A power equipment control management system and method based on big data analysis

Abstract

The application discloses a kind of power equipment control management system and method based on big data analysis, specifically related to power equipment management technical field, including power equipment sensor deployment module, power equipment sensor data acquisition module, power equipment health state management module, power equipment energy efficiency optimization module, power equipment fault early warning module and power equipment fault evaluation module, the operating state data of each monitoring point of power equipment is collected by sensor in the application, power equipment health index is calculated, power equipment health management is carried out, power equipment energy efficiency optimization index is calculated, and power equipment energy-saving optimization processing is carried out, with clear management priority, analyze power equipment fault early warning coefficient, by comparing fault early warning coefficient with preset value, secondary comparison of sensor data and safety threshold, a double verification mechanism is built, the management accuracy and efficiency of power equipment are improved, and the safe and stable operation of power system is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power equipment management, more particularly, the present application relates to a power equipment control management system and method based on big data analysis. BACKGROUND

[0002] With the acceleration of urbanization and rapid economic development, the demand for energy and power continues to grow, and the scale and complexity of the power system also increase. As an important part of the power system, the operation state of the power equipment is directly related to the safety, stability and efficient operation of the power system.

[0003] With the rapid development of new generation information technologies such as Internet of Things, big data and cloud computing, new solutions are provided for the management of power equipment in industrial parks managed by property management. By installing sensors, smart meters and other devices on power equipment, real-time collection of equipment operation data can be achieved, and big data analysis technology can be used to analyze and process the data, thereby reducing operation and maintenance costs and ensuring the safe and stable operation of the power system.

[0004] However, in actual use, there are still some shortcomings, such as the traditional power equipment management method mainly relies on manual inspection and regular maintenance, which requires a lot of manpower and time, and the management method is inefficient. Manual inspection is difficult to find potential hidden dangers inside the equipment, which can easily lead to equipment failure and even accidents;

[0005] The traditional power equipment management method lacks effective collection and analysis of equipment operation data, lacks intelligent management function of power equipment, and is difficult to make scientific decisions and optimize management. SUMMARY

[0006] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide a power equipment control management system and method based on big data analysis, which is used to solve the problems raised in the background art.

[0007] To achieve the above-mentioned purpose, the present application provides the following technical scheme: a power equipment control management system based on big data analysis, comprising:

[0008] A power equipment sensor deployment module is used to install sensors at each monitoring point of the power equipment and number the sensors at each monitoring point of the power equipment.

[0009] A power equipment sensor data acquisition module is used to acquire the operation state data of each monitoring point of the power equipment through the sensors. The power equipment sensor data acquisition module includes a mechanical equipment data acquisition unit and an equipment energy consumption data acquisition unit.

[0010] The power equipment health state management module: according to the mechanical equipment data collected by the mechanical equipment data acquisition unit, the power equipment health index of each monitoring point of the power equipment is calculated, and the power equipment health management is carried out.

[0011] The power equipment energy efficiency optimization module: according to the equipment energy consumption data collected by the equipment energy consumption data acquisition unit, the power equipment energy efficiency optimization index of each monitoring point of the power equipment is calculated, and the power equipment energy saving optimization processing is carried out.

[0012] The power equipment fault early warning module: according to the power equipment health index and the power equipment energy efficiency optimization index of each monitoring point of the power equipment, the power equipment fault early warning coefficient of each monitoring point of the power equipment is calculated.

[0013] The power equipment fault evaluation module: the power equipment fault early warning coefficient of each monitoring point of the power equipment is obtained, compared with the preset power equipment fault early warning coefficient, and processed.

[0014] Preferably, the power equipment sensor deployment module specifically comprises:

[0015] For setting m monitoring points according to the monitoring needs of the power equipment, installing sensors at the monitoring points, and sequentially numbering all sensors of each monitoring point of the power equipment as 1, 2,... i,... n.

[0016] Preferably, the power equipment sensor data acquisition module specifically comprises:

[0017] The mechanical equipment data acquisition unit: the equipment temperature of each monitoring point of the power equipment is collected by the temperature sensor, marked as The vibration frequency of each monitoring point of the power equipment is collected by the vibration sensor, marked as , wherein u=1, 2,... m, u represents the number of the u-th monitoring point;

[0018] The equipment energy consumption data acquisition unit: the equipment electric energy consumption and the equipment load rate of each monitoring point of the power equipment are collected by the intelligent electric meter, respectively marked as , The equipment start-stop times of each monitoring point of the power equipment are collected by the switch state sensor, marked as .

[0019] Preferably, the calculation formula of the power equipment health index is:

[0020]

[0021] , wherein represents the power equipment health index of the u-th monitoring point, represents the equipment temperature of the u-th monitoring point, represents a preset device temperature, represents a device vibration frequency of the u-th monitoring point, represents a maximum value of the device vibration frequency, represents a minimum value of the device vibration frequency, e represents a natural constant;

[0022] When the device vibration frequency is greater, the difference between the maximum value of the device vibration frequency and the current device vibration frequency is smaller, and the device temperature exceeds the preset range, the power equipment health index is higher, indicating that the device has a fault, otherwise, the power equipment health index is lower, indicating that the device is in a healthy running state.

