A mountain line fault detection and early warning method, system, device and storage medium

Abstract

The present invention relates to the field of fault detection technology, and discloses a method, system, device and storage medium for detecting and warning faults in mountainous areas, including obtaining fault traveling wave signals on segmented lines, locating fault points, and obtaining line fault points; collecting field data of line fault points, determining fault types and corresponding sets of influencing factors; weighted summing the sets of influencing factors according to standard weights, and obtaining a first fault score corresponding to the fault type; performing segmented inspections on power grid lines, extracting influencing factors from inspection data according to the sets of influencing factors, and performing weighted summing to obtain a second fault score; and performing a fault warning reminder in response to the second fault score being greater than the first fault score. The present invention realizes accurate positioning of mountainous line fault points through traveling wave detection, realizes accurate mountainous line fault warnings by performing comprehensive scoring calculations on influencing factors when line faults occur, and effectively reduces the frequency of mountainous line faults.

Description

Technical Field

[0001] The present invention relates to the technical field of fault detection, and in particular to a method, system, device and storage medium for detecting and warning faults of a line in a mountainous area. Background Art

[0002] According to the on-site operation and maintenance experience of distribution networks in mountainous areas, distribution lines in mountainous areas are easily affected by lightning strikes, wildfires, mudslides, landslides and tree growth, which may cause power outages. In addition, traffic conditions in mountainous areas are poor. If a line fault occurs and the cause of the fault is unknown, the on-site operation and maintenance personnel will have a huge workload and their work will be difficult, resulting in insufficient efficiency in restoring power after a fault. The lines in mountainous areas are long, the failure rate is high, and the line inspection time is long, which seriously affects the user experience of mountain users.

[0003] At present, the commonly used fault diagnosis methods for power distribution lines in mountainous areas are mostly to perform fault diagnosis by analyzing the causes of the fault after the fault occurs. However, the limitations of this method are that, on the one hand, it lacks specific positioning of the fault and has the problem of insufficient efficiency in line fault re-pointing; on the other hand, it is unable to provide early warning of the fault, thereby failing to effectively reduce the frequency of faults in mountainous areas. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a mountain line fault detection and early warning method, system, equipment and storage medium, so as to solve the problem that the existing mountain lines have insufficient line fault restoration efficiency and cannot provide early warning of faults, thereby effectively reducing the frequency of faults in mountain lines.

[0005] In a first aspect, the present invention provides a method for detecting and warning faults of a line in a mountainous area, the method comprising:

[0006] Divide the mountain power grid lines into sections, obtain the fault traveling wave signals on the sectioned lines, and locate the fault points according to the fault traveling wave signals to obtain the line fault points;

[0007] Collecting on-site data of line fault points through inspection robots, and determining the fault type and the corresponding set of influencing factors based on the on-site data;

[0008] Performing a weighted summation on each influencing factor in the influencing factor set according to a preset standard weight, to obtain a first fault score corresponding to the fault type;

[0009] Performing segmented inspections on the power grid lines by using the inspection robot to obtain inspection data of each segmented line, extracting influencing factors from the inspection data according to the influencing factor set, and performing weighted summation according to the standard weights to obtain a second fault score;

[0010] The second fault score is compared with the first fault score, and in response to the second fault score being greater than the first fault score, a fault early warning reminder is issued.

[0011] Furthermore, the steps of segmenting the mountain power grid line, acquiring the fault traveling wave signal on the segmented line, and locating the fault point according to the fault traveling wave signal to obtain the line fault point include:

[0012] The mountain power grid lines are segmented according to the line material, and the fault traveling wave signals on the segmented lines are obtained through traveling wave sensors;

[0013] A traveling wave feature is extracted from the fault traveling wave signal, and based on the traveling wave feature, a distance from the fault point to the traveling wave sensor is calculated to obtain the line fault point.

[0014] Furthermore, the distance from the fault point to the traveling wave sensor is expressed by the following formula:

[0015] D=V i ×t i

[0016] Where V i is the propagation speed of the traveling wave in the i-th line, t i is the time from the occurrence of the fault in the i-th line to the receipt of the fault traveling wave signal by the traveling wave sensor, and D is the distance from the fault point to the traveling wave sensor.

[0017] Furthermore, the step of collecting on-site data of the line fault point by the inspection robot and determining the fault type and the corresponding set of influencing factors according to the on-site data includes:

[0018] Collecting on-site data of line fault points by inspection robots, wherein the on-site data includes fault point data, environmental data and line data;

[0019] Determine the fault type according to the fault point data, and extract environmental influencing factors and line influencing factors from the environmental data and the line data respectively;

[0020] The various environmental influencing factors are combined into an environmental influencing factor set, and the various line influencing factors are combined into a line influencing factor set.

[0021] Furthermore, the first fault score is expressed by the following formula:

[0022]

[0023] Where, wE i represents the standard weight of the i-th environmental influencing factor, DE irepresents the i-th environmental influencing factor, n represents the total number of environmental influencing factors, wL j represents the standard weight of the jth line influencing factor, DL j represents the jth line influencing factor, and m represents the total number of line influencing factors.

