Method and system for detecting temperature and humidity of inflatable composite insulated cross arm

Abstract

The invention relates to the technical field of high-voltage power transmission, in particular to a temperature and humidity detection method and system for an inflatable composite insulated cross arm, and the method comprises the steps: collecting the temperature and humidity data of a key part of the inflatable composite insulated cross arm through a temperature and humidity sensor; determining the environment state by analyzing the change rule of internal temperature and humidity data, and analyzing the influence of the environment state on the leakage current and the flashover voltage; analyzing, calculating and learning the received data through a GABP neural network; and presenting the data analyzed in real time and the predicted data to a user interface in the form of images. The method has the advantages that data can be utilized more effectively through a network structure in which the GABP neural network is combined with the genetic algorithm, the sensor can provide more comprehensive environment and equipment state information through the multifunctional integrated sensor, and the monitoring accuracy and reliability are improved.

Description

Technical Field

[0001] The invention relates to the technical field of high-voltage power transmission, and in particular to a method and system for detecting temperature and humidity of an air-filled composite insulating cross arm. Background Art

[0002] Transmission towers are an indispensable and important part of high-voltage overhead transmission lines. They are the backbone of the power grid and have a huge impact on the safety, economy, and reliability of the low-voltage power grid. In the past, transmission towers were mostly made of steel because of its easy connection and high strength. But at the same time, steel will also increase the width of the transmission corridor and the height of the transmission tower, making the cost of line construction higher.

[0003] Nowadays, the material of transmission towers has been developing towards high-performance fiber-reinforced composite materials. The crossarms of transmission towers have begun to use this composite material. Among them, the inflatable composite insulating crossarm has the advantages of strong insulation performance, good corrosion resistance, high strength and light weight. However, the inflatable composite insulating crossarm often encounters complex natural environment during use, and it is also subjected to high pressure and mechanical force, which will cause internal air pressure leakage, further leading to internal surface flashover, threatening the safety of power grid operation. Summary of the invention

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

[0005] Therefore, the purpose of the present invention is to provide a method for detecting temperature and humidity of an inflatable composite insulating crossarm, which can solve the problem that after the sealing of the inflatable composite insulating crossarm fails, the internal temperature and humidity change and its insulation performance is affected.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for detecting temperature and humidity of an inflatable composite insulating crossarm, which comprises collecting temperature and humidity data of key parts of the inflatable composite insulating crossarm through a temperature and humidity sensor;

[0007] By analyzing the changing patterns of its internal temperature and humidity data, the environmental state is determined, and its impact on leakage current and flashover voltage is analyzed;

[0008] Analyze, calculate and learn the received data through GA_BP neural network;

[0009] The real-time analysis data and predicted data are presented in the form of images on the user interface.

[0010] As a preferred solution of the method for detecting temperature and humidity of the inflatable composite insulation crossarm of the present invention, the temperature and humidity sensor is installed on the inner side of the inflatable composite insulation crossarm to collect temperature and humidity data at multiple points inside the inflatable composite insulation crossarm in real time.

[0011] As a preferred scheme of the method for detecting temperature and humidity of the inflatable composite insulation cross-arm of the present invention, wherein: leakage current refers to the phenomenon of unintentional current flow in a circuit or electronic equipment. This current is usually caused by equipment insulation failure, component damage or improper design, which will lead to circuit performance degradation and equipment failure; flashover voltage refers to the voltage between two electrodes at the moment before the flashover occurs, wherein flashover refers to the breakdown between two electrodes along the surface of solid insulation in the gas under the action of high voltage. The flashover voltage is affected by factors such as the surface state and shape of the solid insulation, and is always lower than or equal to the spark discharge voltage of the gas gap with the same electrode structure and the same distance.

[0012] As a preferred solution of the method for detecting temperature and humidity of the inflatable composite insulating crossarm of the present invention, the GA_BP neural network is provided with historical data. By comparing the real-time data collected by the temperature and humidity sensor with the historical data, the GA_BP neural network identifies whether the current environmental state is normal or whether there are abnormal changes.

