early warning response in a timely manner.

[0017] Compared with the prior art, the present invention provides a control method and system for electromechanical equipment integrated with a building intelligent system, which has the following beneficial effects: (1) The present invention can ensure the accuracy of time series analysis by detecting the stationarity and non-stationarity of pre-processed operating data and performing multi-order differentiation on non-stationary operating data. It can also perform time series analysis on operating data in a stable state, which helps to gain an in-depth understanding of the energy consumption status at different time points. At the same time, based on the time series analysis results, it predicts the energy consumption trend of each electromechanical equipment, helps to achieve energy consumption management of electromechanical equipment, reduce energy consumption costs, and improve energy utilization efficiency, thereby playing an important role in the control of electromechanical equipment integrated into the building intelligent system.

[0018] (2) The present invention can accurately identify the key variable factors that affect energy consumption by analyzing the energy consumption trend of electromechanical equipment and calculating the correlation coefficient. In combination with the collaborative optimization technology, the operating parameters of the electromechanical equipment can be adjusted and optimized according to the risk factors. This can minimize energy consumption while ensuring the equipment efficiency, thereby achieving dual savings in energy and cost. This collaborative optimization control strategy based on energy consumption prediction and risk factors not only improves energy efficiency and operational performance, but also enhances the overall reliability and economy of the intelligent building system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 is a flow chart of a method for controlling electromechanical equipment integrated with a building intelligent system according to an embodiment of the present invention; Figure 2 It is a principle block diagram of a mechanical and electrical equipment control system integrated with a building intelligent system according to an embodiment of the present invention.

[0021] In the figure: 1. Data interaction module; 2. Energy consumption trend analysis module; 3. Collaborative optimization control module; 4. Abnormal energy consumption monitoring module. DETAILED DESCRIPTION

[0022] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments. They can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in this field should be able to understand other possible implementation methods and advantages of the present invention.

[0023] According to an embodiment of the present invention, a method and system for controlling electromechanical equipment integrated with a building intelligent system are provided.

[0024] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to the electromechanical equipment control method of building intelligent system integration according to an embodiment of the present invention, the electromechanical equipment control method of building intelligent system integration includes the following steps: S1. Integrate all mechanical and electrical equipment interfaces in the building intelligent system to establish communication and interactive connections between the operating data of each mechanical and electrical equipment.

[0025] It should be noted that in the integrated building intelligent system, the integration of all electromechanical equipment interfaces and the establishment of communication and interactive connections between the operating data of each device is a key technical challenge, which involves multiple steps, including hardware interface integration, software platform development and data communication protocol implementation, which specifically include: 1. Hardware interface integration A compatibility assessment of existing electromechanical equipment is required to determine the interface type and communication requirements of each device.

[0026] Communications gateways and adapters: Install communications gateways or adapters for non-standard or legacy equipment so they can connect to modern smart building management systems.

[0027] Unified interface design: Develop or adopt unified hardware interface standards to ensure seamless integration of new installations and existing equipment.

[0028] 2. Software platform development Centralized Control System: Develop or deploy a centralized control system (such as a building automation system, BAS) that is able to communicate with all devices and control their operation.

[0029] User Interface: Design an intuitive user interface that enables operators to easily monitor and control connected equipment.

[0030] Data processing and analysis: Integrated data processing and analysis tools to extract insights from operational data to support maintenance and energy optimization decisions.

[0031] 3. Data communication protocol Standardized protocol adoption: Standardized communication protocols (such as BACnet, Modbus, KNX, etc.) are adopted, which support data exchange and interoperability between different devices.

[0032] Security measures: Implement data encryption and security protocols to protect device data from unauthorized access and cyber attacks.

[0033] Network topology design: Design efficient network topology to ensure the stability and real-time performance of data transmission and reduce latency.

[0034] 4. Testing and Verification System Integration Testing: Perform comprehensive system integration testing to verify that all devices communicate and function as expected.

[0035] Performance evaluation: Regularly evaluate system performance to ensure that there are no communication delays and data is transmitted accurately and without errors.

[0036] S2. Perform time series analysis on the operating data of electromechanical equipment, and predict the energy consumption trend of each electromechanical equipment based on the time series analysis results.

