Electric shock prevention alarm method and device for power transmission line and computer equipment

Abstract

The invention relates to a power transmission line anti-electric shock alarm method and device and computer equipment. The method comprises the following steps: acquiring environment sensing data and power transmission monitoring data of a power transmission line, and inputting the data into a hybrid architecture electric shock model to obtain electric shock distance prediction data; inputting the environment sensing data and the power transmission monitoring data into a physical driving electric shock model to obtain electric shock distance calculation data; under the condition that a difference value between the electric shock distance prediction data and the electric shock distance calculation data is greater than a preset error value, performing error analysis on the difference value to obtain data error factor information; according to the data error factor information, model parameters of the hybrid architecture electric shock model and the physical drive electric shock model are adjusted and recalculated until a difference value is smaller than a preset error value, and electric shock distance output data are obtained; and determining anti-electric shock alarm information according to the electric shock distance output data and the biological sensing data. The accuracy of electric shock prevention alarm of the power transmission line can be improved.

Description

Technical Field

[0001] The present application relates to the field of smart grid technology, and in particular to a method, device and computer equipment for preventing electric shock alarm for power transmission lines. Background Art

[0002] Currently, 10kV distribution lines are widely distributed, and many of them cross highways. When large vehicles pass through distribution lines that cross highways, if the line is not high enough from the ground, people will privately raise the distribution lines, which may cause electric shock accidents. In traditional technologies, video surveillance, drone inspections and other means are mainly used to remotely detect the safety conditions around transmission lines. When a creature is found to be at risk of electric shock, a warning message is sent to the creature through the warning device on the transmission line. However, traditional technologies require long-term processing of images obtained from video surveillance and drone inspections. Since images are easily affected by various objective factors and there are improper processing during image processing, the accuracy of transmission line anti-electric shock alarms cannot meet the safety needs of protecting life and property. Summary of the invention

[0003] Based on this, it is necessary to provide a power transmission line anti-electric shock alarm method, device and computer equipment that can improve the accuracy of the power transmission line anti-electric shock alarm to meet the safety needs of protecting life and property in response to the above technical problems.

[0004] In a first aspect, the present application provides a transmission line anti-electric shock alarm method, comprising:

[0005] Obtain environmental perception data, power transmission monitoring data, and biological perception data of power transmission lines;

[0006] Inputting the environmental perception data and the power transmission monitoring data into the hybrid architecture electric shock model of the power transmission line to obtain electric shock distance prediction data;

[0007] And, inputting the environmental perception data and the power transmission monitoring data into the physical drive electric shock model of the power transmission line to obtain electric shock distance calculation data;

[0008] When the difference between the electric shock distance prediction data and the electric shock distance calculation data is greater than a preset error value, performing error analysis on the difference according to the environmental perception data and the power transmission monitoring data to obtain data error factor information;

[0009] According to the data error factor information, the model parameters of the hybrid architecture electric shock model and the physical drive electric shock model are adjusted and recalculated respectively until the difference value is less than the preset error value, and the electric shock distance output data is obtained;

[0010] The anti-electric shock alarm information of the power transmission line is determined according to the electric shock distance output data and the biological perception data.

[0011] In a second aspect, the present application also provides a transmission line anti-electric shock alarm device, comprising:

[0012] A line data acquisition module is used to acquire environmental perception data, power transmission monitoring data and biological perception data of the power transmission line;

[0013] An electric shock distance prediction module, used for inputting the environmental perception data and the power transmission monitoring data into the hybrid architecture electric shock model of the power transmission line to obtain electric shock distance prediction data;

[0014] An electric shock distance calculation module, used for inputting the environmental perception data and the power transmission monitoring data into the physical drive electric shock model of the power transmission line to obtain electric shock distance calculation data;

[0015] A calculation error analysis module, used for performing error analysis on the difference between the electric shock distance prediction data and the electric shock distance calculation data to obtain data error factor information according to the environmental perception data and the power transmission monitoring data when the difference between the electric shock distance prediction data and the electric shock distance calculation data is greater than a preset error value;

[0016] An electric shock distance determination module is used to adjust and recalculate the model parameters of the hybrid architecture electric shock model and the physical drive electric shock model according to the data error factor information, until the difference value is less than a preset error value, to obtain electric shock distance output data;

[0017] The warning information generation module is used to determine the anti-electric shock warning information of the power transmission line according to the electric shock distance output data and the biological perception data.

[0018] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements any step of a power transmission line anti-electric shock alarm method when executing the computer program.

[0019] The above-mentioned transmission line anti-electric shock alarm method, device and computer equipment, by inputting the environmental perception data and transmission monitoring data of the transmission line into the hybrid architecture electric shock model and the physical drive electric shock model at the same time, respectively calculates the electric shock distance prediction data and the electric shock distance calculation data, and judges the error by comparing the difference between the two values; when the error value exceeds the preset threshold, the error analysis is performed based on the environmental perception data and the transmission monitoring data, the key factors affecting the calculation accuracy are identified, and the model parameters are adjusted in a targeted manner, and the calculation process is optimized until the error is reduced to an acceptable range, thereby ensuring the accuracy of the output data of the electric shock distance; further combined with the biological perception data, the calculated electric shock distance output data is correlated and analyzed with the actual biological activity situation, so as to accurately identify the potential electric shock risk and generate the corresponding anti-electric shock alarm information. It can not only improve the reliability of the electric shock distance calculation, reduce false alarms and missed alarms, effectively improve the accuracy of the transmission line anti-electric shock alarm to meet the safety needs of protecting life and property, but also enhance the safety protection capability of the transmission line, provide efficient and accurate technical support for the intelligent monitoring and early warning of the transmission line, and improve the safety and stability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 A diagram showing an application environment of a power transmission line anti-electric shock alarm method in one embodiment;

[0022] Figure 2 A schematic diagram of a flow chart of a method for preventing electric shock alarm in a power transmission line according to an embodiment;

[0023] Figure 3 A schematic diagram of a flow chart of a method for obtaining electric shock distance calculation data in one embodiment;

[0024] Figure 4 A schematic flow chart of a method for obtaining a breakdown electric field in one embodiment;

[0025] Figure 5 A schematic flow chart of a method for obtaining a corona critical electric field in one embodiment;

[0026] Figure 6 A schematic diagram of a flow chart of a method for obtaining electric shock distance calculation data in one embodiment;

[0027] Figure 7A schematic flow chart of a method for obtaining data error factor information in another embodiment;

[0028] Figure 8 It is a structural block diagram of a power transmission line anti-electric shock alarm device in one embodiment;

[0029] Fig. 9 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0031] The present application provides a method for preventing electric shock from occurring in a power transmission line, which can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. Among them, the server 104 can be implemented with an independent server or a server cluster composed of multiple servers.

[0032] In an exemplary embodiment, Figure 2 As shown, a transmission line anti-electric shock alarm method is provided, and the method is applied to Figure 1 The server in the example is used to illustrate, including the following steps 202 to 212. Among them:

[0033] Step 202, obtaining environmental perception data, power transmission monitoring data and biological perception data of the power transmission line.

