Electrical monitoring management method and system based on intelligent analysis and early warning, and storage medium

Abstract

The invention discloses an electrical monitoring management method and system based on intelligent analysis and early warning, and a storage medium, and relates to the technical field of data processing. Firstly, characteristic parameters of the electrical monitoring sensor are counted and processed to obtain configuration information of the electrical monitoring sensor, and then the electrical monitoring sensor is pre-configured, so that the sensor is ensured to work in an optimal state, the accuracy and reliability of data acquisition are improved, and acquired historical operation data of electrical equipment are synchronously extracted and analyzed; the long-term operation trend and performance change of the equipment can be known, then the electrical equipment is monitored in real time through the pre-configured electrical monitoring sensor, a monitoring and early warning result is obtained through comprehensive analysis in combination with the operation efficiency verification value of the electrical equipment, the potential fault hidden danger of the electrical equipment can be found in time, and the safety of the electrical equipment is improved. And finally, monitoring and early warning verification is carried out according to the monitoring and early warning result of the electrical equipment, electrical monitoring and early warning verification information is obtained, and early warning is executed, so that the early warning accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an electrical monitoring management method, system and storage medium based on intelligent analysis and early warning. Background Art

[0002] In the field of electrical monitoring and management technology, with the continuous improvement of the level of industrial automation, the complexity of equipment operation and the difficulty of management have also increased. Preventing equipment failures while ensuring production efficiency has become one of the core challenges for improving production safety and stability. The operating status of electrical equipment directly affects the continuity of production and product quality. Electrical monitoring and management systems based on data acquisition have gradually been applied to real-time monitoring of electrical equipment. By intelligently analyzing the real-time data of the equipment operating status, it is possible to accurately analyze the failures of the equipment and issue early warnings, thereby achieving precise control of the equipment operating status. Prior art, such as an electrical equipment health management system and method announced in the invention patent with announcement number: CN109102189B, establishes a mapping relationship through data fitting, inputs operating parameters, and analyzes the final result according to the mapping relationship, and continuously corrects the data fitting method according to the final result, and continuously updates the data fitting method. The final result of the analysis is a secondary evaluation prediction result of health assessment and fault prediction; various sensors are installed in electrical equipment and internal data is obtained through communication, and real-time monitoring, regular inspections and other methods are used to collect data.

[0003] The prior art, such as a power plant safety production management system announced in the invention patent with announcement number: CN116629589B, belongs to the field of power system technology, and includes: a monitoring module for real-time monitoring of the working status information of various electrical equipment in the power plant and the operating status information of the staff; a data analysis module for analyzing the acquired working status information and operating status information; a production scheduling center module for managing various electrical equipment and staff based on the analysis results; an emergency management module for receiving emergency management instructions issued by the production scheduling center module, and executing emergency shutdown or startup of various electrical equipment, and reminding and regulating the behavior of staff.

[0004] In combination with the above scheme, it is found that currently in the field of electrical monitoring and management technology, only the working status information of electrical equipment is usually monitored and analyzed. However, there will be problems with inaccurate measurement of monitoring data in the electrical monitoring process, which affects the accuracy of the monitoring results. This not only affects the accuracy of the assessment of the status of electrical equipment, and fails to timely discover and deal with potential hidden faults, but may also lead to the generation of erroneous early warning signals, and there is a risk of misdiagnosis and missed diagnosis, which affects the normal operation of electrical equipment and may even cause safety accidents, which have an adverse effect on production efficiency and equipment life. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides an electrical monitoring management method, system and storage medium based on intelligent analysis and early warning, which can effectively solve the problems involved in the above-mentioned background technology.

[0006] To achieve the above objectives, the first aspect of the present invention is implemented through the following technical solutions: an electrical monitoring management method based on intelligent analysis and early warning, including counting the characteristic parameters of the electrical monitoring sensor, processing the electrical monitoring sensor configuration information, pre-configuring the electrical monitoring sensor based on the electrical monitoring sensor configuration information, and starting the electrical equipment monitoring signal.

[0007] The historical operation data of the electrical equipment collected by the electrical monitoring central controller is extracted synchronously, and the operation efficiency verification value of the electrical equipment is obtained based on the analysis of the historical operation data of the electrical equipment.

[0008] The electrical monitoring central controller receives the electrical equipment monitoring signal and monitors the electrical equipment in real time through the pre-configured electrical monitoring sensors. Combined with the operating efficiency verification value of the electrical equipment, a comprehensive analysis is performed to obtain the monitoring and early warning results of the electrical equipment.

[0009] Monitoring and early warning verification is performed according to the monitoring and early warning results of the electrical equipment to obtain electrical monitoring and early warning verification information, and early warning configuration is executed based on the electrical monitoring and early warning verification information.

[0010] Furthermore, the processing obtains the configuration information of the electrical monitoring sensor, and the specific process is: the characteristic parameters of the electrical monitoring sensor include the performance attenuation coefficient of the electrical monitoring sensor, the power supply fluctuation coefficient of the electrical monitoring sensor, the signal strength deviation value of the electrical monitoring sensor and the average resolution of the electrical monitoring sensor.

[0011] Based on the processing of characteristic parameters of the electrical monitoring sensor, an abnormality evaluation value of the electrical monitoring sensor is obtained, and the abnormality evaluation value of the electrical monitoring sensor is used to comprehensively quantify the abnormal state and performance degradation degree of the electrical monitoring sensor during the monitoring process.

[0012] The electrical monitoring sensor configuration information includes executing sensor adjustment and not executing sensor adjustment.

[0013] The electrical monitoring sensor abnormality assessment value is compared with the set electrical monitoring sensor abnormality assessment threshold. If the electrical monitoring sensor abnormality assessment value is lower than the set electrical monitoring sensor abnormality assessment threshold, the electrical monitoring sensor configuration information is marked as not performing sensor adjustment; otherwise, the electrical monitoring sensor configuration information is marked as performing sensor adjustment.

[0014] Furthermore, the electrical monitoring sensor is pre-configured based on the electrical monitoring sensor configuration information, and the specific process is: extract the electrical monitoring sensor configuration information; if the electrical monitoring sensor configuration information is not to perform sensor adjustment, then continue to pre-configure the electrical monitoring sensor with the current electrical monitoring sensor configuration parameters; if the electrical monitoring sensor configuration information is to perform sensor adjustment, then add the current electrical monitoring sensor configuration parameters to the set electrical monitoring sensor configuration parameter supplementary values ​​as the electrical monitoring sensor configuration target adjustment parameters, and adjust the electrical monitoring sensor configuration parameters up to the electrical monitoring sensor configuration target adjustment parameters.

[0015] Furthermore, the operation efficiency verification value of the electrical equipment is obtained based on the analysis of the historical operation data of the electrical equipment. The specific analysis process is: the historical operation data of the electrical equipment includes the historical average energy consumption value of the electrical equipment, the average displacement value of the components of the electrical equipment, the historical highest speed deviation coefficient of the electrical equipment and the historical highest electromagnetic interference coefficient of the electrical equipment.

[0016] A comprehensive analysis is performed on the historical data of the electrical equipment to obtain an operating efficiency verification value of the electrical equipment, and the operating efficiency verification value of the electrical equipment is used to comprehensively quantify the overall operating efficiency and health status of the electrical equipment.

