Remote operation and maintenance weak current intelligent equipment management platform

Abstract

The invention discloses a remote operation and maintenance weak current intelligent equipment management platform, relates to the technical field of energy consumption management, and realizes whole-process integrated intelligent management from data acquisition, data analysis, decision support to execution control. A platform completes standard access and initialization of weak current equipment through an equipment access module, a real-time monitoring module collects and transmits energy consumption data based on a high-speed communication protocol, and a data analysis module carries out time proofreading and delay correction and calculates an energy consumption index Nzs and an equipment state delay index Dyc; the decision support module establishes a multivariate analysis model based on an energy consumption index Nzs and an equipment state delay index Dyc, dynamically generates regulation and control suggestions, optimizes data acquisition frequency and priority by combining a delay threshold, and synchronously optimizes parameters in real time; and the execution control module automatically generates an instruction, issues the instruction and uploads feedback to realize closed-loop optimization, so that the safety, the energy efficiency and the intelligent level of equipment operation are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy consumption management, and in particular to a weak current intelligent equipment management platform for remote operation and maintenance. Background Art

[0002] The weak current equipment management platform, originating from the development of information technology and accompanied by the rise of the Internet and the Internet of Things, has gradually evolved into a vital tool for modern intelligent management. Initially used for security monitoring, it has gradually expanded with technological advancements to include smart buildings, smart cities, data centers, and other fields. Its development has made the integration and collaborative management of weak current systems more efficient.

[0003] Weak current equipment management platforms have a wide range of applications, but in energy consumption monitoring applications in building automation systems, they often have the following technical shortcomings: Data collection delays: Due to the long response time of sensors or devices, there is a delay in collecting real-time energy consumption data. This not only affects the real-time monitoring, but may also lead to delayed decision-making and make it impossible to implement energy consumption optimization measures in a timely manner. The chain reaction of poor decision-making: Due to data collection delays, the system may make decisions based on outdated or inaccurate data. For example, it may fail to adjust equipment operating status in a timely manner during peak energy consumption periods, resulting in wasted energy and increased costs. Such poor decision-making, in turn, affects data accuracy, creating a vicious cycle of poor data collection and poor decision-making. Summary of the Invention

[0004] In view of the deficiencies in the prior art, the present invention provides a remote operation and maintenance weak current intelligent equipment management platform, which solves the technical shortcomings mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a remote operation and maintenance weak current intelligent equipment management platform, including an equipment access module, a real-time monitoring module, a data analysis module, a decision support module and an execution control module; The device access module is used to connect each weak current device in the building automation system to the management platform, including the identification and initial status setting of the device, and the collection of device status data; The real-time monitoring module is used to collect energy consumption data of each connected device in real time, including current, voltage and power, through a high-speed communication protocol, and transmit the energy consumption data to the data analysis module; The data analysis module is used to receive energy consumption data and equipment status data, and adjust the impact of network and response delays through time series analysis technology to calculate the accurate energy consumption index Nzs and equipment status delay index Dyc; The decision support module is used to build a data analysis model using a linear regression model, perform data analysis on the energy consumption index Nzs and the equipment status delay index Dyc, and generate equipment control suggestions; Secondly, the preset threshold is compared and evaluated with the device status delay index Dyc, and the data collection frequency and data transmission priority are readjusted; The execution control module is used to receive device control suggestions and send instructions to relevant devices in real time for execution, including adjusting the device operating status or switching the operating mode.

[0006] Preferably, the device access module includes a device identification unit and an initial state setting unit; The device identification unit is used to identify the identity of each weak current device to be connected to the building automation system, complete the device information collection and device uniqueness confirmation by reading the unique identification code of the device, and upload the identification result to the management platform for registration and archiving.

[0007] Preferably, the initial state setting unit is used to initialize the operating state of the connected device according to the system preset parameters after the device completes identity identification, including initial settings such as operating mode, network parameters and security level, and synchronize the configured initial state to the management platform database.

[0008] Preferably, the real-time monitoring module includes an energy consumption data acquisition unit and an energy consumption data transmission unit; The energy consumption data acquisition unit is used to collect energy consumption information of each connected device in real time based on a high-speed communication protocol, including continuous monitoring of parameters such as current, voltage and power of the device, and preliminary organization of the collected data; The energy consumption data transmission unit is used to send the collected energy consumption data to the data analysis module in real time through a high-speed communication protocol, thereby achieving high-speed synchronization and accurate transmission of energy consumption information.

