Intelligent electrical equipment early warning monitoring system and method based on multi-sensor interaction

Abstract

The invention discloses an intelligent electrical equipment early warning monitoring system and method based on multi-sensor interaction, and relates to the technical field of electrical equipment early warning management. The intelligent electrical equipment early warning monitoring system based on multi-sensor interaction comprises a data acquisition coincidence judgment module, a data fusion effect judgment module and a monitoring optimization coincidence judgment module. According to the invention, the acquisition quality of the acquired transformer aging monitoring data is evaluated to judge whether to execute data fusion, and then the effect of transformer early warning data fusion is evaluated and whether to execute transformer early warning monitoring optimization is judged. And finally, obtaining the transformer early warning optimization index after the transformer early warning monitoring optimization is executed to judge whether to execute the transformer early warning monitoring resource dynamic priority scheduling or not, thereby realizing the improvement of the aging performance early warning real-time performance of the intelligent transformer. The problem that the aging performance of the intelligent transformer is not timely pre-warned and monitored based on multi-sensor interaction in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of early warning management of electrical equipment, and in particular to an intelligent early warning monitoring system and method for electrical equipment based on multi-sensor interaction. Background Art

[0002] The global manufacturing industry is undergoing a transformation and upgrading towards digitalization, networking and intelligence. Intelligent manufacturing emphasizes the interconnection, data-driven and autonomous decision-making of equipment, and the intelligent early warning monitoring system with multi-sensor interaction is one of the key technologies for realizing intelligent manufacturing. In the field of intelligent electrical equipment, with the development of science and technology, especially driven by technologies such as the Internet of Things, artificial intelligence, big data analysis and machine learning, the management and maintenance of electrical equipment are undergoing profound changes. Traditional equipment management methods mainly rely on manual inspection and regular maintenance, which have problems such as untimely response, unforeseen failures and high maintenance costs. In order to solve these challenges, the intelligent electrical equipment early warning monitoring system based on multi-sensor interaction came into being, becoming an important part of modern industrial production and intelligent manufacturing, and an inevitable choice in dealing with equipment failures and improving production efficiency.

[0003] The existing multi-sensor interactive intelligent electrical equipment early warning monitoring system collects equipment status information in real time through various sensors, including physical quantities such as temperature, vibration, and current, and fuses multi-source data from different sensors to improve the accuracy and reliability of fault diagnosis. Machine learning algorithms (such as decision trees, support vector machines, neural networks, etc.) are used to train models through historical data to identify whether the equipment is in a fault mode, warn of potential faults in advance, and display them visually. By combining data analysis, sensor technology, and intelligent decision-making, the accuracy and timeliness of equipment management can be improved, the impact of equipment failures on production can be reduced, and enterprises can be driven to develop in a more efficient and intelligent direction.

[0004] For example, the patent application with publication number: CN118735106A discloses a building mechanical and electrical equipment management system based on the Internet of Things technology, including: an equipment monitoring module, which is used to dynamically monitor the location, working status and operating parameters of the building mechanical and electrical equipment, and determine the real-time data of the building mechanical and electrical equipment based on the Internet of Things technology; a data processing module, which is used to retrieve, sort and fuse the real-time data of the building mechanical and electrical equipment based on the Internet of Things technology, and determine the characterization data of the building mechanical and electrical equipment based on the Internet of Things technology; an analysis and evaluation module, which is used to analyze and evaluate the characterization data of the building mechanical and electrical equipment based on the standard data of the building mechanical and electrical equipment, and determine the analysis and evaluation results of the building mechanical and electrical equipment based on the Internet of Things technology; an early warning control module, which is used to formulate an early warning control plan for building mechanical and electrical equipment, and perform early warning control on the building mechanical and electrical equipment based on the early warning control plan.

[0005] For example, the invention patent announcement with announcement number: CN115640860B announces a remote maintenance method and system for electromechanical equipment of an industrial cloud service, including: collecting basic data of electromechanical equipment during operation, obtaining electromechanical equipment operation data through automatic identification and analysis of the basic data, obtaining electromechanical equipment fault information through the electromechanical equipment operation data, constructing a matrix of electromechanical equipment operation position data, and obtaining the optimal maintenance distance through the matrix, obtaining the fault information of the electromechanical equipment by calculating the weight analysis ratio of the temperature value to the current value, and judging the fault information of the electromechanical equipment in combination with the visual sensor to obtain a more accurate analysis of the fault information of the electromechanical equipment.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] Smart electrical equipment (such as smart transformers) are usually in more complex and changeable environments (such as power substations, remote areas far away from urban areas, etc.). Sensors may lose data or have inconsistent data during data collection, resulting in the inability to obtain comprehensive monitoring information in a timely manner. At the same time, there are usually strong electromagnetic fields around smart transformers, which may interfere with certain types of sensors (such as current and voltage sensors) and affect the accuracy of data collection.

[0008] Another thing that needs to be considered is that multi-sensor data usually needs to be integrated through fusion algorithms, which usually have a large amount of computation. Especially when there are many types of sensors and complex data dimensions, the data fusion process may cause delays, and there is a problem of untimely early warning monitoring of the aging performance of smart transformers based on multi-sensor interaction. Summary of the invention

[0009] The embodiments of the present application solve the problem in the prior art of untimely early warning monitoring of the aging performance of smart transformers based on multi-sensor interaction by providing an intelligent electrical equipment early warning monitoring system and method based on multi-sensor interaction, thereby improving the real-time performance of early warning of the aging performance of smart transformers.

[0010] The embodiment of the present application provides an intelligent electrical equipment early warning monitoring system based on multi-sensor interaction, including: a data collection compliance judgment module, a data fusion effect judgment module and a monitoring optimization compliance judgment module; the data collection compliance judgment module is used to evaluate the collection quality of the collected transformer aging monitoring data to obtain a transformer collection index, and judge whether to perform data fusion based on the transformer collection index, and the transformer collection index is used to quantitatively evaluate the compliance degree of the quality of the transformer aging monitoring data collected by the sensor; the data fusion effect judgment module is used to evaluate the effect of the transformer early warning data fusion after performing data fusion to obtain a transformer fusion index, and judge whether to perform transformer early warning monitoring optimization based on the transformer fusion index, and the transformer fusion index is used to comprehensively quantify the fusion effect of the transformer aging monitoring data; the monitoring optimization compliance judgment module is used to obtain the transformer early warning optimization index after performing transformer early warning monitoring optimization, and judge whether to perform transformer early warning monitoring resource dynamic priority scheduling based on the transformer early warning optimization index, and the transformer early warning optimization index is used to comprehensively quantify the compliance degree of the transformer early warning monitoring optimization.

