Method for analyzing operation condition of power distribution device based on temperature and humidity sensor

Abstract

The invention discloses a method and system for analyzing the operation condition of a power distribution device based on a temperature and humidity sensor, and relates to the technical field of electric power, and the method comprises the steps: deploying the temperature and humidity sensor at a key position of the power distribution device, and preprocessing the data of the temperature and humidity sensor; constructing a power distribution device operation condition analysis model based on the preprocessed temperature and humidity sensor data; predicting the operation condition of the key position based on a power distribution device operation condition analysis model; based on a manufacturer guide and an equipment specification, determining a normal range of an operation condition; comparing the normal range of the operation condition with the predicted operation condition, dispatching a related department to process the key power distribution device based on a comparison result, and recording a processing result; and optimizing the power distribution device operation condition analysis model based on the processing result. By deploying the temperature and humidity sensors and constructing the analysis model, the operation state of the key position of the power distribution device is monitored, abnormal conditions are found and processed in time, and the maintenance efficiency of the power distribution system is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of electric power systems, and in particular to a method for analyzing the operation status of a power distribution device based on a temperature and humidity sensor. Background Art

[0002] With the continuous improvement of the intelligence and automation level of power systems, temperature and humidity monitoring technology has become one of the key means to ensure the safe and stable operation of distribution equipment. Changes in temperature and humidity will not only affect the insulation performance of electrical equipment, but may also cause corrosion, condensation and other problems. By deploying temperature and humidity sensors at key locations, environmental changes can be monitored in real time, potential risks can be discovered in a timely manner, and early warning and preventive maintenance of the distribution system can be achieved.

[0003] As an efficient machine learning method, support vector machine has shown strong capabilities in areas such as pattern recognition and regression analysis. The distribution device operation analysis model established using SVM can accurately predict the working status of the equipment and improve the prediction accuracy by continuously optimizing the model. This method greatly improves maintenance efficiency and the overall stability of the system. By integrating modern data analysis technology and intelligent algorithms, the power industry is gradually realizing the transition from passive response to active prediction. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method for analyzing the operating conditions of a power distribution device based on a temperature and humidity sensor to solve the problem of passive response in the traditional maintenance mode of the power system.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a method for analyzing the operation of a power distribution device based on a temperature and humidity sensor, which includes deploying a temperature and humidity sensor at a key position of the power distribution device and preprocessing the temperature and humidity sensor data;

[0008] Based on the pre-processed temperature and humidity sensor data, a distribution device operation analysis model is constructed;

[0009] Based on the distribution equipment operation analysis model, predict the operation status of key locations;

[0010] Determine normal range of operation based on manufacturer's guidelines and equipment specifications;

[0011] Compare the normal operating range with the predicted operating conditions, dispatch relevant departments to handle key power distribution devices based on the comparison results, and record the handling results;

[0012] Based on the processing results, the distribution device operation analysis model is optimized.

[0013] As a preferred solution of the method for analyzing the operation status of a power distribution device based on a temperature and humidity sensor of the present invention, wherein: the key positions of the power distribution device include the inside of a power distribution cabinet, the contact parts of the power distribution device, the inside of a transformer cooling oil tank and the cable joints;

[0014] The temperature and humidity sensor data includes temperature value, humidity value, data acquisition timestamp and sensor ID;

[0015] The preprocessing includes aggregating the temperature and humidity sensor data by wired and wireless means, and uploading the aggregated temperature and humidity sensor data to a cloud platform for data cleaning, data interpolation, and data vectorization.

[0016] As a preferred solution of the method for analyzing the operation status of a power distribution device based on a temperature and humidity sensor according to the present invention, a power distribution device operation status analysis model is constructed based on the pre-processed temperature and humidity data. The specific steps are as follows:

[0017] The support vector machine model is trained based on the preprocessed temperature and humidity data as the distribution device operation analysis model, which is expressed as:

[0018] P=∑ i∈SV α i y i exp(-γ||xx i || 2 )+b;

[0019] In the formula, P represents the predicted value of the operation status of the distribution device, α i is the Lagrange multiplier, y i is the target variable for the operation analysis value label, exp(-γ||xx i || 2 ) is the kernel function that maps the original data to a higher dimensional space, and b is the bias term that adjusts the position of the decision boundary;

[0020] Parameter α in the distribution device operation analysis model i and b are solved based on the quadratic programming method.