[0013] As a preferred solution of the method for detecting temperature and humidity of the inflatable composite insulating cross-arm of the present invention, the user interface is responsible for presenting complex data and analysis results to the operation and maintenance personnel in an intuitive and easy-to-understand manner, including displaying real-time data of key parameters inside the current cross-arm, allowing users to query and view historical data, displaying early warning signals for abnormal situations, displaying predicted temperature and humidity change trends, and allowing users to remotely configure sensor parameters.

[0014] To solve the above technical problems, the present invention also provides the following technical solutions: an inflatable composite insulating cross-arm temperature and humidity detection system, comprising a high-precision sensing module, a data acquisition and transmission module connected to the high-precision sensing module, a processing module connected to the data acquisition and transmission module, a prediction and early warning module connected to a neural network algorithm processing unit, and an interactive and remote monitoring module connected to the processing module.

[0015] As a preferred solution of the inflatable composite insulation cross-arm temperature and humidity detection system of the present invention, the high-precision sensing module adopts advanced temperature and humidity sensors and is installed at key positions of the inflatable composite insulation cross-arm; the data acquisition and transmission module transmits the data collected by the sensor to the central processing unit through wireless or wired communication technology; the processing module has a built-in high-performance processor and is equipped with a specially designed neural network algorithm for real-time analysis and learning of temperature and humidity data; the prediction and early warning module combines historical data and real-time analysis results, and the neural network algorithm can predict the future temperature and humidity change trends of the cross-arm, and promptly issue an early warning signal when an abnormality is detected; the interactive and remote monitoring module provides a friendly user operation interface, supports remote monitoring and management functions, and is convenient for operation and maintenance personnel to grasp the status of the cross-arm at any time.

[0016] As a preferred solution of the temperature and humidity detection system of the inflatable composite insulating crossarm of the present invention, the high-precision sensing module includes a temperature sensor and a humidity sensor for real-time monitoring of the temperature and humidity of the inflatable composite insulating crossarm.

[0017] As a preferred solution of the inflatable composite insulation cross-arm temperature and humidity detection system of the present invention, the processing module is equipped with a neural network algorithm designed for temperature and humidity data, which is used to perform real-time analysis and learning of temperature and humidity data and identify potential abnormal patterns.

[0018] As a preferred solution of the temperature and humidity detection system of the inflatable composite insulation cross arm of the present invention, wherein: the interactive and remote monitoring module includes a user operation interface and remote monitoring;

[0019] The prediction and warning module can issue warning signals in time when an anomaly is detected, and display them to operation and maintenance personnel through the user interface.

[0020] Beneficial effects of the present invention: The present invention can more effectively utilize data and improve the generalization ability of the model through the network structure of the GA_BP neural network combined with the genetic algorithm. The multifunctional integrated sensor integrates the collection of temperature, humidity, voltage and current data, so that the sensor can provide more comprehensive environment and equipment status information, thereby improving the accuracy and reliability of monitoring. Through the innovative design method of secondary fusion, the sensor is combined with the insulator body, which achieves a breakthrough in the limitations of traditional equipment, so that the insulator has both mechanical properties and data collection functions, improves the monitoring performance of the insulator, and enables it to monitor its own operating status in real time and discover potential problems in time, thereby improving the safety and reliability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0022] Figure 1 The figure is a flow chart of the method for detecting temperature and humidity of air-filled composite insulation crossarms.

[0023] Figure 2 It is a schematic diagram of the first implementation method of the inflatable composite insulation cross-arm temperature and humidity detection system.

[0024] Figure 3 It is a schematic diagram of the second implementation method of the inflatable composite insulation cross-arm temperature and humidity detection system.

[0025] Figure 4 This is a schematic diagram of the installation of the high-precision sensor module of the inflatable composite insulation cross-arm temperature and humidity detection system.

[0026] Figure 5 This is a schematic diagram of the neural network structure of the temperature and humidity detection system for the air-filled composite insulation cross-arm.