[0011] Furthermore, the specific steps of evaluating the collection quality of the collected transformer aging monitoring data to obtain the transformer collection index are as follows: statistically analyzing the transformer aging monitoring data collected by the monitoring sensor within a preset time interval to obtain the corresponding transformer monitoring average data; obtaining the transformer collection evaluation data within the preset time interval, the transformer collection evaluation data including the total amount of transformer collection monitoring data, the amount of transformer collection monitoring lost data and the transformer collection redundancy correlation coefficient; obtaining the transformer collection index according to the transformer monitoring average data, the transformer collection evaluation data and the transformer collection related reference data obtained from a preset database; the monitoring sensor includes a current sensor, a voltage sensor, a temperature sensor, a vibration sensor and a gas sensor; the transformer monitoring average data includes transformer monitoring average temperature data, transformer monitoring average current data, transformer monitoring average voltage data, transformer average vibration amplitude data and transformer average methane concentration data; the transformer collection related reference data includes transformer monitoring reference value, transformer monitoring reference deviation, reference collection redundancy correlation coefficient and transformer monitoring collection correction factor.

[0012] Furthermore, the specific process of judging whether to perform data fusion based on the transformer collection index is: comparing the transformer collection index with the preset early warning collection threshold range obtained from the preset database; if the transformer collection index is within the preset early warning collection threshold range, data fusion is performed, and the fusion effect of the transformer early warning monitoring data is evaluated to obtain the transformer fusion index; if the transformer collection index exceeds the preset early warning collection threshold range, data fusion is not performed, and the preset personnel are reminded to re-collect the transformer aging monitoring data.

[0013] Furthermore, the specific steps of evaluating the fusion effect of the transformer early warning monitoring data to obtain the transformer fusion index are as follows: obtaining the transformer fusion evaluation data within a preset time interval after executing data fusion; the transformer fusion evaluation data includes transformer acquisition indicators, early warning monitoring fusion data volume, total early warning monitoring data volume, early warning monitoring fusion time, predicted performance aging value and early warning monitoring fusion signal-to-noise ratio; combining the transformer fusion evaluation data with the transformer fusion reference data obtained from a preset database to obtain the transformer fusion index; the transformer fusion reference data includes reference optimal transformer acquisition indicators, reference maximum fusion time, reference predicted performance aging value and reference monitoring signal-to-noise ratio.

[0014] Furthermore, the method for obtaining the transformer fusion index is as follows:

[0015] ;

[0016] In the formula, represents the transformer fusion index, Indicates the number of times data is fused within the preset time interval. , Indicates the total number of data fusions within the preset time interval. represents the transformer collection index of the rth data fusion of transformer aging monitoring data, Indicates the reference optimal transformer acquisition index, represents the amount of early warning monitoring fusion data of the rth data fusion of transformer aging monitoring data, Indicates the total amount of data collected for early warning monitoring within the preset time interval. represents the reference maximum fusion time, represents the early warning monitoring fusion time of the rth data fusion of transformer aging monitoring data, represents the predicted performance aging value of the rth data fusion of transformer aging monitoring data, represents the reference predicted performance aging value, Indicates the fusion signal-to-noise ratio of early warning monitoring at a preset time interval, Indicates the reference monitoring signal-to-noise ratio.

[0017] Furthermore, the specific process of judging whether to execute transformer early warning monitoring optimization based on transformer fusion index is as follows: if the transformer fusion index is within the preset early warning fusion threshold range, the transformer early warning monitoring optimization is not executed, and the transformer aging monitoring data is optimized and uploaded to cloud storage; the optimization processing includes transformer aging monitoring data anomaly detection, transformer aging monitoring data feature extraction and transformer aging monitoring data compression processing; if the transformer fusion index exceeds the preset early warning fusion threshold range, the transformer early warning monitoring optimization is executed, and the transformer early warning optimization index after executing the transformer early warning monitoring optimization is obtained.

[0018] Furthermore, the specific process of the transformer early warning monitoring optimization is as follows: A1, optimizing the monitoring data collection of the transformer, when the transformer fusion index after the monitoring data collection optimization is within the preset early warning fusion threshold range, the transformer early warning monitoring optimization is stopped, otherwise A2 is executed; A2, optimizing the transformer through data fusion, judging that the transformer fusion index after the data fusion optimization is within the preset early warning fusion threshold range, the transformer early warning monitoring optimization is stopped, otherwise A3 is executed; A3, optimizing the early warning monitoring architecture of the transformer, and obtaining the transformer early warning optimization index after the transformer early warning monitoring optimization is executed.

[0019] Furthermore, the specific steps of obtaining the transformer early warning optimization index after executing the transformer early warning monitoring optimization are as follows: obtaining the transformer indicators to be optimized and the transformer optimization evaluation data after executing the transformer early warning monitoring optimization; the transformer indicators to be optimized include the transformer acquisition indicators to be optimized and the transformer fusion indicators to be optimized; the transformer optimization evaluation data include the optimized sampling frequency, the total amount of optimized acquisition early warning monitoring data, the amount of optimized acquisition early warning monitoring lost data and the average predicted performance aging value of the optimized early warning monitoring fusion; the transformer early warning optimization index is obtained according to the transformer indicators to be optimized, the transformer optimization evaluation data and the transformer optimization reference data obtained from the preset database; the transformer optimization reference data include the reference sampling frequency, the reference optimal transformer early warning fusion index and the reference early warning monitoring predicted performance aging value.

[0020] Furthermore, the specific process of judging whether to execute dynamic priority scheduling of transformer early warning monitoring resources based on transformer early warning optimization indicators is as follows: if the transformer early warning optimization indicator is within the preset early warning optimization interval, the dynamic priority scheduling of transformer early warning monitoring resources will not be executed, and at the same time, the transformer fusion indicator will be continuously monitored to see if it is within the preset early warning fusion threshold range; if the transformer early warning optimization indicator exceeds the preset early warning optimization interval, the dynamic priority scheduling of transformer early warning monitoring resources will be executed; the dynamic priority scheduling of transformer early warning monitoring resources includes algorithm priority scheduling and monitoring resource scheduling.

[0021] The embodiment of the present application provides an intelligent electrical equipment early warning monitoring method based on multi-sensor interaction, comprising the following steps: S1, evaluating the collection quality of the collected transformer aging monitoring data to obtain a transformer collection index, judging whether to perform data fusion based on the transformer collection index, and the transformer collection index is used to quantitatively evaluate the compliance degree of the quality of the transformer aging monitoring data collected by the sensor; S2, after performing data fusion, evaluating the effect of the transformer early warning data fusion to obtain a transformer fusion index, judging whether to perform transformer early warning monitoring optimization based on the transformer fusion index, and the transformer fusion index is used to comprehensively quantify the fusion effect of the transformer aging monitoring data; S3, obtaining the transformer early warning optimization index after performing the transformer early warning monitoring optimization, judging whether to perform the dynamic priority scheduling of the transformer early warning monitoring resources based on the transformer early warning optimization index, and the transformer early warning optimization index is used to comprehensively quantify the compliance degree of the transformer early warning monitoring optimization.