[0017] Furthermore, the real-time monitoring of electrical equipment by pre-configured electrical monitoring sensors is specifically carried out as follows: real-time monitoring of electrical equipment by pre-configured electrical monitoring sensors is carried out to obtain real-time monitoring data of electrical equipment, and the real-time monitoring data of electrical equipment includes the electromagnetic interference coefficient, magnetic flux deviation coefficient, vibration frequency, input voltage and input power supply frequency deviation coefficient of the electrical equipment.

[0018] Based on the real-time monitoring data of the electrical equipment, an abnormal operation index of the electrical equipment is obtained, and the abnormal operation index of the electrical equipment is used to comprehensively quantify the real-time operation status and abnormality degree of the electrical equipment.

[0019] Furthermore, the comprehensive analysis obtains monitoring and early warning results of the electrical equipment, and the specific process is: the monitoring and early warning results of the electrical equipment include abnormal operation of the electrical equipment and normal operation of the electrical equipment.

[0020] The abnormal operation verification value of the electrical equipment is obtained by matching the operation efficiency verification value of the electrical equipment, and the abnormal operation index of the electrical equipment is compared with the abnormal operation verification value of the electrical equipment. If the abnormal operation index of the electrical equipment is higher than the abnormal operation verification value of the electrical equipment, the monitoring and early warning result of the electrical equipment is marked as abnormal operation of the electrical equipment; otherwise, the monitoring and early warning result of the electrical equipment is marked as normal operation of the electrical equipment.

[0021] Furthermore, the electrical monitoring and early warning verification information is obtained, and the early warning configuration is executed based on the electrical monitoring and early warning verification information. The specific process is: extract the monitoring and early warning results of the electrical equipment. If the monitoring and early warning results of the electrical equipment are that the electrical equipment is operating normally, no early warning prompt is issued. If the monitoring and early warning results of the electrical equipment are that the electrical equipment is operating abnormally, verification is performed based on the electrical equipment monitoring and early warning results to obtain the electrical equipment monitoring and early warning verification data. The electrical equipment monitoring and early warning verification data includes the thermal distribution anomaly coefficient on the surface of the electrical equipment, the surface structure anomaly coefficient of the electrical equipment, the average voltage of the electrical equipment, and the communication transmission influence coefficient of the electrical equipment.

[0022] A comprehensive analysis is performed on the electrical equipment monitoring and early warning verification data to obtain an early warning verification value of the electrical equipment, and the early warning verification value of the electrical equipment is used to comprehensively quantify the accuracy of monitoring and early warning.

[0023] The electrical monitoring early warning verification information includes electrical equipment failure, electrical equipment abnormality and electrical equipment normality.

[0024] The early warning verification value of the electrical equipment is compared with the set early warning verification first threshold. If the early warning verification value of the electrical equipment is higher than the set early warning verification threshold, the electrical monitoring early warning verification information is marked as an electrical equipment failure, and the electrical monitoring early warning verification information is sent to the management terminal for early warning prompts.

[0025] If the early warning verification value of the electrical equipment is lower than or equal to the set early warning verification first threshold, the early warning verification value of the electrical equipment is compared with the set early warning verification second threshold. If the early warning verification value of the electrical equipment is higher than the set early warning verification second threshold, the electrical monitoring early warning verification information is marked as an electrical equipment abnormality. At the same time, the difference between the early warning verification value of the electrical equipment and the set early warning verification second threshold is recorded as the electrical equipment configuration adjustment control value, and the electrical equipment monitoring configuration parameters are obtained according to the electrical equipment configuration adjustment control value. The electrical equipment is monitored and configured based on the electrical equipment monitoring configuration parameters.

[0026] If the early warning verification value of the electrical equipment is lower than or equal to the set early warning verification second threshold, the electrical monitoring early warning verification information is marked as the electrical equipment is normal, and the electrical equipment continues to be monitored and configured with the current electrical equipment monitoring configuration parameters.

[0027] Furthermore, the early warning verification value of the electrical equipment has specific analysis conditions as follows:

[0028] ; In the formula, Indicates the early warning verification value of electrical equipment. Indicates the thermal distribution anomaly coefficient on the surface of electrical equipment. Indicates the verification correction factor corresponding to the thermal distribution anomaly coefficient of the set electrical equipment surface, Indicates the abnormal coefficient of the surface structure of electrical equipment, Indicates the verification correction factor corresponding to the set electrical equipment surface structure abnormality coefficient, Indicates the average voltage of electrical equipment. Indicates the reference voltage of the set electrical equipment. Indicates the communication transmission influence coefficient of electrical equipment, It represents the verification correction factor corresponding to the communication transmission influence coefficient of the set electrical equipment, and e represents a natural constant.

[0029] like Figure 2 The second aspect of the present invention provides an electrical monitoring and management system based on intelligent analysis and early warning, including: a preconfiguration module, which is used to count the characteristic parameters of the electrical monitoring sensor, process the electrical monitoring sensor configuration information, preconfigure the electrical monitoring sensor based on the electrical monitoring sensor configuration information, and start the electrical equipment monitoring signal.

[0030] The historical data analysis module is used to synchronously extract the historical operation data of the electrical equipment collected by the electrical monitoring central controller, and obtain the operation efficiency verification value of the electrical equipment based on the historical operation data analysis of the electrical equipment.

[0031] The real-time monitoring and analysis module is used for the electrical monitoring central controller to receive the electrical equipment monitoring signal, and to monitor the electrical equipment in real time through the pre-configured electrical monitoring sensors. Combined with the operation efficiency verification value of the electrical equipment, a comprehensive analysis is performed to obtain the monitoring and early warning results of the electrical equipment.

[0032] The early warning execution verification module is used to perform monitoring early warning verification according to the monitoring early warning results of the electrical equipment, obtain electrical monitoring early warning verification information, and execute early warning configuration based on the electrical monitoring early warning verification information.

[0033] A third aspect of the present invention provides a storage medium for storing a program, which, when executed by a processor, implements the above-mentioned electrical monitoring and management method based on intelligent analysis and early warning.

[0034] The present invention has the following beneficial effects:

[0035] (1) The present invention provides an electrical monitoring management method, system and storage medium based on intelligent analysis and early warning. First, the electrical monitoring sensor is pre-configured to achieve adaptive adjustment of electrical monitoring, and the historical operation data of the electrical equipment is simultaneously extracted to provide data support for subsequent equipment maintenance. Then, the electrical equipment is monitored in real time to reduce the possibility of sudden failures of the electrical equipment. Finally, the monitoring and early warning are verified and the early warning is executed based on the monitoring and early warning results of the electrical equipment.

[0036] (2) The present invention pre-configures the electrical monitoring sensor by processing the configuration information of the electrical monitoring sensor, which can timely detect the abnormality of the electrical monitoring sensor and realize the refined pre-configuration of the electrical monitoring sensor, thereby improving the performance and reliability of electrical monitoring, ensuring that the sensor maintains the best working state in a complex and changeable working environment, effectively reducing data acquisition errors, and enhancing the stability and sensitivity of the monitoring signal.