[0027] Figure 6 This is the flow chart of the GA_BP neural network model of the temperature and humidity detection system for the air-filled composite insulation cross-arm. DETAILED DESCRIPTION

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Example 1

[0032] Reference Figure 1 , is an embodiment of the present invention, which provides a method for detecting temperature and humidity of an inflatable composite insulating crossarm, which can solve the problem that after the sealing of the inflatable composite insulating crossarm fails, the internal temperature and humidity change and its insulation performance is affected.

[0033] Specifically, the temperature and humidity data of key parts of the inflatable composite insulation crossarm are collected through the temperature and humidity sensors;

[0034] By analyzing the changing patterns of its internal temperature and humidity data, the environmental state is determined, and its impact on leakage current and flashover voltage is analyzed;

[0035] Analyze, calculate and learn the received data through GA_BP neural network;

[0036] The real-time analysis data and predicted data are presented in the form of images on the user interface.

[0037] Specifically, during the data collection stage, temperature and humidity sensors are used to collect internal temperature and humidity data at key locations of the inflatable composite insulation crossarm. These data are real-time and can reflect the immediate environmental status inside the crossarm.

[0038] Entering the data transmission stage, the collected temperature and humidity data are transmitted to the central processing unit through wireless or wired communication technology, ensuring the real-time and accuracy of the data, and providing a basis for subsequent analysis and processing;

[0039] In the data processing and learning stage, the data is learned through the analysis and calculation of the GA_BP neural network. The GA_BP neural network combines the advantages of the genetic algorithm and the BP neural network. The genetic algorithm optimizes the weights and biases of the BP neural network, thereby improving the generalization ability and accuracy of the model.

[0040] Furthermore, during the neural network training process, the genetic algorithm optimizes the network parameters through selection, crossover and mutation operations, thus improving the learning efficiency and prediction accuracy of the network;

[0041] In the trend prediction and analysis stage, based on the real-time analyzed data and historical data, the GA_BP neural network can predict the future trend of temperature and humidity changes. This prediction helps to identify potential environmental changes in advance so that preventive measures can be taken; by analyzing the changing patterns of temperature and humidity data, the environmental state is determined and its impact on leakage current and flashover voltage is analyzed. This helps to understand how environmental factors affect the operating performance of power equipment.

[0042] The user interface display stage includes data image display and user operation and remote monitoring. The data image display presents the real-time analysis data and predicted data in the form of images to the user interface. The user interface supports multiple chart types, such as line charts, area charts, pie charts, etc., to intuitively display data trends and comparisons. User operation and remote monitoring provide a friendly user operation interface and support remote monitoring and management functions, which makes it convenient for operation and maintenance personnel to grasp the status of the crossarm at any time and make timely responses based on the information provided by the system.

[0043] It should be noted that the algorithm and internal structure used by the neural network of the neural network algorithm processing unit can be modified according to actual needs to adapt to different monitoring environments and needs; the time interval for collecting data can be defined by itself to meet different monitoring frequency requirements.

[0044] In summary, the present invention can more effectively utilize data and improve the generalization ability of the model through the network structure of the GA_BP neural network combined with the genetic algorithm. The multifunctional integrated sensor integrates the collection of temperature, humidity, voltage and current data, so that the sensor can provide more comprehensive environmental and equipment status information, thereby improving the accuracy and reliability of monitoring. Through the innovative design method of secondary fusion, the sensor is combined with the insulator body, which achieves a breakthrough in the limitations of traditional equipment, so that the insulator has both mechanical properties and data collection functions, improves the monitoring performance of the insulator, and enables it to monitor its own operating status in real time and discover potential problems in time, thereby improving the safety and reliability of the power system.

[0045] Example 2

[0046] Reference Figure 1 , which is the second embodiment of the present invention, provides a method for detecting temperature and humidity of an inflatable composite insulating crossarm, which can solve the problem that after the sealing of the inflatable composite insulating crossarm fails, the internal temperature and humidity change and its insulation performance is affected.

[0047] Specifically, the temperature and humidity data of key parts of the inflatable composite insulation crossarm are collected through the temperature and humidity sensors;

[0048] By analyzing the changing patterns of its internal temperature and humidity data, the environmental state is determined, and its impact on leakage current and flashover voltage is analyzed;

[0049] Analyze, calculate and learn the received data through GA_BP neural network;

[0050] The real-time analysis data and predicted data are presented in the form of images on the user interface.