[0009] Preferably, the data analysis module includes a time series analysis unit and an index calculation unit; The timing analysis unit is used to perform time stamp verification and synchronization on the received energy consumption data and device status data, and to correct timing deviations caused by network and response delays; Then, time series analysis is performed, and compensation and adjustment algorithms are used to improve data information based on the impact of network and response delays.

[0010] Preferably, the index calculation unit calculates and obtains the energy consumption index Nzs and the device status delay index Dyc based on the data information of the improved energy consumption data and the device status data. The specific calculation formulas are as follows: ; Among them, P t is the actual power value of the monitoring equipment at time t; Pre f is the rated reference power or industry-standard power of the corresponding equipment; T is the total number of time collected during the statistical period, that is, the number of sampling points; t represents the sequence number of the time sampling point, that is, the time point when the data was collected; ; Among them, t ai Indicates the time point when the device status actually changes under the i-th monitoring command; t ci Indicates the time point when the i-th monitoring command is issued; N is the total number of monitoring commands in the statistical period; i is the sequence number of the monitoring command.

[0011] Preferably, the decision support module includes a data analysis and modeling unit, a control suggestion generation unit and a data acquisition and optimization unit; The data analysis modeling unit is used to call the energy consumption index Nzs and the equipment status delay index Dyc based on the linear regression model, build a multivariate data analysis model, perform correlation analysis on the equipment operation status and energy consumption change trend, and output the analysis results; Preferably, the control suggestion generating unit is used to receive the analysis results of the data analysis and modeling unit, and generate control suggestions for specific equipment in combination with the real-time working conditions of the equipment, including operating parameter optimization, energy consumption fluctuation control and state response adjustment solutions; Finally, the analysis suggestions are pushed to the management platform in the form of actionable instructions.

[0012] Preferably, the data acquisition and optimization unit is used to preset a device state delay threshold D, and compare and evaluate the device state delay index Dyc with the preset threshold; When the device status delay index Dyc exceeds the device status delay threshold D, the data collection frequency and data transmission priority of the relevant devices are automatically adjusted, and the optimization results are synchronized to the data analysis model in real time. Preferably, the execution control module is used to receive the control suggestions generated by the decision support module, and automatically generate operating instructions for the target device based on the control suggestions, and send the instructions in real time through the data communication interface with the relevant equipment, so that the target device adjusts the operating status or switches the operating mode according to the instruction content, and the device uploads the current status and feedback data to the system in real time after the instruction is executed.

[0013] The present invention provides a remote operation and maintenance weak current intelligent equipment management platform. It has the following beneficial effects: (1) This remote operation and maintenance weak current intelligent equipment management platform aims to solve the problem of delay in real-time energy consumption data collection due to the long response time of sensors or equipment in the remote operation and maintenance of existing weak current intelligent equipment. It proposes an integrated intelligent management platform from data collection, data analysis, decision support to execution control. The device access module realizes the standard access and initial state configuration of each weak current equipment in the building automatic control system, and the real-time monitoring module collects and transmits the energy consumption data such as current, voltage, power of the equipment in real time based on the high-speed communication protocol. The data analysis module uses the timing analysis unit to perform time stamp proofreading and network and response delay correction on the received data. The indicator calculation unit calculates the energy consumption index based on this. The data Nzs and the equipment status delay index Dyc are combined to ensure the accuracy and timeliness of energy consumption analysis; the decision support module establishes a multivariate data analysis model through a linear regression model, performs trend analysis on the energy consumption index Nzs and the equipment status delay index Dyc, and generates equipment control suggestions. At the same time, according to the preset equipment status delay threshold D, the equipment status delay index Dyc is compared with the threshold. If the equipment status delay index Dyc exceeds the threshold, the data acquisition optimization unit will adjust the data acquisition frequency and data transmission priority of the equipment in a timely manner. The optimized acquisition parameters and transmission strategy are synchronized to the data analysis model in real time to improve the real-time nature of data feedback, eliminate monitoring blind spots and data lags caused by delays, and realize closed-loop dynamic optimization control. (2) The remote operation and maintenance weak current intelligent equipment management platform is based on the execution control module. After receiving the control suggestions generated based on the latest energy consumption index Nzs and equipment status delay index Dyc analysis, it can automatically generate operation instructions for the target equipment and send them to the target equipment in real time through the data communication interface to adjust the equipment operation status or switch the operation mode. After the instruction is executed, the equipment will upload the current status and feedback data to the system in real time for subsequent continuous monitoring and data optimization. This mechanism can effectively prevent decision-making errors caused by outdated or inaccurate data, thereby reducing the risk of energy waste and increased operation and maintenance costs during peak energy consumption periods, and thus breaking the vicious cycle of decision-making and data updating caused by data collection delays, and realizing safe, efficient, energy-saving and intelligent operation and maintenance of building weak current intelligent equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a schematic diagram of the system framework structure of the remote operation and maintenance weak current intelligent equipment management platform of the present invention. DETAILED DESCRIPTION