[0037] (3) The present invention monitors electrical equipment in real time through pre-configured electrical monitoring sensors, and obtains monitoring and early warning results of the electrical equipment through comprehensive analysis, thereby achieving accurate early warning of the status of the electrical equipment, improving the maintenance capability of the monitoring system, ensuring that the equipment operates at a state close to the optimal efficiency, and reducing maintenance costs and downtime. The combination of real-time monitoring and historical data analysis provides strong data support for equipment health management, thereby enhancing the reliability and service life of the electrical equipment.

[0038] (4) The present invention performs monitoring and early warning verification according to the monitoring and early warning results of the electrical equipment to obtain electrical monitoring and early warning verification information, and executes early warning configuration based on the electrical monitoring and early warning verification information, thereby improving the intelligent management level of the electrical equipment, ensuring the accuracy and timeliness of the early warning system, effectively avoiding false alarms and missed alarms, enhancing the ability to handle equipment failures, providing a scientific basis for preventive maintenance, and being able to dynamically adjust according to the actual operating conditions of the equipment, thereby improving the flexibility of the electrical equipment.

[0039] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic diagram of the method flow of the present invention.

[0041] Figure 2 It is a schematic diagram of system module connection of the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] See also Figure 1As shown, the first aspect of an embodiment of the present invention provides a technical solution: an electrical monitoring management method based on intelligent analysis and early warning, including counting characteristic parameters of electrical monitoring sensors, processing to obtain electrical monitoring sensor configuration information, pre-configuring the electrical monitoring sensors based on the electrical monitoring sensor configuration information, and starting electrical equipment monitoring signals.

[0044] The historical operation data of the electrical equipment collected by the electrical monitoring central controller is extracted synchronously, and the operation efficiency verification value of the electrical equipment is obtained based on the analysis of the historical operation data of the electrical equipment.

[0045] The electrical monitoring central controller receives the electrical equipment monitoring signal and monitors the electrical equipment in real time through the pre-configured electrical monitoring sensors. Combined with the operating efficiency verification value of the electrical equipment, a comprehensive analysis is performed to obtain the monitoring and early warning results of the electrical equipment.

[0046] Monitoring and early warning verification is performed according to the monitoring and early warning results of the electrical equipment to obtain electrical monitoring and early warning verification information, and early warning configuration is executed based on the electrical monitoring and early warning verification information.

[0047] Specifically, the configuration information of the electrical monitoring sensor is processed, and the specific process is: the characteristic parameters of the electrical monitoring sensor include the performance attenuation coefficient of the electrical monitoring sensor, the power supply fluctuation coefficient of the electrical monitoring sensor, the signal strength deviation value of the electrical monitoring sensor and the average resolution of the electrical monitoring sensor.

[0048] It should be noted that the performance attenuation coefficient of the electrical monitoring sensor refers to the degree of performance decline of the sensor relative to the initial performance after long-term use. The absolute value of the difference between the output signal strength of the sensor and the standard signal strength within a certain period of time is divided by the standard signal strength of the sensor, and the obtained value is used as the performance attenuation coefficient of the electrical monitoring sensor. The power supply fluctuation coefficient of the electrical monitoring sensor refers to the ratio of the maximum power supply voltage fluctuation value to the average power supply voltage within a certain period of time. A voltage monitoring instrument can be used to record the power supply voltage within a period of time, and the maximum and minimum voltage values ​​can be found from the recorded data. The difference between the maximum voltage value and the rated voltage value is recorded as the maximum positive fluctuation value, and the difference between the rated voltage value and the minimum voltage value is recorded as the maximum negative fluctuation value. The larger of the absolute values ​​of the maximum positive fluctuation value and the maximum negative fluctuation value is taken. The maximum value is used to obtain the maximum fluctuation value of the power supply voltage, and the recorded power supply voltage is averaged to obtain the average power supply voltage, thereby obtaining the power supply fluctuation coefficient of the electrical monitoring sensor. The signal strength deviation value of the electrical monitoring sensor refers to the absolute value of the difference between the strength of the output signal of the electrical monitoring sensor and the standard signal strength within a certain period of time after the unit is removed. A signal analyzer can be used to measure the output signal of the sensor to obtain the signal strength deviation value of the electrical monitoring sensor. The average resolution of the electrical monitoring sensor refers to the average value of the minimum input change that all electrical monitoring sensors can distinguish after the unit is removed, which can be measured by a high-precision signal generator. For example, the average resolution of the electrical monitoring sensor is 0.1, which means that the electrical monitoring sensor can distinguish changes within 0.1.

[0049] Based on the processing of characteristic parameters of the electrical monitoring sensor, an abnormality evaluation value of the electrical monitoring sensor is obtained, and the abnormality evaluation value of the electrical monitoring sensor is used to comprehensively quantify the abnormal state and performance degradation degree of the electrical monitoring sensor during the monitoring process.

[0050] In this embodiment, the abnormal evaluation value of the electrical monitoring sensor can be obtained by the following analysis method, and the specific analysis conditions are as follows:

[0051] ;

[0052] In the formula, Indicates the abnormal evaluation value of the electrical monitoring sensor. Indicates the performance attenuation coefficient of the electrical monitoring sensor, Indicates the sensor abnormality assessment factor corresponding to the set performance attenuation coefficient, Indicates the power supply fluctuation coefficient of the electrical monitoring sensor, Indicates the sensor abnormality assessment factor corresponding to the set power supply fluctuation coefficient. Indicates the signal strength deviation value of the electrical monitoring sensor. Indicates the sensor abnormality assessment factor corresponding to the set unit signal strength deviation value. represents the average resolution of electrical monitoring sensors, Indicates the set reference resolution.

[0053] It should be added that, in this embodiment, the sensor abnormality assessment factor corresponding to the preset performance attenuation coefficient, the sensor abnormality assessment factor corresponding to the power supply fluctuation coefficient and the sensor abnormality assessment factor corresponding to the unit signal strength deviation value are obtained from the electrical monitoring management database.

[0054] It needs to be explained that the sensor abnormality assessment factors corresponding to the performance attenuation coefficient, the power supply fluctuation coefficient and the unit signal strength deviation value are respectively used to adjust the importance of the performance attenuation coefficient of the electrical monitoring sensor, the power supply fluctuation coefficient of the electrical monitoring sensor and the signal strength deviation value of the electrical monitoring sensor in the process of analyzing the abnormality assessment value of the electrical monitoring sensor. For example, there is a pre-set mapping relationship between the real-time electrical monitoring sensor characteristic parameters and the corresponding sensor abnormality assessment factors in the electrical monitoring management database. The sensor abnormality assessment factors corresponding to the real-time electrical monitoring sensor characteristic parameters can be matched through the pre-set mapping relationship. The performance attenuation coefficient of the electrical monitoring sensor, the power supply fluctuation coefficient of the electrical monitoring sensor and the signal strength deviation value of the electrical monitoring sensor are respectively matched with the pre-set mapping relationship to obtain the sensor abnormality assessment factors corresponding to the performance attenuation coefficient, the power supply fluctuation coefficient and the unit signal strength deviation value.

