Intelligent monitoring system for operation state of distribution transformer

Abstract

The invention relates to the technical field of transformers and intelligent monitoring, and provides an intelligent monitoring system for the running state of a distribution transformer. The sensor network module is installed on a distribution transformer and collects related parameters of the distribution transformer in real time. The data transmission module transmits sensor data of the sensor network module to the data analysis module in real time, and the data analysis module receives the data transmitted by the data transmission module for analysis; the operation monitoring center obtains an analysis result of the data analysis module and displays the analysis result and the operation data of the distribution transformer; an alarm function and an input interface are provided; and the management module memorizes related information according to an analysis result of the data analysis module. By introducing fuzzy logic, when data with high fuzziness is processed, more accurate decision support is provided, and the response speed of the system is increased through a solution scheme of storing corresponding problem description through the management module.

Description

Technical Field

[0001] The invention relates to the technical field of transformer energy efficiency monitoring, in particular to an intelligent monitoring system for the operating status of a distribution transformer. Background Art

[0002] With the rapid development of social economy, the continuous growth of electricity demand, the wide access of distributed energy and the continuous expansion of the power market, distribution transformers, as important power equipment in the power system, have put forward higher requirements on their operating status and performance. Their safe and stable operation is crucial to ensure the quality and reliability of power supply. However, the traditional operation and management methods of distribution transformers have many limitations and cannot meet the needs of intelligent and efficient modern power systems.

[0003] The prior art still has the following deficiencies: 1. Traditional distribution transformer operation monitoring methods often require reaching a certain point variable or other data such as temperature to make a diagnosis. However, the actual situation is that many faults have already occurred quietly before reaching this threshold. This monitoring method that relies on a fixed threshold has great limitations and is prone to delayed diagnosis of faults. However, when the fault has not yet reached this threshold, there are many factors that affect the fault. Objective reasons make it impossible to conduct in-depth research on the distribution transformer at this time to explore whether it is really possible that the operating status may be abnormal. If only threshold monitoring is used, the fault cannot be effectively known in advance. As time goes by, these initial faults may gradually worsen and eventually cause serious faults, which will bring great harm to the stable operation of the power system and may even cause serious consequences such as large-scale power outages.

[0004] 2. In the existing distribution transformer operation status monitoring, the response measures after the fault occurs are not well matched. When a fault occurs, the existing technology usually has a set of emergency response plans, but due to the abnormal state or the large number of fault causes, it may not be possible to match the corresponding emergency plan according to the current abnormal state or match the wrong emergency plan, resulting in the inability to quickly and effectively take measures to contain the fault during the actual processing process, thereby prolonging the power outage time and causing great inconvenience and economic losses to users. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides an intelligent monitoring system for the operating status of a distribution transformer to solve the problems in the prior art.

[0006] The technical solution of the present invention is as follows: An intelligent monitoring system for the operating status of a distribution transformer, comprising: A sensor network module, wherein the sensor network module is used to install sensors on the distribution transformer to monitor the relevant parameters of the distribution transformer in real time and in all directions, including temperature data and voltage data; A data transmission module, which is data-connected to the sensor network module and is used to collect and transmit sensor data in the sensor network module in real time; A data analysis module, which is data-connected to the data transmission module and is used to receive data transmitted by the data transmission module and perform an analysis process including fuzzification, fuzzy reasoning and defuzzification to achieve evaluation, fault diagnosis and prediction of the operating status of the distribution transformer; A management module, which is data-connected to the data analysis module, performs identification and matching based on pre-stored solution information combined with analysis results of the data analysis module, and stores the matching results; An operation monitoring center, which is connected to the data analysis module and the management module, displays the operation status of the distribution transformer, the matching degree of the monitoring data and the solution in real time according to the analysis results of the data analysis module and the storage content of the management module, and provides an alarm function and a data input function; The data analysis module includes a data fuzzification unit, a fuzzy reasoning unit and a data defuzzification unit; The data fuzzification unit is used to define temperature and voltage fuzzy sets according to the operating parameters of the distribution transformer, and actively perform fuzzy processing on the original data; The fuzzy reasoning unit is used to make inferences based on the input monitoring data combined with fuzzy rules. After the data is fuzzified, the fuzzy reasoning unit makes inferences based on the input monitoring data and the pre-set fuzzy rules; The data defuzzification unit is used to convert the result of fuzzy reasoning into clear control instructions or decisions, and convert the obtained fuzzy reasoning result into data or results; The data fuzzification unit obtains relevant parameters of the distribution transformer during normal operation, including temperature and voltage, according to the factory instructions for use of the distribution transformer used, and defines a fuzzy set based on the parameters, the fuzzy set including but not limited to a normal range, a high range, and a low range; Indicates the distribution transformer related parameters transmitted by the data transmission module; The boundary or threshold membership function formula for relevant judgment is: High Range: in, and are two thresholds that define the high range value, Too high, For super high, Indicates the degree to which T belongs to the fuzzy set of high range; 0 means it does not belong to the high range, 1 means it belongs to the ultra-high range, Indicates the degree to which T belongs to the fuzzy set of high range; Normal range: in, Indicates the lower limit of the temperature range in normal working state. Indicates the upper limit of the temperature range in normal working state. It indicates the middle value of the temperature range when the distribution transformer is in normal working condition. 0 indicates that it is not in the normal range. and Indicates the degree to which T belongs to the fuzzy set of normal range; Low Range: in, and are two thresholds that define the low range value, Too low, For ultra-low, 0 means it is not in the low range, 1 means it is in the ultra-low range, Indicates the degree to which T belongs to the fuzzy set of low range. In fuzzy logic, for the relevant parameter T, the degree to which it belongs to the "low range" fuzzy set is measured based on the threshold and membership function. This degree is not an absolute value of 0 or 1, but a fuzzy value between 0 and 1.