[0022] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0023] 1. The collection quality of the collected transformer aging monitoring data is evaluated to determine whether to perform data fusion, and then the effect of the transformer early warning data fusion is evaluated to determine whether to perform transformer early warning monitoring optimization. Finally, the transformer early warning optimization index after the transformer early warning monitoring optimization is obtained to determine whether to perform transformer early warning monitoring resource dynamic priority scheduling, thereby realizing the evaluation of transformer early warning data fusion and monitoring optimization, and then realizing the improvement of the real-time performance warning of the intelligent transformer aging performance, which effectively solves the problem of untimely early warning monitoring of the aging performance of the intelligent transformer based on multi-sensor interaction in the existing technology.

[0024] 2. By obtaining the transformer fusion evaluation data within the preset time interval after executing data fusion, and combining the transformer fusion evaluation data with the transformer fusion reference data obtained from the preset database to obtain the transformer fusion index, the numerical evaluation of the fusion effect of the transformer aging monitoring data is realized, thereby realizing a more accurate evaluation of the fusion effect of the transformer aging monitoring data.

[0025] 3. By obtaining the transformer indicators to be optimized and the transformer optimization evaluation data after executing the transformer early warning monitoring optimization, and obtaining the transformer early warning optimization indicators based on the transformer indicators to be optimized, the transformer optimization evaluation data and the transformer optimization reference data obtained from the preset database, the numerical evaluation of the transformer early warning monitoring optimization compliance degree is realized, thereby realizing a more accurate evaluation of the transformer early warning monitoring optimization compliance degree. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1A schematic diagram of the structure of an intelligent electrical equipment early warning monitoring system based on multi-sensor interaction provided in an embodiment of the present application;

[0027] Figure 2 A schematic diagram showing the change of transformer fusion index along with transformer acquisition index provided in an embodiment of the present application;

[0028] Figure 3 A schematic diagram showing the change of transformer fusion index with early warning monitoring fusion signal-to-noise ratio provided in an embodiment of the present application;

[0029] Figure 4 A flowchart of an intelligent electrical equipment early warning monitoring method based on multi-sensor interaction provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] The embodiment of the present application solves the problem of untimely early warning monitoring of the aging performance of intelligent transformers based on multi-sensor interaction in the prior art by providing an intelligent electrical equipment early warning monitoring system and method based on multi-sensor interaction. The system evaluates the collection quality of the collected transformer aging monitoring data to obtain a transformer collection index, and determines whether to perform data fusion based on the transformer collection index. After performing the data fusion, the effect of the transformer early warning data fusion is evaluated to obtain a transformer fusion index and determine whether to perform transformer early warning monitoring optimization. Finally, the transformer early warning optimization index after performing the transformer early warning monitoring optimization is obtained, and it is determined whether to perform dynamic priority scheduling of transformer early warning monitoring resources based on the transformer early warning optimization index, thereby improving the real-time performance of the early warning of the intelligent transformer aging performance.

[0031] The technical solution in the embodiment of the present application is to solve the problem of untimely early warning monitoring of the aging performance of the smart transformer based on multi-sensor interaction. The overall idea is as follows:

[0032] The collection quality of the collected transformer aging monitoring data is evaluated to determine whether to perform data fusion, and then the effect of the transformer early warning data fusion is evaluated to determine whether to perform transformer early warning monitoring optimization, and finally it is determined whether to perform dynamic priority scheduling of transformer early warning monitoring resources, thereby achieving the effect of improving the real-time performance warning of the intelligent transformer aging performance.

[0033] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0034] like Figure 1As shown, it is a structural schematic diagram of the intelligent electrical equipment early warning monitoring system based on multi-sensor interaction provided by the embodiment of the present application. The intelligent electrical equipment early warning monitoring system based on multi-sensor interaction provided by the embodiment of the present application includes: a data collection compliance judgment module, a data fusion effect judgment module and a monitoring optimization compliance judgment module; the data collection compliance judgment module is used to evaluate the collection quality of the collected transformer aging monitoring data to obtain a transformer collection index, and judge whether to perform data fusion based on the transformer collection index. The transformer collection index is used to quantitatively evaluate the compliance degree of the quality of the transformer aging monitoring data collected by the sensor; the data fusion effect judgment module is used to evaluate the effect of the transformer early warning data fusion after performing data fusion to obtain a transformer fusion index, and judge whether to perform transformer early warning monitoring optimization based on the transformer fusion index. The transformer fusion index is used to comprehensively quantify the fusion effect of the transformer aging monitoring data; the monitoring optimization compliance judgment module is used to obtain the transformer early warning optimization index after performing transformer early warning monitoring optimization, and judge whether to perform transformer early warning monitoring resource dynamic priority scheduling based on the transformer early warning optimization index. The transformer early warning optimization index is used to comprehensively quantify the compliance degree of the transformer early warning monitoring optimization.

[0035] Among them, the transformer aging monitoring data includes transformer monitoring temperature data, transformer monitoring current data, transformer monitoring voltage data, transformer vibration amplitude data and transformer methane concentration data.

[0036] Specifically, the transformer monitoring temperature data is obtained through temperature sensors deployed on key parts of the transformer (such as transformer windings and oil tanks), the transformer monitoring current data and transformer monitoring voltage data are obtained through current sensors and voltage sensors deployed on the output end of the transformer, the transformer vibration amplitude data is obtained through a vibration sensor deployed at the center point of the transformer base, and the transformer methane concentration data is obtained through a gas sensor (methane gas sensor) deployed on the vent of the transformer oil tank.

[0037] By acquiring transformer aging monitoring data through sensors, the collection quality of the collected transformer aging monitoring data can be evaluated, thereby improving the data reliability in the process of intelligent transformer aging performance early warning monitoring through multi-sensor interaction. By improving the reliability of data in the process of intelligent transformer aging performance early warning monitoring, the real-time performance of intelligent transformer aging performance early warning can be improved.

[0038] Furthermore, the specific steps of evaluating the collection quality of the collected transformer aging monitoring data to obtain the transformer collection index are as follows: statistically analyzing the transformer aging monitoring data collected by the monitoring sensor within a preset time interval to obtain the corresponding transformer monitoring average data; obtaining the transformer collection evaluation data within the preset time interval, the transformer collection evaluation data including the total amount of transformer collection monitoring data, the amount of transformer collection monitoring lost data and the transformer collection redundancy correlation coefficient; obtaining the transformer collection index according to the transformer monitoring average data, the transformer collection evaluation data and the transformer collection related reference data obtained from the preset database.

[0039] Among them, the monitoring sensors include current sensors, voltage sensors, temperature sensors, vibration sensors and gas sensors; the transformer monitoring average data include transformer monitoring average temperature data, transformer monitoring average current data, transformer monitoring average voltage data, transformer average vibration amplitude data and transformer average methane concentration data; the transformer acquisition related reference data include transformer monitoring reference value, transformer monitoring reference deviation, reference acquisition redundancy correlation coefficient and transformer monitoring acquisition correction factor.

