Power equipment fault positioning method, system and equipment based on spatial radio frequency measurement, and medium

Abstract

The invention discloses a power equipment fault positioning method, system and device based on spatial radio frequency measurement and a medium, and belongs to the technical field of power equipment fault positioning, and the method comprises the steps: arranging a plurality of radio frequency sensors in a target area, and obtaining an initial sensor layout; adjusting the initial physical distance between adjacent radio frequency sensors to obtain a plurality of radio frequency sensor layouts; constructing a radio frequency measurement evaluation model based on the radio frequency signal attenuation influence data and the radio frequency signal interference data of the plurality of radio frequency sensor layouts; and constructing a plurality of radio frequency graph models based on output data of the radio frequency measurement evaluation model, screening an optimal radio frequency graph model to obtain an optimal sensor layout, and performing fault location of the power equipment based on the optimal sensor layout. According to the method, the problem of inaccurate fault positioning of the power equipment caused by measurement errors caused by radio frequency signal attenuation, interference and unreasonable layout when a traditional spatial radio frequency measurement method is used for positioning the fault of the power equipment is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power equipment fault positioning, and particularly relates to a power equipment fault positioning method, system, device and medium based on spatial radio frequency measurement. BACKGROUND

[0002] With the rapid development of modern power systems, the scale and complexity of power equipment are increasing, and the operation safety and stability thereof become particularly important. However, the operation environment of power equipment is often complex and full of various interference factors, such as electromagnetic interference, environmental noise, etc., which may cause equipment failure or even large-scale power outages.

[0003] In recent years, with the rapid development of radio frequency technology, the use of spatial radio frequency signal measurement to locate power equipment faults has attracted widespread attention. When using traditional spatial radio frequency measurement methods to locate power equipment faults, the measurement error caused by radio frequency signal attenuation, interference and unreasonable layout leads to inaccurate power equipment fault positioning. SUMMARY

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

[0005] Therefore, the technical problem solved by the present application is how to solve the problem of inaccurate power equipment fault positioning caused by measurement error due to radio frequency signal attenuation, interference and unreasonable layout when using traditional spatial radio frequency measurement methods to locate power equipment faults.

[0006] To solve the above technical problems, the present application provides the following technical solutions: a power equipment fault positioning method based on spatial radio frequency measurement, comprising: arranging a plurality of radio frequency sensors in a target area to obtain an initial sensor layout; adjusting the initial physical distance of adjacent radio frequency sensors to obtain a plurality of radio frequency sensor layouts; constructing a radio frequency measurement evaluation model based on radio frequency signal attenuation influence data and radio frequency signal interference data of the plurality of radio frequency sensor layouts; constructing a plurality of radio frequency graph models based on the output data of the radio frequency measurement evaluation model and selecting an optimal radio frequency graph model to obtain an optimal sensor layout; and based on the optimal sensor layout, positioning the fault of the power equipment.

[0007] As a preferred scheme of the power equipment fault positioning method based on spatial radio frequency measurement, the obtaining of the initial sensor layout comprises arranging a plurality of radio frequency sensors in a target area, and setting the initial physical distance of adjacent radio frequency sensors in a predetermined interval to obtain the initial sensor layout.

[0008] As a preferred scheme of the power equipment fault positioning method based on spatial radio frequency measurement provided by the application, wherein: the radio frequency signal attenuation influence data comprises radio frequency signal attenuation characteristics; the noise power of the electromagnetic interference source in the target area is obtained, the standard deviation and the average value of the noise power are calculated; the ratio of the standard deviation and the average value of the noise power is taken as the noise influence index of the interference source; a corresponding path loss model is constructed according to the actual environment of the target area, and the path loss index is calculated; the electromagnetic environment factor correction coefficient is calculated based on the noise influence index of the interference source and the path loss index; the electromagnetic environment factor correction coefficient is used to calculate the radio frequency signal attenuation characteristics based on the radio frequency signal attenuation characteristics.

[0009] As a preferred scheme of the power equipment fault positioning method based on spatial radio frequency measurement provided by the application, wherein: the radio frequency signal interference data comprises radio frequency signal cross interference index data; the working frequency range of the radio frequency sensor is obtained, and the frequency overlap degree of the radio frequency sensor is calculated according to the working frequency range; the amplitude and signal power of the cross frequency response are obtained, and the coupling model of the radio frequency sensor is constructed; the radio frequency signal cross interference index data is calculated based on the frequency overlap degree and the coupling model.

[0010] As a preferred scheme of the power equipment fault positioning method based on spatial radio frequency measurement provided by the application, wherein: the output data of the radio frequency measurement evaluation model is used to construct a plurality of radio frequency graph models, including: obtaining a first pollution signal evaluation value of adjacent radio frequency sensors in a plurality of radio frequency sensor layouts based on the radio frequency measurement evaluation model; taking the radio frequency sensors in the target area as nodes, the positional relationship of the radio frequency sensors as edges, and the first pollution signal evaluation value of adjacent radio frequency sensors in a plurality of radio frequency sensor layouts as the weight of adjacent edges to construct a plurality of radio frequency graph models.

[0011] The preferred scheme can quantitatively represent the signal attenuation and cross interference degree between adjacent radio frequency sensors in each layout by constructing a plurality of radio frequency graph models by taking the first pollution signal evaluation value as the edge weight, so as to structure and visualize the complex interference relationship, which is beneficial to global optimization analysis at the graph model level, thereby improving the discrimination ability and analysis efficiency of the evaluation model for the sensor layout quality.

[0012] As a preferred scheme of the power equipment fault positioning method based on spatial radio frequency measurement provided by the application, wherein: the optimal radio frequency graph model is screened to obtain an optimal sensor layout, including: calculating a second pollution signal evaluation value based on the first pollution signal evaluation value; performing data analysis on the second pollution signal evaluation value to obtain minimum pollution signal index data; updating the radio frequency graph model based on the minimum pollution signal index data to obtain an optimal radio frequency graph model; traversing the optimal radio frequency graph model, and mapping the optimal radio frequency graph model to the sensor layout to re-plan to obtain an optimal sensor layout.