[0023] Preferably, the power equipment health state management module specifically comprises:

[0024] The power equipment health index of each monitoring point of the power equipment is obtained, and each monitoring point is sorted from large to small according to the power equipment health index value. When the power equipment health index is higher, the priority of the power equipment management of the monitoring point is higher. The order of the arranged power equipment monitoring points is obtained, and the numbers of the monitoring points are sent to the industrial park property management personnel in sequence. The property management personnel view the power equipment health index through the industrial park intelligent management platform and process the power equipment fault hidden danger.

[0025] Preferably, the power equipment energy efficiency optimization module specifically comprises:

[0026] S61: Calculate the device energy consumption stability of each monitoring point through the device electric energy consumption and the device load rate:

[0027]

[0028] wherein, represents the device energy consumption stability of the u-th monitoring point, represents the device electric energy consumption of the u-th monitoring point, represents the device electric energy consumption of the u+1-th monitoring point, represents the device load rate of the u-th monitoring point, represents the device load rate of the u+1-th monitoring point, and m represents the number of monitoring points;

[0029] When the device energy consumption stability is greater, it indicates that the fluctuation of the device load rate causes a large change in the device electric energy consumption, and the device energy consumption is obviously affected by the fluctuation of the device load rate, resulting in poor device energy consumption stability. Conversely, when the device energy consumption stability is smaller, it indicates that the fluctuation of the device load rate is small, causing a small change in the device electric energy consumption, and the device energy consumption is stable.

[0030] S62: Calculate the device start-stop energy consumption stability of each monitoring point through the device power consumption and the number of device start-stop times.

[0031]

[0032] wherein, represents the device start-stop energy consumption stability of the u-th monitoring point, represents the number of device start-stop times of the u-th monitoring point.

[0033] When the fluctuation degree of device power consumption is greater and the number of device start-stop times is greater, it indicates that the stability of device power consumption in the process of multiple start-stop is worse, and the device start-stop energy consumption stability is smaller, on the contrary, it indicates that the device power consumption is more stable in the process of start-stop;

[0034] S63: The calculation formula of the power equipment energy efficiency optimization index is:

[0035]

[0036] wherein, represents the power equipment energy efficiency optimization index of the u-th monitoring point, represents the device energy consumption stability of the u-th monitoring point, represents the device start-stop energy consumption stability of the u-th monitoring point.

[0037] S64: Obtain the power equipment energy efficiency optimization index of each monitoring point of the power equipment, and sort each monitoring point from large to small according to the power equipment energy efficiency optimization index value, when the power equipment energy efficiency optimization index is higher, the power equipment energy efficiency optimization potential is greater, and the priority of the power equipment energy efficiency optimization of the monitoring point is higher, obtain the order of the arranged power equipment monitoring points, and send the number of monitoring points to the industrial park property management personnel in turn, and the property management personnel view the device energy consumption of the corresponding monitoring point through the industrial park intelligent management platform. The high energy consumption monitoring point is processed for energy saving.

[0038] Preferably, the calculation formula of the power equipment fault early warning coefficient is:

[0039]

[0040] wherein, represents the power equipment fault early warning coefficient of the u-th monitoring point, represents the power equipment health index of the u-th monitoring point, represents the power equipment energy efficiency optimization index of the u-th monitoring point.

[0041] The higher the power equipment health index is, the more faults the equipment has, the higher the power equipment energy efficiency optimization index is, the greater the energy consumption fluctuation of the equipment is, and the higher the power equipment health index and the power equipment energy efficiency optimization index of a monitoring point are, the higher the risk of equipment operation failure is, and vice versa.

[0042] Preferably, the power equipment fault assessment module specifically comprises:

[0043] The power equipment fault early warning coefficient of each monitoring point of the power equipment is obtained, and is compared with the preset power equipment fault early warning coefficient. If the power equipment fault early warning coefficient of a monitoring point is greater than the preset power equipment fault early warning coefficient, it indicates that the power equipment of the monitoring point has an abnormal operation, and an intelligent analysis process is automatically started immediately, the number of the monitoring point with an abnormality is counted, and the sensor data in the abnormal monitoring point is extracted and compared with the safety threshold set by the sensor. If the sensor data is greater than the safety threshold set by the sensor, it indicates that the state of the power equipment monitored by the sensor has an abnormality, the number of the sensor with an abnormality in the abnormal monitoring point is counted, and the number of the abnormal monitoring point and the number of the abnormal sensor are sent to the industrial park property management personnel. The property management personnel view the operation state of the abnormal power equipment through the industrial park intelligent management platform, obtain the maintenance guidance of the fault, and ensure the safe and stable operation of the power system. Otherwise, it indicates that the power equipment of the monitoring point has no abnormal operation.