[0040] The method to obtain transformer collection indicators is as follows:

[0041] ;

[0042] In the formula, Indicates transformer acquisition indicators, Indicates the type number of the monitoring sensor. , Indicates the total number of monitoring sensor types. Indicates the average transformer monitoring data corresponding to the nth monitoring sensor within the preset time interval, Indicates the transformer monitoring reference value corresponding to the nth monitoring sensor, Indicates the transformer monitoring reference deviation corresponding to the nth monitoring sensor, Indicates the total amount of transformer monitoring data collected within the preset time interval. Indicates the amount of transformer monitoring loss data collected within the preset time interval. It represents the transformer acquisition redundancy correlation coefficient corresponding to the nth monitoring sensor and the jth monitoring sensor within the preset time interval, , represents the reference acquisition redundancy correlation coefficient, Indicates the transformer monitoring acquisition correction factor.

[0043] In this embodiment, the total number of types of monitoring sensors is 5 (i.e. =5), where They correspond to current sensors, voltage sensors, temperature sensors, vibration sensors and gas sensors respectively. The transformer monitoring average data is the average value of the transformer aging monitoring data at a preset time within a preset time interval. The corresponding transformer monitoring average data is obtained by statistically analyzing the transformer monitoring average data corresponding to the preset time interval using LabVIEW mathematical functions (such as the Mean function).

[0044] The total amount of transformer collection and monitoring data is obtained through data acquisition software (such as LabVIEW, MATLAB, PI System, etc.), and the amount of transformer collection and monitoring loss data is obtained through a data loss detection algorithm (such as using a time series analysis algorithm to detect data loss in a programming environment such as MATLAB and Python). Specifically, in MATLAB, data loss can be detected by the isnan function, and the transformer collection redundancy correlation coefficient is obtained through a correlation coefficient calculation tool (such as the corr function in MATLAB). Specifically, the transformer collection redundancy correlation coefficient is calculated by constructing a matrix, where each column represents a time series, and the corr function returns a correlation coefficient matrix, in which each element represents the transformer collection redundancy correlation coefficient between two corresponding time series.

[0045] The aforementioned database is a database for storing various types of setting data established before the design of the intelligent electrical equipment early warning monitoring system based on multi-sensor interaction provided in the embodiment of the present application. The database includes but is not limited to transformer monitoring temperature data, transformer monitoring current data, transformer monitoring voltage data, transformer vibration amplitude data, and transformer methane concentration data, etc., and the various numerical values ​​therein are directly set by technical personnel; for example, the corresponding transformer monitoring reference value is represented by summing and averaging the collected historical monitoring sensor corresponding transformer aging monitoring data, the transformer monitoring reference deviation is represented by summing and averaging the difference between the collected historical monitoring sensor corresponding transformer aging monitoring data and the corresponding transformer monitoring reference value, and the reference acquisition redundant correlation coefficient is represented by summing and averaging the collected historical transformer acquisition redundant correlation coefficient.

[0046] The transformer monitoring and acquisition correction factor is obtained from a preset database and is used to reflect the degree of influence of the factors affecting the quality of transformer aging monitoring data acquisition (such as electromagnetic interference intensity) on the transformer acquisition indicators. The real-time transformer aging monitoring data acquisition quality influencing factors (such as electromagnetic interference intensity) are input into the preset mapping set in the database to obtain the corresponding correction factor.

[0047] The transformer collection index is used to quantitatively evaluate the degree of compliance of the quality of transformer aging monitoring data collected by sensors. Among them, the average transformer monitoring data, the amount of lost data for transformer collection monitoring and the transformer collection redundancy correlation coefficient all have an impact on the transformer collection index. Specifically, as the deviation between the average transformer monitoring data and the corresponding transformer monitoring reference value decreases, the transformer collection index increases accordingly; as the amount of lost data for transformer collection monitoring decreases and the transformer collection redundancy correlation coefficient increases, the transformer collection index increases accordingly.

[0048] In addition, the transformer acquisition index includes parameters from multiple aspects, and each parameter is related and does not exist independently. For example, as the amount of lost data in transformer acquisition monitoring increases, that is, due to data loss, the deviation between the average transformer monitoring data and the corresponding transformer monitoring reference value increases, which in turn leads to a decrease in the transformer acquisition index; in addition, as the transformer acquisition redundancy correlation coefficient decreases, it indicates that the data difference between sensors is large, which may lead to distortion of the average transformer monitoring data, that is, the deviation between the average transformer monitoring data and the corresponding transformer monitoring reference value increases, and the transformer acquisition index also decreases; therefore, considering the correlation and mutual influence between various factors, the transformer acquisition index is obtained through comprehensive analysis, and the numerical evaluation of the quality compliance of the transformer aging monitoring data collected by the sensor is realized. The quality compliance of the transformer aging monitoring data collected by the sensor is judged through the numerical evaluation, thereby achieving the improvement of the accuracy of the evaluation of the quality compliance of the transformer aging monitoring data collected by the sensor.

[0049] Furthermore, the specific process of judging whether to perform data fusion based on the transformer collection index is as follows: comparing the transformer collection index with the preset early warning collection threshold range obtained from the preset database; if the transformer collection index is within the preset early warning collection threshold range, data fusion is performed, and the fusion effect of the transformer early warning monitoring data is evaluated to obtain the transformer fusion index; if the transformer collection index exceeds the preset early warning collection threshold range, data fusion is not performed, and the preset personnel are reminded to re-collect the transformer aging monitoring data.

[0050] In this embodiment, specifically, the preset warning collection threshold range is set by professionals according to standards in the field, for example, the preset warning collection threshold range is set to 0.857 to 0.9; wherein, data fusion is achieved through data fusion methods such as weighted averaging, Kalman filtering or Bayesian fusion method, specifically for example, a Bayesian reasoning data fusion method based on probability theory, which fuses data from different sources according to the posterior probability distribution of calculated sensor data, wherein the prior distribution and likelihood function in the Bayesian reasoning data fusion method calculate the posterior distribution by combining the data of all sensors, and the optimal estimate after fusion is obtained by maximizing the posterior probability or calculating the expected value of the posterior distribution; by combining the preset warning collection threshold range for judgment and data fusion, the accuracy of judging the degree of compliance of the quality of transformer aging monitoring data collected by the sensor is improved.

[0051] Furthermore, the specific steps for evaluating the fusion effect of the transformer early warning monitoring data to obtain the transformer fusion index are as follows: obtain the transformer fusion evaluation data within the preset time interval after the execution of data fusion; the transformer fusion evaluation data includes transformer acquisition indicators, early warning monitoring fusion data volume, total early warning monitoring data volume, early warning monitoring fusion time, predicted performance aging value and early warning monitoring fusion signal-to-noise ratio; the transformer fusion evaluation data is combined with the transformer fusion reference data obtained from the preset database to obtain the transformer fusion index; the transformer fusion reference data includes reference optimal transformer acquisition indicators, reference maximum fusion time, reference predicted performance aging value and reference monitoring signal-to-noise ratio.