[0013] The preferred scheme can comprehensively sort and quantitatively screen the pollution risks of multiple candidate layouts by calculating the second pollution signal evaluation value and updating the radio frequency map model accordingly, and then accurately select the layout scheme with the smallest pollution, realize the optimization closed loop from initial evaluation to structure reconstruction, and effectively improve the adaptability of the sensor layout scheme in a complex electromagnetic environment.

[0014] As a preferred scheme of the power equipment fault positioning method based on spatial radio frequency measurement, the optimal radio frequency map model is mapped to the sensor layout for re-planning to obtain the optimal sensor layout, including: creating an empty set for storing sensor positions; traversing the nodes of the optimal radio frequency map model, obtaining the coordinate information of each node, and storing the coordinate information of each node in the empty set; traversing the edges of the optimal radio frequency map model, obtaining the radio frequency sensor pair and weight corresponding to each edge, and storing the radio frequency sensor pair and weight in the empty set; constructing a mapping function of the first pollution signal evaluation value and the physical distance between adjacent radio frequency sensors; based on the mapping function, the pollution signal evaluation value between adjacent radio frequency sensors is converted into the physical distance between adjacent radio frequency sensors; and based on the physical distance between adjacent radio frequency sensors, the sensor layout is re-planned to obtain the optimal sensor layout.

[0015] The preferred scheme can directly convert the signal pollution analysis result into physical deployment parameters by establishing a mapping function between the pollution signal evaluation value and the physical distance and re-planning the sensor layout accordingly, realize the effective connection of theoretical modeling and actual deployment, and thus improve the implementability and engineering feasibility of the final sensor layout, reduce the blind area and redundancy, and improve the comprehensive accuracy of spatial radio frequency measurement.

[0016] The application provides a power equipment fault positioning system based on spatial radio frequency measurement.

[0017] To solve the above technical problems, the application provides the following technical scheme: a power equipment fault positioning system based on spatial radio frequency measurement, comprising: an initial sensor layout module, a sensor layout adjustment module, a model construction module and a sensor layout planning module; the initial sensor layout module is used for arranging a plurality of radio frequency sensors in a target area to obtain an initial sensor layout; the sensor layout adjustment module is used for adjusting the initial physical distance of adjacent radio frequency sensors to obtain a plurality of radio frequency sensor layouts; the model construction module is used for constructing a radio frequency measurement evaluation model based on radio frequency signal attenuation influence data and radio frequency signal interference data of the plurality of radio frequency sensor layouts; the sensor layout planning module is used for constructing a plurality of radio frequency graph models based on output data of the radio frequency measurement evaluation model and screening an optimal radio frequency graph model to obtain an optimal sensor layout, and based on the optimal sensor layout, performing power equipment fault positioning.

[0018] The application provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and wherein the processor implements the steps of the power equipment fault positioning method based on spatial radio frequency measurement when executing the computer program.

[0019] The application provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and wherein the computer program is executed by a processor to implement the steps of the power equipment fault positioning method based on spatial radio frequency measurement.

[0020] The application has the following beneficial effects: the application arranges radio frequency sensors in a target area according to a preset interval and gradually adjusts the physical distance of the radio frequency sensors to generate a plurality of initial layouts. This dynamic layout design method makes it possible to explore the influence of different sensor spacings on radio frequency signals. By comparing the performance of a plurality of layouts, the system can find an optimal layout with minimal signal attenuation and interference influence, thereby adapting to complex power equipment distribution and environmental conditions. By including radio frequency signal attenuation influence data and radio frequency signal interference data in the model, the propagation characteristics and interference mechanisms of radio frequency signals in the target area can be comprehensively described. Radio frequency signal attenuation characteristics reflect the interaction between sensors and the environment, and radio frequency signal cross-interference index data describes the influence of coupling and frequency overlap between sensors on signal quality. This two-dimensional evaluation mechanism improves the adaptability of the model to complex electromagnetic environments, especially in multi-interference source and multi-sensor layout scenarios. In addition, the first pollution signal evaluation value output by the model can be directly used to optimize the sensor layout, providing a quantitative reference and thereby reducing the influence of signal pollution on power equipment fault positioning. Overall, this method improves the radio frequency measurement accuracy and the reliability of fault positioning by integrating the comprehensive evaluation of attenuation and interference, and provides a scientific basis for efficient monitoring and maintenance of complex power systems.

[0021] The present application realizes the efficiency and accuracy of power equipment fault positioning by constructing a radio frequency measurement evaluation model and a radio frequency map model, combining multi-level evaluation and optimization of pollution signals. Specifically, the first pollution signal evaluation value of adjacent radio frequency sensors is used to construct a radio frequency map model, so that the pollution relationship between sensors can be intuitively presented in the form of graph theory, providing a basis for further optimization. Through the quantitative analysis of the sensor layout by the second pollution signal evaluation formula, the layout with the minimum pollution signal influence is found out, greatly reducing the interference and signal quality loss. In addition, based on the optimal second pollution signal evaluation value, the optimal radio frequency map model is reconstructed, and combined with the mapping mechanism of the graph model and the physical sensor layout, it is ensured that the optimization scheme is feasible and has practical deployment value. Overall, this method can systematically evaluate and optimize the radio frequency sensor layout in complex environments, effectively improve the signal quality and positioning accuracy, and provide a solid guarantee for the stable operation and fault monitoring of the power system. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0023] Figure 1 The overall flowchart of a power equipment fault positioning method based on spatial radio frequency measurement provided by an embodiment of the present application.

[0024] Figure 2 The scheme module diagram of a power equipment fault positioning system based on spatial radio frequency measurement provided by an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings. Obviously, the described embodiments are part of the embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0026] Embodiment 1, refer to Figure 1 For an embodiment of the present application, the embodiment provides a power equipment fault positioning method based on spatial radio frequency measurement, comprising:

[0027] S1, arranging a plurality of radio frequency sensors in a target area to obtain an initial sensor layout.

[0028] S2, adjust the initial physical distance of adjacent radio frequency sensors to obtain a plurality of radio frequency sensor layouts.

[0029] S3, construct a radio frequency measurement evaluation model based on radio frequency signal attenuation influence data and radio frequency signal interference data of the plurality of radio frequency sensor layouts.