[0015] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0016] Example 1 See also Figure 1 , the present invention provides a remote operation and maintenance weak current intelligent equipment management platform, including equipment access module, real-time monitoring module, data analysis module, decision support module and execution control module; The device access module is used to connect each weak current device in the building automation system to the management platform, including the identification and initial status setting of the device, and the collection of device status data; The real-time monitoring module is used to collect energy consumption data of each connected device in real time, including current, voltage and power, through a high-speed communication protocol, and transmit the energy consumption data to the data analysis module; The data analysis module is used to receive energy consumption data and equipment status data, and adjust the impact of network and response delays through time series analysis technology to calculate the accurate energy consumption index Nzs and equipment status delay index Dyc; The decision support module is used to build a data analysis model using a linear regression model, perform data analysis on the energy consumption index Nzs and the equipment status delay index Dyc, and generate equipment control suggestions; Secondly, the preset threshold is compared and evaluated with the device status delay index Dyc, and the data collection frequency and data transmission priority are readjusted; The execution control module is used to receive device control suggestions and send instructions to relevant devices in real time for execution, including adjusting the device operating status or switching the operating mode.

[0017] In this embodiment, the beneficial effects that can be achieved by each module are as follows: The device access module can achieve standardized access and identity recognition for all weak-current devices in the building automation system. It ensures comprehensive collection of device information, uniqueness confirmation, and initialization of operating status configuration through the device's unique identification code, thereby ensuring the integrity and reliability of the underlying data of the management system. The real-time monitoring module uses high-speed communication protocols to collect and accurately transmit equipment current, voltage, power and other energy consumption data in real time, realizing real-time visualization and dynamic management of the operating status of each device; The data analysis module receives and processes energy consumption data and equipment status data, uses time series analysis technology to correct the impact of network and response delays on the data, and calculates the energy consumption index Nzs and equipment status delay index Dyc, providing a highly timely and accurate indicator basis for subsequent operation and maintenance decisions; The decision support module uses a linear regression model to establish multivariate data analysis, fully exploring the comprehensive correlation between the energy consumption index Nzs and the equipment status delay index Dyc, and automatically generating targeted equipment control suggestions. At the same time, by comparing Dyc with the preset delay threshold D, it intelligently adjusts the data collection frequency and data transmission priority, improving the platform's intelligent response and data optimization capabilities. The execution control module can promptly receive control suggestions and automatically generate instructions, which are then sent to the target device in real time through the data communication interface to achieve online adjustment of the device's operating status parameters or switching of the operating mode. After the instructions are executed, the latest status of the device and real-time feedback data are transmitted back, effectively ensuring the timeliness of the operation and maintenance closed loop and the implementation of energy consumption optimization measures.

[0018] Example 2 The device access module includes a device identification unit and an initial state setting unit; The device identification unit is used to identify the identity of each weak current device to be connected to the building automation system, complete the device information collection and device uniqueness confirmation by reading the unique identification code of the device, and upload the identification result to the management platform for registration and archiving.

[0019] The initial state setting unit is used to initialize the operating state of the connected device according to the system preset parameters after the device completes identity identification, including initial settings such as operating mode, network parameters and security level, and synchronize the configured initial state to the management platform database.