[0037] Among them, performing time series analysis on the operation data of electromechanical equipment and predicting the energy consumption trend of each electromechanical equipment based on the time series analysis results includes the following steps: S21, extracting the operation data of each electromechanical device for preprocessing, converting the preprocessed operation data into a time series format, and ensuring that each data point corresponds to a timestamp; S22, detecting the stability and non-stationarity of the pre-processed operation data, and performing multi-order difference on the non-stationary operation data to obtain operation data in a stable state; S23. Perform time series analysis on the operating data under a steady state, and predict the energy consumption trend of each electromechanical equipment in a future time period based on the time series analysis results.

[0038] Among them, performing time series analysis on the operation data in a stable state and predicting the energy consumption trend of each electromechanical equipment in a future time period based on the time series analysis results include the following steps: S231, forming time series data based on the operation data of each electromechanical device in a stable state, and clustering the associated time nodes of each electromechanical device from the time series data; S232, calculating the energy consumption index of each electromechanical device at each associated time node, and evaluating the energy consumption level of the electromechanical device at each associated time node based on the energy consumption index; S233, analyzing and counting energy consumption changes of the electromechanical equipment at each associated time node based on the energy consumption level of the electromechanical equipment at each associated time node; S234. The energy consumption index, energy consumption level and energy consumption change of the electromechanical equipment at each associated time node are integrated as the input of the pre-built seasonal autoregressive integrated moving average model, and the prediction result of the energy consumption trend of each electromechanical equipment is output through the seasonal autoregressive integrated moving average model.

[0039] It should be noted that the Seasonal Autoregressive Integrated Moving Average (SARIMA) model is a model used for time series data analysis, especially for data with seasonal changes. The construction of the SARIMA model mainly includes the following steps: 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 differences to eliminate the trend component of the time series and make the series reach a stable state, and perform seasonal differences to eliminate seasonal effects, especially patterns that appear in fixed periods; use autocorrelation function (ACF) and partial autocorrelation function (PACF) graphs to assist in determining the order of AR (autoregression) and MA (moving average), and select the parameters of seasonal autoregression and moving average based on seasonal ACF and PACF graphs; use maximum likelihood estimation or other suitable methods to estimate model parameters, fit the SARIMA model on the training data, and evaluate the model fit and the white noise characteristics of the residual.

[0040] S3. Based on the prediction results of energy consumption trends, the risk factors affecting the energy consumption of electromechanical equipment are identified, and the electromechanical equipment is collaboratively optimized and controlled based on the risk factors and the collaborative optimization technology.

[0041] Among them, based on the prediction results of energy consumption trends, the risk factors affecting the energy consumption of electromechanical equipment are identified, and based on the risk factors and combined with collaborative optimization technology, the collaborative optimization control of electromechanical equipment includes the following steps: S31. Calculate the correlation coefficients between different variables in the prediction results of the energy consumption trends of various electromechanical equipment, and evaluate the risk factors affecting the energy consumption of electromechanical equipment based on the correlation coefficients.

[0042] Among them, calculating the correlation coefficients between different variables in the prediction results of the energy consumption trends of various electromechanical equipment, and evaluating the risk factors affecting the energy consumption of electromechanical equipment based on the correlation coefficients includes the following steps: S311, based on the prediction results of the energy consumption trends of each electromechanical device, identifying the abnormal energy consumption interval in the energy consumption trend of each electromechanical device, and establishing a fitting association model based on the abnormal energy consumption interval; S312. Preliminarily identify the variable factors that affect the energy consumption of electromechanical equipment, and use the fitting association model to calculate the Pearson correlation coefficient between each variable factor and the energy consumption of electromechanical equipment.

[0043] Among them, the variable factors that affect the energy consumption of electromechanical equipment include: equipment operating status factors, personnel operating mode factors, environmental conditions factors, external variable factors and load conditions factors.

[0044] S313. Perform weighted summation on the calculation results of the Pearson correlation coefficient to obtain a comprehensive correlation matrix.