[0007] Preferably, the fuzzy reasoning unit includes but is not limited to the following rules: Rule 1: Relevant parameters is within the normal range and the relevant parameters It is within the normal range, and the strategy of maintaining monitoring is adopted: Rule 2: Related parameters is a low range and related parameter If the range is high, take emergency maintenance strategy: Among them, W represents the temperature parameter among the related parameters, and U represents the voltage parameter among the related parameters. Indicates logical AND. In fuzzy logic, when ∧ is used for operation, the minimum value of the two is taken to represent the result of logical AND. It is the membership function obtained by fuzzy reasoning, which indicates the degree of strategy adoption.

[0008] Preferably, the calculation result of the data defuzzification unit is Defuzzification using the centroid method: Among them, O is the final clear decision result, R represents the value of the output variable in the membership function obtained by the fuzzy reasoning unit, and the value range is between 0 and 1. Represents the differential symbol, indicating an infinitesimal change, that is, It is an infinitesimal change in the output variable R, which is part of the integral operation.

[0009] Preferably, the operation monitoring center includes an interface unit, an alarm unit and an input unit; The interface unit is used to display the relevant parameters and monitoring status of the distribution transformer to relevant personnel; The alarm unit is used to give an alarm when the data analysis module detects an abnormal state, and the alarm modes include sound alarm, light alarm and information transmission alarm; The input unit is used to provide an input interface for system-related data for staff.

[0010] Preferably, the management module includes an identification unit and a storage unit; the identification unit calculates the matching degree between the problem description and the solution effect according to the problem description, the solution and the solution effect achieved, and measures the ability of the solution to solve the problem; and calculates the selection probability of the solution based on the matching degree between the problem description and the solution effect; the storage unit stores the solution with the highest selection probability identified by the identification unit and the corresponding problem description; Preferably, the calculation formula of the matching degree and the probability calculation of the scheme selection in the recognition unit are as follows: Calculation of matching degree d: Distribute the problem description a and solution b to the feature data space, mark the coordinate values ​​of a and b; the matching degree d is: Where d represents the matching degree. The smaller the matching degree d is, the more likely solution b is to solve problem description a. n represents the total number of solutions and corresponding problem descriptions, that is, the total number of i. i represents the i-th solution and corresponding problem description in the dimensional space. After calculating the matching degree, for the problem description The probability of selection , and the matching degree Proportional, the calculation formula is: Among them, i represents the feature index of the i-th solution and problem description in the dimensional space, j is an index variable used to traverse all solutions; m represents the total number of solutions. By traversing from j=1 to j=m, the matching degrees of all solutions are summed up to calculate the value of the denominator, which is used to determine the probability of a solution being selected.

[0011] Preferably, the sensor network module deploys multiple sensors including voltage sensors and temperature sensors.

[0012] Preferably, the transmission mode of the data transmission module includes wired communication mode and wireless communication mode.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention introduces fuzzy logic control to make the monitoring and control process of distribution transformers more intelligent and efficient. In particular, when processing data with strong uncertainty and ambiguity, the present invention can provide more accurate decision support, thereby reducing the losses caused by misjudgment or delayed processing.