[0020] Furthermore, in the practical application context of unified access and intelligent management of weak-current intelligent devices in building automation systems, the present invention provides a device identification unit and an initial state setting unit to address technical difficulties such as poor identity recognition accuracy, low information entry efficiency, and non-standard initial state configuration during device access. The specific implementation steps are as follows: First, the device access module uses the device identification unit to identify all weak-current smart devices to be connected to the building automation system one by one. It preferably uses a QR code or NFC to automatically read the unique device identification code, that is, the device's factory serial number, and adopts a data verification protocol with an error correction mechanism to ensure the accuracy and traceability of the device's unique information. The device identification unit then uploads all basic attributes collected during the identification process, such as device type, brand, model, and production serial number, to the central management platform database in real time according to a unified data format. The system automatically determines the uniqueness of the device and completes its registration and archiving within the platform. This step requires that data transmission adopt a high-reliability protocol and a uniqueness verification algorithm for conflict detection. After the device identification unit completes the identification and archiving operation, the initial state setting unit automatically performs initial configuration on the newly entered device according to the platform's preset parameter template. Specifically, this includes: operating mode, i.e., always on, timed, or multi-state switching, network parameters, and security level parameters. All initial configuration parameters must be generated according to the management platform's standard template and synchronously pushed to the platform's core database in JSON format. This synchronization process uses redundant storage and an automatic rollback mechanism to improve configuration reliability. Finally, the equipment that has completed the above initialization operations will be written into the equipment life cycle management system in the "registered - configured - to be monitored" status, forming a complete closed loop of equipment access, identity registration, and initialization configuration, and collaboratively providing a unified, standard, low-error underlying foundation for subsequent distributed energy consumption monitoring, status control and data analysis. The equipment identification process and the initial status configuration process are automatically connected and triggered by the access module to ensure that the technical logic of each step is consistent and can be actually implemented. Compared with traditional solutions that are manually entered or do not have standard template configurations, it achieves differentiated upgrades such as automatic and accurate equipment identification, standardized information filing, efficient batch initialization, and zero manual intervention.

[0021] The real-time monitoring module includes an energy consumption data acquisition unit and an energy consumption data transmission unit; The energy consumption data acquisition unit is used to collect energy consumption information of each connected device in real time based on a high-speed communication protocol, including continuous monitoring of parameters such as current, voltage and power of the device, and preliminary organization of the collected data; The energy consumption data transmission unit is used to send the collected energy consumption data to the data analysis module in real time through a high-speed communication protocol, thereby achieving high-speed synchronization and accurate transmission of energy consumption information.

[0022] In this embodiment, the device access module is subdivided into a device identification unit and an initial state setting unit, which can realize the accurate identity recognition and information collection of each weak current device in the building automation system. The unique identification code is used to ensure the uniqueness of the device and complete the registration and archiving of device information, which greatly enhances the reliability and traceability of the underlying data of the management platform. The initial state setting unit performs initial configuration of the newly connected device including the operating mode, network parameters and security level according to the system preset parameters, and synchronizes the configuration information to the platform database to ensure that all devices are included in the unified standardized management system. The real-time monitoring module is further composed of an energy consumption data acquisition unit and an energy consumption data transmission unit. The energy consumption data acquisition unit completes the continuous collection and collation of energy consumption data such as current, voltage, and power of the connected equipment through a high-speed communication protocol, realizing real-time dynamic monitoring of multiple parameters. The energy consumption data transmission unit synchronizes the collected and collated energy consumption data to the data analysis module in real time, providing high-precision and high-timeliness basic data for data analysis; through the above-mentioned functional division of labor and process optimization, the system has greatly improved the access specifications, refined management and data acquisition efficiency of weak current equipment, laying a solid foundation for subsequent data analysis, intelligent decision-making and energy consumption optimization, and effectively ensuring the operation safety, management efficiency and scientific data decision-making of the entire platform.

[0023] Example 3 The data analysis module includes a time series analysis unit and an index calculation unit; The timing analysis unit is used to perform time stamp verification and synchronization on the received energy consumption data and device status data, and to correct timing deviations caused by network and response delays; Then, time series analysis is performed, and compensation and adjustment algorithms are used to improve data information based on the impact of network and response delays.