[0021] As a preferred solution of the method for analyzing the operation status of a power distribution device based on a temperature and humidity sensor according to the present invention, the working status of a key position is predicted based on the power distribution device operation status analysis model, and the specific steps are as follows:

[0022] Based on real-time data, including temperature values, humidity values, data collection timestamps and sensor IDs, the real-time collected temperature and humidity data are input into the distribution device operation analysis model to make predictions for each key location.

[0023] As a preferred solution of the method for analyzing the operation status of a power distribution device based on a temperature and humidity sensor according to the present invention, the normal range of the operation status is determined based on the manufacturer's guide and equipment specifications. The specific steps are as follows:

[0024] Based on the manufacturer's guidelines and equipment specifications for the power distribution unit, determine the operating temperature range threshold and humidity range threshold for each critical location.

[0025] As a preferred solution of the method for analyzing the operation status of a power distribution device based on a temperature and humidity sensor according to the present invention, wherein: comparing the operation status analysis value with the normal range of the operation status, the specific steps are as follows:

[0026] When the predicted equipment operation condition meets the temperature range threshold and humidity range threshold, it is considered normal;

[0027] When the predicted equipment operation meets the temperature range threshold but does not meet the humidity range threshold, it is a Level 1 emergency state;

[0028] When the predicted equipment operation does not meet the temperature range threshold, it is a level 2 emergency.

[0029] As a preferred solution of the method for analyzing the operation status of a power distribution device based on a temperature and humidity sensor according to the present invention, the following specific steps are taken:

[0030] When in a level 1 emergency state, the measures taken are to generate a maintenance work order and immediately notify the maintenance team to go to the site for inspection. The maintenance team will arrive at the site with necessary testing tools, conduct a detailed inspection of key locations, record the inspection results, deal with the cause of the humidity abnormality, and close the work order after completion;

[0031] When in a Level 2 emergency state, the measures taken are to generate an emergency maintenance work order and immediately notify the emergency response team to go to the site. The emergency response team will arrive at the site with necessary testing tools and spare parts, conduct a comprehensive inspection of key locations, and record the inspection results. The emergency response team should diagnose the cause of the fault as soon as possible, and immediately replace the damaged parts and repair the damaged lines to fix the fault, and then close the work order after completion;

[0032] As a preferred solution of the method for analyzing the operation status of a power distribution device based on a temperature and humidity sensor according to the present invention, wherein: based on the processing results, the analysis model of the operation status of the power distribution device is optimized, and the specific steps are as follows:

[0033] Update the training data set of the distribution device operation analysis model according to the processing results, and retrain the distribution device operation analysis model to optimize the model performance;

[0034] Deploy the optimized model to the production environment and replace the original model for real-time prediction;

[0035] Continuously monitor the performance of the optimized model and regularly perform optimization iterations based on newly collected data.

[0036] In a second aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the method for analyzing the operating conditions of a power distribution device based on temperature and humidity sensors as described in the first aspect of the present invention is implemented.

[0037] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the method for analyzing the operating conditions of a power distribution device based on temperature and humidity sensors as described in the first aspect of the present invention is implemented.

[0038] The beneficial effects of the present invention are as follows: by collecting temperature and humidity data in real time, abnormal conditions at key locations of the distribution device can be discovered in time, and measures can be taken in advance to avoid the occurrence of potential faults; by accurately predicting the operating status of the distribution device, the risk of safety accidents caused by equipment failure can be effectively reduced, and the stability and reliability of the power system can be improved; based on the prediction results, a more accurate maintenance plan can be formulated to reduce unnecessary maintenance work and ensure that key components are inspected and repaired in a timely and effective manner; by timely discovering and handling potential problems, unplanned downtime can be significantly reduced, and the availability and work efficiency of the distribution device can be improved; by optimizing maintenance work, high maintenance costs and downtime losses can be reduced, and the overall operating cost can be reduced; through automated monitoring and analysis, the management process of the distribution device is simplified, and the work efficiency of managers is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0040] Figure 1 This is a flow chart of the method for analyzing the operating conditions of a power distribution device based on temperature and humidity sensors in Example 1.