[0014] 2. The present invention identifies and selects the best solution according to the characteristics and objectives of the problem description through the identification unit in the management module, and the storage unit can save these solutions and corresponding problem descriptions for reuse in future operations, thereby improving the response speed and accuracy of the system.

[0015] 3. The present invention realizes real-time monitoring and data analysis of distribution transformers through the sensor network module, ensuring the accuracy and real-time nature of the data. At the same time, the interface unit, alarm unit and input unit of the operation monitoring center provide a friendly input interface and flexible interaction mode, so that relevant staff can easily obtain monitoring data, receive alarm information and perform operation input. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a work flow chart of the present invention.

[0017] Figure 2 It is an analysis flow chart of the data analysis module of the present invention. DETAILED DESCRIPTION

[0018] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0019] like Figure 1-Figure 2 As shown, the present invention provides an intelligent monitoring system for the operating status of a distribution transformer, comprising: A sensor network module, wherein the sensor network module is used to install sensors on the distribution transformer to monitor the relevant parameters of the distribution transformer in real time and in all directions, including temperature data and voltage data; A data transmission module, which is data-connected to the sensor network module and is used to collect and transmit sensor data in the sensor network module in real time; A data analysis module, which is data-connected to the data transmission module and is used to receive data transmitted by the data transmission module and perform an analysis process including fuzzification, fuzzy reasoning and defuzzification to achieve evaluation, fault diagnosis and prediction of the operating status of the distribution transformer; A management module, which is data-connected to the data analysis module, performs identification and matching based on pre-stored solution information combined with analysis results of the data analysis module, and stores the matching results; The operation monitoring center is connected to the data analysis module and the management module. According to the analysis results of the data analysis module and the storage content of the management module, the operation monitoring center displays the operation status of the distribution transformer, the matching degree of the monitoring data and the solution in real time, and provides alarm function and data input function.

[0020] Embodiment 1: In this embodiment, the system is installed in a distribution transformer in a certain area, and the monitoring system monitors the operating status of the distribution transformer through a sensor network module, wherein the sensor network module deploys a variety of sensors including voltage sensors and temperature sensors.

[0021] The data transmission module transmits the voltage data and temperature data of the distribution transformer to the data analysis module in real time through wireless communication. The data analysis module includes a data fuzzification unit, a fuzzy reasoning unit and a data defuzzification unit; The data fuzzification unit is used to define temperature and voltage fuzzy sets according to the operating parameters of the distribution transformer, which is to actively fuzzify the original precise data. For example, for temperature data, the temperature range can be divided into fuzzy sets such as "low temperature", "medium temperature" and "high temperature" based on experience and actual operating requirements; for voltage data, it can also be divided into fuzzy sets such as "low voltage", "normal voltage" and "high voltage". The advantage of this fuzzification process is that it can convert complex precise data into fuzzy language descriptions that are more in line with human thinking and convenient for rule processing.

[0022] The fuzzy reasoning unit is used to make inferences based on the input monitoring data combined with fuzzy rules. After the data is fuzzified, the fuzzy reasoning unit makes inferences based on the input monitoring data, that is, the fuzzified temperature and voltage data and the pre-set fuzzy rules. This step is the core of the whole process. It infers the operating status of the distribution transformer and other related information based on the fuzzified data and rules; The data defuzzification unit is used to convert the results of fuzzy reasoning into clear control instructions or decisions. For example, fuzzy reasoning results in the fuzzy state of the distribution transformer being "relatively dangerous", and defuzzification can be used to derive a specific risk probability.