[0052] The method to obtain the transformer fusion index is as follows:

[0053] ;

[0054] In the formula, represents the transformer fusion index, Indicates the number of times data is fused within the preset time interval. , Indicates the total number of data fusions within the preset time interval. represents the transformer collection index of the rth data fusion of transformer aging monitoring data, Indicates the reference optimal transformer acquisition index, represents the amount of early warning monitoring fusion data of the rth data fusion of transformer aging monitoring data, Indicates the total amount of data collected for early warning monitoring within the preset time interval. represents the reference maximum fusion time, represents the early warning monitoring fusion time of the rth data fusion of transformer aging monitoring data, represents the predicted performance aging value of the rth data fusion of transformer aging monitoring data, represents the reference predicted performance aging value, Indicates the fusion signal-to-noise ratio of early warning monitoring at a preset time interval, Indicates the reference monitoring signal-to-noise ratio.

[0055] In this embodiment, the amount of early warning monitoring fusion data and the total amount of early warning monitoring data are obtained through a data acquisition system (such as SCADA, Supervisory Control and Data Acquisition system). Specifically, the frequency of data acquisition is determined (for example, once per second or once per minute), the amount of data collected by all sensors within a specified time is obtained, the early warning monitoring fusion time is obtained through a big data processing framework (such as Apache Kafka, Spark, etc.), and the aging state of the transformer (such as performance degradation value) is predicted by a machine learning algorithm (such as regression analysis, neural network, etc.) to obtain a predicted performance aging value. Specifically, the aging state of the transformer is predicted by using a historical data training model, and the early warning monitoring fusion signal-to-noise ratio is obtained through a signal analysis tool (such as MATLAB, LabVIEW or Python library NumPy). The early warning monitoring fusion signal-to-noise ratio is the signal-to-noise ratio of the transformer aging monitoring data after data fusion.

[0056] The reference optimal transformer acquisition index is generally set as the maximum value of the preset early warning acquisition threshold range. The reference maximum fusion time is represented by the maximum value of the collected historical early warning monitoring fusion time. The reference predicted performance aging value is represented by the summation and averaging of the collected historical predicted performance aging values. The reference monitoring signal-to-noise ratio is represented by the summation and averaging of the collected historical early warning monitoring fusion signal-to-noise ratios.

[0057] The following settings are made: the total number of data fusions within the preset time interval is 1, the reference optimal transformer collection index is 0.9, the amount of early warning monitoring fusion data is 400 bytes, the total amount of collected early warning monitoring data is 500 bytes, the reference maximum fusion time is 300 seconds, the early warning monitoring fusion time is 150 seconds, the prediction performance aging value is 0.6, the reference prediction performance aging value is 0.5, and the reference monitoring signal-to-noise ratio is 45; Figure 2 As shown, it is a schematic diagram of the change of the transformer fusion index with the transformer acquisition index provided in the embodiment of the present application. Specifically, the early warning monitoring fusion signal-to-noise ratio is set to 40. As the deviation degree between the transformer acquisition index and the reference optimal transformer acquisition index decreases, the transformer fusion index increases accordingly; Figure 3 As shown, it is a schematic diagram of the change of the transformer fusion index with the early warning monitoring fusion signal-to-noise ratio provided in the embodiment of the present application. Specifically, the transformer acquisition index is set to 0.8. As the early warning monitoring fusion signal-to-noise ratio increases, the transformer fusion index also increases accordingly.

[0058] The transformer fusion index is used to comprehensively quantify the fusion effect of transformer aging monitoring data. Specifically, the transformer fusion index includes multiple parameters, among which the transformer acquisition index, the amount of early warning monitoring fusion data, the early warning monitoring fusion time, the predicted performance aging value and the early warning monitoring fusion signal-to-noise ratio all have an impact on the transformer fusion index; specifically, with the increase of transformer acquisition index, the amount of early warning monitoring fusion data and the early warning monitoring fusion signal-to-noise ratio, the transformer fusion index increases accordingly; with the decrease of early warning monitoring fusion time, the transformer fusion index increases accordingly; with the decrease of the deviation between the predicted performance aging value and the reference predicted performance aging value, the transformer fusion index also increases accordingly.

[0059] It should be added that the various parameters in the transformer fusion index are related and do not exist independently. For example, with the increase of transformer acquisition indicators, the amount of early warning monitoring fusion data increases accordingly, and the increase in the amount of early warning monitoring fusion data can help improve the signal-to-noise ratio, because more data can average out noise and enhance signals, that is, the early warning monitoring fusion signal-to-noise ratio increases accordingly, and the transformer fusion index also increases accordingly; in addition, the transformer acquisition indicators also affect the predicted performance aging value. Specifically, with the increase of transformer acquisition indicators, the deviation between the predicted performance aging value and the reference predicted performance aging value decreases, and the transformer fusion index increases accordingly. At the same time, with the increase of the amount of early warning monitoring fusion data, the early warning monitoring fusion signal-to-noise ratio increases accordingly, and the transformer fusion index also increases accordingly; therefore, through a quantitative method, the correlation and mutual influence between various factors are considered, and the transformer fusion index is obtained through comprehensive analysis, which realizes the numerical evaluation of the transformer aging monitoring data fusion effect, and the transformer aging monitoring data fusion effect is judged through numerical evaluation, thereby realizing a more accurate evaluation of the transformer aging monitoring data fusion effect.

[0060] Furthermore, the specific process of judging whether to execute transformer early warning monitoring optimization based on the transformer fusion index is as follows: comparing the transformer fusion index with the preset early warning fusion threshold range obtained from the preset database; if the transformer fusion index is within the preset early warning fusion threshold range, the transformer early warning monitoring optimization is not executed, and the transformer aging monitoring data is optimized and uploaded to the cloud storage; the optimization processing includes transformer aging monitoring data anomaly detection, transformer aging monitoring data feature extraction and transformer aging monitoring data compression processing; if the transformer fusion index exceeds the preset early warning fusion threshold range, the transformer early warning monitoring optimization is executed, and the transformer early warning optimization index after the transformer early warning monitoring optimization is executed is obtained.

[0061] In this embodiment, the preset warning fusion threshold range is set by professionals according to standards in the field. For example, the transformer collection index range is set to 0.8 to 0.9, the warning monitoring fusion data volume range is 80% to 100% of the total warning monitoring data volume collected, the total warning monitoring data volume range is 400 bytes to 900 bytes, the warning monitoring fusion time range is 100 seconds to 300 seconds, the predicted performance aging value range is 0 to 1, and the warning monitoring fusion signal-to-noise ratio range is 40 to 60, thereby determining the preset warning fusion threshold range.