[0055] In this implementation scheme, the performance attenuation coefficient of the electrical monitoring sensor, the power supply fluctuation coefficient of the electrical monitoring sensor, the signal strength deviation value of the electrical monitoring sensor and the average resolution of the electrical monitoring sensor are correlated and do not exist independently. For example, the performance attenuation coefficient usually has a certain relationship with the power supply fluctuation coefficient. Power supply fluctuation will affect the stability of the sensor during operation, thereby exacerbating performance attenuation. A large performance attenuation coefficient may cause signal strength changes, thereby increasing the signal strength deviation value. A large performance attenuation coefficient may also cause the average resolution of the sensor to decrease. The signal strength deviation value of the electrical monitoring sensor is directly related to the quality of the sensor output signal. A large signal strength deviation value usually means that the signal is interfered with during transmission, affecting the average resolution of the electrical monitoring sensor. A low resolution will result in insufficient capture of signal changes, affecting the accuracy of the final measurement results. Comprehensive analysis of the electrical monitoring sensor abnormality assessment value can accurately assess the abnormal state of the sensor, thereby ensuring the accuracy of the monitoring data and the stability of the electrical monitoring sensor.

[0056] The electrical monitoring sensor configuration information includes executing sensor adjustment and not executing sensor adjustment.

[0057] The electrical monitoring sensor abnormality assessment value is compared with the set electrical monitoring sensor abnormality assessment threshold. If the electrical monitoring sensor abnormality assessment value is lower than the set electrical monitoring sensor abnormality assessment threshold, the electrical monitoring sensor configuration information is marked as not performing sensor adjustment; otherwise, the electrical monitoring sensor configuration information is marked as performing sensor adjustment.

[0058] Specifically, the electrical monitoring sensor is pre-configured based on the electrical monitoring sensor configuration information, and the specific process is: extract the electrical monitoring sensor configuration information; if the electrical monitoring sensor configuration information is to not perform sensor adjustment, then continue to pre-configure the electrical monitoring sensor with the current electrical monitoring sensor configuration parameters; if the electrical monitoring sensor configuration information is to perform sensor adjustment, then add the current electrical monitoring sensor configuration parameters to the set electrical monitoring sensor configuration parameter supplementary values ​​as the electrical monitoring sensor configuration target adjustment parameters, and adjust the electrical monitoring sensor configuration parameters up to the electrical monitoring sensor configuration target adjustment parameters.

[0059] It should be noted that the electrical monitoring sensor configuration parameters include the cutoff frequency of the filter, the baud rate of the communication transmission and the self-test period. The analysis of the characteristic parameters of the electrical monitoring sensor can reflect the working status and performance level of the electrical monitoring sensor. If the performance of the electrical monitoring sensor is unqualified and the subsequent electrical equipment monitoring is carried out with the original default electrical monitoring sensor configuration parameters, the accuracy of the monitoring data may be reduced, and even key fault warning signals may be missed. Therefore, it is necessary to monitor and configure the electrical monitoring sensor in combination with the electrical monitoring sensor configuration parameters to ensure that the sensor can operate in the best state and provide reliable data support for subsequent electrical equipment monitoring.

[0060] Specifically, the operating efficiency verification value of the electrical equipment is obtained based on the analysis of the historical operating data of the electrical equipment. The specific analysis process is: the historical operating data of the electrical equipment includes the historical average energy consumption value of the electrical equipment, the average displacement value of the components of the electrical equipment, the historical highest speed deviation coefficient of the electrical equipment and the historical highest electromagnetic interference coefficient of the electrical equipment.

[0061] It should be noted that the historical average energy consumption value of electrical equipment refers to the average value of the energy consumption of electrical equipment within a certain time range, which can be measured by an electric energy meter. The component displacement average value of electrical equipment refers to the average value of the displacement of all components of the electrical equipment relative to the initial position, which can be measured by a displacement sensor to obtain the displacement of each component relative to the initial position, and then the component displacement average value of the electrical equipment is obtained by mean processing. The historical maximum speed deviation coefficient of electrical equipment refers to the value after the unit is removed from the highest deviation value between the actual speed of the historical electrical equipment and the set speed, which can be measured by a speed sensor to obtain the actual speed of the electrical equipment, and then the historical maximum speed deviation coefficient of the electrical equipment is obtained. The historical maximum electromagnetic interference coefficient of electrical equipment refers to the highest ratio of the electromagnetic interference intensity generated by the historical electrical equipment during operation to the electromagnetic interference intensity during normal operation, which can be measured by an electromagnetic interference testing instrument to obtain the electromagnetic interference intensity, and then the historical maximum electromagnetic interference coefficient of the electrical equipment is obtained.

[0062] A comprehensive analysis is performed on the historical data of the electrical equipment to obtain an operating efficiency verification value of the electrical equipment, and the operating efficiency verification value of the electrical equipment is used to comprehensively quantify the overall operating efficiency and health status of the electrical equipment.

[0063] In this embodiment, the operating efficiency verification value of the electrical equipment can be obtained by the following analysis method, and the specific analysis conditions are as follows:

[0064] ;

[0065] In the formula, Indicates the operating efficiency verification value of electrical equipment. Indicates the historical average energy consumption value of electrical equipment. Indicates the set reference energy consumption value. Indicates the average displacement of components of electrical equipment, It indicates the operating efficiency verification correction factor corresponding to the set average displacement value of the unit component. Indicates the historical maximum speed deviation coefficient of the electrical equipment. Indicates the operating efficiency verification correction factor corresponding to the set historical maximum speed deviation coefficient. Indicates the highest electromagnetic interference coefficient in history for electrical equipment, It represents the operating performance verification correction factor corresponding to the set historical highest electromagnetic interference coefficient, and e represents a natural constant.

[0066] It should be added that, in this embodiment, the operating performance verification correction factor corresponding to the preset unit component displacement average value, the operating performance verification correction factor corresponding to the historical highest speed deviation coefficient, and the operating performance verification correction factor corresponding to the historical highest electromagnetic interference coefficient are obtained from the electrical monitoring management database.

[0067] It needs to be explained that the operating efficiency verification correction factors corresponding to the unit component displacement average value, the historical maximum speed deviation coefficient and the historical maximum electromagnetic interference coefficient are respectively used to adjust the importance of the component displacement average value of electrical equipment, the historical maximum speed deviation coefficient of electrical equipment and the historical maximum electromagnetic interference coefficient of electrical equipment in the process of analyzing and obtaining the operating efficiency verification value of electrical equipment. For example, there is a pre-set mapping relationship between the historical operating data of electrical equipment and the corresponding operating efficiency verification correction factors in the electrical monitoring management database. The pre-set mapping relationship can be used to match the operating efficiency verification correction factors corresponding to the real-time historical operating data of the electrical equipment. The component displacement average value of the electrical equipment, the historical maximum speed deviation coefficient of the electrical equipment and the historical maximum electromagnetic interference coefficient of the electrical equipment are respectively matched with the pre-set mapping relationship to obtain the operating efficiency verification correction factors corresponding to the unit component displacement average value, the historical maximum speed deviation coefficient and the historical maximum electromagnetic interference coefficient.

[0068] It should be noted that an increase in the absolute value of the difference between the historical average energy consumption value of electrical equipment and the set reference energy consumption value, the average displacement of components of electrical equipment, the historical maximum speed deviation coefficient of electrical equipment, and the historical maximum electromagnetic interference coefficient of electrical equipment means that there is a problem with the equipment and its operating efficiency is reduced. Therefore, the operating efficiency verification value of the electrical equipment will become smaller.