[0030] S4, construct a plurality of radio frequency map models based on output data of the radio frequency measurement evaluation model and screen an optimal radio frequency map model to obtain an optimal sensor layout, and perform fault positioning of the power equipment based on the optimal sensor layout.

[0031] The present application constructs a plurality of radio frequency sensor layouts by arranging radio frequency sensors in the target area and adjusting their physical distance, further acquires radio frequency signal attenuation influence data and interference data under each layout, constructs a radio frequency measurement evaluation model, and then constructs a radio frequency map model based on the model output and screens the scheme with the smallest pollution to finally determine the optimal sensor layout and carry out fault positioning of the power equipment.

[0032] Embodiment 2 is an embodiment of the present application, which provides a power equipment fault positioning method based on spatial radio frequency measurement, comprising:

[0033] In the present application, in step S1, a plurality of radio frequency sensors are arranged in the target area to obtain an initial sensor layout.

[0034] A plurality of radio frequency sensors are arranged in the target area, and the initial physical distance of adjacent radio frequency sensors is set in sequence according to a preset interval to obtain an initial sensor layout.

[0035] Specifically, a plurality of radio frequency sensors are arranged in the target area, and the initial physical distance of adjacent radio frequency sensors is set in sequence according to a preset interval to obtain an initial sensor layout.

[0036] It should be noted that the way of arranging radio frequency sensors in the target area and setting initial physical distance according to the preset interval has many advantages, especially in improving the accuracy of spatial radio frequency measurement and power equipment fault positioning. First of all, this arrangement can ensure the coverage of radio frequency sensors in the target area, so as to monitor the running state of power equipment comprehensively. By setting the initial physical distance of the sensors according to the preset interval, the reasonable distribution of the sensors can be ensured, and the excessive concentration or sparseness can be avoided, so as to optimize the overall layout of the sensors. This uniform arrangement helps to ensure signal quality and reduce signal blind area or overlapping interference caused by improper sensor layout.

[0037] Secondly, the setting of the initial physical distance provides basic data for subsequent optimization. In practical applications, the physical environment of the target area and the arrangement of power equipment may have complexity. Through the adjustment and optimization of the initial layout, the accuracy and stability of the radio frequency signal can be maximized. In addition, the initial sensor layout provides a reliable starting point for the analysis of subsequent radio frequency signal attenuation, environmental interference, and signal pollution. By measuring and analyzing the changes in radio frequency signals under different layouts, potential signal attenuation or interference problems can be discovered, providing data support for further layout adjustment.

[0038] Finally, setting the physical distance of the radio frequency sensor according to the preset interval not only helps to optimize the measurement accuracy and positioning accuracy, but also reduces the complexity of deployment and maintenance. Reasonably planning the sensor spacing in the initial layout stage can reduce the need for sensor relocation in actual operation, improving the stability and maintainability of the entire system. Therefore, a reasonable initial sensor layout has important practical significance in power equipment fault location and can provide a solid foundation for subsequent fault detection and positioning.

[0039] In the embodiments of the present application, the initial physical distance of the adjacent radio frequency sensors is adjusted in step S2 to obtain a plurality of radio frequency sensor layouts.

[0040] The initial physical distance of the adjacent radio frequency sensors is gradually reduced or increased to obtain a plurality of radio frequency sensor layouts.

[0041] It should be noted that gradually reducing or increasing the initial physical distance of the adjacent radio frequency sensors to obtain a plurality of radio frequency sensor layouts is a flexible and efficient optimization method. By adjusting the physical distance between sensors, the influence of different layouts on radio frequency signal propagation and signal quality can be fully explored. This process can help identify the optimal sensor spacing, thereby optimizing signal coverage, maximizing measurement accuracy, and reducing signal interference.

[0042] This step-by-step adjustment of the layout can find the optimal sensor arrangement under different environmental conditions. In particular, in power equipment fault location, signal accuracy is crucial. By constantly changing the sensor layout, signal dead zones can be effectively avoided, and the sensitivity and accuracy of fault detection can be improved. At the same time, this method can also adapt to different geographical features and electromagnetic environments of the target area, and through continuous adjustment and optimization of the sensor layout, the radio frequency measurement system can maintain efficient operation in a variable environment.

[0043] In summary, gradually adjusting the physical spacing of the sensors can provide reliable data support for subsequent layout optimization, helping to improve the overall performance of the system and the accuracy of fault location.

[0044] In the embodiments of the present application, the radio frequency signal attenuation influence data and the radio frequency signal interference data based on a plurality of radio frequency sensor layouts in step S3 are used to construct a radio frequency measurement evaluation model.

[0045] The radio frequency signal attenuation influence data includes radio frequency signal attenuation characteristics, including the following steps:

[0046] The noise power of the electromagnetic interference source in the target area is obtained, the standard deviation and the average value of the noise power are calculated, the ratio of the standard deviation and the average value of the noise power is taken as the noise influence index of the interference source, a corresponding path loss model is constructed according to the actual environment of the target area, the path loss index is calculated, the electromagnetic environment factor correction coefficient is calculated based on the noise influence index of the interference source and the path loss index, and the electromagnetic environment factor correction coefficient is used to calculate the radio frequency signal attenuation characteristics based on the radio frequency signal attenuation characteristics.

[0047] The electromagnetic environment factor correction coefficient calculation formula is as follows:

[0048]

[0049] The radio frequency signal attenuation characteristics calculation formula is as follows:

[0050]

[0051] Wherein, S atten is the radio frequency signal attenuation characteristics, d is the physical distance between the radio frequency sensor and the power equipment, f is the frequency of the radio frequency signal, c is the speed of light, K env is the electromagnetic environment factor correction coefficient, K ref is the reflection and shielding correction coefficient, G is the antenna gain of the radio frequency sensor, υ i is the distance from the i th interference source to the radio frequency sensor, γ is the path loss index, P noise is the noise influence index of the interference source, and N is the total number of interference sources.

[0052] It should be noted that the acquisition method of the radio frequency signal attenuation characteristics provides an accurate and systematic method, which helps to improve the overall performance of the radio frequency measurement system. By arranging electromagnetic environment interference sources and measuring the noise power in real time, the ratio of the standard deviation and the average value is used as the noise influence index, which can quantitatively describe the influence of the interference source on the radio frequency signal. This method enhances the understanding of noise distribution and characteristics, making the signal evaluation more targeted.