[0041] Figure 2 This is a determination diagram of the operating status of the power distribution device in Example 1. DETAILED DESCRIPTION

[0042] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0043] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0044] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0045] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a method for analyzing the operation status of a power distribution device based on a temperature and humidity sensor, comprising the following steps:

[0046] S1. Deploy temperature and humidity sensors at key locations of the power distribution device and pre-process the temperature and humidity sensor data, including the following steps:

[0047] Deploy temperature and humidity sensors (with accuracy and range meeting the requirements of the corresponding locations) at key locations of the power distribution device, including but not limited to inside the power distribution cabinet, contact points of the power distribution device, inside the transformer cooling oil tank, and cable joints. The temperature and humidity sensor data includes temperature value, humidity value, data collection timestamp, and sensor ID.

[0048] Preprocessing includes aggregating temperature and humidity sensor data through wired and wireless methods, and uploading the aggregated temperature and humidity sensor data to the cloud platform for data cleaning, data interpolation and data vectorization to ensure data quality and make it suitable for subsequent data analysis and modeling.

[0049] Data cleaning removes invalid or erroneous data; data interpolation fills in missing values; data vectorization facilitates computer processing.

[0050] It should be noted that abnormal temperature and humidity are usually precursors to equipment failure. Real-time monitoring can detect potential problems early, thus avoiding major accidents. For key equipment such as distribution cabinets and contact parts, excessive temperature or humidity may lead to electrical performance degradation or even damage. Monitoring these parameters helps improve the overall reliability and service life of the system. Data analysis can help determine the best maintenance cycle and implement condition-based maintenance (CBM) instead of traditional time-based maintenance (TBM), which can significantly reduce unnecessary maintenance costs and extend equipment life. Effective monitoring and preprocessing of temperature and humidity data can not only help power system managers to detect and solve potential problems in a timely manner, but also improve the overall performance and economic benefits of the system through data analysis. These measures are an important part of modern smart grids and industrial Internet of Things applications.

[0051] S2. Based on the pre-processed temperature and humidity sensor data, a distribution device operation analysis model is constructed, including the following steps:

[0052] The preprocessed temperature and humidity data are used to train the support vector machine model as the distribution device operation analysis model, which is expressed as:

[0053] P=∑ i∈SV α i y i exp(-γ||xx i || 2 )+b;

[0054] In the formula, P represents the predicted value of the operation status of the distribution device, α i is the Lagrange multiplier, obtained by solving the quadratic programming problem during training. Only support vectors (i.e., those sample points closest to the decision boundary) have non-zero Lagrange multiplier values, y i is the target variable for the running analysis value label, which indicates the true category of the training sample, exp(-γ||xx i || 2 ) is the kernel function that maps the original data to a higher dimensional space. It is used to map the data in the original low dimensional space to a higher dimensional space so that the decision boundary between the data can be found more easily. b is the bias term that adjusts the position of the decision boundary. It is used to adjust the position of the decision boundary so that the classifier can correctly divide the positive and negative samples.

[0055] Evaluate the model performance on the test set to ensure that the model generalizes well.

[0056] The trained model is deployed to the production environment to predict the operation of the power distribution device in real time.

[0057] It should be noted that the support vector machine model still performs well in the case of small samples, which is particularly useful for power system monitoring data, because sometimes it may be difficult to obtain a large amount of training data; the support vector machine model is trained using labeled temperature and humidity data. During the training process, the support vector machine model will find the optimal support vector and decision boundary, and minimize the classification error by adjusting the Lagrange multiplier and bias term.

[0058] S3. Based on the distribution device operation analysis model, predict the operation status of key locations, including the following steps:

[0059] The real-time data is vectorized, including temperature value, humidity value, data acquisition timestamp and sensor ID, and the vectorized real-time data is input into the distribution device operation analysis model to make predictions for each key location (including but not limited to the inside of the distribution cabinet, the contact parts of the distribution device, the inside of the transformer cooling oil tank and the cable joints).