[0069] In this implementation scheme, the historical average energy consumption value of the electrical equipment, the average displacement value of the components of the electrical equipment, the historical maximum speed deviation coefficient of the electrical equipment and the historical maximum electromagnetic interference coefficient of the electrical equipment are correlated with each other, and do not exist independently. For example, the average energy consumption value and the maximum speed deviation coefficient of the electrical equipment show a certain negative correlation. When the maximum speed deviation coefficient is large, more energy is usually required to maintain the operation of the equipment, resulting in an increase in the average energy consumption value. The larger average displacement value of the components may cause mechanical vibration and irregular operation, thereby increasing the maximum electromagnetic interference coefficient and affecting the stability of the electrical equipment. Comprehensive analysis to obtain the operating efficiency verification value of the electrical equipment can more accurately identify the potential failure risks and performance changes of the electrical equipment and improve the operating efficiency of the electrical equipment.

[0070] Specifically, the electrical equipment is monitored in real time by means of pre-configured electrical monitoring sensors, and the specific process is: the electrical equipment is monitored in real time by means of pre-configured electrical monitoring sensors to obtain real-time monitoring data of the electrical equipment, and the real-time monitoring data of the electrical equipment includes the electromagnetic interference coefficient, magnetic flux deviation coefficient, vibration frequency, input voltage and input power supply frequency deviation coefficient of the electrical equipment.

[0071] It should be noted that the electromagnetic interference coefficient of electrical equipment refers to the maximum ratio of the electromagnetic interference intensity generated by the electrical equipment during operation to the electromagnetic interference intensity during normal operation. The electromagnetic interference intensity can be measured using an electromagnetic interference test instrument to obtain the electromagnetic interference coefficient of the electrical equipment. The magnetic flux deviation coefficient of the electrical equipment is used to evaluate the magnetic performance stability of the electrical equipment. The absolute value of the difference between the actual magnetic flux of the electrical equipment and the rated magnetic flux is measured by a fluxmeter, and then the absolute value is divided by the rated magnetic flux to obtain the magnetic flux deviation coefficient. The vibration frequency and input voltage can be measured and analyzed using a vibration sensor and a voltmeter respectively. The input power supply frequency deviation coefficient can be obtained by measuring the absolute value of the difference between the actual power supply frequency and the standard frequency using a power quality analyzer, and then dividing the absolute value by the standard frequency.

[0072] Based on the real-time monitoring data of the electrical equipment, an abnormal operation index of the electrical equipment is obtained, and the abnormal operation index of the electrical equipment is used to comprehensively quantify the real-time operation status and abnormality degree of the electrical equipment.

[0073] In this embodiment, the abnormal operation index of the electrical equipment can be obtained by the following analysis method, and the specific analysis conditions are as follows:

[0074] ;

[0075] In the formula, Indicates the abnormal operation index of electrical equipment. Indicates the electromagnetic interference coefficient of electrical equipment, Indicates the operation abnormality assessment factor corresponding to the set electromagnetic interference coefficient. Indicates the magnetic flux deviation coefficient of electrical equipment, Indicates the operation abnormality assessment factor corresponding to the set magnetic flux deviation coefficient, Indicates the vibration frequency of electrical equipment. Indicates the set reference vibration frequency, Indicates the input voltage of electrical equipment. Indicates the set reference input voltage, Indicates the input power frequency deviation coefficient, Indicates the operation abnormality assessment factor corresponding to the set input power frequency deviation coefficient.

[0076] It should be added that, in this embodiment, the operation abnormality assessment factor corresponding to the preset electromagnetic interference coefficient, the operation abnormality assessment factor corresponding to the magnetic flux deviation coefficient, and the operation abnormality assessment factor corresponding to the input power supply frequency deviation coefficient are obtained from the electrical monitoring management database.

[0077] It needs to be explained that the operation abnormality assessment factors corresponding to the electromagnetic interference coefficient, magnetic flux deviation coefficient and input power frequency deviation coefficient are respectively used to adjust the importance of the electromagnetic interference coefficient, magnetic flux deviation coefficient and input power frequency deviation coefficient of the electrical equipment in the process of analyzing the abnormal operation index of the electrical equipment. For example, there is a pre-set mapping relationship between the real-time monitoring data of the electrical equipment and the corresponding operation abnormality assessment factors in the electrical monitoring management database. The operation abnormality assessment factors corresponding to the real-time monitoring data of the real-time electrical equipment can be matched through the pre-set mapping relationship. The electromagnetic interference coefficient, magnetic flux deviation coefficient and input power frequency deviation coefficient of the electrical equipment are respectively matched with the pre-set mapping relationship to obtain the operation abnormality assessment factors corresponding to the electromagnetic interference coefficient, magnetic flux deviation coefficient and input power frequency deviation coefficient.

[0078] In this implementation scheme, the electromagnetic interference coefficient, magnetic flux deviation coefficient, vibration frequency, input voltage and input power frequency deviation coefficient of the electrical equipment are correlated and do not exist independently. For example, if the input power frequency deviation coefficient is large, the speed and load of the motor may be unstable, resulting in fluctuations in the magnetic field of the motor, which in turn affects the electromagnetic interference coefficient of the electrical equipment. The deviation of the input voltage and the input power frequency deviation coefficient may affect the electromagnetic interference coefficient of the equipment at the same time, and the vibration frequency may aggravate the electromagnetic interference coefficient by inducing resonance of the electrical system. The deviation of the input power frequency may cause changes in the input current of the equipment when the frequency fluctuation of the power supply grid is large, thereby causing input voltage fluctuations. Comprehensive analysis can be used to obtain the abnormal operation index of the electrical equipment, which can more accurately analyze the abnormal operation of the equipment, thereby achieving comprehensive monitoring of the status of the electrical equipment, providing more targeted maintenance measures, and helping to extend the life of the equipment and improve operating efficiency.

[0079] Specifically, the monitoring and early warning results of the electrical equipment are obtained through comprehensive analysis, and the specific process is: the monitoring and early warning results of the electrical equipment include abnormal operation of the electrical equipment and normal operation of the electrical equipment.

[0080] The abnormal operation verification value of the electrical equipment is obtained by matching the operation efficiency verification value of the electrical equipment, and the abnormal operation index of the electrical equipment is compared with the abnormal operation verification value of the electrical equipment. If the abnormal operation index of the electrical equipment is higher than the abnormal operation verification value of the electrical equipment, the monitoring and early warning result of the electrical equipment is marked as abnormal operation of the electrical equipment; otherwise, the monitoring and early warning result of the electrical equipment is marked as normal operation of the electrical equipment.

[0081] It should be supplemented that, the abnormal operation verification value of the electrical equipment is obtained by matching the operation efficiency verification value of the electrical equipment, and the operation efficiency verification value of the electrical equipment is matched with the abnormal operation verification value corresponding to the operation efficiency verification value interval of each electrical equipment stored in the electrical monitoring management database, and the abnormal operation verification value corresponding to the interval in which the operation efficiency verification value of the electrical equipment is located is counted and recorded as the abnormal operation verification value of the electrical equipment.