[0034] Among them, environmental perception data can be various types of data reflecting the environmental conditions around the transmission lines, mainly including temperature, humidity, wind speed, wind direction, atmospheric pressure, precipitation, degree of air ionization, electromagnetic field strength, lightning activity, etc.

[0035] Among them, the transmission monitoring data can be various monitoring information reflecting the operating status of the transmission line and power parameters, including the voltage, current, phase angle, electric field strength, power load, frequency fluctuation, corona discharge, transmission line temperature and insulation status of the transmission line.

[0036] Among them, biosensing data can be information on biological activities around transmission lines collected using technologies such as infrared detection, video monitoring, millimeter-wave radar, and lidar, covering biological types (such as birds, large mammals, and personnel), biological volume, movement trajectory, activity frequency, and behavioral patterns of organisms approaching transmission lines.

[0037] Specifically, the system obtains environmental perception data, power transmission monitoring data and biological perception data around the transmission line through a variety of sensor equipment and monitoring systems. The environmental perception data includes external environmental parameters such as temperature and humidity, wind speed, rainfall, and atmospheric electric field. The power transmission monitoring data covers the operating status information of the power transmission system such as conductor voltage, current, and power frequency electric field strength. The biological perception data is obtained by infrared detection, high-definition camera, radar perception and other technologies, and is mainly used to identify the activities of birds, animals and other organisms. These data are uploaded to the data processing center or server in real time through wireless transmission, optical fiber communication and other methods.

[0038] Step 204, input the environmental perception data and the power transmission monitoring data into the hybrid architecture electric shock model of the power transmission line to obtain electric shock distance prediction data.

[0039] Among them, the hybrid architecture electric shock model can be an intelligent prediction model that integrates multiple different machine learning algorithms and is specifically used to calculate the distance to electric shock. The model is composed of multiple algorithms such as deep neural network (DNN), support vector machine (SVM), random forest (RF), gradient boosting decision tree (GBDT), etc. It can use the characteristic correlation of environmental perception data and power transmission monitoring data to make high-precision predictions on the critical distance to electric shock.

[0040] Among them, the electric shock distance prediction data can be the result calculated by the hybrid architecture electric shock model, which indicates the critical distance at which an organism or human body may be electrocuted under the current environment and transmission line conditions.

[0041] Specifically, the environmental perception data and power transmission monitoring data are normalized, denoised, and feature enhanced by using data preprocessing technology, and key influencing factors such as transmission line voltage, current, ambient temperature and humidity, and electromagnetic field strength are extracted by feature engineering methods, and input into each sub-model for calculation. The preprocessed environmental perception data and preprocessed power transmission monitoring data are input into the hybrid architecture electric shock model, which is composed of a variety of different machine learning models, including deep neural network (DNN), support vector machine (SVM), random forest (RF), gradient boosting decision tree (GBDT), etc. Among them, DNN can learn complex nonlinear relationships through deep network structure, SVM is good at dealing with boundary classification problems between high-dimensional features, RF and GBDT can improve generalization ability by using the integrated learning strategy of multiple decision trees, and adopt weighted fusion strategy to comprehensively calculate the output results of each sub-model, and optimize the model combination through adaptive weight adjustment mechanism to dynamically adapt to different transmission line states and environmental changes. Finally, the hybrid architecture electric shock model generates electric shock distance prediction data.

[0042] Step 206, input the environmental perception data and the power transmission monitoring data into the physical drive electric shock model of the power transmission line to obtain electric shock distance calculation data.

[0043] Among them, the physical driven electric shock model can be a mathematical modeling method based on electromagnetic field theory, potential distribution calculation and human equivalent resistance model, which is specially used to calculate the critical distance of electric shock. The model uses the physical parameters such as voltage, current, conductor structure, electromagnetic field distribution, and air breakdown characteristics of the transmission line, combined with the electrical characteristics of the human body or organism, to calculate the risk of electric shock under different environmental conditions and provide electric shock distance data that meets the theoretical calculation standards.

[0044] Among them, the electric shock distance calculation data can be the result calculated by a physical-driven electric shock model, which is derived based on strict electromagnetic field theory and mathematical formulas, and represents the critical distance of electric shock for organisms or humans calculated by the physical model under the current operating conditions of the transmission line.

[0045] Specifically, the environmental perception data and the power transmission monitoring data are simultaneously input into the physical driven electric shock model, and the electrical parameters of the transmission line, environmental conditions, and electrical characteristics of the human body or organisms are comprehensively considered based on the electromagnetic field theory and the operating characteristics of the transmission line to achieve accurate calculation of the critical distance of electric shock. In the calculation process, the physical driven electric shock model uses the electromagnetic field distribution equation to calculate the electric field strength at different positions around the transmission line, and combines the rated voltage, current, phase distance, wire material and other parameters of the high-voltage wire to construct a three-dimensional electric field distribution model. Further, based on the equivalent resistance model of the human body or organism, the safe voltage threshold that the organism may withstand under different environmental conditions (such as humidity, temperature, air ionization degree, etc.) is calculated; at the same time, the physical driven electric shock model also combines the grounding conditions, corona discharge effect, humidity The impact of factors such as the field strength of the air breakdown field is corrected to ensure that it adapts to the complex transmission line environment. Finally, the induced potential, electric field gradient, and possible electric shock current when the human body or organism enters the electric field area are comprehensively calculated to determine the critical distance of electric shock under different working conditions and obtain the electric shock distance calculation data.

[0046] Step 208, when the difference between the electric shock distance prediction data and the electric shock distance calculation data is greater than the preset error value, an error analysis is performed on the difference value based on the environmental perception data and the power transmission monitoring data to obtain data error factor information.

[0047] The preset error value may be the maximum error range allowed when calculating the electric shock distance.

[0048] Among them, the data error factor information can be the main factors affecting the accuracy of electric shock distance calculation identified by the system during the error analysis process, including environmental parameter fluctuations, sensor measurement errors, transmission line load changes, model training deviations, etc.

[0049] Specifically, the electric shock distance prediction data calculated by the hybrid architecture electric shock model is compared with the electric shock distance calculation data calculated by the physical drive electric shock model to analyze the degree of difference between the two. If the difference between the calculated electric shock distance prediction data and the calculated data of the physical drive electric shock model exceeds the preset error value, the key variables that may cause calculation errors will be screened based on environmental perception data, power transmission monitoring data and historical calculation records, including temperature and humidity, wind speed, electromagnetic field strength, conductor conditions (such as current fluctuations, voltage offsets), etc. Then, the error decomposition method is used to split the error sources into categories such as data measurement errors, model structure errors, environmental dynamic change errors, and input parameter deviations, and the impact weights of each category of errors are calculated. For example, if the error mainly comes from changes in environmental parameters, the system will focus on analyzing the impact of humidity and air ionization on the electric field distribution; if the error comes from abnormal monitoring data, the sensor data may need to be denoised or calibrated. In addition, statistical analysis methods, such as principal component analysis (PCA) or Bayesian reasoning, will be further used to evaluate the correlation of error factors to determine which variables have the most significant impact on the deviation of electric shock distance calculation; data error factor information will be determined based on the error analysis results.

[0050] Step 210, according to the data error factor information, the model parameters of the hybrid architecture electric shock model and the physical drive electric shock model are adjusted and recalculated respectively until the difference value is less than the preset error value, and the electric shock distance output data is obtained.