[0024] The index calculation unit calculates and obtains the energy consumption index Nzs and the device status delay index Dyc based on the data information of the completed energy consumption data and the device status data. The specific calculation formulas are as follows: ; Among them, P t is the actual power value of the monitoring equipment at time t; Pre f is the rated reference power or industry-standard power of the corresponding equipment; T is the total number of time collected during the statistical period, that is, the number of sampling points; t represents the sequence number of the time sampling point, that is, the time point when the data was collected; ; Among them, t ai Indicates the time point when the device status actually changes under the i-th monitoring command; t ci Indicates the time point when the i-th monitoring command is issued; N is the total number of monitoring commands in the statistical period; i is the sequence number of the monitoring command.

[0025] The decision support module includes a data analysis and modeling unit, a control suggestion generation unit and a data acquisition and optimization unit; The data analysis modeling unit is used to call the energy consumption index Nzs and the equipment status delay index Dyc based on the linear regression model, build a multivariate data analysis model, perform correlation analysis on the equipment operation status and energy consumption change trend, and output the analysis results; The control suggestion generating unit is used to receive the analysis results of the data analysis and modeling unit and generate control suggestions for specific equipment based on the real-time operating conditions of the equipment, including operating parameter optimization, energy consumption fluctuation control and state response adjustment solutions; Finally, the analysis suggestions are pushed to the management platform in the form of actionable instructions.

[0026] The data acquisition and optimization unit is used to preset a device state delay threshold D and compare and evaluate the device state delay index Dyc with the preset threshold; When the device status delay index Dyc exceeds the device status delay threshold D, the data collection frequency and data transmission priority of the relevant devices are automatically adjusted, and the optimization results are synchronized to the data analysis model in real time.

[0027] Furthermore, in the actual application scenario of refined building energy consumption management, the decision support module specifically includes a data analysis and modeling unit, a control suggestion generation unit, and a data collection and optimization unit to meet the intelligent and data-based requirements for the operation status and energy consumption control of large-scale weak current equipment. The specific operation process is as follows: First, the data analysis modeling unit uses the obtained energy consumption index Nzs and equipment status delay index Dyc based on the regression algorithm model, preferably the linear regression model, to construct a multivariate data analysis model with the energy consumption index Nzs and the equipment status delay index Dyc as independent variables and the energy consumption performance score and the response performance score as dependent variables. The model is used to correlate and analyze the energy consumption change trend and operating status response characteristics of the equipment in the current and historical operating periods. The collection and preprocessing of each parameter are completed by the upper module and archived and submitted here. The data analysis modeling unit outputs analysis labels, which serve as the data basis for the control suggestion generation unit; The control suggestion generation unit then receives the multivariate analysis results from the analysis and modeling unit, combines them with the synchronously acquired real-time equipment operating parameters, compares historical operating modes and threshold rules, optimizes the hierarchical decision-making method, and outputs specific control suggestions in a parameterized presentation, including: Increase or decrease target equipment operating parameters, adjust energy consumption allocation thresholds, optimize operation scheduling strategies, and reconfigure equipment response priorities. The generated results are automatically packaged into standard JSON control instructions, which are pushed to the weak current equipment management system through the platform server to execute specific operating instructions and record the process receipt. Next, the data collection optimization unit presets a device status delay threshold D, specifically using the empirical threshold derived from the data analysis model or the threshold recommended by industry standards as a reference. It compares the current Dyc with the threshold D in real time and automatically increases the sampling frequency for devices that exceed the threshold, prioritizing the collection cycle from 10s to 3s. It also prioritizes the data transmission channel, upgrading it to an independent data channel and adding QoS guarantees. The adjustment action is automatically and synchronously fed back to the analysis model parameter management module through the interface service. After closing the collection optimization, a trace is left in the receipt table, maintaining the one-way technical logic coherence of the entire upstream and downstream processes of the decision support module, forming a technically progressive and continuously implementable automated closed loop; The solution has clear technical features such as digital modeling, parameterized indicator calculation, automatic generation of actionable control suggestions, and real-time adjustment and optimization of acquisition scheduling. It overcomes the defects of traditional solutions such as reliance on manual intervention, parameter calibration delays, and separation and lack of linkage between sub-modules. It achieves innovative improvements at multiple levels such as strong consistency, dynamic hierarchical adjustment, and standard data output, and can be directly put into actual engineering applications.