[0045] It should be noted that the Pearson correlation coefficient is also called the Pearson product-moment correlation coefficient. It is a statistic used to measure the degree of linear correlation between two variables. The Pearson correlation coefficient is widely used in data analysis to evaluate the linear dependence 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 that have the greatest impact on energy consumption can be identified, thereby carrying out effective energy management and control.

[0046] The calculation formula of Pearson correlation coefficient is: ; In the formula, r represents the Pearson correlation coefficient; represents the observed value of the variable factor for the i-th data point; Represents the energy consumption value of electromechanical equipment of the i-th variable factor; It represents the average value of the observed values ​​of the variable factors; Indicates the average value of energy consumption of electromechanical equipment.

[0047] S314. Use the comprehensive correlation matrix to measure the correlation between each variable factor and the energy consumption of electromechanical equipment, and comprehensively sort the correlations, and use the variable factors corresponding to the correlations within a preset range as risk factors.

[0048] S32. Establish a collaborative optimization model based on risk factors, and use the collaborative optimization model combined with the predicted results of the energy consumption trend of each electromechanical equipment to dynamically control the operating parameters of each electromechanical equipment.

[0049] Among them, establishing a collaborative optimization model based on 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: S321. Establish a digital model of electromechanical equipment, and generate an energy consumption impact matrix based on the digital model of electromechanical equipment and risk factors; S322, classifying electromechanical equipment with similar energy consumption patterns into the same electromechanical equipment group according to the energy consumption impact matrix, and calculating the limit load of the electromechanical equipment group; S323, taking the minimum energy consumption of the electromechanical equipment group as the optimization goal, the control parameter as the decision variable, and the limit load as the constraint condition, establishing a collaborative optimization model based on the electromechanical equipment group; S324. Formulate a collaborative operation optimization algorithm based on the collaborative optimization model, and use the collaborative operation optimization algorithm to dynamically control the operating parameters of each electromechanical equipment.

[0050] S33. Integrate the collaborative optimization model into the building intelligent system to ensure that all mechanical and electrical equipment can receive dynamic adjustment instructions sent by the building intelligent system.

[0051] S4. Establish an excessive energy consumption early warning monitoring model, monitor the abnormal energy consumption of electromechanical equipment through the excessive energy consumption early warning monitoring model, and perform early warning response in a timely manner.

[0052] Among them, establishing an excessive energy consumption early warning monitoring model, monitoring abnormal energy consumption of electromechanical equipment through the excessive energy consumption early warning monitoring model, and timely executing early warning response includes the following steps: S41, extracting the operation data of electromechanical equipment to formulate a normal energy consumption baseline for each electromechanical equipment, and setting an abnormal energy consumption threshold for each electromechanical equipment; S42. Using a deep learning network to establish an excessive energy consumption early warning monitoring model, and using the excessive energy consumption early warning monitoring model to monitor the energy consumption of electromechanical equipment, and at the same time generate an energy consumption baseline for the electromechanical equipment; S43. When it is monitored 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 a preset threshold, it indicates that the electromechanical equipment has abnormal energy consumption, and an early warning response is executed.

[0053] like Figure 2 As shown, according to another embodiment of the present invention, a mechanical and electrical equipment control system integrated with a building intelligent system is also provided, and the mechanical and electrical equipment control system integrated with 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; 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; Energy consumption trend analysis module 2, used to perform time series analysis on the operation data of electromechanical equipment, and predict the energy consumption trend of each electromechanical equipment based on the time series analysis results; Collaborative optimization control module 3 identifies risk factors affecting the energy consumption of electromechanical equipment based on the prediction results of energy consumption trends, and performs collaborative optimization control on electromechanical equipment based on risk factors combined with collaborative optimization technology; The abnormal energy consumption monitoring module 4 is used to establish an excessive energy consumption early warning monitoring model, monitor the abnormal energy consumption of electromechanical equipment through the excessive energy consumption early warning monitoring model, and execute early warning response in time.