[0051] The electric shock distance output data may be the critical distance of electric shock finally calculated after error analysis and model optimization.

[0052] Specifically, the main factors affecting the calculation accuracy are identified from the data error factor information, such as environmental parameter fluctuations, data acquisition noise, transmission line load changes, or deviations between model assumptions and actual working conditions, and then the adaptive optimization method is used to adjust the parameters of the two types of models respectively: for the hybrid architecture electric shock model, the machine learning model will be retrained to optimize the hyperparameters (such as the number of layers, learning rate, regularization coefficient of the neural network, or the number of decision trees of the random forest) to enhance the model's adaptability to dynamic changes in the environment; for the physical drive electric shock model, the electromagnetic field calculation formula, potential distribution parameters, air breakdown field strength threshold and other key physical variables in the model are adjusted based on the error correction strategy to make it more in line with the actual working conditions. In addition, an iterative optimization mechanism is used to perform multiple rounds of calculation adjustments. After each adjustment, the electric shock distance is recalculated and the calculation error is compared again until the error value is less than the preset error value. In order to improve the optimization efficiency, when adjusting the parameters of the two models, intelligent optimization algorithms such as particle swarm optimization (PSO), genetic algorithm (GA) or gradient descent method can be used to globally optimize the model parameters to ensure that the adjusted model can maintain a high calculation accuracy under various working conditions. The output data of electric shock distance after error convergence is obtained through multiple iterative adjustments.

[0053] Step 212, determining the anti-electric shock alarm information of the power transmission line according to the electric shock distance output data and the biological perception data.

[0054] Among them, the anti-electric shock alarm information can be based on the electric shock distance output data and biological perception data. The system evaluates the electric shock risks that may be faced by organisms or personnel around the transmission lines and generates real-time warning information.

[0055] Specifically, the biological behavior analysis of biological perception data is performed using target detection algorithms (such as YOLO, Faster R-CNN) or deep learning models to determine whether the organism is in a potential electric shock hazard area or has a tendency to enter an electric shock hazard area; then the biological analysis results (i.e., real-time location information or biological action trends) are matched and calculated with the electric shock distance output data to determine whether the distance between the organism and the transmission line has approached or exceeded the electric shock safety critical value. If the actual distance between the organism and the high-voltage wire is lower than the safety threshold, the system will immediately trigger the anti-electric shock alarm mechanism, generate anti-electric shock alarm information and push it to the transmission line operation and maintenance center, on-site inspection personnel or drone inspection system.

[0056] In order to reduce the false alarm rate, the residence time corresponding to the biological behavior trend of the organism and the change of the electromagnetic environment can be combined to further analyze whether the organism has the possibility of continuously approaching or entering the dangerous area, so as to distinguish the risk of electric shock from short-term passing and long-term stay. In the case of determining that there is a risk, according to different biological species and risk levels, intelligent repelling devices (such as sonic bird repellers and laser repelling systems) are linked to take proactive protective measures to reduce the probability of electric shock accidents and obtain subsequent anti-electric shock alarm information.

[0057] In the above-mentioned transmission line anti-electric shock alarm method, the environmental perception data and transmission monitoring data of the transmission line are simultaneously input into the hybrid architecture electric shock model and the physical drive electric shock model to calculate the electric shock distance prediction data and the electric shock distance calculation data respectively, and the error is judged by comparing the difference between the two values; when the error value exceeds the preset threshold, the error analysis is performed based on the environmental perception data and the transmission monitoring data, the key factors affecting the calculation accuracy are identified, and the model parameters are adjusted in a targeted manner to optimize the calculation process until the error is reduced to an acceptable range, thereby ensuring the accuracy of the electric shock distance output data; further combined with the biological perception data, the calculated electric shock distance output data is correlated with the actual biological activity situation, so as to accurately identify the potential electric shock risk and generate the corresponding anti-electric shock alarm information. It can not only improve the reliability of electric shock distance calculation, reduce false alarms and missed alarms, effectively improve the accuracy of transmission line anti-electric shock alarms to meet the safety needs of protecting life and property, but also enhance the safety protection capabilities of transmission lines, provide efficient and accurate technical support for intelligent monitoring and early warning of transmission lines, and improve the safety and stability of power systems.

[0058] In an exemplary embodiment, Figure 3 As shown, the environmental perception data and the power transmission monitoring data are input into the physical drive electric shock model of the power transmission line to obtain the electric shock distance calculation data, including steps 302 to 306. Among them:

[0059] Step 302: Substitute the environmental perception data and the power transmission monitoring data into the breakdown voltage calculation item of the physical driven electric shock model to obtain the breakdown electric field of the transmission line.

[0060] The breakdown voltage calculation item can be a mathematical expression used in the physical driven electric shock model to calculate the minimum voltage required for air to undergo electrical breakdown. This calculation item is based on Paschen's Law or the air dielectric breakdown model, and combines factors such as the voltage of the transmission line, electric field distribution, air humidity, temperature, and atmospheric pressure to calculate the voltage threshold that causes ionization in the air and causes breakdown under specific environmental conditions.

[0061] The breakdown electric field can be the minimum electric field strength that causes the air medium to ionize and form a discharge channel under certain environmental conditions, usually expressed in kilovolts per meter (kV / m). This value is calculated from the breakdown voltage calculation item and is affected by environmental factors (such as air humidity, temperature, pollution level, etc.).

[0062] Specifically, the key electrical parameters of the transmission line, such as conductor voltage, current, phase distance, conductor radius, transmission line height, etc., and the environmental perception data, including temperature, humidity, atmospheric pressure, air ionization degree, etc., are simultaneously substituted into the breakdown voltage calculation formula of the physical driven electric shock model, and the minimum electric field strength for electric breakdown of the air under the current environmental conditions is calculated according to Paschen's Law or the air dielectric breakdown formula. The correction coefficient of the relative humidity of the air to the electric field strength and the electric field distribution characteristics under different types of transmission lines (such as overhead lines and ultra-high voltage transmission lines) are considered in the calculation process. Finally, the specific value of the breakdown electric field of the transmission line is obtained, which represents that when a biological or human body approaches the transmission line, the air may be electrically broken down under this electric field strength, thereby causing the risk of electric shock.

[0063] Step 304: Substitute the environmental sensing data and the power transmission monitoring data into the corona discharge calculation item of the physical driven electric shock model to obtain the corona critical electric field of the power transmission line.

[0064] Among them, the corona discharge calculation item can be a mathematical expression used in the physical driven electric shock model to calculate whether the electric field strength on the surface of the transmission line conductor reaches the critical value of corona discharge. This calculation item takes into account the influencing factors such as the conductor surface potential gradient, conductor radius, transmission line voltage, air humidity, and atmospheric pollution level, and calculates the critical voltage or electric field strength for corona discharge in the transmission line under specific environmental conditions.

[0065] The corona critical electric field can be the minimum electric field strength for corona discharge to occur on the surface of the transmission line conductor. This value depends on factors such as the rated voltage of the transmission line, the shape and size of the conductor, air humidity, and pollutant deposition. When the electric field strength around the transmission line exceeds the corona critical electric field, the air molecules on the surface of the conductor are ionized, resulting in corona discharge.