[0023] The data fuzzification unit obtains relevant parameters of the distribution transformer when it is working normally according to the factory-related instructions for use of the distribution transformer used, and defines a fuzzy set based on the parameters, and the fuzzy set includes but is not limited to a normal range, a high range, and a low range; Relevant parameters of the data transmission module input, such as operating environment temperature parameter W and voltage parameter U, are represented as ; The boundary or threshold membership function formula for relevant judgment is: High Range: in, and are two thresholds that define the high range value, Too high, For super high, Indicates the degree to which T belongs to the fuzzy set of high range; 0 means it does not belong to the high range, 1 means it belongs to the ultra-high range, Indicates the degree to which T belongs to the fuzzy set of high range. This degree indicates that the situation is in a fuzzy transition state between normal and abnormal, which means that there is a trend towards abnormality but it has not reached complete abnormality. From the decision-making perspective, preventive measures may be needed, which is different from the situation where no treatment is required when it is completely normal and immediate correction is required when it is completely abnormal. Normal range: in, Indicates the lower limit of the temperature range in normal working state. Indicates the upper limit of the temperature range in normal working state. It indicates the middle value of the temperature range when the distribution transformer is in normal working condition. 0 indicates that it is not in the normal range. and Indicates the degree to which T belongs to the fuzzy set of normal range; Low Range: in, and are two thresholds that define the low range value, Too low, For ultra-low, 0 means it is not in the low range, 1 means it is in the ultra-low range, Indicates the degree to which T belongs to the fuzzy set of low range. In fuzzy logic, for temperature, the degree to which it belongs to the "low range" fuzzy set is measured by a specific threshold-based membership function. This degree is not an absolute value of 0 or 1, but a fuzzy value between 0 and 1. Among the voltage and data transmitted by the data transmission module, for example, there is a set of data, the current temperature value ℃, current voltage value volt; The membership function of temperature data is determined as follows: =80, which is the lower limit threshold of the high range. =90℃, which is the upper limit threshold of the excessive range; Calculated based on the judgment The degree of belonging to the high-range fuzzy set is is 0.8; Lower limit of normal temperature range =65℃, upper limit =75℃, the middle value is =67.5℃; =60℃, which is the lower limit threshold of the low range. =40℃, which is the ultra-low temperature threshold; The voltage data membership function is determined as follows: =240V, which is the lower threshold of the high voltage range, =255V, which is the ultra-high voltage threshold; Normal voltage range lower limit =220V, upper limit =230 V, mid-range It is 225 volts.

[0024] =220V, which is the lower limit of the low range, =180V, which is the ultra-low voltage threshold. The current voltage value is 185V. According to the corresponding membership function, the degree to which U belongs to the low-range fuzzy set is calculated. is 0.5; After the above judgment, the temperature in this set of data is in the too high range and the voltage is in the too low range. If the membership function obtained after the judgment is directly 1, it means that the distribution transformer has fallen into a fault, and the information is immediately transmitted to the management module to arrange maintenance; Next, the fuzzy reasoning unit confirms the fuzzy rules. In the data transmitted this time, the two sets of data, voltage and temperature, include but are not limited to the following rules; Rule 1: Relevant parameters is within the normal range and the relevant parameters It is within the normal range, and the strategy of maintaining monitoring is adopted: Rule 2: Related parameters is a low range and related parameter If the range is high, take emergency maintenance strategy: Among them, W represents the temperature parameter among the related parameters, and U represents the voltage parameter among the related parameters. Indicates logical AND. In fuzzy logic, when ∧ is used for operation, the minimum value of the two is taken to represent the result of logical AND. It is the membership function obtained by fuzzy reasoning, which indicates the degree of strategy adopted.

[0025] After judgment, this set of fuzzy data belongs to rule 2, that is, to adopt the strategy of emergency maintenance, and the degree of adopting the emergency maintenance strategy is ; The data defuzzification unit uses the centroid method to defuzzify, and calculates according to the following formulas: ; Among them, O is the final clear decision result, R represents the value of the output variable in the membership function obtained by the fuzzy reasoning unit, and the value range is between 0 and 1. Represents the differential symbol, indicating an infinitesimal change, that is, It is the infinitesimal change of the output variable R, which is part of the integral operation. The purpose of defuzzification is to transform the fuzzy result after fuzzy reasoning into a clear result that can be directly used in practical operations, and to transform the originally fuzzy concept that there may be something wrong with the distribution transformer into a specific and measurable indicator. By introducing fuzzy logic control, the monitoring process of distribution transformers is made more intelligent and efficient. In particular, when processing data with strong uncertainty and ambiguity, the present invention can provide more accurate decision support, thereby reducing losses caused by misjudgment or delayed processing.

[0026] Embodiment 2: Figure 1-Figure 2 As shown, in this embodiment, the distribution transformer has already had an abnormal operation, and a corresponding solution needs to be matched to solve the abnormal operation problem of the distribution transformer; The operation monitoring center includes an interface unit, an alarm unit and an input unit; The interface unit is used to display the relevant parameters and monitoring status of the distribution transformer to relevant personnel; The alarm unit is used to give an alarm when the data analysis module detects an abnormal state. The alarm modes include sound alarm, light alarm and information transmission alarm. The input unit is used to provide staff with an input interface for system-related data.