[0062] Among them, anomaly detection of transformer aging monitoring data is achieved through anomaly detection algorithms (such as anomaly detection based on mean and standard deviation), among which, the value exceeding the mean plus or minus three times the standard deviation is an outlier; transformer aging monitoring data feature extraction is achieved by using numpy in Python library to calculate time domain features and scipy or pywt library to calculate wavelet transform. Specifically, numpy in Python library is used to obtain the kurtosis and skewness of transformer aging monitoring data to achieve transformer aging monitoring data feature extraction; transformer aging monitoring data compression processing is achieved through data encoding compression (such as Huffman coding). Specifically, the generated Huffman coding is used to encode the original data, and each symbol is replaced by the corresponding binary code to form compressed data; by combining the preset early warning fusion threshold range for judgment and transformer early warning monitoring optimization, a more accurate judgment of the transformer aging monitoring data fusion effect is achieved.

[0063] It should be added that the specific process of transformer early warning monitoring optimization is as follows: A1, optimize the monitoring data collection of the transformer. When the transformer fusion index after the monitoring data collection optimization is within the preset early warning fusion threshold range, stop the transformer early warning monitoring optimization, otherwise execute A2; A2, perform data fusion optimization on the transformer, and determine that the transformer fusion index after the data fusion optimization is within the preset early warning fusion threshold range, then stop the transformer early warning monitoring optimization, otherwise execute A3; A3, optimize the early warning monitoring architecture of the transformer, and at the same time obtain the transformer early warning optimization index after the transformer early warning monitoring optimization is executed.

[0064] In this embodiment, monitoring data collection optimization is achieved through an adaptive sampling algorithm (such as an event-driven sampling strategy). For example, a static threshold is set (such as a temperature exceeding 80 degrees Celsius, a current exceeding 300A), and the trigger sampling condition is exceeding the static threshold; data fusion optimization is achieved through a time series analysis method (such as ARIMA, -Autoregressive Integrated Moving Average). For example, the statsmodels library in Python provides an ARIMA model implementation, in which the time series data is stabilized by a differential operation. ADF (Augmented Dickey-Fuller) test is usually used to check whether the data is stable. If the data is non-stationary, differentials are performed until the data is stable; the early warning monitoring architecture is optimized through a cloud platform and the Internet of Things. For example, a stream processing framework (such as Apache Kafka, ApacheFlink) is used for real-time processing of data streams, in which Flink provides exactly-once semantics and at-least-once semantics to ensure that data will not be lost during real-time data processing; through transformer early warning monitoring optimization, the reliability of early warning monitoring is improved when the fusion effect of transformer aging monitoring data is abnormal.

[0065] Furthermore, the specific steps for obtaining the transformer early warning optimization index after executing the transformer early warning monitoring optimization are as follows: obtain the transformer indicators to be optimized and the transformer optimization evaluation data after executing the transformer early warning monitoring optimization; the transformer indicators to be optimized include the transformer acquisition indicators to be optimized and the transformer fusion indicators to be optimized; the transformer optimization evaluation data include the optimized sampling frequency, the total amount of optimized acquisition early warning monitoring data, the amount of optimized acquisition early warning monitoring lost data and the average predicted performance aging value of the optimized early warning monitoring fusion; the transformer early warning optimization index is obtained according to the transformer indicators to be optimized, the transformer optimization evaluation data and the transformer optimization reference data obtained from the preset database; the transformer optimization reference data include the reference sampling frequency, the reference optimal transformer early warning fusion index and the reference early warning monitoring predicted performance aging value.

[0066] The method to obtain the transformer early warning optimization index is as follows:

[0067] ;

[0068] In the formula, Represents the transformer early warning optimization index, Indicates the transformer collection index to be optimized, represents the optimized sampling frequency, represents the reference sampling frequency, Indicates the total amount of data collected for early warning monitoring is optimized. Indicates the amount of data lost by optimizing the collection warning monitoring. represents the transformer fusion index to be optimized, It refers to the optimal transformer early warning fusion index. It represents the average prediction performance aging value of the optimized early warning monitoring fusion, Indicates the reference early warning monitoring predicted performance aging value.

[0069] In this embodiment, the transformer collection index to be optimized refers to the transformer collection index after transformer early warning monitoring optimization is performed, and the transformer fusion index to be optimized refers to the transformer fusion index after transformer early warning monitoring optimization is performed.

[0070] The optimized sampling frequency is obtained through wavelet transform tools (such as the cwt (continuous wavelet transform) function in MATLAB). Specifically, the maximum frequency component in the signal is obtained through the time-frequency diagram of wavelet transform. The total amount of optimized collected early warning monitoring data refers to the total amount of transformer collected monitoring data after the transformer early warning monitoring optimization is executed. The amount of lost data for optimized collected early warning monitoring refers to the amount of lost data for transformer collected monitoring after the transformer early warning monitoring optimization is executed. The average predicted performance aging value of the optimized early warning monitoring fusion refers to the predicted performance aging value after the transformer early warning monitoring optimization is executed.

[0071] The reference sampling frequency is represented by summing and averaging the collected historical optimized sampling frequencies, and the reference optimal transformer early warning fusion index is represented by summing and averaging the collected historical transformer early warning fusion indicators within the preset early warning fusion threshold range.

[0072] Specific assumptions: the transformer acquisition index to be optimized is 0.9, the reference sampling frequency is 50 Hz, the total amount of optimized acquisition early warning monitoring data is 300 bytes, the reference optimal transformer early warning fusion index is 0.9, and the reference early warning monitoring prediction performance aging value is 0.98; the transformer early warning optimization index can be calculated through the above method, and the change statistics of the transformer early warning optimization index are shown in Table 1:

[0073] Table 1 Statistics of changes in transformer early warning optimization indicators

[0074]

[0075] It can be seen from the first and second groups of data in Table 1 that with the increase of the optimization sampling frequency, the amount of lost data in the optimized collection early warning monitoring decreases, and the transformer early warning optimization index increases accordingly. In addition, as can be seen from the second, third and fourth groups of data, with the increase of the fusion index of the transformer to be optimized, the deviation between the average predicted performance aging value of the optimized early warning monitoring fusion and the predicted performance aging value of the reference early warning monitoring decreases, and the transformer early warning optimization index also increases accordingly.

[0076] The transformer early warning optimization index is used to comprehensively quantify the degree of compliance of transformer early warning monitoring optimization. Among them, the transformer early warning optimization index contains multiple parameters, and each parameter is related and does not exist independently. For example, the optimization sampling frequency directly determines the precision of data collection. The increase in the optimization sampling frequency helps to capture more detailed information and reduce the loss of key signals, thereby reducing the amount of lost data in the optimization acquisition early warning monitoring, and thus increasing the transformer early warning optimization index; in addition, with the increase in the optimization sampling frequency and the decrease in the amount of lost data in the optimization acquisition early warning monitoring, the fusion index of the transformer to be optimized increases, and thus the transformer early warning optimization index also increases. At the same time, with the increase in the fusion index of the transformer to be optimized, the deviation between the average predicted performance aging value of the optimized early warning monitoring fusion and the predicted performance aging value of the reference early warning monitoring is reduced; therefore, through a quantitative method, the correlation and mutual influence between various factors are considered, and the transformer early warning optimization index is obtained through a comprehensive analysis, which realizes the numerical evaluation of the degree of compliance of the transformer early warning monitoring optimization, and the degree of compliance of the transformer early warning monitoring optimization is judged through the numerical evaluation, so as to better carry out the early warning monitoring of the intelligent transformer aging performance.