[0051] Furthermore, the temperature and humidity sensors are installed on the inner side of the air-filled composite insulation cross arm to collect the temperature and humidity data at multiple points inside the air-filled composite insulation cross arm in real time.

[0052] Furthermore, leakage current refers to the unintentional current flow phenomenon in circuits or electronic equipment. This current is usually caused by equipment insulation failure, component damage or improper design, which will lead to circuit performance degradation and equipment failure; flashover voltage refers to the voltage between two electrodes at the moment before the flashover occurs, where flashover refers to the breakdown between two electrodes along the surface of solid insulation in the gas under the action of high voltage. The flashover voltage is affected by factors such as the surface state and shape of the solid insulation, and is always lower than or equal to the spark discharge voltage of the gas gap with the same electrode structure and the same distance.

[0053] Furthermore, historical data is provided in the GA_BP neural network. By comparing the real-time data collected by the temperature and humidity sensor with the historical data, the GA_BP neural network identifies whether the current environmental state is normal or whether there are abnormal changes. The GA_BP neural network is a neural network that combines genetic algorithm (GA) and back propagation (BP).

[0054] It should be noted that for the temperature and humidity detection of air-filled composite insulation crossarms, the specific steps of GA_BP neural network weight optimization include: initializing the population, fitness function, selection, crossover, mutation, iterative evolution, and extraction of the optimal solution.

[0055] Initializing the population includes combining all weights and biases in the neural network into an individual, which constitutes a population, where each individual represents a set of possible weight and bias values ​​for the forward propagation calculation of the neural network;

[0056] The fitness function is the error between the actual output of the neural network and the true value. The fitness function evaluates the performance of each individual, that is, how they perform in the neural network. Specifically, in the temperature and humidity detection of the inflatable composite insulation crossarm, the fitness function evaluation usually involves calculating the error between the predicted value and the actual value, such as the mean square error (MSE);

[0057] The selection includes, through the principle of “survival of the fittest”, selecting the individuals with the highest fitness to reproduce in the next generation, which means that individuals with smaller errors have a higher chance of being selected;

[0058] Crossover includes simulating the mating process in biological genetics, where the selected individuals produce offspring through a crossover operation. During the crossover process, the weights and biases of the two parent individuals are combined in some way to generate new individuals.

[0059] Mutation involves randomly changing certain weights and bias values ​​in individuals at a certain mutation rate. This mutation operation can increase the diversity of the population and prevent the algorithm from converging to a local optimal solution too early.

[0060] Iterative evolution involves the evolution of a population over multiple generations through repeated selection, crossover, and mutation operations. In each generation, the fitness of individuals is evaluated and selection is performed based on these fitness values ​​in the hope of gradually evolving individuals with better performance.

[0061] The extraction of the optimal solution includes, after a certain number of generations of evolution, selecting the individual with the highest fitness, that is, the individual with the smallest error, from the population, and extracting its weight and bias as the optimal parameters of the neural network.

[0062] The MSE calculation method is as follows:

[0063]

[0064] Among them, y i is the actual observed value of the i-th sample that the model should predict, is the model's predicted value for the ith sample of the actual observation, and n is the number of samples, that is, the total number of data points in the data set.

[0065] Furthermore, the user interface is responsible for presenting complex data and analysis results to the operation and maintenance personnel in an intuitive and easy-to-understand manner, which includes displaying real-time data of key parameters inside the current crossarm, allowing users to query and view historical data, displaying early warning signals for abnormal situations, showing predicted temperature and humidity change trends, and allowing users to remotely configure sensor parameters.

[0066] In summary, the present invention can more effectively utilize data and improve the generalization ability of the model through the network structure of the GA_BP neural network combined with the genetic algorithm. The multifunctional integrated sensor integrates the collection of temperature, humidity, voltage and current data, so that the sensor can provide more comprehensive environmental and equipment status information, thereby improving the accuracy and reliability of monitoring. Through the innovative design method of secondary fusion, the sensor is combined with the insulator body, which achieves a breakthrough in the limitations of traditional equipment, so that the insulator has both mechanical properties and data collection functions, improves the monitoring performance of the insulator, and enables it to monitor its own operating status in real time and discover potential problems in time, thereby improving the safety and reliability of the power system.