[0028] In this embodiment, the data analysis and decision support module uses the timing analysis unit to perform unified time stamp calibration and synchronization on the received energy consumption data and equipment status data, which solves the timing deviation caused by network and response delays and ensures the consistency and accuracy of the analysis data; through compensation and adjustment algorithms, the synchronized energy consumption and status data are improved, providing a highly reliable information basis for downstream analysis. The various acquisition parameters include the actual monitoring power value P t , Rated reference power Pre f , the total time collected during the statistical period T, the actual time point of device status change t ai , monitoring command issuance time t ci The total number of monitoring commands N within the statistical period is collected in a standard acquisition format and strictly calibrated, further providing a complete and effective data source for the calculation of the energy consumption index Nzs and the device status delay index Dyc; The energy consumption index Nzs reflects the actual energy consumption level of the equipment relative to the rated power during the statistical period, laying a quantitative foundation for equipment energy efficiency evaluation and energy management. The equipment status delay index Dyc quantifies the equipment's response delay to instructions in real time, helping to monitor the timeliness and coordinated response capabilities of equipment operation throughout the entire process. The decision support module uses a linear regression model to establish an analysis modeling unit, and conducts multivariate correlation modeling on the key indicators of energy consumption index Nzs and equipment status delay index Dyc to achieve a comprehensive evaluation of equipment energy consumption and operating efficiency. The control suggestion generation unit combines the analysis results with the actual operating conditions of the equipment to automatically output targeted control suggestions, including operating parameter optimization, energy consumption fluctuation control, and state response adjustment to ensure the optimal operating state of the equipment. The generated analysis suggestions are converted into specific operation instructions through the management platform to achieve closed-loop scheduling. By presetting the equipment status delay threshold D through the data acquisition optimization unit, the current equipment status delay index Dyc is automatically compared with the threshold D, the data acquisition frequency and data transmission priority are dynamically adjusted, and the above optimization measures are promptly fed back to the data analysis model to achieve closed-loop optimization of the entire process of data acquisition, analysis, decision-making, and execution. Through the continuous collection, precise calculation and intelligent application of data at all levels, the system effectively realizes real-time perception, quantitative evaluation and dynamic regulation of the operating efficiency and energy consumption level of weak current equipment, greatly improving the standardization of platform data operation, the intelligence of equipment management and the refinement of operation and maintenance.

[0029] Example 4 The execution control module is used to receive the control suggestions generated by the decision support module, and automatically generate operating instructions for the target device based on the control suggestions, and send the instructions in real time through the data communication interface with the relevant equipment, so that the target device adjusts the operating status or switches the operating mode according to the instruction content, and the device uploads the current status and feedback data to the system in real time after the instruction is executed.

[0030] In this embodiment, the execution control module assumes the core responsibilities of decision-making implementation and equipment linkage in the building intelligent operation and maintenance system. It automatically receives the control suggestions generated by the decision support module, converts them into precise and executable operation instructions, and sends them to each target device. It uses the data communication interface between the devices to realize real-time synchronous push of instructions, ensuring that the instruction content continuously covers the entire equipment operation process based on the optimal parameter settings. After executing the instructions, each device immediately uploads the current actual status and feedback data to the system, building a complete closed loop of analysis-decision-execution-feedback, greatly improving the intelligence and automation level of the system response, ensuring the rapid tuning and dynamic adaptation of core energy consumption and status indicators such as Nzs and Dyc, and effectively supporting the overall energy efficiency improvement, operation safety and standardization, process and efficiency of operation and maintenance management of the building's weak current equipment group.

[0031] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. Remote operation and maintenance of weak current intelligent equipment management platform, characterized by: It includes equipment access module, real-time monitoring module, data analysis module, decision support module and execution control module; The device access module is used to connect each weak current device in the building automation system to the management platform, including the identification and initial status setting of the device, and the collection of device status data; The real-time monitoring module is used to collect energy consumption data of each connected device in real time, including current, voltage and power, through a high-speed communication protocol, and transmit the energy consumption data to the data analysis module; The data analysis module is used to receive energy consumption data and equipment status data, and adjust the impact of network and response delays through time series analysis technology to calculate the accurate energy consumption index Nzs and equipment status delay index Dyc; The decision support module is used to build a data analysis model using a linear regression model, perform data analysis on the energy consumption index Nzs and the equipment status delay index Dyc, and generate equipment control suggestions; Secondly, the preset threshold is compared and evaluated with the device status delay index Dyc, and the data collection frequency and data transmission priority are readjusted; The execution control module is used to receive device control suggestions and send instructions to relevant devices in real time for execution, including adjusting the device operating status or switching the operating mode.