[0066] Specifically, since corona discharge is a phenomenon caused by air ionization around high-voltage transmission lines, it may affect the electric field distribution of transmission lines and change the accuracy of electric shock risk assessment. Therefore, the electrical parameters of the transmission line, such as the conductor surface potential gradient, wire type, phase distance, insulator structure, etc., and the environmental perception data (such as humidity, atmospheric pollution, air pressure), are simultaneously substituted into the corona discharge calculation formula of the physical driven electric shock model to calculate the critical electric field strength of the transmission line to generate corona discharge in the current environment. In the calculation process, the influencing factors such as corona starting voltage, wire surface roughness, and humidity correction coefficient are considered to ensure the accuracy of the calculation results. Finally, the specific value of the corona critical electric field of the transmission line is obtained, which is used to judge whether there is a corona discharge phenomenon around the transmission line and its influence on the electric shock distance.

[0067] Step 306, weighted fusion of the breakdown electric field and the corona critical electric field of the transmission line to obtain electric shock distance calculation data.

[0068] Specifically, since the breakdown electric field and the corona critical electric field have different effects on the risk of electric shock under different transmission conditions and environmental conditions, the system adopts a weight distribution method to adaptively adjust the weight ratio of the two according to the voltage level of the transmission line, environmental factors (such as humidity, pollution level), line structure and other factors. For example, in an environment with high humidity or severe pollution, the impact of corona discharge on the electric field distribution is greater, so the weight of the corona critical electric field will increase accordingly; while in a dry environment or under ultra-high voltage transmission lines, the risk of air breakdown is higher, so the weight of the breakdown electric field will increase. After the system weightedly calculates the two electric field values, combined with the human body or biological equivalent resistance model, it derives the safe distance when different organisms approach the transmission line, and finally obtains the electric shock distance calculation data.

[0069] In this embodiment, by substituting the environmental perception data and the power transmission monitoring data into the breakdown voltage calculation item and the corona discharge calculation item of the physical drive electric shock model, the breakdown electric field and the corona critical electric field are obtained respectively, and then the two are weighted and fused, and finally a more accurate electric shock distance data is calculated, which can fully consider the influence of environmental factors (such as temperature, humidity, and air pressure) around the transmission line on the air ionization characteristics; at the same time, combined with the operating status of the transmission line (such as voltage, current, and electric field distribution), it is ensured that the calculated electric shock distance is not only theoretically rigorous, but also can adapt to complex and changeable actual working conditions, and can more comprehensively reflect the electric field characteristics around the transmission line, improve the accuracy of electric shock risk assessment, reduce false alarms and omissions, and provide more efficient and accurate technical support for the safety monitoring of the power system. After the weighted fusion is adopted, the calculation results are more stable, which can adapt to different types of transmission lines and environmental changes, improve the reliability of the anti-electric shock warning system, reduce the risk of electric shock accidents of organisms or personnel near the transmission line, and ensure the safe and stable operation of the power grid.

[0070] In an exemplary embodiment, Figure 4 As shown, the environmental perception data and the power transmission monitoring data are substituted into the breakdown voltage calculation item of the physical drive electric shock model to obtain the breakdown electric field of the transmission line, including steps 402 to 410. Among them:

[0071] Step 402: Analyze the variation law of the ionization energy of the power transmission line according to the power transmission monitoring data to obtain the ionization energy variation analysis data of the power transmission line.

[0072] Among them, the law of ionization energy variation can describe the characteristics of the energy required for ionization of air molecules when they are subjected to the electric field around the transmission line, which varies with external conditions. This law is affected by factors such as voltage, current, and load fluctuations of the transmission line, and is closely related to environmental conditions (such as air pressure, temperature, humidity, etc.).

[0073] Among them, the ionization energy change analysis data can be a quantitative result calculated based on the transmission line monitoring data and environmental sensor data, which characterizes the change in the ionization energy demand of the air around the transmission line under different operating conditions. This data is usually calculated from parameters such as the voltage, current, line electric field strength and atmospheric pressure of the transmission line, and is used to measure the minimum energy required for air molecules to ionize in the current environment.

[0074] Specifically, the operating parameters of the transmission line are obtained from the transmission monitoring data, including the conductor voltage, current amplitude, phase angle, electric field intensity distribution, and instantaneous load fluctuation of the transmission line. These data can reflect the electrical characteristics of the transmission line under different working conditions, thereby affecting the degree of air ionization. At the same time, the atmospheric pressure p around the transmission line is extracted from the environmental sensing data. This parameter determines the density of air molecules, which directly affects the mean free path and collision frequency of electrons. Since the ionization process around the transmission line depends on the collision effect of air molecules, the higher the air pressure p, the smaller the free motion range of electrons, the higher the collision frequency, and the more likely ionization occurs. The electrode spacing d represents the distance between the transmission line conductors or the minimum spacing between the conductors and the organism. This value affects the electric field gradient, thereby determining the frequency of electron acceleration and collision. On the other hand, parameter B, as an empirical coefficient in Paschen's law, is closely related to the ionization characteristics of the gas and is affected by the electrical conditions of the transmission line, the composition of the surrounding air, and the pollution level. The B value is dynamically adjusted through historical experimental data and the operating information of the transmission line, so as to finally multiply the empirical coefficient B, the gas pressure p and the electrode spacing d to obtain the ionization energy change analysis data.

[0075] Step 404: Analyze the electron impact ionization law of the transmission line according to the transmission monitoring data to obtain electron impact ionization analysis data of the transmission line.

[0076] Among them, the law of electron impact ionization can be described as the physical mechanism by which electrons collide with air molecules and trigger the ionization process under the action of a high-voltage electric field. This law is affected by factors such as electric field strength, electron drift velocity, air pressure, and electrode spacing. Generally speaking, under strong electric field conditions, electrons gain higher energy, and after collision, it is easier to ionize air molecules and release more free electrons, thereby triggering an avalanche ionization effect; in an environment of high pressure or small electrode spacing, the density of air molecules increases, shortening the mean free path of electrons, resulting in an increase in the collision frequency, thereby accelerating the ionization process.

[0077] Among them, the electron impact ionization analysis data can be a quantitative indicator calculated based on the transmission line monitoring data, describing the possibility and trend of electron impact ionization of air molecules around the transmission line under different electric field strengths. This data is calculated through parameters such as electric field strength, electrode spacing, and air pressure, and is used to evaluate the critical conditions for air ionization.

[0078] Specifically, the electron impact ionization law of the air around the transmission line is analyzed. Three key variables are involved in the calculation process: the empirical parameter A of Paschen's Law, the air pressure p, and the electrode spacing d. Among them, the empirical parameter A is an empirical coefficient reflecting the gas ionization characteristics. The system dynamically adjusts it through the operating characteristics of the transmission line and historical experimental data to ensure the calculation accuracy; the air pressure p is obtained in real time by the environmental monitoring system, and its size affects the density of air molecules, thereby determining the mean free path and collision probability of electrons; the higher the air pressure, the denser the air molecules, and the easier it is for electrons to collide with molecules and ionize during the drift process; the electrode spacing d represents the distance between the transmission line conductors or the minimum distance between the conductors and the organism. This value affects the electric field gradient, thereby determining the frequency of electron acceleration and collision. The system multiplies these three variables and takes the natural logarithm, that is, (ln(A·p·d)), to obtain the electron impact ionization adjustment data, which is used to quantify the ionization trend of the air around the transmission line under the action of electron collision.