[0060] S4. Determine the normal range of operation based on the manufacturer's guidelines and equipment specifications, including the following steps:

[0061] Obtain the user manual, operating guide, and technical specification documents provided by the manufacturer of the power distribution unit and review the relevant sections in those documents to find the recommended operating ranges for temperature and humidity.

[0062] For each critical location, find the recommended operating temperature range given in the manufacturer's guide, including the minimum operating temperature and the maximum operating temperature, find the recommended operating humidity range, including the minimum relative humidity and the maximum relative humidity, and determine the operating temperature range threshold and humidity range threshold for each critical location.

[0063] S5. Compare the normal operating range with the predicted operating conditions, dispatch relevant departments to handle the key power distribution devices based on the comparison results, and record the processing results, including the following steps:

[0064] When the predicted device operation meets the temperature range threshold and the humidity range threshold, it is a normal state; when the predicted device operation meets the temperature range threshold but does not meet the humidity range threshold, it is a level one emergency state; when the predicted device operation does not meet the temperature range threshold, it is a level two emergency state.

[0065] When in a level 1 emergency state, the measures taken are to generate a maintenance work order (create a maintenance task order to record the inspection and maintenance work that needs to be done), and immediately notify the maintenance team to go to the site for inspection. The maintenance team arrives at the site with necessary testing tools, conducts a detailed inspection of key locations, and records the inspection results (records all inspection results, including problems found and measures taken), handles the cause of the humidity abnormality, and closes the work order after completion;

[0066] When in a level 2 emergency state, the measures taken are to generate an emergency maintenance work order (similar to the level 1 emergency state, but more urgent) and immediately notify the emergency response team (a team of experienced technicians) to go to the site. The emergency response team arrives at the site with necessary testing tools and spare parts, conducts a comprehensive inspection of key locations, and records the inspection results. The emergency response team should diagnose the cause of the fault as soon as possible. If necessary, immediately replace the damaged parts and repair the damaged lines to repair the fault, and close the work order after completion.

[0067] S6. Based on the processing results, optimizing the distribution device operation analysis model includes the following steps:

[0068] The training data set of the distribution device operation analysis model is updated according to the processing results, and the distribution device operation analysis model is retrained to optimize the model performance, with the aim of improving the accuracy and reliability of the model by adding new and verified data points.

[0069] The optimized model is deployed to the production environment to replace the original model for real-time prediction, with the aim of enabling the model to better adapt to the current actual situation and improve prediction accuracy.

[0070] Continuously monitor the performance of the optimized model and regularly repeat the above steps based on newly collected data to maintain the effectiveness of the model and ensure that the model can always accurately reflect the actual operating status of the distribution device.

[0071] This embodiment also provides a computer device, which is suitable for the method of analyzing the operating status of a distribution device based on temperature and humidity sensors, and includes: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the method of analyzing the operating status of a distribution device based on temperature and humidity sensors as proposed in the above embodiment.

[0072] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0073] The present embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for analyzing the operating conditions of a power distribution device based on a temperature and humidity sensor as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (Static Random Access Memory, SRAM for short), an electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, EEPROM for short), an erasable programmable read-only memory (Erasable Programmable Read Only Memory, EPROM for short), a programmable read-only memory (Programmable Red-Only Memory, PROM for short), a read-only memory (Read-Only Memory, ROM for short), a magnetic memory, a flash memory, a disk or an optical disk.

[0074] In summary, the present invention proposes a method for analyzing the operation status of a distribution device based on a temperature and humidity sensor, by analyzing the problems of insulation degradation, corrosion, condensation, etc. caused by temperature and humidity changes in the distribution device in the power system. By deploying temperature and humidity sensors in key locations such as inside the distribution cabinet and contact points, real-time monitoring of environmental changes, and using support vector machines (SVM) to build an operation status analysis model, the working status of the equipment can be accurately predicted. This method combines the manufacturer's guidelines and equipment specifications to determine the normal operating range. When the predicted results exceed the normal range, a maintenance team or emergency response team can be dispatched for processing according to different levels of urgency, and the model can be optimized based on the processing results. This solution solves the problem of passive response in the traditional maintenance mode, realizes early warning and preventive maintenance of the distribution system, and thus improves the safety and reliability of the power system.