[0067] Example 3

[0068] Reference Figure 2 to Figure 6 , which is the third embodiment of the present invention, provides an inflatable composite insulation cross-arm temperature and humidity detection system, which can solve the problem that after the sealing of the inflatable composite insulation cross-arm fails, the internal temperature and humidity change and its insulation performance is affected.

[0069] Specifically, a high-precision sensing module 100, a data acquisition and transmission module 200 connected to the high-precision sensing module 100, a processing module 300 connected to the data acquisition and transmission module 200, a prediction and early warning module 400 connected to the neural network algorithm processing unit, and an interaction and remote monitoring module 500 connected to the processing module 300.

[0070] Furthermore, the high-precision sensing module 100 adopts advanced temperature and humidity sensors 102, which are installed at key positions of the inflatable composite insulating crossarm; the data acquisition and transmission module 200 transmits the data collected by the sensor to the central processing unit through wireless or wired communication technology; the processing module 300 has a built-in high-performance processor and is equipped with a specially designed neural network algorithm for real-time analysis and learning of temperature and humidity data; the prediction and early warning module 400 combines historical data and real-time analysis results, and the neural network algorithm can predict the future temperature and humidity change trends of the crossarm, and promptly issue early warning signals when an abnormality is detected; the interactive and remote monitoring module 500 provides a user-friendly operation interface and supports remote monitoring and management functions, making it convenient for operation and maintenance personnel to grasp the status of the crossarm at any time.

[0071] Furthermore, the high-precision sensing module 100 includes a temperature sensor 101 and a humidity sensor 102 for real-time monitoring of the temperature and humidity of the inflatable composite insulating crossarm.

[0072] Furthermore, the processing module 300 is equipped with a neural network algorithm designed for temperature and humidity data, which is used to perform real-time analysis and learning of the temperature and humidity data and identify potential abnormal patterns.

[0073] Furthermore, the interaction and remote monitoring module 500 includes a user operation interface and remote monitoring;

[0074] The prediction and warning module 400 can issue a warning signal in time when an abnormality is detected, and display it to the operation and maintenance personnel through the user operation interface.

[0075] Specifically, Figure 3 As shown in the figure, the structure diagram of the inflatable composite insulation cross-arm temperature and humidity detection device based on the neural network, the leftmost side of the figure is the inflatable composite insulation cross-arm, and a temperature and humidity sensor 102 is placed inside the composite cross-arm. In order to maximize the processing speed of the neural network model, this neural network adopts a model pruning operation. The neural network structure diagram is shown in the figure. Figure 5 shown.

[0076] Among them, BP neural network is a multi-layer feedforward network trained by the error back propagation algorithm. The BP neural network structure consists of an input layer, a hidden layer, and an output layer. The data information transmission between the nodes in each layer is divided into two stages: the first stage is the forward propagation of the signal, from the input layer through the hidden layer, and finally to the output layer; the second stage is the reverse propagation of the error, from the output layer to the hidden layer, and finally to the input layer, and the weights and biases from the hidden layer to the output layer and from the input layer to the hidden layer are adjusted in turn.

[0077] In a neural network, neurons need to pass through an activation function. Figure 2 and Figure 3The figure shows the topological structure of a single hidden layer of a BP neural network. Among them, x1, x2, ..., xn are the actual inputs of the network; y is the actual output of the network; u1, u2, ..., um are the network neurons; v11, v12, ..., vnm are the weight elements from the input layer to the hidden layer; ω1, ω2, ..., ωm are the weight matrix elements from the hidden layer to the output layer.

[0078] like Figure 4 As shown in FIG. 1 , it is a schematic diagram of the installation of the high-precision sensor module 100, that is, a simplified cross-sectional diagram of the inflatable composite insulating crossarm. Compared with the installation position of the traditional sensor, Figure 4 The sensor location shown also allows for more effective detection of partial discharge signals within the equipment while avoiding distortion of the electric field and threatening the safety of the device.