[0079] Step 406: Adjust the electron impact ionization analysis data according to the environmental sensing data to obtain electron impact ionization adjustment data.

[0080] Among them, the electron impact ionization adjustment data can be the optimization result obtained by correcting the calculated electron impact ionization analysis data in combination with the environmental perception data (such as temperature, humidity, wind speed, air ion concentration, etc.). Since environmental factors such as temperature, humidity, and wind speed can affect the motion characteristics of electrons and the ionization threshold of air molecules, the system compensates for the impact of these environmental changes on the calculation results by adjusting the electron impact ionization analysis data. For example, in a high humidity environment, water vapor in the air will lower the threshold of air ionization, making the ionization process more likely to occur, so the system will lower the corrected ionization threshold; in a strong wind environment, since the ionized particles in the air are more likely to diffuse, the system may need to improve the calculation results of electron impact ionization to match the actual working conditions.

[0081] Specifically, since the first environmental correction function f1 (T, H, W, I) represents the influence of the environment on the air ionization characteristics, where T is the measured temperature, H is the relative humidity, W is the measured wind speed, and I is the ion concentration in the air; in actual calculations, temperature affects the thermal motion rate of air molecules and the electron drift velocity. Higher temperatures usually reduce the density of air molecules and increase the mean free path of electrons, thereby affecting the ionization probability; humidity changes the ionization threshold of air through the polarization effect of water vapor molecules, and a high humidity environment usually reduces the breakdown strength of air; wind speed affects the electric field distribution around the transmission line and the diffusion rate of ionized particles in the air. Under high wind speed conditions, ionized electrons may be quickly taken away from the electric field action area, weakening the ionization effect; the ion concentration in the air determines the number of initial charged particles. A higher ion concentration may enhance conductivity and reduce the effect of electric field strength on electron impact ionization. After substituting the environmental sensing data into f1(T, H, W, I) to calculate the environmental impact correction coefficient, the natural logarithm of the environmental impact correction coefficient is taken and used to adjust the electron impact ionization analysis data calculated in the previous step to compensate for the error caused by the change in environmental conditions. Finally, the electron impact ionization data is obtained.

[0082] Step 408: Divide the ionization energy variation analysis data by the electron impact ionization adjustment data to obtain the breakdown voltage of the transmission line.

[0083] Specifically, the ionization energy change analysis data is divided by the electron impact ionization adjustment data to characterize the minimum voltage required for air molecules to undergo electrical breakdown under current environmental conditions. This calculation method is based on Paschen's Law and the physical properties of the air medium. By considering factors such as electric field strength, molecular ionization energy, and electron collision cross section, the threshold voltage for electrical breakdown of air is deduced. Finally, the breakdown voltage of the transmission line is obtained.

[0084] Step 410, determining the breakdown electric field of the transmission line according to the breakdown voltage of the transmission line.

[0085] Specifically, since the breakdown electric field refers to the minimum electric field strength when air is ionized and loses its insulation properties, it is generally calculated by the breakdown voltage and electric field distribution formula. Therefore, the breakdown voltage of the transmission line is divided by the electrode spacing d to obtain the breakdown electric field of the transmission line.

[0086] The breakdown electric field of the transmission line is expressed as:

[0087]

[0088]

[0089] Among them, E b is the breakdown electric field; V bis the breakdown voltage; d is the electrode spacing; A is the Townsend coefficient prefactor; B is the reciprocal factor of the ionization coefficient; p is the atmospheric pressure; f1(T,H,W,I) is the first environmental correction function; T is the measured temperature; T ref is the reference temperature; α is the temperature effect power law factor, which is determined by fitting the temperature-related experimental data; is the amplification effect of the motion of gas molecules; H is the relative humidity; H ref is the reference humidity; μ is the humidity change adjustment factor, which is obtained based on the statistical analysis of the humidity response curve; is the smoothing saturation effect when the humidity deviates from the reference value; W is the measured wind speed; W ref is the reference wind speed; γ is the correction factor for wind speed effect, which is determined by experimental calibration of the effect of wind speed on ionization phenomena; is the exponential attenuation of the effect on ion diffusion when the wind speed increases; I is the ion concentration in the air; I ref is the concentration reference value; δ is the concentration periodic modulation factor, which is obtained by fitting the experimental relationship between ion concentration and ionization effect; is the periodic modulation effect of ion concentration.

[0090] In this embodiment, by analyzing the ionization energy variation law and electron impact ionization law of the transmission line based on the transmission monitoring data, the electron impact ionization analysis data is dynamically adjusted in combination with the environmental perception data, the breakdown voltage is accurately calculated, and the breakdown electric field of the transmission line is further determined. It is possible to comprehensively consider the operating state of the transmission line (such as voltage, current, electric field distribution) and the influence of external environmental factors (such as temperature, humidity, air pressure) on the air ionization process, thereby compensating for the calculation deviation caused by the dynamic change of the environment. By adjusting the electron impact ionization data to match it with the actual working conditions, the accuracy of the breakdown voltage calculation is improved, thereby enhancing the reliability of the breakdown electric field prediction. Further, it can be more flexibly adapted to different types of transmission lines and environmental conditions, improve the accuracy of electric shock risk assessment, reduce false alarms and missed reports, and provide more accurate basic data support for the transmission line anti-electric shock warning system, thereby effectively reducing the risk of electric shock to organisms or personnel around the transmission line, and ensuring the safety and stability of the power grid operation.

[0091] In an exemplary embodiment, Figure 5 As shown, the environmental sensing data and the power transmission monitoring data are substituted into the corona discharge calculation item of the physical driven electric shock model to obtain the corona critical electric field of the power transmission line, including steps 502 to 504. Among them:

[0092] Step 502: Analyze the nonlinear influence of corona discharge according to the environmental sensing data to obtain nonlinear analysis data of the corona phenomenon.

[0093] Among them, corona discharge can be a common gas discharge phenomenon around high-voltage transmission lines. When the electric field strength on the surface of the conductor exceeds the critical corona electric field of the air, the surrounding air molecules are ionized, resulting in weak glow discharge, accompanied by noise, electromagnetic radiation and ozone generation. Corona discharge usually occurs on the surface of high-voltage conductors, insulators or at the tip with a high electric field gradient. Its intensity is affected by the transmission line voltage, conductor radius, phase-to-phase distance and environmental factors (such as humidity, air pressure, temperature, etc.).

[0094] Among them, nonlinear effects can be that the relationship between variables is not a simple linear proportional relationship, but presents complex nonlinear characteristics with changes in certain conditions. In corona discharge analysis, temperature and humidity have nonlinear effects on the ionization characteristics of air and the discharge process. For example, an increase in temperature will change the density of air molecules and the electron drift rate, causing the initial electric field strength of corona discharge to change nonlinearly; an increase in humidity will reduce the ionization threshold of the air, but this change is not linear, but changes dynamically with factors such as the rate of change of humidity and the polarization effect of water vapor in the air.