[0075] Example 2

[0076] Referring to Table 1, which is the second embodiment of the present invention, in order to further verify the advancement of the present invention, experimental simulation data of a method for analyzing the operating conditions of a power distribution device based on temperature and humidity sensors are provided.

[0077] A typical power distribution room was selected as the test site, which contains key locations such as distribution cabinets, contact points, transformers and cable connectors.

[0078] Temperature and humidity sensors with accuracy and range that meet the requirements of the corresponding locations were deployed. A total of 20 sensors were installed inside the distribution cabinet (4), at the contact points (4), inside the transformer cooling oil tank (4), and at the cable joints (8).

[0079] A server is prepared to receive and process sensor data and upload it to the cloud platform for pre-processing.

[0080] User manuals and technical specification documents from the power distribution unit manufacturer were obtained to determine the normal operating temperature and humidity ranges at each key location.

[0081] In the data preprocessing stage, the temperature and humidity sensor data are aggregated through wired and wireless methods and uploaded to the cloud platform for data cleaning, data interpolation, and data vectorization.

[0082] A distribution device operation analysis model based on support vector machine was constructed. The model was trained using historical temperature and humidity data, and the model performance was evaluated on the test set to ensure that the model had good generalization ability.

[0083] The trained model is deployed to the production environment to predict the operation of key locations of the power distribution equipment in real time.

[0084] Based on the guidelines and technical specification documents provided by the manufacturer, the normal range of operating temperature and humidity was determined for each critical location.

[0085] The real-time data is vectorized and input into the distribution device operation analysis model for prediction.

[0086] According to the prediction results, when key locations are in different emergency states, corresponding measures are taken, including generating maintenance work orders or emergency maintenance work orders, and dispatching maintenance teams or emergency response teams for on-site processing.

[0087] Based on the processing results, the training dataset is updated, the model is retrained to optimize performance, and the optimized model is deployed to the production environment.

[0088] Record the data collected during the test. The table will contain data records of 20 sensors, each record includes sensor ID, temperature value, humidity value, acquisition timestamp, predicted operating status and other information, as shown in Table 1:

[0089] Table 1 Distribution equipment monitoring data experimental record table

[0090]

[0091]

[0092] By analyzing the above table data, we can draw the following conclusions:

[0093] The operating status predicted by the model is completely consistent with the actual status, indicating that the model has a high prediction accuracy. There were no false positives or negatives in all the records, which further proves the good performance of the model. All sensor readings are within the manufacturer's recommended operating range, indicating that the distribution device is in normal operation. Even under high temperature and high humidity conditions (such as S19 and S20), the model can still accurately predict that the equipment is in normal condition, showing the robustness of the model under extreme conditions. By monitoring the temperature and humidity changes at key locations in real time, the invention can timely detect potential failure risks and take corresponding preventive measures. Compared with traditional manual inspection methods, the invention can significantly reduce the downtime caused by equipment failure and improve the reliability and efficiency of the overall system. Due to the use of the support vector machine model, the invention can handle complex nonlinear relationships, thereby more accurately predicting the changing trend of the equipment operating status. In this experiment, although all records were in normal condition, the responsiveness and optimization potential of the model can be further tested by introducing abnormal data (such as simulating temperature or humidity values ​​above the normal range). By continuously collecting new data and updating the training set, the model performance can be continuously optimized to adapt to changes in the operating environment of the distribution device.

[0094] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for analyzing the operation status of a power distribution device based on a temperature and humidity sensor, characterized in that: include, Deploy temperature and humidity sensors at key locations of power distribution equipment and pre-process the temperature and humidity sensor data; Based on the pre-processed temperature and humidity sensor data, a distribution device operation analysis model is constructed; Based on the distribution equipment operation analysis model, predict the operation status of key locations; Determine normal range of operation based on manufacturer's guidelines and equipment specifications; Compare the normal operating range with the predicted operating conditions, dispatch relevant departments to handle key power distribution devices based on the comparison results, and record the handling results; Based on the processing results, the distribution device operation analysis model is optimized.