[0044] Preferably, a power equipment control management method based on big data analysis comprises the following steps:

[0045] Step S01: power equipment sensor deployment: used for installing sensors at each monitoring point of the power equipment, and numbering the sensors at each monitoring point of the power equipment;

[0046] Step S02: power equipment sensor data acquisition: used for acquiring the operation state data of each monitoring point of the power equipment through the sensors, the power equipment sensor data acquisition comprising a mechanical equipment data acquisition sub-step and a device energy consumption data acquisition sub-step, and the operation state data comprising mechanical equipment data and device energy consumption data;

[0047] Step S03: power equipment health state management: used for receiving the operation state data transmitted by the power equipment sensor data acquisition step, calculating the power equipment health index of each monitoring point of the power equipment according to the mechanical equipment data collected by the mechanical equipment data acquisition sub-step, and performing power equipment health management;

[0048] Step S04: power equipment energy efficiency optimization: for receiving the operation state data transmitted by the power equipment sensor data collection step, calculating the power equipment energy efficiency optimization index of each monitoring point of the power equipment according to the equipment energy consumption data collected by the equipment energy consumption data collection substep, and performing power equipment energy saving optimization processing;

[0049] Step S05: power equipment fault early warning: calculating the power equipment fault early warning coefficient of each monitoring point of the power equipment according to the power equipment health index and the power equipment energy efficiency optimization index of each monitoring point of the power equipment;

[0050] Step S06: power equipment fault evaluation: obtaining the power equipment fault early warning coefficient of each monitoring point of the power equipment, comparing with the preset power equipment fault early warning coefficient, and processing.

[0051] Technical effects and advantages of the present application:

[0052] 1. The application provides a kind of power equipment control management system and method based on big data analysis, the operating state data of each monitoring point of power equipment is collected by sensor, according to the mechanical equipment data collected by mechanical equipment data acquisition unit, the health index of power equipment of each monitoring point of power equipment is calculated, according to the equipment energy consumption data collected by equipment energy consumption data acquisition unit, the power equipment energy efficiency optimization index of each monitoring point of power equipment is calculated, further analysis obtains the power equipment failure early warning coefficient of each monitoring point of power equipment, compared with the preset power equipment failure early warning coefficient, if the power equipment failure early warning coefficient of certain monitoring point is greater than the preset power equipment failure early warning coefficient, then it indicates that the power equipment operation of this monitoring point exists anomaly, immediately automatically start intelligent analysis process, the monitoring point number of existence anomaly is counted, and the sensor data in the abnormal monitoring point is extracted, compared with the safety threshold set by sensor for second time, if sensor data is greater than the safety threshold set by sensor, then it indicates that the power equipment state monitored by this sensor exists anomaly, the number of sensor monitoring anomaly in abnormal monitoring point is counted, and the abnormal monitoring point number, abnormal sensor number is sent to industrial park property management personnel, the running state of abnormal power equipment is viewed by property management personnel through industrial park intelligent management platform, obtain the maintenance guidance of failure, ensure the safe and stable operation of power system, otherwise, it indicates that the power equipment operation of this monitoring point is normal, through the comprehensive collection and depth analysis of power equipment operation data, the overall evaluation of equipment health status is realized, by analyzing the energy consumption data of equipment, it is beneficial to quickly identify power equipment energy efficiency bottleneck, by comparing fault early warning coefficient with preset value, and the second comparison of sensor data and safety threshold, double verification mechanism is built, data processing speed is fast and accuracy is high, improve the management efficiency of power equipment, ensure the safe and stable operation of power system, the present application is applicable to the scene such as commercial complex, industrial park managed by property management company, can help property management company to improve the management efficiency of power equipment, reduce operation and maintenance cost, guarantee the safe and stable operation of power system;

[0053] 2, The application provides a power equipment control management system and method based on big data analysis, which obtains the power equipment health indexes of each monitoring point of the power equipment, sorts the monitoring points from large to small according to the power equipment health index values, the higher the power equipment health index, the higher the priority of the power equipment management of the monitoring point, obtains the order of the arranged power equipment monitoring points, and sends the numbers of the monitoring points to the industrial park property management personnel in turn, the property management personnel views the power equipment health index through the industrial park intelligent management platform, and processes the power equipment fault hidden danger, obtains the power equipment energy efficiency optimization index of each monitoring point of the power equipment, sorts the monitoring points from large to small according to the power equipment energy efficiency optimization index values, the higher the power equipment energy efficiency optimization index, the greater the power equipment energy efficiency optimization potential, the higher the priority of the power equipment energy efficiency optimization of the monitoring point, obtains the order of the arranged power equipment monitoring points, and sends the numbers of the monitoring points to the industrial park property management personnel in turn, the property management personnel views the equipment energy consumption of the corresponding monitoring point through the industrial park intelligent management platform, and processes the high-energy-consumption monitoring point, through the health management priority sorting and the energy efficiency optimization classification promotion, the management priority is clear, and the management efficiency is improved, so that the operation condition of the equipment can be deeply understood, a scientific and reasonable equipment maintenance plan and energy efficiency optimization strategy are formulated, and the power consumption of the park is reduced. BRIEF DESCRIPTION OF DRAWINGS

[0054] Figure 1 It is a structural schematic diagram of the power equipment control management system based on big data analysis.