[0082] It should be added that matching the abnormal operation verification value of electrical equipment with the operation efficiency verification value of electrical equipment can help to make real-time adjustments to the early warning standards based on actual usage, and thus accurately judge the abnormal operation status of the equipment. If the operation efficiency verification value of the electrical equipment is small, it means that there is a problem with the electrical equipment, and the matched abnormal operation verification value of the electrical equipment is also small, which helps to discover potential problems with the equipment in a timely manner and make fault warnings more accurate.

[0083] Specifically, electrical monitoring and early warning verification information is obtained, and early warning configuration is performed based on the electrical monitoring and early warning verification information. The specific process is: extract the monitoring and early warning results of the electrical equipment. If the monitoring and early warning results of the electrical equipment are that the electrical equipment is operating normally, no early warning prompt is issued. If the monitoring and early warning results of the electrical equipment are that the electrical equipment is operating abnormally, verification is performed based on the electrical equipment monitoring and early warning results to obtain electrical equipment monitoring and early warning verification data. The electrical equipment monitoring and early warning verification data includes the thermal distribution anomaly coefficient on the surface of the electrical equipment, the surface structure anomaly coefficient of the electrical equipment, the average voltage of the electrical equipment, and the communication transmission influence coefficient of the electrical equipment.

[0084] It should be noted that the thermal distribution anomaly coefficient on the surface of electrical equipment refers to the value of the deviation between the average temperature of the surface of the electrical equipment and the average temperature under normal working conditions after removing the unit. The thermal distribution anomaly coefficient can be obtained by using a thermal imaging camera combined with data image processing technology. The surface structural anomaly coefficient of electrical equipment refers to the number of cracks on the surface of the electrical equipment after removing the unit. The number of cracks on the surface of the electrical equipment can be measured by a three-dimensional scanner. The average voltage of the electrical equipment refers to the average value of the working voltage of the electrical equipment within a certain period of time. The communication transmission influence coefficient of the electrical equipment refers to the frequency of restricted data transmission or control of the wind turbine set due to communication transmission delays or interruptions. The communication transmission influence coefficient can be obtained by recording the communication interruption time through SCADA (supervisory control and data acquisition system), and the ratio of the communication interruption time to the total monitoring time is used as the communication transmission influence coefficient.

[0085] A comprehensive analysis is performed on the electrical equipment monitoring and early warning verification data to obtain an early warning verification value of the electrical equipment, and the early warning verification value of the electrical equipment is used to comprehensively quantify the accuracy of monitoring and early warning.

[0086] Specifically, the early warning verification value of electrical equipment has the following specific analysis conditions:

[0087] ;

[0088] In the formula, Indicates the early warning verification value of electrical equipment. Indicates the thermal distribution anomaly coefficient on the surface of electrical equipment. Indicates the verification correction factor corresponding to the thermal distribution anomaly coefficient of the set electrical equipment surface, Indicates the abnormal coefficient of the surface structure of electrical equipment, Indicates the verification correction factor corresponding to the set electrical equipment surface structure abnormality coefficient, Indicates the average voltage of electrical equipment. Indicates the reference voltage of the set electrical equipment. Indicates the communication transmission influence coefficient of electrical equipment, It represents the verification correction factor corresponding to the communication transmission influence coefficient of the set electrical equipment, and e represents a natural constant.

[0089] It should be added that, in this embodiment, the verification correction factor corresponding to the preset thermal distribution anomaly coefficient of the electrical equipment surface, the verification correction factor corresponding to the electrical equipment surface structure anomaly coefficient and the verification correction factor corresponding to the communication transmission influence coefficient of the electrical equipment are obtained from the electrical monitoring management database.

[0090] It needs to be explained that the verification correction factors corresponding to the thermal distribution anomaly coefficient on the surface of electrical equipment, the structural anomaly coefficient on the surface of electrical equipment and the communication transmission influence coefficient of electrical equipment are respectively used to adjust the importance of the thermal distribution anomaly coefficient on the surface of electrical equipment, the structural anomaly coefficient on the surface of electrical equipment and the communication transmission influence coefficient of electrical equipment in the process of analyzing and obtaining the early warning verification value of electrical equipment. For example, there is a pre-set mapping relationship between the electrical equipment monitoring and early warning verification data and the corresponding verification correction factors in the electrical monitoring management database. The verification correction factors corresponding to the real-time electrical equipment monitoring and early warning verification data can be matched through the pre-set mapping relationship. The thermal distribution anomaly coefficient on the surface of electrical equipment, the structural anomaly coefficient on the surface of electrical equipment and the communication transmission influence coefficient of electrical equipment are respectively matched with the pre-set mapping relationship to obtain the verification correction factors corresponding to the thermal distribution anomaly coefficient on the surface of electrical equipment, the structural anomaly coefficient on the surface of electrical equipment and the communication transmission influence coefficient of electrical equipment.

[0091] In this implementation scheme, there is a correlation between the thermal distribution anomaly coefficient on the surface of the electrical equipment, the surface structure anomaly coefficient of the electrical equipment, the average voltage of the electrical equipment and the communication transmission influence coefficient of the electrical equipment, and they do not exist independently. For example, the thermal distribution anomaly coefficient may be related to the average voltage of the electrical equipment. When the voltage is unstable or overloaded, the electrical equipment will generate too much heat. The voltage abnormality may directly affect the thermal distribution anomaly coefficient of the equipment. Too high an average voltage may cause the equipment to overheat, and unstable voltage may cause signal quality to deteriorate, affecting the communication transmission influence coefficient of the electrical equipment. Comprehensive analysis can obtain the early warning verification value of the electrical equipment, which can quantify the operating health of the equipment and reduce the possibility of false alarms or missed alarms.

[0092] The electrical monitoring early warning verification information includes electrical equipment failure, electrical equipment abnormality and electrical equipment normality.

[0093] The early warning verification value of the electrical equipment is compared with the set early warning verification first threshold. If the early warning verification value of the electrical equipment is higher than the set early warning verification threshold, the electrical monitoring early warning verification information is marked as an electrical equipment failure, and the electrical monitoring early warning verification information is sent to the management terminal for early warning prompts.

[0094] If the early warning verification value of the electrical equipment is lower than or equal to the set early warning verification first threshold, the early warning verification value of the electrical equipment is compared with the set early warning verification second threshold. If the early warning verification value of the electrical equipment is higher than the set early warning verification second threshold, the electrical monitoring early warning verification information is marked as an electrical equipment abnormality. At the same time, the difference between the early warning verification value of the electrical equipment and the set early warning verification second threshold is recorded as the electrical equipment configuration adjustment control value, and the electrical equipment monitoring configuration parameters are obtained according to the electrical equipment configuration adjustment control value. The electrical equipment is monitored and configured based on the electrical equipment monitoring configuration parameters.

[0095] It should be added that the electrical equipment monitoring configuration parameters are obtained by matching the electrical equipment configuration adjustment control values, matching the electrical equipment configuration adjustment control values ​​with the electrical equipment monitoring configuration parameters corresponding to each electrical equipment configuration adjustment control value interval stored in the electrical monitoring management database, counting the electrical equipment monitoring configuration parameters corresponding to the interval in which the electrical equipment configuration adjustment control values ​​are located, and recording them as electrical equipment monitoring configuration parameters, and matching the data collection frequency of the electrical equipment with the power supply redundancy adjustment to obtain the electrical equipment monitoring configuration parameters. For example, if the electrical monitoring early warning verification information is that the electrical equipment is abnormal, the data collection frequency of the electrical equipment is changed from once per minute to once every 10 seconds, and the power supply redundancy is adjusted from one main power supply to one main power supply and one backup power supply.