[0079] like Figure 6 As shown in the figure, it is a flow chart of the GA_BP neural network model. First, the weight parameters are initialized and encoded, and then the conformity of the collected data is calculated to see if it meets the conditions. If it does not meet the conditions, it is associated with the normal data. After data transformation, new data is generated to obtain the optimal temperature and humidity thresholds. The error with the standard temperature and humidity data is calculated. If the corresponding error conditions are met, simulation prediction is performed to determine the final output result. When initializing the threshold, real number encoding is used to connect the weights and thresholds between each layer, and 1 real number string is 1 individual. The encoding length calculation formula is as follows:

[0080] 1=x·m+m+m·s+s

[0081] Among them, 1 is the coding length; is the number of input layer nodes of the BP neural network; is the number of hidden layer nodes; is the number of output layer nodes.

[0082] The workflow of the entire system is as follows: the high-precision sensing module 100 collects temperature and humidity data inside the inflatable insulating crossarm, the high-precision sensing module 100 transmits the collected temperature and humidity data to the processing module 300 through the data acquisition and transmission module 200, the processing module 300 analyzes, calculates and learns the data, the processing module 300 predicts future temperature and humidity change trends based on real-time analysis data and historical data, and the processing module 300 presents the real-time analysis data and predicted data in the form of images to the interactive and remote monitoring module 500.

[0083] In summary, the present invention can more effectively utilize data and improve the generalization ability of the model through the network structure of the GA_BP neural network combined with the genetic algorithm. The multifunctional integrated sensor integrates the collection of temperature, humidity, voltage and current data, so that the sensor can provide more comprehensive environmental and equipment status information, thereby improving the accuracy and reliability of monitoring. Through the innovative design method of secondary fusion, the sensor is combined with the insulator body, which achieves a breakthrough in the limitations of traditional equipment, so that the insulator has both mechanical properties and data collection functions, improves the monitoring performance of the insulator, and enables it to monitor its own operating status in real time and discover potential problems in time, thereby improving the safety and reliability of the power system.

[0084] Importantly, it should be noted that the construction and arrangement of the present application shown in a plurality of different exemplary embodiments are only exemplary. Although only a few embodiments are described in detail in this disclosure, it should be readily understood by those who refer to this disclosure that many modifications are possible without substantially departing from the novel teachings and advantages of the subject matter described in the application (e.g., mounting arrangements, use of materials, color, changes in orientation, etc.). For example, the element shown as integrally formed may be composed of a plurality of parts or elements, the position of the element may be inverted or otherwise changed, and the nature or number or position of the discrete element may be altered or changed. Therefore, all such modifications are intended to be included within the scope of the present invention. The order or sequence of any process or method steps may be changed or reordered according to an alternative embodiment. In the claims, any "bracket plus function" clause is intended to cover the structure of the execution function described herein, and is not only structurally equivalent but also equivalent structure. Without departing from the scope of the present invention, other replacements, modifications, changes and omissions may be made in the design, operating conditions and arrangement of the exemplary embodiments. Therefore, the present invention is not limited to a specific embodiment, but extends to a variety of modifications that still fall within the scope of the appended claims.

[0085] Additionally, in an effort to provide a concise description of example embodiments, all features of an actual implementation may not be described.

[0086] It should be understood that in the development of any actual implementation, as in any engineering or design project, numerous implementation-specific decisions may be made. Such a development effort may be complex and time-consuming, but for those of ordinary skill having the benefit of this disclosure, the development effort will be a routine task of design, fabrication, and production without undue experimentation.

[0087] 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 detecting temperature and humidity of an inflatable composite insulation cross arm, characterized in that: include, The temperature and humidity data of key parts of the inflatable composite insulation crossarm are collected through temperature and humidity sensors; By analyzing the changing patterns of its internal temperature and humidity data, the environmental state is determined, and its impact on leakage current and flashover voltage is analyzed; Analyze, calculate and learn the received data through GA_BP neural network; The real-time analysis data and predicted data are presented in the form of images on the user interface.