[0095] Among them, the nonlinear analysis data of the corona phenomenon can be a data set calculated by analyzing the nonlinear effects of environmental factors (such as temperature and humidity) on the behavior of corona discharge. This data describes how the critical electric field of corona discharge is adjusted with changes in environmental conditions and is calculated through numerical modeling (such as multivariate regression, neural network, etc.). For example, the data may include the ionization threshold of air under different temperature and humidity conditions, the change trend of the starting electric field intensity of corona discharge, etc.

[0096] Specifically, since the occurrence of corona discharge is closely related to the dielectric properties of air, changes in temperature and humidity will lead to nonlinear changes in the motion characteristics, conductivity and ionization threshold of air molecules. For example, when the temperature rises, the thermal motion of air molecules is enhanced, and the mean free path between molecules increases, resulting in a decrease in the probability of electron impact ionization, thereby increasing the initial electric field strength of corona discharge; while in a low temperature environment, the density of air molecules increases and the mean free path of electrons is shortened, making impact ionization more likely to occur, reducing the critical electric field of corona discharge. In addition, an increase in humidity means an increase in the water vapor content in the air. Water molecules have a strong polarization ability, which can reduce the ionization energy threshold of the air, making corona discharge more likely to occur, thereby reducing the critical corona electric field strength around the transmission line. By using a numerical modeling method to quantify the deviation effect of the measured temperature relative to the reference temperature and the influence of the change in relative humidity relative to the reference humidity, nonlinear analysis data of the corona phenomenon are finally obtained.

[0097] Step 504, based on the nonlinear analysis data of the corona phenomenon, the initial critical electric field of the physical driven electric shock model is corrected to obtain the corona critical electric field.

[0098] Among them, the initial critical electric field can be the minimum electric field strength required for corona discharge to occur in the air under idealized or theoretical calculation conditions. This value is usually calculated by a mathematical model based on basic physical parameters such as the rated voltage, conductor radius, and phase-to-phase distance of the transmission line, and does not directly consider the dynamic changes of environmental factors.

[0099] Specifically, the initial critical electric field is usually calculated by a theoretical model, such as an ideal value derived from basic physical parameters such as conductor radius, transmission line voltage level, and phase-to-phase distance. However, due to the complexity of the actual transmission line operating environment, this theoretical value may not accurately reflect the actual corona discharge conditions. Therefore, the system uses a correction factor based on the nonlinear analysis data of the corona phenomenon to calculate the correction factor under different environmental conditions, which is applied to the initial critical electric field to adjust the original theoretical value to make it more suitable for the actual transmission line operating environment. Finally, the corrected corona critical electric field is obtained.

[0100] The expression of the corona critical electric field of the transmission line is:

[0101]

[0102] Among them, E c is the corona critical electric field; E0 is the initial critical electric field under standard conditions; ξ is the air density factor; f2(T,H) is the second environment correction function; λ is the environmental coupling influence adjustment coefficient, which is determined by fitting the experimental data of the influence of temperature and humidity on the critical electric field of charge; T is the measured temperature; T ref is the reference temperature; is the deviation effect of the measured temperature relative to the reference temperature; H is the relative humidity; H ref is the reference humidity; is the effect of the change in relative humidity relative to the reference humidity.

[0103] In this embodiment, by analyzing environmental perception data, evaluating the nonlinear effects of factors such as temperature and humidity on corona discharge, and based on the nonlinear analysis data of the corona phenomenon, the initial critical electric field of the physical drive electric shock model is corrected, so as to obtain a corona critical electric field that is more in line with the actual working conditions, and dynamically adjust the calculation parameters to make it more accurately adapt to the corona discharge characteristics under different environmental conditions. For example, in a high humidity environment, the polarization of water vapor will reduce the ionization threshold of the air, thereby accelerating the occurrence of corona discharge, and in a low temperature environment, the density of air molecules increases, which may increase the intensity of the corona starting electric field. The calculation process is optimized by nonlinear analysis, so that the calculation accuracy of the corona critical electric field is greatly improved, thereby reducing errors and improving the accuracy of electric shock distance assessment. Finally, the electric field characteristics around the transmission line can be more accurately identified, the electric shock risk prediction ability of the transmission line can be improved, and the false alarm and missed alarm can be reduced, providing more scientific and reliable technical support for power safety monitoring and anti-electric shock warning.

[0104] In an exemplary embodiment, Figure 6 As shown, the breakdown electric field and the corona critical electric field of the transmission line are weighted and fused to obtain the electric shock distance calculation data, including steps 602 to 606. Among them:

[0105] Step 602, weighted fusion of the breakdown electric field and the corona critical electric field of the transmission line to obtain the initial electric shock critical electric field of the transmission line.

[0106] Among them, the initial critical electric field for electric shock can be the electric field strength value calculated by weighted fusion of the breakdown electric field and the corona critical electric field, which represents the minimum electric field strength that may cause electric shock risk around the transmission line under ideal conditions. The breakdown electric field determines the minimum electric field value for air ionization to form a conductive channel, while the corona critical electric field reflects the minimum electric field threshold for corona discharge on the surface of the transmission line conductor.

[0107] Specifically, the breakdown electric field is the minimum electric field strength for air ionization and formation of a discharge path, while the corona critical electric field represents the minimum electric field strength for corona discharge to occur on the conductor surface. Since the two have different effects on the risk of electric shock under different working conditions, the system adopts a weight distribution strategy to adaptively distribute the weights of the two according to factors such as the voltage level of the transmission line, environmental conditions (such as humidity, air pressure, etc.), and conductor shape. For example, in a high-humidity environment, the impact of corona discharge is greater, and the system will increase the weight of the corona critical electric field. In high-voltage UHV transmission lines, the risk of air breakdown is more significant, so the weight of the breakdown electric field will increase; through weighted calculation, the system obtains the initial critical electric field for electric shock. The formula for weighted fusion is E th =ρE B +(1-ρ)E C, where ρ is the weighting coefficient, which is determined by factors such as the voltage level of the transmission line, environmental conditions, and conductor shape, E th is the initial critical electric field of electric shock after fusion.

[0108] Step 604, performing local electric field correction on the initial critical electric field for electric shock according to the space charge effect information of the power transmission line to obtain a corrected critical electric field for electric shock.

[0109] The corrected critical electric field for electric shock may be an electric field strength value obtained by locally correcting the initial critical electric field in combination with the space charge effect information around the transmission line.

[0110] Specifically, since the space charge effect information is the local electric field distortion phenomenon caused by corona discharge, charge drift and charged particle aggregation around the transmission line, this effect will cause the electric field to increase or decrease in certain areas, thereby affecting the actual critical electric field value for electric shock. The space charge distribution information around the transmission line is obtained through space charge monitoring equipment or numerical simulation methods, and the initial critical electric field for electric shock is locally corrected based on the space charge distribution information. For example, if the monitoring data shows that there is a high concentration of charged particles near the transmission line, the local electric field may be enhanced, increasing the risk of electric shock, so the system will appropriately increase the critical electric field value for electric shock; and if the space charge effect causes the local electric field to weaken, the system will correspondingly reduce the critical electric field for electric shock to obtain a corrected critical electric field for electric shock, where the formula for local electric field correction is E′ th (deff,t)=E th g(deff,t), where g(r,t) is the spatial charge distribution information, E′ th (deff,t) is the corrected critical electric field for electric shock.