[0077] Furthermore, the specific process of judging whether to execute dynamic priority scheduling of transformer early warning monitoring resources based on the transformer early warning optimization index is as follows: comparing the transformer early warning optimization index with the preset early warning optimization threshold range obtained from the preset database; if the transformer early warning optimization index is within the preset early warning optimization interval, the dynamic priority scheduling of transformer early warning monitoring resources is not executed, and at the same time, the transformer fusion index is continuously monitored to see if it is within the preset early warning fusion threshold range; if the transformer early warning optimization index exceeds the preset early warning optimization interval, the dynamic priority scheduling of transformer early warning monitoring resources is executed; the dynamic priority scheduling of transformer early warning monitoring resources includes algorithm priority scheduling and monitoring resource scheduling.

[0078] In this embodiment, specifically, the preset early warning optimization threshold range is set by professionals according to standards in the field. For example, the collection index range of the transformer to be optimized is set to 0.8 to 0.9, the optimized sampling frequency range is 30 Hz to 100 Hz, the total data volume of the optimized collection early warning monitoring range is 100 bytes to 900 bytes, the lost data volume of the optimized collection early warning monitoring range is 20 bytes to 50 bytes, the fusion index range of the transformer to be optimized is 0.7 to 1, and the average predicted performance aging value range of the optimized early warning monitoring fusion is 0.3 to 1, thereby determining the preset early warning optimization threshold range.

[0079] Among them, algorithm priority scheduling is achieved through multi-level priority algorithms (such as shortest job priority, shortest remaining time priority, etc.). For example, when a task is scheduled for execution, the task with the shortest execution time is selected; monitoring resource scheduling is achieved through multi-task scheduling optimization algorithms (such as scheduling based on priority queues). For example, monitoring tasks are placed in queues according to their priorities, and the system allocates resources according to the priority order of the tasks. Specifically, priority queue scheduling can optimize resource allocation by first processing high-priority tasks (such as the temperature monitoring sensor shows that the oil temperature exceeds the safety threshold of 65 degrees Celsius to 80 degrees Celsius) and then processing low-priority tasks (such as archiving and storing historical operation data); by combining the preset early warning optimization threshold range for judgment and performing dynamic priority scheduling of transformer early warning monitoring resources, a more accurate judgment of the degree of compliance with the transformer early warning monitoring optimization is achieved.

[0080] like Figure 4 As shown, it is a flow chart of the intelligent electrical equipment early warning monitoring method based on multi-sensor interaction provided in an embodiment of the present application, and the method includes the following steps: S1, evaluating the collection quality of the collected transformer aging monitoring data to obtain a transformer collection index, judging whether to perform data fusion based on the transformer collection index, and the transformer collection index is used to quantitatively evaluate the compliance degree of the quality of the transformer aging monitoring data collected by the sensor; S2, after performing data fusion, evaluating the effect of the transformer early warning data fusion to obtain a transformer fusion index, judging whether to perform transformer early warning monitoring optimization based on the transformer fusion index, and the transformer fusion index is used to comprehensively quantify the fusion effect of the transformer aging monitoring data; S3, obtaining the transformer early warning optimization index after performing transformer early warning monitoring optimization, judging whether to perform transformer early warning monitoring resource dynamic priority scheduling based on the transformer early warning optimization index, and the transformer early warning optimization index is used to comprehensively quantify the compliance degree of the transformer early warning monitoring optimization.

[0081] In this embodiment, the transformer aging monitoring data is collected by sensors, thereby realizing the evaluation of the transformer aging monitoring data collection quality and the transformer early warning data fusion, and judging whether to perform transformer early warning monitoring optimization is based on the transformer early warning data fusion effect, thereby realizing more timely early warning monitoring of the aging performance of the intelligent transformer in the process of multi-sensor interaction.

[0082] To summarize, the embodiment of the present application evaluates the collection quality of the collected transformer aging monitoring data to determine whether to perform data fusion, then evaluates the effect of the transformer early warning data fusion and determines whether to perform transformer early warning monitoring optimization, and finally obtains the transformer early warning optimization index after performing the transformer early warning monitoring optimization to determine whether to perform transformer early warning monitoring resource dynamic priority scheduling, thereby realizing the evaluation of transformer early warning data fusion and monitoring optimization, and further realizing the improvement of the real-time performance warning of the intelligent transformer aging performance, effectively solving the problem of untimely early warning monitoring of the aging performance of the intelligent transformer based on multi-sensor interaction in the prior art.

[0083] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0084] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0085] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0086] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0087] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0088] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. Intelligent electrical equipment early warning monitoring system based on multi-sensor interaction, characterized in that: include: Data collection compliance judgment module, data fusion effect judgment module and monitoring optimization compliance judgment module; The data collection compliance judgment module is used to evaluate the collection quality of the collected transformer aging monitoring data to obtain a transformer collection index, and determine whether to perform data fusion based on the transformer collection index. The transformer collection index is used to quantitatively evaluate the compliance degree of the quality of the transformer aging monitoring data collected by the sensor; The data fusion effect judgment module is used to evaluate the effect of transformer early warning data fusion after data fusion is performed to obtain a transformer fusion index, and judge whether to perform transformer early warning monitoring optimization based on the transformer fusion index. The transformer fusion index is used to comprehensively quantify the fusion effect of transformer aging monitoring data; The monitoring optimization compliance judgment module is used to obtain the transformer early warning optimization index after executing the transformer early warning monitoring optimization, and judge whether to execute the dynamic priority scheduling of transformer early warning monitoring resources based on the transformer early warning optimization index. The transformer early warning optimization index is used to comprehensively quantify the compliance degree of the transformer early warning monitoring optimization.

2. The intelligent electrical equipment early warning monitoring system based on multi-sensor interaction as claimed in claim 1, characterized in that: The specific steps of evaluating the collection quality of the collected transformer aging monitoring data to obtain the transformer collection index are as follows: Performing statistical analysis on the transformer aging monitoring data collected by the monitoring sensor within a preset time interval to obtain the corresponding transformer monitoring average data; Acquire transformer collection and evaluation data within a preset time interval, wherein the transformer collection and evaluation data includes a total amount of transformer collection and monitoring data, an amount of transformer collection and monitoring lost data, and a transformer collection redundancy correlation coefficient; Obtain transformer acquisition indicators based on transformer monitoring average data, transformer acquisition evaluation data, and transformer acquisition related reference data obtained from a preset database; The monitoring sensors include current sensors, voltage sensors, temperature sensors, vibration sensors and gas sensors; The transformer monitoring average data includes transformer monitoring average temperature data, transformer monitoring average current data, transformer monitoring average voltage data, transformer average vibration amplitude data and transformer average methane concentration data; The transformer acquisition related reference data includes a transformer monitoring reference value, a transformer monitoring reference deviation, a reference acquisition redundancy correlation coefficient and a transformer monitoring acquisition correction factor.