[0027] By operating the interface unit, alarm unit and input unit of the monitoring center, an interactive method is provided, so that relevant staff can easily obtain monitoring data, receive alarm information and perform operational input.

[0028] The management module includes an identification unit and a storage unit; The identification unit clarifies the problem description and solution as well as the solution effect achieved, calculates the match between the goal and the solution effect, and measures the ability of the solution to solve the problem; The storage unit memorizes the solutions with high matching degree identified by the identification unit and the corresponding problem description, and can store some solution plans in advance according to the determination in the first embodiment, so as to respond quickly when the fault determination result appears; The calculation formula for the matching degree in the recognition unit and the probability of solution selection are calculated as follows: Calculation of matching degree d: Distribute the problem description a and solution b to the feature data space, mark the coordinate values ​​of a and b; the matching degree d is: Where d represents the matching degree. The smaller the matching degree d is, the more likely solution b is to solve problem description a. n represents the total number of solutions and corresponding problem descriptions, that is, the total number of i. i represents the i-th solution and corresponding problem description in the dimensional space. After calculating the matching degree, for the problem description The probability of selection , and the matching degree Proportional, the calculation formula is: Among them, i represents the feature index of the i-th solution and problem description in the dimensional space, j is an index variable used to traverse all solutions; m represents the total number of solutions. By traversing from j=1 to j=m, the matching degrees of all solutions are summed up to calculate the value of the denominator, which is used to determine the probability of the solution being selected. At the same time, it can also be selected based on the degree of matching. The solution with high matching degree is recommended first, and the solution with low matching degree is selected as an alternative. When the matching degrees are equal, they are selected according to the above probability.

[0029] The recognition unit identifies and selects the optimal solution based on the characteristics and objectives of the problem description, and the storage unit is able to save these solutions and the corresponding problem description for reuse in future operations, thereby improving the response speed and accuracy of the system.

[0030] The recognition unit in the management module of this system calculates the matching degree by distributing the problem description and solution to the feature data space. This matching degree calculation method based on the feature space can more accurately measure the degree of fit between the solution and the problem.

[0031] For example, when faced with a complex fault scenario (problem description) caused by both high temperature and low voltage, the system can accurately evaluate the matching degree of different solutions, avoiding the situation where emergency plans are incorrectly matched due to the numerous causes of the fault, thereby improving the pertinence and effectiveness of fault handling.

[0032] The embodiments of the present invention are provided for the purpose of illustration and description. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations of the present invention. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. An intelligent monitoring system for the operating status of a distribution transformer, characterized in that: include: A sensor network module, wherein the sensor network module is used to install sensors on the distribution transformer to monitor the relevant parameters of the distribution transformer in real time and in all directions, including temperature data and voltage data; A data transmission module, which is data-connected to the sensor network module and is used to collect and transmit sensor data in the sensor network module in real time; A data analysis module, which is data-connected to the data transmission module and is used to receive data transmitted by the data transmission module and perform an analysis process including fuzzification, fuzzy reasoning and defuzzification to achieve evaluation, fault diagnosis and prediction of the operating status of the distribution transformer; A management module, which is data-connected to the data analysis module, performs identification and matching based on pre-stored solution information combined with analysis results of the data analysis module, and stores the matching results; An operation monitoring center, which is connected to the data analysis module and the management module, displays the operation status of the distribution transformer, the matching degree of the monitoring data and the solution in real time according to the analysis results of the data analysis module and the storage content of the management module, and provides an alarm function and a data input function; The data analysis module includes a data fuzzification unit, a fuzzy reasoning unit and a data defuzzification unit; The data fuzzification unit is used to define temperature and voltage fuzzy sets according to the operating parameters of the distribution transformer, and actively perform fuzzy processing on the original data; The fuzzy reasoning unit is used to make inferences based on the input monitoring data combined with fuzzy rules. After the data is fuzzified, the fuzzy reasoning unit makes inferences based on the input monitoring data and the pre-set fuzzy rules; The data defuzzification unit is used to convert the result of fuzzy reasoning into clear control instructions or decisions, and convert the obtained fuzzy reasoning result into data or results; The data fuzzification unit obtains relevant parameters of the distribution transformer during normal operation according to the factory instructions of the distribution transformer used, including temperature and voltage, and defines a fuzzy set according to the parameters, the fuzzy set including but not limited to a normal range, a high range, and a low range; Indicates the distribution transformer related parameters transmitted by the data transmission module; The boundary or threshold membership function formula is: High Range: in, and are two thresholds that define the high range value, Too high, For super high, Indicates the degree to which T belongs to the fuzzy set of high range; 0 means it does not belong to the high range, 1 means it belongs to the ultra-high range, Indicates the degree to which T belongs to the fuzzy set of high range; Normal range: in, Indicates the lower limit of the temperature range in normal working state. Indicates the upper limit of the temperature range in normal working state. It indicates the middle value of the temperature range when the distribution transformer is in normal working condition. 0 indicates that it is not in the normal range. and Indicates the degree to which T belongs to the fuzzy set of normal range; Low Range: in, and are two thresholds that define the low range value, Too low, For ultra-low, 0 means it is not in the low range, 1 means it is in the ultra-low range, Indicates the degree to which T belongs to the fuzzy set of low range. In fuzzy logic, for the relevant parameter T, the degree to which it belongs to the "low range" fuzzy set is measured based on the threshold and membership function. This degree is not an absolute value of 0 or 1, but a fuzzy value between 0 and 1.