[0055] Figure 2 It is a flowchart of the power equipment control management method based on big data analysis. DETAILED DESCRIPTION

[0056] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the application.

[0057] Please refer to Figure 1 As shown in the figure, the application provides a power equipment control management system based on big data analysis, which comprises a power equipment sensor deployment module, a power equipment sensor data acquisition module, a power equipment health state management module, a power equipment energy efficiency optimization module, a power equipment fault early warning module and a power equipment fault evaluation module.

[0058] The power equipment sensor deployment module is connected with a power equipment sensor data collection module, the power equipment sensor data collection module is connected with a power equipment health state management module and a power equipment energy efficiency optimization module, the power equipment health state management module and the power equipment energy efficiency optimization module are connected with a power equipment fault early warning module, and the power equipment fault early warning module is connected with a power equipment fault evaluation module.

[0059] The power equipment sensor deployment module is used for installing sensors at each monitoring point of the power equipment and numbering the sensors at each monitoring point of the power equipment, and scientific sensor configuration management is used to ensure the comprehensiveness of sensor data collection and provide high-quality data support for intelligent management of the power equipment.

[0060] In a possible design, the power equipment sensor deployment module specifically includes:

[0061] The power equipment sensor deployment module is used for installing sensors at each monitoring point of the power equipment and numbering the sensors at each monitoring point of the power equipment, and scientific sensor configuration management is used to ensure the comprehensiveness of sensor data collection and provide high-quality data support for intelligent management of the power equipment.

[0062] The power equipment sensor data collection module is used for collecting operation state data of each monitoring point of the power equipment through sensors, and the power equipment sensor data collection module includes a mechanical equipment data collection unit and an equipment energy consumption data collection unit, and the operation state data includes mechanical equipment data and equipment energy consumption data; the comprehensive and accurate data collected can provide strong support for performance evaluation of the power equipment and realize sharing and interaction of data.

[0063] In a possible design, the power equipment sensor data collection module specifically includes:

[0064] The mechanical equipment data collection unit collects equipment temperature of each monitoring point of the power equipment through a temperature sensor and marks the equipment temperature as The mechanical equipment data collection unit collects equipment temperature of each monitoring point of the power equipment through a temperature sensor and marks the equipment temperature as , collects equipment vibration frequency of each monitoring point of the power equipment through a vibration sensor, and marks the equipment vibration frequency as , where u=1, 2,...m, and u represents the number of the u th monitoring point.

[0065] The equipment energy consumption data collection unit collects equipment electric energy consumption of each monitoring point of the power equipment through a smart meter, marks the equipment electric energy consumption as , collects equipment load rate of each monitoring point of the power equipment through a smart meter, marks the equipment load rate as , and collects equipment start-stop times of each monitoring point of the power equipment through a switch state sensor and marks the equipment start-stop times as

[0066] The power equipment health state management module is used for receiving the operation state data transmitted by the power equipment sensor data acquisition module, calculating the power equipment health indexes of each monitoring point of the power equipment according to the mechanical equipment data collected by the mechanical equipment data acquisition unit, and performing power equipment health management; through comprehensive collection and deep analysis of the power equipment operation data, comprehensive evaluation and predictive maintenance of the equipment health state are realized.

[0067] In a possible design, the calculation formula of the power equipment health index is as follows:

[0068]

[0069] wherein, represents the power equipment health index of the u th monitoring point, represents the equipment temperature of the u th monitoring point, represents the preset equipment temperature, represents the equipment vibration frequency of the u th monitoring point, represents the maximum value of the equipment vibration frequency, represents the minimum value of the equipment vibration frequency, and e represents the natural constant;

[0070] When the equipment vibration frequency is greater, the difference between the maximum value of the equipment vibration frequency and the current equipment vibration frequency is smaller, and the equipment temperature exceeds the preset range, the power equipment health index is higher, indicating that the equipment has a fault, otherwise, the power equipment health index is lower, indicating that the equipment is in a healthy running state.

[0071] In a possible design, the power equipment health state management module specifically includes:

[0072] The power equipment health indexes of each monitoring point of the power equipment are obtained, and each monitoring point is sorted from large to small according to the power equipment health index values; when the power equipment health index is higher, the priority of the power equipment management of the monitoring point is higher; the order of the arranged power equipment monitoring points is obtained, and the numbers of the monitoring points are sent to the industrial park property management personnel in sequence; the property management personnel view the power equipment health indexes through the industrial park intelligent management platform and process the power equipment fault hidden dangers.

[0073] The power equipment energy efficiency optimization module is used for receiving the operation state data transmitted by the power equipment sensor data acquisition module, calculating the power equipment energy efficiency optimization indexes of each monitoring point of the power equipment according to the equipment energy consumption data collected by the equipment energy consumption data acquisition unit, and performing power equipment energy saving optimization processing; through analysis of the energy consumption data of the equipment, it is beneficial to quickly identify the power equipment energy efficiency bottleneck and propose energy saving optimization suggestions.