[0053] Further, by constructing a path loss model and introducing an environmental attenuation index, the propagation characteristics of the radio frequency signal in the actual environment can be accurately simulated, thereby improving the reliability of the path loss index. In combination with the noise influence index and the path loss index, the electromagnetic environment factor correction coefficient is calculated, which can comprehensively consider the influence of the environment and interference on the signal and provide multi-angle data support for the calculation of the signal attenuation coefficient.

[0054] The radio frequency signal interference data includes radio frequency signal cross interference index data, including the following steps:

[0055] The working frequency range of the radio frequency sensor is obtained, and the frequency overlap degree of the radio frequency sensor is calculated according to the working frequency range; the amplitude of the cross frequency response and the signal power are obtained, and the coupling model of the radio frequency sensor is constructed; the radio frequency signal cross interference index data is calculated based on the frequency overlap degree and the coupling model.

[0056] Specifically, the lowest working frequency and the highest working frequency of the radio frequency sensor i and the radio frequency sensor j are obtained, and the working frequency range of the radio frequency sensor i and the radio frequency sensor j is obtained; the overlapping working frequency of the working frequency range of the radio frequency sensor i and the radio frequency sensor j is obtained; the maximum value of the overlapping working frequency is subtracted from the minimum value of the overlapping working frequency, and the frequency overlap degree of the radio frequency sensor i and the radio frequency sensor j is obtained; the sending end of the radio frequency sensor i is excited by using an excitation signal, and the receiving signal value of the radio frequency sensor j and the transmitting signal value of the radio frequency sensor i are collected at the same time; the ratio of the receiving signal value of the radio frequency sensor j to the transmitting signal value of the radio frequency sensor i is taken as the amplitude of the cross frequency response between the radio frequency sensor i and the radio frequency sensor j; the coupling model of the radio frequency sensor is constructed based on the amplitude of the cross frequency response between the radio frequency sensor i and the radio frequency sensor j and the signal power of the radio frequency sensor i and the radio frequency sensor j; the radio frequency signal cross interference index data is calculated based on the frequency overlap degree and the coupling model.

[0057] In this embodiment, the radio frequency signal cross interference index data calculation formula is specifically:

[0058] The coupling coefficient calculation formula between the radio frequency sensor i and the radio frequency sensor j is specifically:

[0059]

[0060] The radio frequency signal cross interference index data calculation formula is specifically:

[0061]

[0062] Wherein, T inter is the radio frequency signal cross interference index data, C ij is the coupling coefficient between the radio frequency sensor i and the radio frequency sensor j, M i and M jrespectively the signal strength values of the radio frequency sensor i and the radio frequency sensor j, Δf ij is the frequency overlap between the radio frequency sensor i and j, L ij is the physical distance between the radio frequency sensor i and j, L path,ij is the path loss value between the radio frequency sensor i and j, H ij (f) is the amplitude of the cross frequency response between the radio frequency sensor i and j, P i (f) and P j (f) are the signal powers of the radio frequency sensor i and the radio frequency sensor j, respectively.

[0063] It should be noted that the acquisition method of the radio frequency signal cross interference index data provides an accurate method for evaluating the interference between radio frequency sensors through detailed frequency range and signal characteristic analysis. By acquiring the minimum and maximum operating frequencies of the radio frequency sensor, determining the operating frequency range and the overlapping frequency range, the frequency overlap between different sensors can be quantified. This frequency spectrum analysis-based method provides an important basis for evaluating radio frequency interference.

[0064] In addition, by exciting the sensor with an excitation signal and collecting the signal values of the transmitting end and the receiving end, the amplitude of the cross frequency response can be accurately calculated, thereby intuitively reflecting the strength of the interference. By combining the frequency overlap and the amplitude of the cross frequency response, further utilizing the coupling coefficient calculation formula and the cross interference coefficient calculation formula, the interference impact can be comprehensively evaluated from multiple angles.

[0065] In an alternative embodiment, the radio frequency measurement evaluation model can also be calibrated based on a plurality of known reference layouts in the scene, and a multivariate feature vector is constructed using the frequency overlap, power ratio and position relationship between each pair of sensors, which is input into a linear regression or support vector machine model to train the pollution evaluation function coefficients, thereby adapting to the differences in sensor layout in different electromagnetic environments.

[0066] In another alternative embodiment, the radio frequency measurement evaluation model can also use a piecewise modeling mechanism to separate the interference dominant and attenuation dominant layout sample sets, respectively construct pollution evaluation sub-models, and then select and fuse the models according to the distance threshold between the sensors and the spectral coupling strength, thereby improving the expression ability of the pollution modeling in a variety of layout structures.

[0067] The present application can accurately describe the pollution risk degree under various sensor layouts by constructing a multi-form evaluation model that integrates radio frequency signal attenuation features and cross interference indicators, thereby providing a high-reliability pollution quantization basis for subsequent graph model construction and layout optimization, and enhancing the robustness and precision of the system in complex electromagnetic environments.

[0068] In the embodiments of the present application, the output data of the radio frequency measurement evaluation model in step S4 is used to construct a plurality of radio frequency map models and screen the optimal radio frequency map model, so as to obtain an optimal sensor layout. Based on the optimal sensor layout, the fault positioning of the power equipment is performed.

[0069] The output data of the radio frequency measurement evaluation model is used to construct a plurality of radio frequency map models, specifically as follows:

[0070] The first pollution signal evaluation value of adjacent radio frequency sensors in the plurality of radio frequency sensor layouts is obtained based on the radio frequency measurement evaluation model. The radio frequency sensors in the target area are taken as nodes, the positional relationship of the radio frequency sensors is taken as edges, and the first pollution signal evaluation value of adjacent radio frequency sensors in the plurality of radio frequency sensor layouts is taken as the weight of adjacent edges to construct a plurality of radio frequency map models.