2. The intelligent monitoring system for the operating status of a distribution transformer according to claim 1, characterized in that: The fuzzy reasoning unit includes but is not limited to the following rules: Rule 1: Relevant parameters is within the normal range and the relevant parameters It is within the normal range, and the strategy of maintaining monitoring is adopted: Rule 2: Related parameters is a low range and related parameter If the range is high, take emergency maintenance strategy: Among them, W represents the temperature parameter among the related parameters, and U represents the voltage parameter among the related parameters. Indicates logical AND. In fuzzy logic, when ∧ is used for operation, the minimum value of the two is taken to represent the result of logical AND. It is the membership function obtained by fuzzy reasoning, which indicates the degree of strategy adopted.

3. The intelligent monitoring system for the operating status of a distribution transformer according to claim 2, characterized in that: The calculation result of the data defuzzification unit Defuzzification using the centroid method: Among them, O is the final clear decision result, R represents the value of the output variable in the membership function obtained by the fuzzy reasoning unit, and the value range is between 0 and 1. Represents the differential symbol, indicating an infinitesimal change, that is, It is an infinitesimal change in the output variable R, which is part of the integral operation.

4. The intelligent monitoring system for the operating status of a distribution transformer according to claim 1, characterized in that: The operation monitoring center includes an interface unit, an alarm unit and an input unit; The interface unit is used to display the relevant parameters and monitoring status of the distribution transformer to personnel; The alarm unit is used to give an alarm when the data analysis module detects an abnormal state, and the alarm modes include sound alarm, light alarm and information transmission alarm; The input unit is used to provide an input interface for system data for the staff.

5. The intelligent monitoring system for the operating status of a distribution transformer according to claim 1, characterized in that: The management module includes an identification unit and a storage unit; the identification unit calculates the matching degree between the problem description and the solution effect according to the problem description and the solution and the achieved solution effect, and measures the ability of the solution to solve the problem; And the probability of selecting a solution is calculated based on the match between the problem description and the solution effect; The storage unit stores the solution with the highest selection probability identified by the identification unit and the corresponding problem description.

6. The intelligent monitoring system for the operating status of a distribution transformer according to claim 5, characterized in that: The calculation formula of the matching degree in the recognition unit and the probability calculation of the scheme selection are as follows: Calculation of matching degree d: Distribute the problem description a and solution b to the feature data space, mark the coordinate values ​​of a and b; the matching degree d is: Where d represents the matching degree. The smaller the matching degree d is, the more likely solution b is to solve problem description a. n represents the total number of solutions and corresponding problem descriptions, that is, the total number of i. i represents the i-th solution and corresponding problem description in the dimensional space. After calculating the matching degree, for the problem description The probability of selection , and the matching degree Proportional, the calculation formula is: Among them, i represents the feature index of the i-th solution and problem description in the dimensional space, j is an index variable used to traverse all solutions; m represents the total number of solutions. By traversing from j=1 to j=m, the matching degrees of all solutions are summed up to calculate the value of the denominator, which is used to determine the probability of a solution being selected.

7. The intelligent monitoring system for the operating status of a distribution transformer according to claim 1, characterized in that: The sensor network module deploys a variety of sensors including voltage sensors and temperature sensors.

8. The intelligent monitoring system for the operating status of a distribution transformer according to claim 1, characterized in that: The transmission mode of the data transmission module includes wired communication mode and wireless communication mode.

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