[0074] In a possible design, the power equipment energy efficiency optimization module specifically includes:

[0075] S01: Calculate the equipment energy consumption stability of each monitoring point through the equipment electric energy consumption and the equipment load rate:

[0076]

[0077] wherein, represents the equipment energy consumption stability of the u-th monitoring point, represents the equipment electric energy consumption of the u-th monitoring point, represents the equipment electric energy consumption of the u+1-th monitoring point, represents the equipment load rate of the u-th monitoring point, represents the equipment load rate of the u+1-th monitoring point, and m represents the number of monitoring points;

[0078] When the equipment energy consumption stability is greater, it indicates that the fluctuation of the equipment load rate causes a large change in the equipment electric energy consumption, the energy consumption of the equipment is obviously affected by the fluctuation of the load rate, and the equipment energy consumption stability is poor. Conversely, when the equipment energy consumption stability is smaller, it indicates that the fluctuation of the equipment load rate is small, causing a small change in the equipment electric energy consumption, and the equipment energy consumption is stable.

[0079] S02: Calculate the equipment start-stop energy consumption stability of each monitoring point through the equipment electric energy consumption and the equipment start-stop number:

[0080]

[0081] wherein, represents the equipment start-stop energy consumption stability of the u-th monitoring point, represents the equipment start-stop number of the u-th monitoring point;

[0082] When the fluctuation degree of the equipment electric energy consumption is greater and the equipment start-stop number is more, it indicates that the stability of the electric energy consumption of the equipment in the multiple start-stop processes is poorer, and the equipment start-stop energy consumption stability is smaller. Conversely, it indicates that the electric energy consumption of the equipment is more stable in the start-stop process.

[0083] S03: The calculation formula of the power equipment energy efficiency optimization index is:

[0084]

[0085] wherein, represents the power equipment energy efficiency optimization index of the u-th monitoring point, represents the equipment energy consumption stability of the u-th monitoring point, represents the equipment start-stop energy consumption stability of the u-th monitoring point;

[0086] S04: Obtain the power equipment energy efficiency optimization index of each monitoring point of the power equipment, and sort each monitoring point from large to small according to the power equipment energy efficiency optimization index value. The higher the power equipment energy efficiency optimization index, the greater the power equipment energy efficiency optimization potential, and the higher the priority of the power equipment energy efficiency optimization of the monitoring point. The order of the arranged power equipment monitoring points is obtained, and the numbers of the monitoring points are sent to the industrial park property management personnel in turn. The property management personnel view the device energy consumption of the corresponding monitoring point through the industrial park intelligent management platform, and perform energy-saving processing on the monitoring points with high energy consumption.

[0087] The power equipment fault early warning module: according to the power equipment health index and the power equipment energy efficiency optimization index of each monitoring point of the power equipment, the power equipment fault early warning coefficient of each monitoring point of the power equipment is calculated; the deep fusion of data is realized, the data collected by different sensors are integrated, the state of the power equipment is reflected in all directions, and the accurate fault prediction capability is possessed.

[0088] In a possible design, the calculation formula of the power equipment fault early warning coefficient is:

[0089]

[0090] Among them, represents the power equipment fault early warning coefficient of the u-th monitoring point, represents the power equipment health index of the u-th monitoring point, represents the power equipment energy efficiency optimization index of the u-th monitoring point;

[0091] The higher the power equipment health index, the more likely the device has a fault. The higher the power equipment energy efficiency optimization index, the greater the fluctuation of the device energy consumption. If the power equipment health index and the power equipment energy efficiency optimization index of a monitoring point are higher, the risk of device operation failure is high, otherwise, the risk of device operation failure is low.

[0092] The power equipment fault evaluation module: obtains the power equipment fault early warning coefficient of each monitoring point of the power equipment, compares and processes the power equipment fault early warning coefficient with a preset power equipment fault early warning coefficient; by comparing the fault early warning coefficient with the preset value, and comparing the sensor data with the safety threshold twice, a double verification mechanism is constructed, the data processing speed is fast and the accuracy is high, the power equipment management efficiency is improved, and the safe and stable operation of the power system is ensured.

[0093] In a possible design, the power equipment fault evaluation module specifically includes:

[0094] The power equipment fault early warning coefficient of each monitoring point of the power equipment is acquired, and is compared with a preset power equipment fault early warning coefficient. If the power equipment fault early warning coefficient of a certain monitoring point is greater than the preset power equipment fault early warning coefficient, it indicates that the power equipment operation of the monitoring point is abnormal, and an intelligent analysis process is automatically started immediately, the number of the monitoring point with the abnormality is counted, and the sensor data in the abnormal monitoring point is extracted and compared with a safety threshold set by the sensor. If the sensor data is greater than the safety threshold set by the sensor, it indicates that the state of the power equipment monitored by the sensor is abnormal, the number of the sensor monitoring the abnormality in the abnormal monitoring point is counted, and the number of the abnormal monitoring point and the number of the abnormal sensor are sent to the industrial park property management personnel. The property management personnel view the operation state of the abnormal power equipment through the industrial park intelligent management platform, obtain the maintenance guidance of the fault, and ensure the safe and stable operation of the power system. Otherwise, it indicates that the power equipment operation of the monitoring point is normal.