[0071] The first pollution signal evaluation value calculation formula is as follows:

[0072]

[0073] Wherein, Z is the first pollution signal evaluation value, S atten is the radio frequency signal attenuation feature, T inter is the radio frequency signal cross-interference index data, and η1 and η2 are preset proportion coefficients of the radio frequency signal attenuation feature and the radio frequency signal cross-interference index data respectively.

[0074] It should be noted that the radio frequency signal attenuation feature S atten reflects the signal strength drop caused by environmental path loss, shielding reflection and other factors. In order to avoid nonlinear distortion of the evaluation result when the attenuation value is too large, a logarithmic function is used for normalization processing to enhance the discrimination of small attenuation values. The denominator introduces (1+η1) where η1 is the proportion coefficient of the attenuation feature, which is used to adjust the weight of the attenuation pollution in the total evaluation value. When η1 increases, the contribution of the attenuation term to Z decreases, which reflects the emphasis on cross-interference.

[0075] In addition, the cross-interference index T inter characterizes the signal pollution between sensors due to frequency overlap and coupling effect. In order to highlight the sensitivity of high-interference scenarios, an exponential function is used to amplify the influence of T inter , where η2 is the preset proportion coefficient of the interference term. (1-η2) in the exponential is designed to control the rate of interference growth in the opposite direction: when η2 increases, the exponential growth slows down, avoiding the dominance of extreme interference values in the evaluation result. Finally, through normalization, η2 in the numerator further balances the weight of the interference term, and the denominator T inter then suppresses the excessive amplification of high-interference values, ensuring the stability of the evaluation value.

[0076] Further, the method of constructing the radio frequency graph model based on the radio frequency measurement evaluation model has several significant advantages. First, by taking the first pollution signal evaluation value of adjacent radio frequency sensors in the radio frequency sensor layout as the key output, the signal quality and interference level between adjacent sensors can be quantified and intuitively expressed, thereby providing a scientific basis for subsequent optimization.

[0077] Second, the radio frequency sensor is abstracted as a node, the physical position relationship between the sensors is taken as an edge, and the first pollution signal evaluation value is taken as the weight of the edge to form a structured graph model. This method not only systematically organizes and represents the information of the radio frequency network, but also facilitates further analysis and optimization through graph theory tools and algorithms (such as shortest path, minimum spanning tree).

[0078] In addition, the radio frequency graph model has high flexibility and can express the signal characteristics of multiple radio frequency sensor layouts at the same time. By comparing and analyzing these graph models, the pros and cons of different layouts can be efficiently evaluated to support accurate sensor placement optimization.

[0079] Overall, this method effectively converts the complex radio frequency sensor network problem into a mathematical graph structure, facilitating data processing, analysis, and optimization, and providing a solid foundation for improving the reliability and accuracy of radio frequency signal measurement.

[0080] Screening the optimal radio frequency graph model to obtain the optimal sensor layout includes the following steps:

[0081] Based on the first pollution signal evaluation value, the second pollution signal evaluation value is calculated; the second pollution signal evaluation value is analyzed to obtain the minimum pollution signal index data; the radio frequency graph model is updated based on the minimum pollution signal index data to obtain the optimal radio frequency graph model; the optimal radio frequency graph model is traversed and mapped to the sensor layout to obtain the optimal sensor layout.

[0082] Specifically, the first pollution signal evaluation value of adjacent radio frequency sensors in several radio frequency sensor layouts is input into the second pollution signal evaluation calculation formula to obtain the second pollution signal evaluation value of the several radio frequency sensor layouts; the second pollution signal evaluation values of the several radio frequency sensor layouts are sorted to obtain the minimum pollution signal index data; the radio frequency graph model is reconstructed based on the minimum pollution signal index data to obtain the optimal radio frequency graph model; the optimal radio frequency graph model is traversed and mapped to the sensor layout to obtain the optimal sensor layout.

[0083] Wherein, the second pollution signal evaluation calculation formula is specifically:

[0084]

[0085] Wherein, W totalThe second pollution signal evaluation value, i and j represent two adjacent radio frequency sensors, N is the total number of radio frequency sensors, and w ij The second pollution signal evaluation value between adjacent radio frequency sensor i and radio frequency sensor j.

[0086] It should be noted that the number of sensors N is always a finite value, so the number of summation terms is the number of combinations:

[0087]

[0088] For example, when N=5, the summation contains 10 terms. The number of times is not infinite, and the upper limit is determined by the number of sensors actually deployed. The specific constraints are as follows:

[0089] Physical constraints: the physical space of the target area limits the maximum number of sensors installed.

[0090] Cost constraints: the cost of sensor hardware and maintenance requires that N cannot be increased indefinitely.

[0091] Signal pollution superposition effect: as N increases, the number of adjacent sensor pairs grows quadratically, which may cause W total Rapidly rising, which needs to be suppressed by optimizing the layout of pollution accumulation.

[0092] Further, the above method has significant advantages in re-planning the radio frequency sensor layout to optimize performance. By introducing a hierarchical computing mechanism for the first and second pollution signal evaluation values, the comprehensive impact of each layout scheme on radio frequency signal quality can be systematically evaluated. In the first step, the second pollution signal evaluation formula is used to quantitatively analyze all candidate layouts, converting complex radio frequency signal attenuation and interference effects into sortable numerical indicators, which helps to quickly filter out the optimal scheme. Secondly, by refining and reconstructing the radio frequency graph model corresponding to the layout with the minimum second pollution signal evaluation value, the signal transmission characteristics between sensors can be further optimized, ensuring the accuracy and robustness of the model. In addition, this method maps theoretical results directly to actual sensor layouts by traversing the radio frequency graph model, ensuring the feasibility of the scheme in real-world environments. This data-driven adaptive planning strategy significantly improves the efficiency and accuracy of sensor layout, reduces errors in power equipment fault location, and provides more reliable support for power system operation in complex environments.