3. The intelligent electrical equipment early warning monitoring system based on multi-sensor interaction as claimed in claim 1, characterized in that: The specific process of judging whether to perform data fusion based on transformer collection indicators is as follows: Compare the transformer collection index with a preset warning collection threshold range obtained from a preset database; If the transformer collection index is within the preset warning collection threshold range, data fusion is performed, and the fusion effect of the transformer warning monitoring data is evaluated to obtain the transformer fusion index; If the transformer collection index exceeds the preset warning collection threshold range, data fusion will not be performed, and the preset personnel will be reminded to re-collect the transformer aging monitoring data.

4. The intelligent electrical equipment early warning monitoring system based on multi-sensor interaction as claimed in claim 3, characterized in that: The specific steps of evaluating the fusion effect of transformer early warning monitoring data to obtain transformer fusion indicators are as follows: Obtain transformer fusion evaluation data within a preset time interval after executing data fusion; The transformer fusion evaluation data includes transformer collection indicators, early warning monitoring fusion data volume, total collection early warning monitoring data volume, early warning monitoring fusion time, predicted performance aging value and early warning monitoring fusion signal-to-noise ratio; The transformer fusion evaluation data is combined with the transformer fusion reference data obtained from a preset database to obtain a transformer fusion index; The transformer fusion reference data includes a reference optimal transformer acquisition index, a reference maximum fusion time, a reference predicted performance aging value, and a reference monitoring signal-to-noise ratio.

5. The intelligent electrical equipment early warning monitoring system based on multi-sensor interaction as claimed in claim 4, characterized in that: The method for obtaining the transformer fusion index is as follows: ； In the formula, represents the transformer fusion index, Indicates the number of times data is fused within the preset time interval. , Indicates the total number of data fusions within the preset time interval. represents the transformer collection index of the rth data fusion of transformer aging monitoring data, Indicates the reference optimal transformer acquisition index, represents the amount of early warning monitoring fusion data of the rth data fusion of transformer aging monitoring data, Indicates the total amount of data collected for early warning monitoring within the preset time interval. represents the reference maximum fusion time, represents the early warning monitoring fusion time of the rth data fusion of transformer aging monitoring data, represents the predicted performance aging value of the rth data fusion of transformer aging monitoring data, represents the reference predicted performance aging value, Indicates the fusion signal-to-noise ratio of early warning monitoring at a preset time interval, Indicates the reference monitoring signal-to-noise ratio.

6. The intelligent electrical equipment early warning monitoring system based on multi-sensor interaction as claimed in claim 1, characterized in that: The specific process of judging whether to perform transformer early warning monitoring optimization based on transformer fusion indicators is as follows: If the transformer fusion index is within the preset warning fusion threshold, the transformer warning monitoring optimization will not be performed, and the transformer aging monitoring data will be optimized and uploaded to the cloud storage; The optimization process includes transformer aging monitoring data anomaly detection, transformer aging monitoring data feature extraction and transformer aging monitoring data compression processing; If the transformer fusion index exceeds the preset early warning fusion threshold range, the transformer early warning monitoring optimization is executed, and the transformer early warning optimization index after the transformer early warning monitoring optimization is executed is obtained.

7. The intelligent electrical equipment early warning monitoring system based on multi-sensor interaction as claimed in claim 6, characterized in that: The specific process of transformer early warning monitoring optimization is as follows: A1, optimize the monitoring data collection of the transformer. When the transformer fusion index after the monitoring data collection optimization is within the preset early warning fusion threshold range, stop the transformer early warning monitoring optimization, otherwise execute A2; A2, perform data fusion optimization on the transformer, and determine that the transformer fusion index after data fusion optimization is within the preset warning fusion threshold range, then stop the transformer early warning monitoring optimization, otherwise execute A3; A3, optimize the early warning monitoring architecture of the transformer, and obtain the transformer early warning optimization index after executing the transformer early warning monitoring optimization.

8. The intelligent electrical equipment early warning monitoring system based on multi-sensor interaction as claimed in claim 6, characterized in that: The specific steps of obtaining the transformer early warning optimization index after performing transformer early warning monitoring optimization are as follows: Obtain the transformer indicators to be optimized and transformer optimization evaluation data after performing transformer early warning monitoring optimization; The transformer indicators to be optimized include transformer acquisition indicators to be optimized and transformer fusion indicators to be optimized; The transformer optimization evaluation data includes optimizing the sampling frequency, optimizing the total amount of data collected for early warning monitoring, optimizing the amount of lost data collected for early warning monitoring, and optimizing the average predicted performance aging value of early warning monitoring fusion; Obtaining transformer early warning optimization indicators according to transformer indicators to be optimized, transformer optimization evaluation data and transformer optimization reference data obtained from a preset database; The transformer optimization reference data includes a reference sampling frequency, a reference optimal transformer early warning fusion index, and a reference early warning monitoring prediction performance aging value.

9. The intelligent electrical equipment early warning monitoring system based on multi-sensor interaction as claimed in claim 1, characterized in that: The specific process of judging whether to perform dynamic priority scheduling of transformer early warning monitoring resources based on transformer early warning optimization indicators is as follows: If the transformer early warning optimization index is within the preset early warning optimization interval, the dynamic priority scheduling of transformer early warning monitoring resources will not be performed, and the transformer fusion index will be continuously monitored to see if it is within the preset early warning fusion threshold range; If the transformer early warning optimization index exceeds the preset early warning optimization interval, the dynamic priority scheduling of transformer early warning monitoring resources is executed; The transformer early warning monitoring resource dynamic priority scheduling includes algorithm priority scheduling and monitoring resource scheduling.

10. An intelligent electrical equipment early warning monitoring method based on multi-sensor interaction, characterized in that: The following steps are involved: S1, evaluating the collection quality of the collected transformer aging monitoring data to obtain a transformer collection index, and judging whether to perform data fusion based on the transformer collection index, wherein the transformer collection index is used to quantitatively evaluate the degree of compliance of the quality of the transformer aging monitoring data collected by the sensor; S2, after executing data fusion, evaluating the effect of transformer early warning data fusion to obtain a transformer fusion index, and judging whether to perform transformer early warning monitoring optimization based on the transformer fusion index, wherein the transformer fusion index is used to comprehensively quantify the fusion effect of transformer aging monitoring data; S3, obtaining the transformer early warning optimization index after executing the transformer early warning monitoring optimization, and judging whether to execute the dynamic priority scheduling of transformer early warning monitoring resources based on the transformer early warning optimization index, wherein the transformer early warning optimization index is used to comprehensively quantify the compliance degree of the transformer early warning monitoring optimization.

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