[0095] Referring to Figure 2 In the embodiment, it is specifically pointed out that the present application provides a power equipment control management method based on big data analysis, comprising the following steps:

[0096] Step S01: Power equipment sensor deployment: used for installing sensors at each monitoring point of the power equipment, and numbering the sensors at each monitoring point of the power equipment;

[0097] Step S02: Power equipment sensor data acquisition: used for acquiring the operation state data of each monitoring point of the power equipment through the sensors. The power equipment sensor data acquisition includes a mechanical equipment data acquisition sub-step and a device energy consumption data acquisition sub-step. The operation state data includes mechanical equipment data and device energy consumption data.

[0098] Step S03: Power equipment health state management: used for receiving the operation state data transmitted by the power equipment sensor data acquisition step, calculating the power equipment health index of each monitoring point of the power equipment according to the mechanical equipment data acquired by the mechanical equipment data acquisition sub-step, and performing power equipment health management;

[0099] Step S04: Power equipment energy efficiency optimization: used for receiving the operation state data transmitted by the power equipment sensor data acquisition step, calculating the power equipment energy efficiency optimization index of each monitoring point of the power equipment according to the device energy consumption data acquired by the device energy consumption data acquisition sub-step, and performing power equipment energy saving optimization processing;

[0100] Step S05: Power equipment fault early warning: calculating the power equipment fault early warning coefficient of each monitoring point of the power equipment according to the power equipment health index and the power equipment energy efficiency optimization index of each monitoring point of the power equipment;

[0101] Step S06: power equipment failure evaluation: obtaining the power equipment failure early warning coefficient of each monitoring point of the power equipment, comparing with the preset power equipment failure early warning coefficient, and processing.

[0102] In the embodiment, it needs to be specifically pointed out that, the present application collects the running state data of each monitoring point of the power equipment through the sensor, calculates the power equipment health index of each monitoring point of the power equipment according to the mechanical equipment data collected by the mechanical equipment data acquisition unit, calculates the power equipment energy efficiency optimization index of each monitoring point of the power equipment according to the equipment energy consumption data collected by the equipment energy consumption data acquisition unit, further analyzes to obtain the power equipment failure early warning coefficient of each monitoring point of the power equipment, compares with the preset power equipment failure early warning coefficient, if the power equipment failure early warning coefficient of a monitoring point is greater than the preset power equipment failure early warning coefficient, it indicates that the power equipment running of the monitoring point is abnormal, immediately automatically starts the intelligent analysis process, counts the monitoring point number of the abnormal monitoring point, and extracts the sensor data in the abnormal monitoring point, compares with the safety threshold set by the sensor for the second time, if the sensor data is greater than the safety threshold set by the sensor, it indicates that the power equipment state monitored by the sensor is abnormal, counts the number of sensors monitoring abnormal in the abnormal monitoring point, sends the abnormal monitoring point number and the abnormal sensor number to the industrial park property management personnel, the property management personnel views the running state of the abnormal power equipment through the industrial park intelligent management platform, obtains the fault maintenance guidance, ensures the safe and stable operation of the power system, otherwise, it indicates that the power equipment running of the monitoring point is normal, through the comprehensive collection and deep analysis of the power equipment running data, the overall evaluation of the equipment health state is realized, through the analysis of the energy consumption data of the equipment, it is beneficial to quickly identify the power equipment energy efficiency bottleneck, through the comparison of the failure early warning coefficient with the preset value, and the second comparison of the sensor data with the safety threshold, a double verification mechanism is constructed, the data processing speed is fast and the accuracy is high, the power equipment management efficiency is improved, the safe and stable operation of the power system is ensured, the present application is applicable to the scenes of commercial complexes, industrial parks and the like managed by property management companies, can help the property management companies to improve the management efficiency of the power equipment, reduce the operation and maintenance cost, and ensure the safe and stable operation of the power system;

[0103] The application obtains the power equipment health indexes of each monitoring point of the power equipment, and sorts each monitoring point from large to small according to the power equipment health index values, the higher the power equipment health index, the higher the priority of the power equipment management of the monitoring point, obtains the order of the arranged power equipment monitoring points, and sends the numbers of the monitoring points to the industrial park property management personnel in turn, the property management personnel views the power equipment health indexes through the industrial park intelligent management platform, and processes the power equipment fault hidden dangers, obtains the power equipment energy efficiency optimization indexes of each monitoring point, and sorts each monitoring point from large to small according to the power equipment energy efficiency optimization index values, the higher the power equipment energy efficiency optimization index, the greater the power equipment energy efficiency optimization potential, the higher the priority of the power equipment energy efficiency optimization of the monitoring point, obtains the order of the arranged power equipment monitoring points, and sends the numbers of the monitoring points to the industrial park property management personnel in turn, the property management personnel views the equipment energy consumption of the corresponding monitoring points through the industrial park intelligent management platform, and processes the high energy consumption monitoring points, through the health management priority sorting and the energy efficiency optimization hierarchical promotion, the management priority is clear, and the management efficiency is improved, so that the operation condition of the equipment can be deeply understood, the scientific and reasonable equipment maintenance plan and the energy efficiency optimization strategy are formulated, and the power consumption of the park is reduced.