[0024] Furthermore, before the step of weighted summing the influencing factors in the influencing factor set according to the preset standard weight, the method further includes:

[0025] Extracting a set of historical influencing factors corresponding to the fault type from the historical inspection fault results, and normalizing the set of historical influencing factors according to the types and quantities of the influencing factors to obtain normalized weights;

[0026] The standard weight is dynamically adjusted according to the normalized weight to obtain the adjusted standard weight.

[0027] Furthermore, the normalized weight is expressed by the following formula:

[0028]

[0029] Where pE i represents the normalized weight of the i-th historical environmental influencing factor, cE i represents the i-th historical environmental influencing factor, n1 represents the total number of historical environmental influencing factors, and pL j represents the normalized weight of the influencing factor of the jth historical route, cL j represents the jth historical route influencing factor, and m1 represents the total number of historical route influencing factors;

[0030] The adjusted standard weights are expressed using the following formula:

[0031] w′E i =α·wE i +(1-α)·pE i

[0032] w′L j =α·wL j +(1-α)·pL

[0033] Where w′E i represents the adjusted standard weight of the i-th environmental influencing factor, wE i represents the standard weight of the i-th environmental influencing factor, w′L j represents the adjusted standard weight of the jth line influencing factor, wL j represents the standard weight of the j-th line influencing factor, and α represents the adjustment factor.

[0034] In a second aspect, the present invention provides a mountain line fault detection and early warning system, the system comprising:

[0035] The fault location module is used to segment the mountain power grid line, obtain the fault traveling wave signal on the segmented line, and locate the fault point according to the fault traveling wave signal to obtain the line fault point;

[0036] A data acquisition module, used to collect on-site data of line fault points through an inspection robot, and determine the fault type and a corresponding set of influencing factors based on the on-site data;

[0037] A scoring calculation module, used for performing weighted summation on each influencing factor in the influencing factor set according to a preset standard weight to obtain a first fault score;

[0038] a fault warning module, configured to perform segmented inspections on power grid lines by using an inspection robot, obtain inspection data of each segmented line, extract influencing factors from the inspection data according to the influencing factor set, and perform weighted summation according to the standard weights to obtain a second fault score;

[0039] The second fault score is compared with the first fault score, and in response to the second fault score being greater than the first fault score, a fault early warning reminder is issued.

[0040] In a third aspect, an embodiment of the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0041] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0042] The present invention provides a method, system, device and storage medium for detecting and warning line faults in mountainous areas. The present invention realizes accurate positioning of the fault point of the mountainous area line by detecting the fault traveling wave signal, realizes judgment of the degree of external influence on the line fault by performing comprehensive scoring calculation on the environmental influence factors and line influence factors when the line fault occurs, and realizes accurate mountainous area line fault warning by performing comprehensive scoring judgment on the fault warning, thereby effectively reducing the frequency of mountainous area line faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a flow chart of a method for detecting and warning faults of a line in a mountainous area according to an embodiment of the present invention;

[0044] Figure 2It is a structural schematic diagram of a mountain line fault detection and early warning system in an embodiment of the present invention;

[0045] Figure 3 It is a diagram of the internal structure of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] See also Figure 1 The first embodiment of the present invention provides a method for detecting and warning line faults in mountainous areas, which includes steps S10 to S50:

[0048] Step S10, segmenting the mountain power grid line, acquiring the fault traveling wave signal on the segmented line, and locating the fault point according to the fault traveling wave signal to obtain the line fault point;

[0049] Step S20, collecting field data of the line fault point by the inspection robot, and determining the fault type and the corresponding influencing factor set according to the field data;

[0050] Step S30, performing weighted summation on each influencing factor in the influencing factor set according to a preset standard weight, to obtain a first fault score corresponding to the fault type;

[0051] Step S40, performing segmented inspection on the power grid line by using the inspection robot to obtain inspection data of each segmented line, extracting influencing factors from the inspection data according to the influencing factor set, and performing weighted summation according to the standard weight to obtain a second fault score;

[0052] Step S50: compare the second fault score with the first fault score, and in response to the second fault score being greater than the first fault score, issue a fault warning reminder.

[0053] In the present invention, firstly, the fault location is performed by detecting the traveling wave signal of the power grid line of the mountain distribution network, and the specific steps include:

[0054] The mountain power grid lines are segmented according to the line material, and the fault traveling wave signals on the segmented lines are obtained through traveling wave sensors;

[0055] A traveling wave feature is extracted from the fault traveling wave signal, and based on the traveling wave feature, a distance from the fault point to the traveling wave sensor is calculated to obtain the line fault point.