[0111] Step 606, using Newton iteration method to solve and correct the critical electric field of electric shock, and obtain electric shock distance calculation data.

[0112] Among them, the Newton iteration method is a numerical solution method for nonlinear equations, which is often used to calculate the relationship between electric field strength and electric shock distance. In the calculation of electric shock distance, the system needs to solve the mathematical model of correcting the critical electric field of electric shock and the distance of electric shock. The Newton iteration method first selects an initial guess value (usually the corrected critical electric field of electric shock), and then calculates the error based on the Taylor expansion, and continuously updates the calculated value through iteration, so that it gradually approaches the true solution. In each iteration process, the system calculates the error of the current solution and uses its derivative information to adjust the next calculation direction until the error converges to a preset range.

[0113] Specifically, a mathematical model between the critical electric field for electric shock and the distance for electric shock is established, usually based on the electric field distribution equation, the human body equivalent resistance model, and the electrical characteristic equation of the transmission line. The Newton iteration method is then used to solve the model. The initial value is taken as the corrected critical electric field for electric shock, and the optimal solution is gradually approached through iterative calculations. In each iteration, the error is calculated and the input value for the next calculation is adjusted until it converges to the preset error range. Finally, the system obtains the calculation data for the distance for electric shock.

[0114] In this embodiment, the initial critical electric field for electric shock is calculated by weighted fusion of the breakdown electric field and the corona critical electric field, and the local electric field is corrected in combination with the space charge effect information of the transmission line. Finally, the Newton iteration method is used to solve the corrected critical electric field for electric shock, and the distance of electric shock is accurately calculated. The complexity of air ionization around the transmission line is fully considered, and the influence of air breakdown, corona discharge and space charge effect on the electric field distribution is comprehensively considered, so that the calculation results are more in line with the actual transmission conditions. The space charge effect correction link can effectively compensate for the error caused by local electric field distortion, and the Newton iteration method can quickly converge to the optimal solution, improving the calculation efficiency and accuracy. The use of multi-level optimization makes the calculation of electric shock distance more accurate, which can significantly reduce false alarms and missed alarms, improve the reliability of the transmission line anti-electric shock warning system, provide scientific technical support for power system safety monitoring, effectively reduce the risk of electric shock to organisms or personnel around the transmission line, and ensure the safe and stable operation of the power grid.

[0115] In an exemplary embodiment, Figure 7 As shown, according to the environmental perception data and the power transmission monitoring data, the difference value is analyzed for error to obtain the data error factor information, including steps 702 to 704. Among them:

[0116] Step 702: Perform variance-based sensitivity analysis on the difference values ​​based on the environmental perception data and the power transmission monitoring data to obtain data error factor contribution information.

[0117] Among them, variance-based sensitivity analysis can be a statistical method used to quantify the impact of input variables on system output results. The core idea is to evaluate the importance of each variable by calculating how changes in input variables affect the variance of the output.

[0118] Among them, the data error factor contribution information can be a data set calculated by variance-based sensitivity analysis, which characterizes the contribution of different environmental variables and transmission parameters to the error in the calculation of the electric shock distance. This information is usually expressed in the form of contribution rate or weight value, reflecting the influence of each variable on the error. For example, if the contribution rate of humidity is 40%, the contribution rate of voltage fluctuation is 30%, and the contribution rate of wind speed is 20%, it means that humidity is the main influencing factor of the error.

[0119] Specifically, key variables are extracted from environmental perception data and transmission monitoring data, including environmental factors such as temperature, humidity, air pressure, wind speed, air pollution level, and transmission parameters such as voltage, current, conductor spacing, load fluctuation, and electric field distribution of transmission lines. In order to quantify the contribution of each variable to the error, the system establishes an input-output response relationship model, with key variables of environmental perception data and transmission monitoring data as input, and the electric shock distance calculation error (i.e., the difference between the electric shock distance prediction data and the electric shock distance calculation data) as output, and analyzes the influence of each variable change on the calculation error. Subsequently, Sobol sensitivity analysis combined with decomposition variance analysis (ANOVA) is used to calculate the variance contribution of each variable and evaluate its influence weight on the error. For example, high humidity will reduce the air ionization threshold and enhance the corona discharge effect, thereby affecting the calculation accuracy of the corona critical electric field; voltage fluctuations in transmission lines may lead to uneven electric field distribution, which increases the deviation of the breakdown electric field calculation results; wind speed will accelerate the diffusion of charged particles, affect the space charge effect, and then change the local electric field strength, increasing the calculation error of the electric shock critical electric field. Finally, the contribution information of data error factors is generated based on the analysis results.

[0120] Step 704, performing multi-level fuzzy reasoning on the data error factor contribution information to obtain data error factor information.

[0121] Among them, multi-level fuzzy reasoning can be a reasoning method based on fuzzy logic, which is suitable for dealing with complex systems with strong uncertainty and deriving the final decision results through multi-level fuzzy rules.

[0122] Specifically, a fuzzy reasoning rule base is constructed based on the environmental perception data and transmission monitoring data of the transmission line, and the error influencing factors are divided into multiple levels (such as low impact, medium impact, and high impact). Then, the membership function is used to calculate the degree of belonging of different error factors in each level, and the fuzzy reasoning rules are combined for deduction. For example, if the variance-based sensitivity analysis shows that humidity contributes a high degree to the error, and the current humidity is abnormally high, the system may infer that "the enhanced corona discharge effect leads to a large error in the calculation of the electric shock distance". The interaction of different error factors is comprehensively evaluated through a multi-level fuzzy reasoning method, and finally the data error factor information is output.

[0123] In this embodiment, the influence of environmental perception data and power transmission monitoring data on the calculation error of electric shock distance is evaluated by variance-based sensitivity analysis, and the contribution of different factors to the error is quantified. Then, multi-level fuzzy reasoning is used for comprehensive analysis to identify key error sources and generate data error factor information, which can dynamically adapt to the complex power transmission line environment and accurately identify the main factors affecting the accuracy of electric shock distance calculation, such as temperature, humidity, power transmission voltage fluctuation, wind speed, air ion concentration, etc. The influence weight of each variable on the error is further determined by variance analysis, and fuzzy reasoning can effectively deal with uncertain factors, derive the potential correlation of error sources, make error identification more intelligent and adaptable, not only improve the accuracy of error analysis, but also provide a reliable basis for subsequent model optimization and parameter adjustment, thereby optimizing the accuracy of electric shock distance calculation, reducing false alarms and missed alarms, and improving the reliability of the power transmission line anti-electric shock warning system, providing more accurate technical support for power grid safety monitoring, reducing the risk of electric shock accidents, and ensuring the safe and stable operation of the power system.