[0054] In summary, with the help of the above-mentioned technical scheme 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 operating data and performing multi-order differences on the non-stationary operating data, and perform time series analysis on the operating data in a stable state, which is helpful to deeply understand the energy consumption status at different time points, and at the same time predict the energy consumption trend of each electromechanical equipment based on the time series analysis results, which is helpful to realize the energy consumption management of electromechanical equipment, reduce energy consumption costs, and improve energy utilization efficiency, and thus play 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 operating parameters of the electromechanical equipment according to the risk factors, which can minimize energy consumption while ensuring the equipment efficiency, and achieve dual savings in energy and cost. This collaborative optimization control strategy based on energy consumption prediction and risk factors not only improves energy efficiency and operating performance, but also enhances the overall reliability and economy of the intelligent building system.

[0055] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. All aspects shall be within the spirit and principle of the present invention.

Claims

1. A method for controlling electromechanical equipment integrated with a building intelligent system, characterized in that: The electromechanical equipment control method integrated with the building intelligent system comprises the following steps: S1. Integrate all mechanical and electrical equipment interfaces in the building intelligent system to establish communication and interactive connections between the operating data of each mechanical and electrical equipment; S2. Perform time series analysis on the operation data of electromechanical equipment, and predict the energy consumption trend of each electromechanical equipment based on the time series analysis results; S3. Based on the prediction results of energy consumption trends, identify the risk factors that affect the energy consumption of electromechanical equipment, and conduct collaborative optimization control of electromechanical equipment based on the risk factors and in combination with collaborative optimization technology; S4. Establish an excessive energy consumption early warning monitoring model, monitor the abnormal energy consumption of electromechanical equipment through the excessive energy consumption early warning monitoring model, and perform early warning response in a timely manner.

2. The electromechanical equipment control method for building intelligent system integration according to claim 1, characterized in that: The time series analysis of the operation data of the electromechanical equipment and the prediction of the energy consumption trend of each electromechanical equipment based on the time series analysis result include the following steps: S21, extracting the operation data of each electromechanical device for preprocessing, converting the preprocessed operation data into a time series format, and ensuring that each data point corresponds to a timestamp; S22, detecting the stability and non-stationarity of the pre-processed operation data, and performing multi-order difference on the non-stationary operation data to obtain operation data in a stable state; S23. Perform time series analysis on the operating data under a steady state, and predict the energy consumption trend of each electromechanical equipment in a future time period based on the time series analysis results.

3. The electromechanical equipment control method for building intelligent system integration according to claim 2, characterized in that: The time series analysis of the operation data in a stable state and the prediction of the energy consumption trend of each electromechanical equipment in a future time period based on the time series analysis result include the following steps: S231, forming time series data based on the operation data of each electromechanical device in a stable state, and clustering the associated time nodes of each electromechanical device from the time series data; S232, calculating the energy consumption index of each electromechanical device at each associated time node, and evaluating the energy consumption level of the electromechanical device at each associated time node based on the energy consumption index; S233, analyzing and counting energy consumption changes of the electromechanical equipment at each associated time node based on the energy consumption level of the electromechanical equipment at each associated time node; S234. The energy consumption index, energy consumption level and energy consumption change of the electromechanical equipment at each associated time node are integrated as the input of the pre-built seasonal autoregressive integrated moving average model, and the prediction result of the energy consumption trend of each electromechanical equipment is output through the seasonal autoregressive integrated moving average model.

4. The electromechanical equipment control method for building intelligent system integration according to claim 3 is characterized in that: The method of identifying the risk factors affecting the energy consumption of electromechanical equipment based on the prediction results of the energy consumption trend, and performing collaborative optimization control on the electromechanical equipment based on the risk factors in combination with the collaborative optimization technology includes the following steps: S31. Calculate the correlation coefficients between different variables in the prediction results of the energy consumption trends of various electromechanical equipment, and evaluate the risk factors affecting the energy consumption of electromechanical equipment based on the correlation coefficients; S32. Establish a collaborative optimization model based on risk factors, and use the collaborative optimization model in combination with the prediction results of the energy consumption trend of each electromechanical equipment to dynamically control the operating parameters of each electromechanical equipment; S33. Integrate the collaborative optimization model into the building intelligent system to ensure that all mechanical and electrical equipment can receive dynamic adjustment instructions sent by the building intelligent system.