[0104] Finally: the above only for the preferred embodiments of the application, and not for limiting the application, any modification, equivalent replacement, improvement, etc. within the spirit and principle of the application, should be included in the protection scope of the application.

Claims

1. A power equipment control management system based on big data analysis, characterized in that, The application relates to an industrial park intelligent management platform and method. The application comprises the following: A power equipment sensor deployment module is used for installing sensors at each monitoring point of power equipment and numbering the sensors at each monitoring point of the power equipment; A power equipment sensor data acquisition module is used for acquiring operation state data of each monitoring point of the power equipment through the sensors, and the power equipment sensor data acquisition module comprises a mechanical equipment data acquisition unit and an equipment energy consumption data acquisition unit; A power equipment health state management module is used for calculating power equipment health indexes of each monitoring point of the power equipment according to the mechanical equipment data acquired by the mechanical equipment data acquisition unit and performing power equipment health management; wherein, a power equipment health index represented as the u-th monitoring point, an equipment temperature represented as the u-th monitoring point, an equipment temperature represented as a preset, an equipment vibration frequency represented as the u-th monitoring point, a maximum value of the equipment vibration frequency, a minimum value of the equipment vibration frequency, e represents a natural constant; The calculation formula of the power equipment health index is as follows: When the equipment vibration frequency is larger, the difference between the maximum equipment vibration frequency and the current equipment vibration frequency is smaller, and the equipment temperature exceeds the preset range, the power equipment health index is higher, which indicates that the equipment has a fault, and vice versa, the power equipment health index is lower, which indicates that the equipment operation state is healthy; A power equipment energy efficiency optimization module is used for calculating power equipment energy efficiency optimization indexes of each monitoring point of the power equipment according to the equipment energy consumption data acquired by the equipment energy consumption data acquisition unit and performing power equipment energy saving optimization processing; The power equipment energy efficiency optimization module is specifically as follows: wherein, the device energy consumption stability represented as the u-th monitoring point, the device electric energy consumption represented as the u-th monitoring point, the device electric energy consumption represented as the u+1-th monitoring point, the device load rate represented as the u-th monitoring point, the device load rate represented as the u+1-th monitoring point, m represents the number of monitoring points; S61: the equipment energy consumption stability of each monitoring point is calculated through the equipment electric energy consumption and the equipment load rate: When the equipment energy consumption stability is larger, it is indicated that the equipment load rate fluctuation causes the equipment electric energy consumption to change in a large amplitude, the equipment energy consumption is obviously affected by the load rate fluctuation, and the equipment energy consumption stability is poor, and vice versa, when the equipment energy consumption stability is smaller, it is indicated that the equipment load rate fluctuation is small, the equipment electric energy consumption changes in a small amplitude, and the equipment energy consumption stability is good; wherein, a device start-stop energy stability represented as the u-th monitoring point, a device start-stop frequency represented as the u-th monitoring point; S62: the equipment start-stop energy consumption stability of each monitoring point is calculated through the equipment electric energy consumption and the equipment start-stop times: When the fluctuation degree of the equipment electric energy consumption is larger and the equipment start-stop times are more, it is indicated that the equipment electric energy consumption stability is poorer in the multiple start-stop processes, and the equipment start-stop energy consumption stability is smaller, and vice versa, it is indicated that the equipment electric energy consumption is more stable in the start-stop process; wherein, an energy efficiency optimization index of the power equipment represented as the u-th monitoring point, an energy consumption stability of the equipment represented as the u-th monitoring point, an energy consumption stability of the equipment start-stop represented as the u-th monitoring point; S63: the calculation formula of the power equipment energy efficiency optimization index is as follows: S64: the power equipment energy efficiency optimization indexes of each monitoring point of the power equipment are acquired, each monitoring point is sorted from large to small according to the power equipment energy efficiency optimization index values, when the power equipment energy efficiency optimization index is higher, the power equipment energy efficiency optimization potential is larger, the priority of the power equipment energy efficiency optimization of the monitoring point is higher, the order of the arranged power equipment monitoring points is acquired, the numbers of the monitoring points are sequentially sent to industrial park property management personnel, the property management personnel view the equipment energy consumption of the corresponding monitoring points through an industrial park intelligent management platform, and the monitoring points with high energy consumption are subjected to energy saving treatment; A power equipment fault early warning module is used for calculating power equipment fault early warning coefficients of each monitoring point of the power equipment according to the power equipment health indexes and the power equipment energy efficiency optimization indexes of each monitoring point of the power equipment. The power equipment fault evaluation module: obtains the power equipment fault early warning coefficient of each monitoring point of the power equipment, compares with the preset power equipment fault early warning coefficient, and processes; The power equipment fault evaluation module is specifically: The power equipment fault early warning coefficient of each monitoring point of the power equipment is obtained, and compared with the preset power equipment fault early warning coefficient. If the power equipment fault early warning coefficient of a monitoring point is greater than the preset power equipment fault early warning coefficient, it indicates that the power equipment operation of the monitoring point is abnormal, and the intelligent analysis process is started immediately and automatically. The number of monitoring points with abnormalities is counted, and the sensor data in the abnormal monitoring point is extracted. The sensor data is compared with the safety threshold set by the sensor. If the sensor data is greater than the safety threshold set by the sensor, it indicates that the power equipment state monitored by the sensor is abnormal. The number of sensors monitoring abnormality in the abnormal monitoring point is counted. The abnormal monitoring point number and abnormal sensor number are sent to the industrial park property management personnel. The property management personnel view the operation state of the abnormal power equipment through the industrial park intelligent management platform, obtain the fault maintenance guidance, ensure the safe and stable operation of the power system, and vice versa. It indicates that the power equipment operation of the monitoring point is normal.