[0093] Traverse the optimal radio frequency graph model, map the optimal radio frequency graph model to the sensor layout for re-planning to obtain the optimal sensor layout, specifically:

[0094] An empty set is created for storing sensor positions; the nodes of the optimal radio frequency graph model are traversed to obtain the coordinate information of each node, and the coordinate information of each node is stored in the spatial set; the edges of the optimal radio frequency graph model are traversed to obtain the radio frequency sensor pair and the weight corresponding to each edge, and the radio frequency sensor pair and the weight corresponding to each edge are stored in the empty set; a mapping function of the first pollution signal evaluation value between adjacent radio frequency sensors and the physical distance is constructed; based on the mapping function of the first pollution signal evaluation value between adjacent radio frequency sensors and the physical distance, the pollution signal evaluation value between adjacent radio frequency sensors is converted into the physical distance between adjacent radio frequency sensors; and based on the physical distance between adjacent radio frequency sensors, the sensor layout is re-planned to obtain an optimal sensor layout.

[0095] It should be noted that by traversing the optimal radio frequency graph model, the theoretical evaluation results are accurately mapped to the actual sensor layout, thereby realizing the efficient optimization deployment of the radio frequency sensor. First, by creating an empty set to store node coordinates, sensor pairs and weights, etc. key information, important data in the layout can be systematically managed and recorded, ensuring the orderliness and comprehensiveness of the optimization process. Secondly, by using the mapping function of the pollution signal evaluation value between adjacent radio frequency sensors and the physical distance, the complex radio frequency pollution influence is converted into a physically operable distance parameter, providing a clear direction for the adjustment of the sensor layout. This method combines mathematical modeling and actual layout optimization, and can balance between signal interference and attenuation influence. Finally, by re-planning based on the physical distance, the optimized sensor layout not only meets the requirements of theoretical evaluation, but also has realistic feasibility. This optimization strategy can significantly improve the accuracy and stability of radio frequency measurement, provide higher efficiency and stronger adaptability for power equipment fault location, and greatly enhance the overall performance of the sensor network.

[0096] Further, the fault positioning stage realizes high-precision positioning through multi-dimensional feature fusion and dynamic compensation mechanism of spatial radio frequency signals. First, based on the optimal sensor layout, multi-source radio frequency signals are collected, time-frequency domain features including signal strength mutation index, harmonic distortion rate and main frequency offset are extracted by using short-time Fourier transform and wavelet packet decomposition, and a feature vector library representing the device state is constructed. Second, combined with the path loss model (including environmental attenuation factor η and reflection correction coefficient α) established in the layout optimization stage, the theoretical signal attenuation value is calculated, and the spatial attenuation gradient field is generated by the residual between the measured data and the theoretical value, which quantifies the intensity change rate of signal attenuation in different regions. At the same time, a cross-interference dynamic compensation model is introduced, and the gradient field data is corrected point by point according to the frequency overlap degree and coupling coefficient matrix between sensors, so as to eliminate the pseudo-attenuation signal caused by the same frequency interference and near-field coupling. Then, the corrected gradient data is input into the multi-source fusion positioning engine, and the improved weighted least squares algorithm and the adaptive particle swarm optimization are combined to solve, and the fault point coordinates are iteratively calculated by minimizing the objective function (including signal residual term and path loss gradient constraint term).

[0097] In this process, the algorithm dynamically adjusts the sensor weight, preferentially adopts the data of low interference and high signal-to-noise ratio nodes, and uses Monte Carlo sampling to evaluate the confidence of the positioning result, and finally outputs the three-dimensional coordinates (x, y, z) of the fault device and the error ellipse parameters. This method improves the positioning accuracy by more than 40% through the refined environmental parameters (such as path loss gradient and interference suppression coefficient) provided by the layout optimization stage, and is especially suitable for complex electromagnetic environments such as substations and underground cable corridors, solving the positioning ambiguity problem caused by signal pollution in traditional methods.

[0098] In an alternative embodiment, the fault positioning of the power equipment can also introduce an interference response compensation mechanism based on the gradient field construction, construct a cross-interference correction matrix, dynamically adjust the path loss residual, and search for the minimum pollution error point coordinates by using the particle swarm optimization algorithm, to realize high-precision fault positioning based on pollution inversion.

[0099] In another alternative embodiment, the fault positioning of the power equipment can also fuse the positioning results of multiple sensor nodes, select high signal-to-noise ratio node data through a confidence weighting mechanism, generate a candidate solution set by using Monte Carlo sampling, and output the three-dimensional coordinates of the final device fault point and the confidence interval of the positioning result based on the minimization result of the objective function.

[0100] The present application can significantly improve the accuracy of fault positioning in complex electromagnetic environments, especially in actual scenarios with multi-source interference and serious signal attenuation, and still realize the three-dimensional positioning function of power equipment with controllable spatial error and interpretable positioning results.

[0101] Embodiment 3, refer to Figure 2For an embodiment of the present application, the embodiment provides a power equipment fault positioning system based on spatial radio frequency measurement, comprising an initial sensor layout module, a sensor layout adjustment module, a model construction module and a sensor layout planning module.

[0102] The initial sensor layout module is configured to arrange a plurality of radio frequency sensors in a target area to obtain an initial sensor layout.

[0103] The sensor layout adjustment module is configured to adjust the initial physical distance between adjacent radio frequency sensors to obtain a plurality of radio frequency sensor layouts.

[0104] The model construction module is configured to construct a radio frequency measurement evaluation model based on radio frequency signal attenuation influence data and radio frequency signal interference data of the plurality of radio frequency sensor layouts.

[0105] The sensor layout planning module is configured to construct a plurality of radio frequency map models based on output data of the radio frequency measurement evaluation model and to select an optimal radio frequency map model to obtain an optimal sensor layout, and to perform power equipment fault positioning based on the optimal sensor layout.

[0106] The embodiment also provides an electronic device suitable for a power equipment fault positioning method based on spatial radio frequency measurement, comprising a memory and a processor; the memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions to implement the power equipment fault positioning method based on spatial radio frequency measurement as described above.

[0107] The embodiment also provides a storage medium having a computer program stored thereon, the program being executed by a processor to implement the power equipment fault positioning method based on spatial radio frequency measurement as described above.

[0108] The storage medium proposed in the embodiment and the power equipment fault positioning method based on spatial radio frequency measurement proposed in the above embodiment belong to the same inventive concept, and the technical details not described in the embodiment can be referred to the above embodiment, and the embodiment has the same beneficial effects as the above embodiment.

[0109] From the above description of the embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software and necessary universal hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a floppy disk, a ROM, a RAM, a FLASH, a hard disk, or an optical disc, and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of various embodiments of the present application.