[0096] It should be noted that the electrical equipment monitoring configuration parameters include the data collection frequency and power supply redundancy of the electrical equipment. The analysis of the electrical equipment monitoring early warning verification data can reflect the actual operating status and potential risk level of the electrical equipment. If the electrical equipment early warning verification information is inaccurate, subsequent monitoring with the original default electrical equipment monitoring configuration parameters may result in inaccurate electrical equipment performance testing and failure to identify faults or abnormalities, which may ultimately cause equipment shutdown or damage. Therefore, it is necessary to configure the electrical equipment in combination with the electrical equipment monitoring configuration parameters. The larger the electrical equipment configuration adjustment control value, the more abnormal the electrical equipment is, and the larger the data collection frequency and power supply redundancy of the matched electrical equipment are.

[0097] If the early warning verification value of the electrical equipment is lower than or equal to the set early warning verification second threshold, the electrical monitoring early warning verification information is marked as the electrical equipment is normal, and the electrical equipment continues to be monitored and configured with the current electrical equipment monitoring configuration parameters.

[0098] like Figure 2 The second aspect of the present invention provides an electrical monitoring and management system based on intelligent analysis and early warning, including: a preconfiguration module, which is used to count the characteristic parameters of the electrical monitoring sensor, process the electrical monitoring sensor configuration information, preconfigure the electrical monitoring sensor based on the electrical monitoring sensor configuration information, and start the electrical equipment monitoring signal.

[0099] The historical data analysis module is used to synchronously extract the historical operation data of the electrical equipment collected by the electrical monitoring central controller, and obtain the operation efficiency verification value of the electrical equipment based on the historical operation data analysis of the electrical equipment.

[0100] The real-time monitoring and analysis module is used for the electrical monitoring central controller to receive the electrical equipment monitoring signal, and to monitor the electrical equipment in real time through the pre-configured electrical monitoring sensors. Combined with the operation efficiency verification value of the electrical equipment, a comprehensive analysis is performed to obtain the monitoring and early warning results of the electrical equipment.

[0101] The early warning execution verification module is used to perform monitoring early warning verification according to the monitoring early warning results of the electrical equipment, obtain electrical monitoring early warning verification information, and execute early warning configuration based on the electrical monitoring early warning verification information.

[0102] It should be noted that the electrical monitoring and management system based on intelligent analysis and early warning also includes an electrical monitoring management database for storing a first parameter set, a second parameter set, a third parameter set and a fourth parameter set obtained by analyzing historical data.

[0103] The first parameter set includes a sensor abnormality assessment factor corresponding to the performance attenuation coefficient, a sensor abnormality assessment factor corresponding to the power supply fluctuation coefficient, a sensor abnormality assessment factor corresponding to the unit signal strength deviation value, a reference resolution, an electrical monitoring sensor abnormality assessment threshold, and an electrical monitoring sensor configuration parameter supplementary value.

[0104] The second parameter set includes reference energy consumption values, operating efficiency verification correction factors corresponding to the average unit component displacement, operating efficiency verification correction factors corresponding to the historical highest speed deviation coefficient, operating efficiency verification correction factors corresponding to the historical highest electromagnetic interference coefficient, and abnormal operation verification values ​​corresponding to the operating efficiency verification value range of each electrical equipment.

[0105] The third parameter set includes an operation abnormality assessment factor corresponding to the electromagnetic interference coefficient, an operation abnormality assessment factor corresponding to the magnetic flux deviation coefficient, a reference vibration frequency, a reference input voltage, and an operation abnormality assessment factor corresponding to the input power frequency deviation coefficient.

[0106] The fourth parameter set includes the verification correction factor corresponding to the thermal distribution anomaly coefficient on the surface of the electrical equipment, the verification correction factor corresponding to the surface structure anomaly coefficient of the electrical equipment, the reference voltage of the electrical equipment, the verification correction factor corresponding to the communication transmission influence coefficient of the electrical equipment, the first threshold of early warning verification, the second threshold of early warning verification and the electrical equipment monitoring configuration parameters corresponding to the configuration adjustment control value interval of each electrical equipment.

[0107] A third aspect of the present invention provides a storage medium for storing a program, which, when executed by a processor, implements the above-mentioned electrical monitoring and management method based on intelligent analysis and early warning.

[0108] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0109] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well.

Claims

1. An electrical monitoring and management method based on intelligent analysis and early warning, characterized in that: include: Counting characteristic parameters of electrical monitoring sensors, processing to obtain configuration information of the electrical monitoring sensors, pre-configuring the electrical monitoring sensors based on the configuration information of the electrical monitoring sensors, and starting electrical equipment monitoring signals; Synchronously extract the historical operation data of the electrical equipment collected by the electrical monitoring central controller, and obtain the operation efficiency verification value of the electrical equipment based on the analysis of the historical operation data of the electrical equipment; The electrical monitoring central controller receives the electrical equipment monitoring signal and monitors the electrical equipment in real time through the pre-configured electrical monitoring sensors. Combined with the operating efficiency verification value of the electrical equipment, the comprehensive analysis obtains the monitoring and early warning results of the electrical equipment. Monitoring and early warning verification is performed according to the monitoring and early warning results of the electrical equipment to obtain electrical monitoring and early warning verification information, and early warning configuration is executed based on the electrical monitoring and early warning verification information.

2. The electrical monitoring and management method based on intelligent analysis and early warning according to claim 1 is characterized in that: The processing obtains the electrical monitoring sensor configuration information, and the specific process is as follows: The characteristic parameters of the electrical monitoring sensor include a performance attenuation coefficient of the electrical monitoring sensor, a power supply fluctuation coefficient of the electrical monitoring sensor, a signal strength deviation value of the electrical monitoring sensor, and an average resolution of the electrical monitoring sensor; Based on the characteristic parameters of the electrical monitoring sensor, an abnormality evaluation value of the electrical monitoring sensor is obtained, wherein the abnormality evaluation value of the electrical monitoring sensor is used to comprehensively quantify the abnormal state and performance degradation degree of the electrical monitoring sensor during the monitoring process; The electrical monitoring sensor configuration information includes executing sensor adjustment and not executing sensor adjustment; The electrical monitoring sensor abnormality assessment value is compared with the set electrical monitoring sensor abnormality assessment threshold. If the electrical monitoring sensor abnormality assessment value is lower than the set electrical monitoring sensor abnormality assessment threshold, the electrical monitoring sensor configuration information is marked as not performing sensor adjustment; otherwise, the electrical monitoring sensor configuration information is marked as performing sensor adjustment.

3. The electrical monitoring and management method based on intelligent analysis and early warning according to claim 2 is characterized in that: The electrical monitoring sensor is pre-configured based on the electrical monitoring sensor configuration information, and the specific process is as follows: Extract the electrical monitoring sensor configuration information. If the electrical monitoring sensor configuration information is to not perform sensor adjustment, continue to pre-configure the electrical monitoring sensor with the current electrical monitoring sensor configuration parameters. If the electrical monitoring sensor configuration information is to perform sensor adjustment, add the current electrical monitoring sensor configuration parameters to the set electrical monitoring sensor configuration parameter supplementary values ​​as the electrical monitoring sensor configuration target adjustment parameters, and adjust the electrical monitoring sensor configuration parameters up to the electrical monitoring sensor configuration target adjustment parameters.