2. The power equipment control management system based on big data analysis according to claim 1, characterized in that: The power equipment sensor deployment module is specifically: For setting m monitoring points according to the monitoring requirements of the power equipment, installing sensors in the monitoring points, and sequentially numbering all sensors of each monitoring point of the power equipment as 1, 2,... i,... n.

3. The power equipment control management system based on big data analysis according to claim 1, characterized in that: The power equipment sensor data acquisition module is specifically: The mechanical equipment data acquisition unit: through the temperature sensor to collect the equipment temperature of each monitoring point of the power equipment, marked as , through the vibration sensor to collect the equipment vibration frequency of each monitoring point of the power equipment, marked as , wherein u=1,2,...m, u represents the number of the u-th monitoring point; The device energy consumption data collection unit: through the intelligent electric meter, the device electric energy consumption of each monitoring point of the electric power device and the device load rate are collected, and are respectively marked as , , through the switch state sensor, the device start-stop times of each monitoring point of the electric power device are collected, and are marked as .

4. The power equipment control management system based on big data analysis according to claim 1, characterized in that: The power equipment health state management module is specifically: The power equipment health index of each monitoring point of the power equipment is obtained, and each monitoring point is sorted from large to small according to the power equipment health index value. The higher the power equipment health index, the higher the priority of the power equipment management of the monitoring point. The order of the arranged power equipment monitoring points is obtained, and the number of the monitoring points is sequentially sent to the industrial park property management personnel. The property management personnel view the power equipment health index through the industrial park intelligent management platform and process the power equipment fault hidden danger.

5. The power equipment control management system based on big data analysis according to claim 1, characterized in that: The calculation formula of the power equipment fault early warning coefficient is: wherein, a power equipment failure early warning coefficient represented as the u-th monitoring point, a power equipment health index represented as the u-th monitoring point, a power equipment energy efficiency optimization index represented as the u-th monitoring point; The higher the power equipment health index, the higher the risk of equipment failure. The higher the power equipment energy efficiency optimization index, the greater the equipment energy consumption fluctuation. The higher the power equipment health index and the power equipment energy efficiency optimization index of a monitoring point, the higher the risk of equipment operation failure. Conversely, it indicates that the equipment operation failure risk is low. 6.A power equipment control management method based on big data analysis, using the power equipment control management system based on big data analysis according to any one of claims 1-5, characterized in that: The following steps are included: Step S01: Power equipment sensor deployment: for installing sensors in each monitoring point of the power equipment, and numbering the sensors of each monitoring point of the power equipment; Step S02: Power equipment sensor data acquisition: for collecting the running state data of each monitoring point of the power equipment through the sensor. The power equipment sensor data acquisition includes mechanical equipment data acquisition substep and equipment energy consumption data acquisition substep. The running state data includes mechanical equipment data and equipment energy consumption data; Step S03: Power equipment health state management: for receiving the operation state data transmitted by the power equipment sensor data collection step, calculating the power equipment health index of each monitoring point of the power equipment according to the mechanical equipment data collected by the mechanical equipment data collection sub-step, and performing power equipment health management; Step S04: Power equipment energy efficiency optimization: for receiving the operation state data transmitted by the power equipment sensor data collection step, calculating the power equipment energy efficiency optimization index of each monitoring point of the power equipment according to the equipment energy consumption data collected by the equipment energy consumption data collection sub-step, and performing power equipment energy saving optimization processing; Step S05: Power equipment fault early warning: calculating the power equipment fault early warning coefficient of each monitoring point of the power equipment according to the power equipment health index and the power equipment energy efficiency optimization index of each monitoring point of the power equipment; Step S06: Power equipment fault evaluation: obtaining the power equipment fault early warning coefficient of each monitoring point of the power equipment, comparing with the preset power equipment fault early warning coefficient, and processing.

Images