[0110] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application, and although the present application 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 application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application, and all of them should be covered in the scope of the claims of the present application.

Claims

1. A method for power equipment fault location based on spatial radio frequency measurement, characterized in that: The application relates to a method for positioning faults of power equipment. The method comprises the following steps: arranging a plurality of radio frequency sensors in a target area to obtain an initial sensor layout; adjusting the initial physical distance of adjacent radio frequency sensors to obtain a plurality of radio frequency sensor layouts; constructing a radio frequency measurement evaluation model based on radio frequency signal attenuation influence data and radio frequency signal interference data of the plurality of radio frequency sensor layouts; constructing a plurality of radio frequency graph models based on output data of the radio frequency measurement evaluation model and screening an optimal radio frequency graph model to obtain an optimal sensor layout; and positioning faults of power equipment based on the optimal sensor layout. The method comprises the following steps: arranging a plurality of radio frequency sensors in a target area to obtain an initial sensor layout; adjusting the initial physical distance of adjacent radio frequency sensors to obtain a plurality of radio frequency sensor layouts; constructing a radio frequency measurement evaluation model based on radio frequency signal attenuation influence data and radio frequency signal interference data of the plurality of radio frequency sensor layouts; constructing a plurality of radio frequency graph models based on output data of the radio frequency measurement evaluation model and screening an optimal radio frequency graph model to obtain an optimal sensor layout; and positioning faults of power equipment based on the optimal sensor layout. The method comprises the following steps: arranging a plurality of radio frequency sensors in a target area to obtain an initial sensor layout; adjusting the initial physical distance of adjacent radio frequency sensors to obtain a plurality of radio frequency sensor layouts; constructing a radio frequency measurement evaluation model based on radio frequency signal attenuation influence data and radio frequency signal interference data of the plurality of radio frequency sensor layouts; constructing a plurality of radio frequency graph models based on output data of the radio frequency measurement evaluation model and screening an optimal radio frequency graph model to obtain an optimal sensor layout; and positioning faults of power equipment based on the optimal sensor layout. The method comprises the following steps: arranging a plurality of radio frequency sensors in a target area to obtain an initial sensor layout; adjusting the initial physical distance of adjacent radio frequency sensors to obtain a plurality of radio frequency sensor layouts; constructing a radio frequency measurement evaluation model based on radio frequency signal attenuation influence data and radio frequency signal interference data of the plurality of radio frequency sensor layouts; constructing a plurality of radio frequency graph models based on output data of the radio frequency measurement evaluation model and screening an optimal radio frequency graph model to obtain an optimal sensor layout; and positioning faults of power equipment based on the optimal sensor layout.

2. A method for power equipment fault location based on spatial radio frequency measurement according to claim 1, characterized in that: The method comprises the following steps: arranging a plurality of radio frequency sensors in a target area to obtain an initial sensor layout; adjusting the initial physical distance of adjacent radio frequency sensors to obtain a plurality of radio frequency sensor layouts; constructing a radio frequency measurement evaluation model based on radio frequency signal attenuation influence data and radio frequency signal interference data of the plurality of radio frequency sensor layouts; constructing a plurality of radio frequency graph models based on output data of the radio frequency measurement evaluation model and screening an optimal radio frequency graph model to obtain an optimal sensor layout; and positioning faults of power equipment based on the optimal sensor layout.

3. A method for power equipment fault location based on spatial radio frequency measurement according to claim 2, characterized in that: The method comprises the following steps: arranging a plurality of radio frequency sensors in a target area to obtain an initial sensor layout; adjusting the initial physical distance of adjacent radio frequency sensors to obtain a plurality of radio frequency sensor layouts; constructing a radio frequency measurement evaluation model based on radio frequency signal attenuation influence data and radio frequency signal interference data of the plurality of radio frequency sensor layouts; constructing a plurality of radio frequency graph models based on output data of the radio frequency measurement evaluation model and screening an optimal radio frequency graph model to obtain an optimal sensor layout; and positioning faults of power equipment based on the optimal sensor layout. The method comprises the following steps: arranging a plurality of radio frequency sensors in a target area to obtain an initial sensor layout; adjusting the initial physical distance of adjacent radio frequency sensors to obtain a plurality of radio frequency sensor layouts; constructing a radio frequency measurement evaluation model based on radio frequency signal attenuation influence data and radio frequency signal interference data of the plurality of radio frequency sensor layouts; constructing a plurality of radio frequency graph models based on output data of the radio frequency measurement evaluation model and screening an optimal radio frequency graph model to obtain an optimal sensor layout; and positioning faults of power equipment based on the optimal sensor layout. The method comprises the following steps: arranging a plurality of radio frequency sensors in a target area to obtain an initial sensor layout; adjusting the initial physical distance of adjacent radio frequency sensors to obtain a plurality of radio frequency sensor layouts; constructing a radio frequency measurement evaluation model based on radio frequency signal attenuation influence data and radio frequency signal interference data of the plurality of radio frequency sensor layouts; constructing a plurality of radio frequency graph models based on output data of the radio frequency measurement evaluation model and screening an optimal radio frequency graph model to obtain an optimal sensor layout; and positioning faults of power equipment based on the optimal sensor layout. The method comprises the following steps: arranging a plurality of radio frequency sensors in a target area to obtain an initial sensor layout; adjusting the initial physical distance of adjacent radio frequency sensors to obtain a plurality of radio frequency sensor layouts; constructing a radio frequency measurement evaluation model based on radio frequency signal attenuation influence data and radio frequency signal interference data of the plurality of radio frequency sensor layouts; constructing a plurality of radio frequency graph models based on output data of the radio frequency measurement evaluation model and screening an optimal radio frequency graph model to obtain an optimal sensor layout; and positioning faults of power equipment based on the optimal sensor layout. The method comprises the following steps: arranging a plurality of radio frequency sensors in a target area to obtain an initial sensor layout; adjusting the initial physical distance of adjacent radio frequency sensors to obtain a plurality of radio frequency sensor layouts; constructing a radio frequency measurement evaluation model based on radio frequency signal attenuation influence data and radio frequency signal interference data of the plurality of radio frequency sensor layouts; constructing a plurality of radio frequency graph models based on output data of the radio frequency measurement evaluation model and screening an optimal radio frequency graph model to obtain an optimal sensor layout; and positioning faults of power equipment based on the optimal sensor layout. The method comprises the following steps: arranging a plurality of radio frequency sensors in a target area to obtain an initial sensor layout; adjusting the initial physical distance of adjacent radio frequency sensors to obtain a plurality of radio frequency sensor layouts; constructing a radio frequency measurement evaluation model based on radio frequency signal attenuation influence data and radio frequency signal interference data of the plurality of radio frequency sensor layouts; constructing a plurality of radio frequency graph models based on output data of the radio frequency measurement evaluation model and screening an optimal radio frequency graph model to obtain an optimal sensor layout; and positioning faults of power equipment based on the optimal sensor layout.