2. The remote operation and maintenance weak current intelligent equipment management platform according to claim 1 is characterized by: The device access module includes a device identification unit and an initial state setting unit; The device identification unit is used to identify the identity of each weak current device to be connected to the building automation system, complete the device information collection and device uniqueness confirmation by reading the unique identification code of the device, and upload the identification result to the management platform for registration and archiving.

3. The remote operation and maintenance weak current intelligent equipment management platform according to claim 2 is characterized by: The initial state setting unit is used to initialize the operating state of the connected device according to the system preset parameters after the device completes identity identification, including initial settings such as operating mode, network parameters and security level, and synchronize the configured initial state to the management platform database.

4. The remote operation and maintenance weak current intelligent equipment management platform according to claim 1 is characterized by: The real-time monitoring module includes an energy consumption data acquisition unit and an energy consumption data transmission unit; The energy consumption data acquisition unit is used to collect energy consumption information of each connected device in real time based on a high-speed communication protocol, including continuous monitoring of parameters such as current, voltage and power of the device, and preliminary organization of the collected data; The energy consumption data transmission unit is used to send the collected energy consumption data to the data analysis module in real time through a high-speed communication protocol, thereby achieving high-speed synchronization and accurate transmission of energy consumption information.

5. The remote operation and maintenance weak current intelligent equipment management platform according to claim 1 is characterized by: The data analysis module includes a time series analysis unit and an index calculation unit; The timing analysis unit is used to perform time stamp verification and synchronization on the received energy consumption data and device status data, and to correct timing deviations caused by network and response delays; Then, time series analysis is performed, and compensation and adjustment algorithms are used to improve data information based on the impact of network and response delays.

6. The remote operation and maintenance weak current intelligent equipment management platform according to claim 1 is characterized by: The index calculation unit calculates and obtains the energy consumption index Nzs and the device status delay index Dyc based on the data information of the completed energy consumption data and the device status data. The specific calculation formulas are as follows: ； Among them, P t is the actual power value of the monitoring equipment at time t; Pre f is the rated reference power or industry-standard power of the corresponding equipment; T is the total number of time collected during the statistical period, that is, the number of sampling points; t represents the sequence number of the time sampling point, that is, the time point when the data was collected; ； Among them, t ai Indicates the time point when the device status actually changes under the i-th monitoring command; t ci Indicates the time point when the i-th monitoring command is issued; N is the total number of monitoring commands in the statistical period; i is the sequence number of the monitoring command.

7. The remote operation and maintenance weak current intelligent equipment management platform according to claim 1 is characterized by: The decision support module includes a data analysis and modeling unit, a control suggestion generation unit and a data acquisition and optimization unit; The data analysis modeling unit is used to call the energy consumption index Nzs and the equipment status delay index Dyc based on the linear regression model, build a multivariate data analysis model, perform correlation analysis on the equipment operation status and energy consumption change trend, and output the analysis results.

8. The remote operation and maintenance weak current intelligent equipment management platform according to claim 1 is characterized by: The control suggestion generating unit is used to receive the analysis results of the data analysis and modeling unit and generate control suggestions for specific equipment based on the real-time operating conditions of the equipment, including operating parameter optimization, energy consumption fluctuation control and state response adjustment solutions; Finally, the analysis suggestions are pushed to the management platform in the form of actionable instructions.

9. The remote operation and maintenance weak current intelligent equipment management platform according to claim 1 is characterized by: The data acquisition and optimization unit is used to preset a device state delay threshold D and compare and evaluate the device state delay index Dyc with the preset threshold; When the device status delay index Dyc exceeds the device status delay threshold D, the data collection frequency and data transmission priority of the relevant devices are automatically adjusted, and the optimization results are synchronized to the data analysis model in real time.

10. The remote operation and maintenance weak current intelligent equipment management platform according to claim 1 is characterized by: The execution control module is used to receive the control suggestions generated by the decision support module, and automatically generate operating instructions for the target device based on the control suggestions, and send the instructions in real time through the data communication interface with the relevant equipment, so that the target device adjusts the operating status or switches the operating mode according to the instruction content, and the device uploads the current status and feedback data to the system in real time after the instruction is executed.

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