5. The electromechanical equipment control method for building intelligent system integration according to claim 4, characterized in that: The step of calculating the correlation coefficients between different variables in the prediction results of the energy consumption trends of various electromechanical devices and evaluating the risk factors affecting the energy consumption of electromechanical devices based on the correlation coefficients includes the following steps: S311, based on the prediction results of the energy consumption trends of each electromechanical device, identifying the abnormal energy consumption interval in the energy consumption trend of each electromechanical device, and establishing a fitting association model based on the abnormal energy consumption interval; S312. Preliminarily identify the variable factors that affect the energy consumption of electromechanical equipment, and use the fitting association model to calculate the Pearson correlation coefficient between each variable factor and the energy consumption of electromechanical equipment; S313, performing 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 electromechanical equipment, and comprehensively sort the correlations, and use the variable factors corresponding to the correlations within a preset range as risk factors.

6. A method for controlling electromechanical equipment for building intelligent system integration according to claim 5, characterized in that: The variable factors that affect the energy consumption of electromechanical equipment include: equipment operating status factors, personnel operating mode factors, environmental conditions factors, external variable factors and load conditions factors.

7. A method for controlling electromechanical equipment for building intelligent system integration according to claim 6, characterized in that: The method of establishing a collaborative optimization model based on risk factors and dynamically controlling the operating parameters of each electromechanical device by using the collaborative optimization model in combination with the prediction results of the energy consumption trend of each electromechanical device includes the following steps: S321. Establish a digital model of electromechanical equipment, and generate an energy consumption impact matrix based on the digital model of electromechanical equipment and risk factors; S322, classifying electromechanical equipment with similar energy consumption patterns into the same electromechanical equipment group according to the energy consumption impact matrix, and calculating the limit load of the electromechanical equipment group; S323, taking the minimum energy consumption of the electromechanical equipment group as the optimization goal, the control parameter as the decision variable, and the limit load as the constraint condition, establishing a collaborative optimization model based on the electromechanical equipment group; S324. Formulate a collaborative operation optimization algorithm based on the collaborative optimization model, and use the collaborative operation optimization algorithm to dynamically control the operating parameters of each electromechanical equipment.

8. The electromechanical equipment control method for building intelligent system integration according to claim 7, characterized in that: The calculation formula of the Pearson correlation coefficient is: ; In the formula, r represents the Pearson correlation coefficient; represents the observed value of the variable factor for the i-th data point; Represents the energy consumption value of electromechanical equipment of the i-th variable factor; It represents the average value of the observed values ​​of the variable factors; Indicates the average value of energy consumption of electromechanical equipment.

9. The electromechanical equipment control method for building intelligent system integration according to claim 8, characterized in that: The method of establishing an excessive energy consumption early warning monitoring model, monitoring abnormal energy consumption of electromechanical equipment through the excessive energy consumption early warning monitoring model, and timely executing an early warning response includes the following steps: S41, extracting the operation data of electromechanical equipment to formulate a normal energy consumption baseline for each electromechanical equipment, and setting an abnormal energy consumption threshold for each electromechanical equipment; S42. Using a deep learning network to establish an excessive energy consumption early warning monitoring model, and using the excessive energy consumption early warning monitoring model to monitor the energy consumption of electromechanical equipment, and at the same time generate an energy consumption baseline for the electromechanical equipment; S43. When it is monitored 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 a preset threshold, it indicates that the electromechanical equipment has abnormal energy consumption, and an early warning response is executed.

10. A mechanical and electrical equipment control system for building intelligent system integration, used to implement the mechanical and electrical equipment control method for building intelligent system integration according to any one of claims 1 to 9, characterized in that: The mechanical and electrical equipment control system 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; The data interaction module is used to integrate all electromechanical equipment interfaces in the building intelligent system and establish a communication interaction connection between the operation data of each electromechanical equipment; The energy consumption trend analysis module 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; The collaborative optimization control module identifies the risk factors that affect 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 based on the risk factors and in combination with the collaborative optimization technology; The abnormal energy consumption monitoring module is used to establish an excessive energy consumption early warning monitoring model, monitor the abnormal energy consumption of electromechanical equipment through the excessive energy consumption early warning monitoring model, and execute early warning response in a timely manner.

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