2. The method for analyzing the operation status of a power distribution device based on a temperature and humidity sensor according to claim 1, characterized in that: The key positions of the power distribution device include the inside of the power distribution cabinet, the contact parts of the power distribution device, the inside of the transformer cooling oil tank and the cable joints; The temperature and humidity sensor data includes temperature value, humidity value, data acquisition timestamp and sensor ID; The preprocessing includes aggregating the temperature and humidity sensor data by wired and wireless means, and uploading the aggregated temperature and humidity sensor data to a cloud platform for data cleaning, data interpolation, and data vectorization.

3. The method for analyzing the operation status of a power distribution device based on a temperature and humidity sensor according to claim 1, characterized in that: Based on the pre-processed temperature and humidity data, a distribution device operation analysis model is constructed. The specific steps are as follows: The support vector machine model is trained based on the preprocessed temperature and humidity data as the distribution device operation analysis model, which is expressed as: P=∑ i∈SV a i y i exp(-γ||xx i || 2 )+b; In the formula, P represents the predicted value of the operation status of the distribution device, α i is the Lagrange multiplier, y i is the target variable for the operation analysis value label, exp(-γ||xx i || 2 ) is the kernel function that maps the original data to a higher dimensional space, and b is the bias term that adjusts the position of the decision boundary; Parameter α in the distribution device operation analysis model i and b are solved based on the quadratic programming method.

4. The method for analyzing the operation status of a power distribution device based on a temperature and humidity sensor according to claim 3, characterized in that: Based on the distribution device operation analysis model, the working status of key locations is predicted. The specific steps are as follows: Based on real-time data, including temperature values, humidity values, data collection timestamps and sensor IDs, the real-time collected temperature and humidity data are input into the distribution device operation analysis model to make predictions for each key location.

5. The method for analyzing the operation status of a power distribution device based on a temperature and humidity sensor according to claim 4, characterized in that: Determine the normal range of operation based on the manufacturer's guidelines and equipment specifications. Follow these steps: Based on the manufacturer's guidelines and equipment specifications for the power distribution unit, determine the operating temperature range threshold and humidity range threshold for each critical location.

6. The method for analyzing the operation status of a power distribution device based on a temperature and humidity sensor according to claim 5, characterized in that: Compare the operating condition analysis value with the normal operating condition range. The specific steps are as follows: When the predicted equipment operation condition meets the temperature range threshold and humidity range threshold, it is considered normal; When the predicted equipment operation meets the temperature range threshold but does not meet the humidity range threshold, it is a Level 1 emergency state; When the predicted equipment operation does not meet the temperature range threshold, it is a level 2 emergency.

7. The method for analyzing the operation status of a power distribution device based on a temperature and humidity sensor according to claim 6, characterized in that: Based on the comparison, the relevant departments are dispatched to process the key parts and the processing results are recorded. The specific steps are as follows: When in a level 1 emergency state, the measures taken are to generate a maintenance work order and immediately notify the maintenance team to go to the site for inspection. The maintenance team will arrive at the site with necessary testing tools, conduct a detailed inspection of key locations, record the inspection results, deal with the cause of the humidity abnormality, and close the work order after completion; When in a level 2 emergency state, the measures taken are to generate an emergency maintenance work order and immediately notify the emergency response team to go to the site. The emergency response team will arrive at the site with necessary testing tools and spare parts, conduct a comprehensive inspection of key locations, and record the inspection results. The emergency response team should diagnose the cause of the fault as soon as possible, and immediately take measures such as replacing damaged parts and repairing damaged lines to repair the fault, and close the work order after completion.

8. The method for analyzing the operation status of a power distribution device based on a temperature and humidity sensor according to claim 7, characterized in that: Based on the processing results, the distribution device operation analysis model is optimized. The specific steps are as follows: Update the training data set of the distribution device operation analysis model according to the processing results, and retrain the distribution device operation analysis model to optimize the model performance; Deploy the optimized model to the production environment and replace the original model for real-time prediction; Continuously monitor the performance of the optimized model and regularly perform optimization iterations based on newly collected data.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for analyzing the operating conditions of a power distribution device based on a temperature and humidity sensor as described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for analyzing the operating conditions of a power distribution device based on a temperature and humidity sensor as described in any one of claims 1 to 8 are implemented.

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