[0124] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0125] Based on the same inventive concept, the embodiment of the present application also provides a transmission line anti-electric shock alarm device for implementing the above-mentioned transmission line anti-electric shock alarm method. Figure 8 As shown, it includes: a line data acquisition module 802, an electric shock distance prediction module 804, an electric shock distance calculation module 806, a calculation error analysis module 808, an electric shock distance determination module 810 and a warning information generation module 812. The implementation solution for solving the problem provided by the device is similar to the implementation solution recorded in the above method. Therefore, the specific limitations in one or more transmission line anti-electric shock alarm device embodiments provided below can be found in the above limitations on a transmission line anti-electric shock alarm method, which will not be repeated here.

[0126] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig. 9The computer device includes a processor, a memory, an input / output interface (I / O for short) and a communication interface. Those skilled in the art will understand that Fig. 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0127] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0128] In one embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0129] In one embodiment, a computer program product or computer program is provided, the computer program product or computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in the above-mentioned method embodiments.

[0130] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0131] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0132] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0133] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for preventing electric shock alarm in a power transmission line, characterized in that: The method comprises: Obtain environmental perception data, power transmission monitoring data, and biological perception data of power transmission lines; Inputting the environmental perception data and the power transmission monitoring data into the hybrid architecture electric shock model of the power transmission line to obtain electric shock distance prediction data; And, inputting the environmental perception data and the power transmission monitoring data into the physical drive electric shock model of the power transmission line to obtain electric shock distance calculation data; When the difference between the electric shock distance prediction data and the electric shock distance calculation data is greater than a preset error value, performing error analysis on the difference according to the environmental perception data and the power transmission monitoring data to obtain data error factor information; According to the data error factor information, the model parameters of the hybrid architecture electric shock model and the physical drive electric shock model are adjusted and recalculated respectively until the difference value is less than the preset error value, and the electric shock distance output data is obtained; The anti-electric shock alarm information of the power transmission line is determined according to the electric shock distance output data and the biological perception data.

2. The method according to claim 1, characterized in that: The step of inputting the environmental perception data and the power transmission monitoring data into the physical drive electric shock model of the power transmission line to obtain electric shock distance calculation data includes: Substituting the environmental perception data and the power transmission monitoring data into the breakdown voltage calculation item of the physical driven electric shock model to obtain the breakdown electric field of the power transmission line; Substituting the environmental perception data and the power transmission monitoring data into the corona discharge calculation item of the physical driven electric shock model to obtain the corona critical electric field of the power transmission line; The breakdown electric field of the transmission line and the corona critical electric field are weighted and fused to obtain the electric shock distance calculation data.

3. The method according to claim 2, characterized in that Substituting the environmental perception data and the power transmission monitoring data into the breakdown voltage calculation item of the physical drive electric shock model to obtain the breakdown electric field of the power transmission line includes: Analyzing the ionization energy variation law of the transmission line according to the transmission monitoring data to obtain ionization energy variation analysis data of the transmission line; Analyzing the electron impact ionization law of the transmission line according to the transmission monitoring data to obtain electron impact ionization analysis data of the transmission line; According to the environmental sensing data, the electron impact ionization analysis data is adjusted to obtain electron impact ionization adjustment data; Dividing the ionization energy variation analysis data by the electron impact ionization adjustment data to obtain a breakdown voltage of the transmission line; The breakdown electric field of the transmission line is determined according to the breakdown voltage of the transmission line.

4. The method according to claim 3, characterized in that The breakdown electric field of the transmission line is expressed as: Among them, E b is the breakdown electric field; V b is the breakdown voltage; d is the electrode spacing; A is the Townsend coefficient prefactor; B is the reciprocal factor of the ionization coefficient; p is the atmospheric pressure; f1(T,H,W,I) is the first environmental correction function; T is the measured temperature; T ref is the reference temperature; α is the power law factor of temperature effect; is the amplification effect of the motion of gas molecules; H is the relative humidity; H ref is the reference humidity; μ is the humidity change adjustment factor; is the smoothing saturation effect when the humidity deviates from the reference value; W is the measured wind speed; W ref is the reference wind speed; γ is the wind speed correction coefficient; is the exponential attenuation of the effect on ion diffusion when the wind speed increases; I is the ion concentration in the air; I ref is the concentration reference value; δ is the concentration periodic modulation factor; is the periodic modulation effect of ion concentration.

5. The method according to claim 2, characterized in that: Substituting the environmental perception data and the power transmission monitoring data into the corona discharge calculation item of the physical driven electric shock model to obtain the corona critical electric field of the power transmission line includes: Analyzing the nonlinear influence of corona discharge according to the environmental sensing data to obtain nonlinear analysis data of corona phenomenon; According to the nonlinear analysis data of the corona phenomenon, the initial critical electric field of the physical driven electric shock model is corrected to obtain the corona critical electric field.

6. The method according to claim 5, characterized in that The expression of the corona critical electric field of the transmission line is: Among them, E c is the corona critical electric field; E0 is the initial critical electric field under standard conditions; ξ is the air density factor; f2(T,H) is the second environmental correction function; λ is the environmental coupling influence adjustment coefficient; T is the measured temperature; T ref is the reference temperature; is the deviation effect of the measured temperature relative to the reference temperature; H is the relative humidity; H ref is the reference humidity; is the effect of the change in relative humidity relative to the reference humidity.

7. The method according to claim 2, characterized in that: The weighted fusion of the breakdown electric field of the transmission line and the corona critical electric field to obtain the electric shock distance calculation data includes: Weighted fusion of the breakdown electric field of the transmission line and the corona critical electric field to obtain the initial electric shock critical electric field of the transmission line; According to the space charge effect information of the transmission line, the initial electric shock critical electric field is locally corrected to obtain a corrected electric shock critical electric field; The Newton iteration method is used to solve the modified critical electric field of electric shock to obtain the electric shock distance calculation data.

8. The method according to any one of claims 1 to 7, characterized in that: The performing error analysis on the difference value according to the environmental perception data and the power transmission monitoring data to obtain data error factor information includes: According to the environmental perception data and the power transmission monitoring data, a variance-based sensitivity analysis is performed on the difference value to obtain data error factor contribution information; Multi-level fuzzy reasoning is performed on the data error factor contribution information to obtain the data error factor information.

9. A transmission line anti-electric shock alarm device, characterized in that: The device comprises: A line data acquisition module is used to acquire environmental perception data, power transmission monitoring data and biological perception data of the power transmission line; An electric shock distance prediction module, used for inputting the environmental perception data and the power transmission monitoring data into the hybrid architecture electric shock model of the power transmission line to obtain electric shock distance prediction data; An electric shock distance calculation module, used for inputting the environmental perception data and the power transmission monitoring data into the physical drive electric shock model of the power transmission line to obtain electric shock distance calculation data; A calculation error analysis module, used for performing error analysis on the difference between the electric shock distance prediction data and the electric shock distance calculation data to obtain data error factor information according to the environmental perception data and the power transmission monitoring data when the difference between the electric shock distance prediction data and the electric shock distance calculation data is greater than a preset error value; An electric shock distance determination module is used to adjust and recalculate the model parameters of the hybrid architecture electric shock model and the physical drive electric shock model according to the data error factor information, until the difference value is less than a preset error value, to obtain electric shock distance output data; The warning information generation module is used to determine the anti-electric shock warning information of the power transmission line according to the electric shock distance output data and the biological perception data.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

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