4. The electrical monitoring and management method based on intelligent analysis and early warning according to claim 1 is characterized in that: The operation efficiency verification value of the electrical equipment is obtained by analyzing the historical operation data of the electrical equipment. The specific analysis process is as follows: The historical operation data of the electrical equipment includes the historical average energy consumption value of the electrical equipment, the average displacement value of the components of the electrical equipment, the historical highest speed deviation coefficient of the electrical equipment and the historical highest electromagnetic interference coefficient of the electrical equipment; A comprehensive analysis is performed on the historical data of the electrical equipment to obtain an operating efficiency verification value of the electrical equipment, and the operating efficiency verification value of the electrical equipment is used to comprehensively quantify the overall operating efficiency and health status of the electrical equipment.

5. The electrical monitoring and management method based on intelligent analysis and early warning according to claim 1 is characterized in that: The electrical equipment is monitored in real time by the pre-configured electrical monitoring sensor, and the specific process is as follows: Real-time monitoring of electrical equipment by pre-configured electrical monitoring sensors to obtain real-time monitoring data of electrical equipment, wherein the real-time monitoring data of electrical equipment includes electromagnetic interference coefficient, magnetic flux deviation coefficient, vibration frequency, input voltage and input power frequency deviation coefficient of the electrical equipment; Based on the real-time monitoring data of the electrical equipment, an abnormal operation index of the electrical equipment is obtained, and the abnormal operation index of the electrical equipment is used to comprehensively quantify the real-time operation status and abnormality degree of the electrical equipment.

6. The electrical monitoring and management method based on intelligent analysis and early warning according to claim 1 is characterized in that: The comprehensive analysis obtains the monitoring and early warning results of the electrical equipment, and the specific process is as follows: The monitoring and early warning results of the electrical equipment include abnormal operation of the electrical equipment and normal operation of the electrical equipment; The abnormal operation verification value of the electrical equipment is obtained by matching the operation efficiency verification value of the electrical equipment, and the abnormal operation index of the electrical equipment is compared with the abnormal operation verification value of the electrical equipment. If the abnormal operation index of the electrical equipment is higher than the abnormal operation verification value of the electrical equipment, the monitoring and early warning result of the electrical equipment is marked as abnormal operation of the electrical equipment; otherwise, the monitoring and early warning result of the electrical equipment is marked as normal operation of the electrical equipment.

7. The electrical monitoring and management method based on intelligent analysis and early warning according to claim 1 is characterized in that: The electrical monitoring early warning verification information is obtained, and the early warning configuration is performed based on the electrical monitoring early warning verification information. The specific process is as follows: Extracting the monitoring and early warning results of the electrical equipment. If the monitoring and early warning results of the electrical equipment show that the electrical equipment is operating normally, no early warning prompt is given. If the monitoring and early warning results of the electrical equipment show that the electrical equipment is operating abnormally, verification is performed based on the monitoring and early warning results of the electrical equipment to obtain the monitoring and early warning verification data of the electrical equipment. The monitoring and early warning verification data of the electrical equipment includes the thermal distribution abnormality coefficient on the surface of the electrical equipment, the surface structure abnormality coefficient of the electrical equipment, the average voltage of the electrical equipment, and the communication transmission influence coefficient of the electrical equipment; Comprehensively analyzing the electrical equipment monitoring and early warning verification data to obtain an early warning verification value for the electrical equipment, the early warning verification value for the electrical equipment being used to comprehensively quantify the accuracy of monitoring and early warning; The electrical monitoring early warning verification information includes electrical equipment failure, electrical equipment abnormality and electrical equipment normality; Compare the early warning verification value of the electrical equipment with the set early warning verification first threshold. If the early warning verification value of the electrical equipment is higher than the set early warning verification threshold, mark the electrical monitoring early warning verification information as an electrical equipment failure, and send the electrical monitoring early warning verification information to the management terminal for early warning prompt; If the early warning verification value of the electrical equipment is lower than or equal to the set early warning verification first threshold, the early warning verification value of the electrical equipment is compared with the set early warning verification second threshold. If the early warning verification value of the electrical equipment is higher than the set early warning verification second threshold, the electrical monitoring early warning verification information is marked as an electrical equipment abnormality. The difference between the early warning verification value of the electrical equipment and the set early warning verification second threshold is simultaneously recorded as the electrical equipment configuration adjustment control value. The electrical equipment monitoring configuration parameters are matched according to the electrical equipment configuration adjustment control value, and the electrical equipment is monitored and configured based on the electrical equipment monitoring configuration parameters. If the early warning verification value of the electrical equipment is lower than or equal to the set early warning verification second threshold, the electrical monitoring early warning verification information is marked as the electrical equipment is normal, and the electrical equipment continues to be monitored and configured with the current electrical equipment monitoring configuration parameters.

8. The electrical monitoring and management method based on intelligent analysis and early warning according to claim 7 is characterized in that: The early warning verification value of the electrical equipment is specifically analyzed under the following conditions: ； In the formula, Indicates the early warning verification value of electrical equipment. Indicates the thermal distribution anomaly coefficient on the surface of electrical equipment. Indicates the verification correction factor corresponding to the thermal distribution anomaly coefficient of the set electrical equipment surface, Indicates the abnormal coefficient of the surface structure of electrical equipment, Indicates the verification correction factor corresponding to the set electrical equipment surface structure abnormality coefficient, Indicates the average voltage of electrical equipment. Indicates the reference voltage of the set electrical equipment. Indicates the communication transmission influence coefficient of electrical equipment, It represents the verification correction factor corresponding to the communication transmission influence coefficient of the set electrical equipment, and e represents a natural constant.

9. Electrical monitoring and management system based on intelligent analysis and early warning, including: A pre-configuration module is used to count characteristic parameters of electrical monitoring sensors, process and obtain configuration information of the electrical monitoring sensors, pre-configure the electrical monitoring sensors based on the configuration information of the electrical monitoring sensors, and start electrical equipment monitoring signals; The historical data analysis module is used to synchronously extract the historical operation data of the electrical equipment collected by the electrical monitoring central controller, and obtain the operation efficiency verification value of the electrical equipment based on the historical operation data analysis of the electrical equipment; Real-time monitoring and analysis module, which is used for the electrical monitoring central controller to receive the electrical equipment monitoring signal, and to monitor the electrical equipment in real time through the pre-configured electrical monitoring sensor, and to obtain the monitoring and early warning results of the electrical equipment through comprehensive analysis combined with the operation efficiency verification value of the electrical equipment; The early warning execution verification module is used to perform monitoring early warning verification according to the monitoring early warning results of the electrical equipment, obtain electrical monitoring early warning verification information, and execute early warning configuration based on the electrical monitoring early warning verification information.

10. A storage medium for storing a program, characterized in that: When the program is executed by the processor, the electrical monitoring and management method based on intelligent analysis and early warning as described in any one of claims 1 to 8 is implemented.

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