2. The method for detecting temperature and humidity of an inflatable composite insulation cross arm as claimed in claim 1, characterized in that: The temperature and humidity sensor is installed on the inner side of the air-filled composite insulating cross arm to collect temperature and humidity data at multiple points inside the air-filled composite insulating cross arm in real time.

3. The method for detecting temperature and humidity of an inflatable composite insulation cross arm as claimed in claim 2, characterized in that: The leakage current refers to the unintentional current flow phenomenon in a circuit or electronic device. This current is usually caused by equipment insulation failure, component damage or improper design, which will lead to circuit performance degradation and equipment failure; the flashover voltage refers to the voltage between the two electrodes at the moment before the flashover occurs, where flashover refers to the breakdown between the two electrodes along the surface of the solid insulation in the gas under the action of high voltage. The flashover voltage is affected by factors such as the surface state and shape of the solid insulation, and is always lower than or equal to the spark discharge voltage of the gas gap with the same electrode structure and the same distance.

4. The method for detecting temperature and humidity of an inflatable composite insulation cross arm as claimed in claim 3, characterized in that: The GA_BP neural network is provided with historical data. By comparing the real-time data collected by the temperature and humidity sensor with the historical data, the GA_BP neural network identifies whether the current environmental state is normal or whether there is an abnormal change.

5. The method for detecting temperature and humidity of an inflatable composite insulation cross arm as claimed in claim 4, characterized in that: The user interface is responsible for presenting complex data and analysis results to operation and maintenance personnel in an intuitive and easy-to-understand manner, including displaying real-time data of key parameters inside the current crossarm, allowing users to query and view historical data, displaying early warning signals for abnormal situations, showing predicted temperature and humidity change trends, and allowing users to remotely configure sensor parameters.

6. A temperature and humidity detection system for an inflatable composite insulation cross arm, which is applicable to a method for detecting temperature and humidity of an inflatable composite insulation cross arm, characterized in that: include, A high-precision sensing module (100), a data acquisition and transmission module (200) connected to the high-precision sensing module (100), a processing module (300) connected to the data acquisition and transmission module (200), a prediction and early warning module (400) connected to the processing module (300), and an interaction and remote monitoring module (500) connected to the processing module (300).

7. The inflatable composite insulation cross arm temperature and humidity detection system as claimed in claim 6, characterized in that: The high-precision sensing module (100) uses an advanced temperature and humidity sensor (102) installed at a key position of the inflatable composite insulation crossarm; the data acquisition and transmission module (200) transmits the data collected by the sensor to a central processor through wireless or wired communication technology; the processing module (300) has a built-in high-performance processor and a specially designed neural network algorithm for real-time analysis and learning of temperature and humidity data; The prediction and warning module (400) combines historical data and real-time analysis results, and the neural network algorithm can predict the future temperature and humidity change trend of the crossarm, and issue a warning signal in time when an abnormality is detected; the interaction and remote monitoring module (500) provides a user-friendly operation interface, supports remote monitoring and management functions, and facilitates operation and maintenance personnel to grasp the status of the crossarm at any time.

8. The inflatable composite insulation cross-arm temperature and humidity detection system as claimed in claim 7, characterized in that: The high-precision sensing module (100) comprises a temperature sensor (101) and a humidity sensor (102), which are used to monitor the temperature and humidity of the inflatable composite insulating crossarm in real time.

9. The inflatable composite insulation cross arm temperature and humidity detection system as claimed in claim 8, characterized in that: The processing module (300) is equipped with a neural network algorithm designed for temperature and humidity data, and is used to perform real-time analysis and learning of the temperature and humidity data and identify potential abnormal patterns.

10. The inflatable composite insulation cross arm temperature and humidity detection system as claimed in claim 9, characterized in that: The interaction and remote monitoring module (500) includes a user operation interface and remote monitoring; The prediction and warning module (400) can issue a warning signal in time when an abnormality is detected, and display it to the operation and maintenance personnel through the user operation interface.

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