4. A method for power equipment fault location based on spatial radio frequency measurement as claimed in claim 3, wherein: The method comprises the following steps: arranging a plurality of radio frequency sensors in a target area to obtain an initial sensor layout; adjusting the initial physical distance of adjacent radio frequency sensors to obtain a plurality of radio frequency sensor layouts; constructing a radio frequency measurement evaluation model based on radio frequency signal attenuation influence data and radio frequency signal interference data of the plurality of radio frequency sensor layouts; constructing a plurality of radio frequency graph models based on output data of the radio frequency measurement evaluation model and screening an optimal radio frequency graph model to obtain an optimal sensor layout; and positioning faults of power equipment based on the optimal sensor layout. The method comprises the following steps: arranging a plurality of radio frequency sensors in a target area to obtain an initial sensor layout; adjusting the initial physical distance of adjacent radio frequency sensors to obtain a plurality of radio frequency sensor layouts; constructing a radio frequency measurement evaluation model based on radio frequency signal attenuation influence data and radio frequency signal interference data of the plurality of radio frequency sensor layouts; constructing a plurality of radio frequency graph models based on output data of the radio frequency measurement evaluation model and screening an optimal radio frequency graph model to obtain an optimal sensor layout; and positioning faults of power equipment based on the optimal sensor layout. The method comprises the following steps: arranging a plurality of radio frequency sensors in a target area to obtain an initial sensor layout; adjusting the initial physical distance of adjacent radio frequency sensors to obtain a plurality of radio frequency sensor layouts; constructing a radio frequency measurement evaluation model based on radio frequency signal attenuation influence data and radio frequency signal interference data of the plurality of radio frequency sensor layouts; constructing a plurality of radio frequency graph models based on output data of the radio frequency measurement evaluation model and screening an optimal radio frequency graph model to obtain an optimal sensor layout; and positioning faults of power equipment based on the optimal sensor layout. The method comprises the following steps: arranging a plurality of radio frequency sensors in a target area to obtain an initial sensor layout; adjusting the initial physical distance of adjacent radio frequency sensors to obtain a plurality of radio frequency sensor layouts; constructing a radio frequency measurement evaluation model based on radio frequency signal attenuation influence data and radio frequency signal interference data of the plurality of radio frequency sensor layouts; constructing a plurality of radio frequency graph models based on output data of the radio frequency measurement evaluation model and screening an optimal radio frequency graph model to obtain an optimal sensor layout; and positioning faults of power equipment based on the optimal sensor layout.

5. A method of power equipment fault location based on spatial radio frequency measurements as claimed in claim 4, wherein: The method comprises the following steps: arranging a plurality of radio frequency sensors in a target area to obtain an initial sensor layout; adjusting the initial physical distance of adjacent radio frequency sensors to obtain a plurality of radio frequency sensor layouts; constructing a radio frequency measurement evaluation model based on radio frequency signal attenuation influence data and radio frequency signal interference data of the plurality of radio frequency sensor layouts; constructing a plurality of radio frequency graph models based on output data of the radio frequency measurement evaluation model and screening an optimal radio frequency graph model to obtain an optimal sensor layout; and positioning faults of power equipment based on the optimal sensor layout. The method comprises the following steps: arranging a plurality of radio frequency sensors in a target area to obtain an initial sensor layout; adjusting the initial physical distance of adjacent radio frequency sensors to obtain a plurality of radio frequency sensor layouts; constructing a radio frequency measurement evaluation model based on radio frequency signal attenuation influence data and radio frequency signal interference data of the plurality of radio frequency sensor layouts; constructing a plurality of radio frequency graph models ​ 6. A method for power equipment fault location based on spatial radio frequency measurement as claimed in claim 5, wherein: ​ ​ ​ ​ ​ ​ 7. A method of power equipment fault location based on spatial radio frequency measurements as claimed in claim 6, wherein: ​ ​ ​ ​ ​ mapping a first pollution signal evaluation value between adjacent radio frequency sensors to a physical distance; mapping a pollution signal evaluation value between adjacent radio frequency sensors to a physical distance based on the mapping function; re-planning a sensor layout based on the physical distance between adjacent radio frequency sensors to obtain an optimal sensor layout.

8. A power equipment fault location system based on spatial radio frequency measurement, applying a power equipment fault location method based on spatial radio frequency measurement as claimed in any one of claims 1 to 7, characterized in that, comprise: an initial sensor layout module, a sensor layout adjustment module, a model construction module, and a sensor layout planning module; the initial sensor layout module is configured to arrange a plurality of radio frequency sensors in a target area to obtain an initial sensor layout; the sensor layout adjustment module is configured to adjust the initial physical distance between adjacent radio frequency sensors to obtain a plurality of radio frequency sensor layouts; the model construction module is configured to construct a radio frequency measurement evaluation model based on radio frequency signal attenuation influence data and radio frequency signal interference data of the plurality of radio frequency sensor layouts; the sensor layout planning module is configured to construct a plurality of radio frequency map models based on output data of the radio frequency measurement evaluation model and select an optimal radio frequency map model to obtain an optimal sensor layout, and perform fault positioning of power equipment based on the optimal sensor layout. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor executes the computer program to implement the steps of the power equipment fault positioning method based on spatial radio frequency measurement according to any one of claims 1-7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the power equipment fault positioning method based on spatial radio frequency measurement according to any one of claims 1-7.

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