[0056] In this embodiment, in order to ensure the accuracy of circuit fault location, the power grid line is first segmented according to the material of the line to obtain multiple segmented lines, and then a traveling wave sensor is installed to capture the fault traveling wave signal on the corresponding segmented line, and the traveling wave characteristics are extracted from the fault traveling wave signal. According to the traveling wave characteristics and the length of the segmented line, the distance from the fault point to the traveling wave sensor is calculated, wherein the traveling wave characteristics include the propagation speed of the traveling wave, the time to reach the corresponding line point, etc., and the distance from the fault point to the traveling wave sensor is calculated using the following formula:

[0057] D=V i ×t i

[0058] Where V i is the propagation speed of the traveling wave in the i-th line, t i is the time from the occurrence of the fault in the i-th line to the receipt of the fault traveling wave signal by the traveling wave sensor, and D is the distance from the fault point to the traveling wave sensor.

[0059] After determining the fault point of the line, in order to better obtain the on-site data of the fault, the inspection robot is controlled to fly to the fault point to collect on-site data, and the fault type and influencing factors of the fault point are determined based on the on-site data. The specific steps include:

[0060] Collecting on-site data of line fault points by inspection robots, wherein the on-site data includes fault point data, environmental data and line data;

[0061] Determine the fault type according to the fault point data, and extract environmental influencing factors and line influencing factors from the environmental data and the line data respectively;

[0062] The various environmental influencing factors are combined into an environmental influencing factor set, and the various line influencing factors are combined into a line influencing factor set.

[0063] In this embodiment, the field data collected by the inspection robot includes the fault point data of the segmented line, as well as the environmental data and line data around the fault point. Fault diagnosis is performed on the fault point data to determine the fault type, and environmental influencing factors and line influencing factors that affect the fault type are extracted from the environmental data and line data respectively to construct an environmental influencing factor set and a line influencing factor set, wherein the environmental influencing factors include weather factors such as temperature, humidity, wind, rainfall and snow, and the line influencing factors include line factors such as current, voltage and line material.

[0064] In fact, most line faults are caused by the combined effect of multiple influencing factors. However, for different fault types, the degree of influence of the influencing factors on the faults is also different. In order to accurately characterize the type of fault, in this embodiment, a scoring index calculation method based on the degree of influence of the influencing factors on the fault type is adopted. Specifically, by pre-setting the standard weight of each influencing factor for each fault type, and by weighted summing, the fault score of the fault type is determined. The fault scoring formula is:

[0065]

[0066] Where, wE i represents the standard weight of the i-th environmental influencing factor, DE i represents the i-th environmental influencing factor, n represents the total number of environmental influencing factors, wL j represents the standard weight of the jth line influencing factor, DL j represents the jth line influencing factor, and m represents the total number of line influencing factors.

[0067] Through the above steps, the present invention realizes the fault location of the mountain power grid line. On the basis of realizing the fault location, the present invention can also realize the early warning of the fault by locating the fault type and performing fault inspection on other segmented lines. Specifically, after calculating the fault score of the fault type, the fault score is used as the fault threshold. At the same time, the inspection robot continues to be controlled to inspect other segmented lines of the mountain power grid line, thereby obtaining the inspection data, selecting the environmental data and line data of the segmented line from the inspection data, and extracting the environmental influencing factors and line influencing factors from the environmental data and line data respectively according to the influencing factor set corresponding to the fault type located above, and calculating the fault score of the segmented line according to the fault scoring formula. Since the environmental data and line data of each segmented line are relatively similar at the current moment, the fault score of the actual fault is used as the scoring threshold, and the fault score of other segmented lines is compared with the scoring threshold. If the fault score of the segmented line is greater than the scoring threshold, it means that the segmented line has a high possibility of this type of fault. At this time, a fault warning reminder can be issued to remind the inspection personnel to pre-process the segmented line as soon as possible to avoid the occurrence of the fault.

[0068] In a preferred embodiment, in order to ensure that the fault score can more accurately characterize the fault type, the present invention provides a method for dynamically adjusting the standard weights of influencing factors, and the specific steps include:

[0069] Extracting a set of historical influencing factors corresponding to the fault type from the historical fault inspection results, and normalizing the set of historical influencing factors according to the types and quantities of the influencing factors to obtain normalized weights;

[0070] The standard weight is dynamically adjusted according to the normalized weight to obtain the adjusted standard weight.

[0071] In this embodiment, the previously preset standard weight is dynamically adjusted according to the historical inspection fault data obtained by the current line inspection. Specifically, the fault data is extracted from the historical inspection data, and the historical influencing factor set corresponding to the fault type is extracted from the fault data. The types of environmental influencing factors and line influencing factors in the set and the number of corresponding types are counted, and normalized and sorted to obtain the normalized weight:

[0072]

[0073] Where pE i represents the normalized weight of the i-th historical environmental influencing factor, cE i represents the i-th historical environmental influencing factor, n1 represents the total number of historical environmental influencing factors, and pL j represents the normalized weight of the influencing factor of the jth historical route, cL j represents the jth historical route influencing factor, and m1 represents the total number of historical route influencing factors.

[0074] Then adjust the current standard weight according to the normalized weight to obtain the adjusted standard weight:

[0075] w′E i =α·wE i +(1-α)·pE i

[0076] w′L j =α·wL j +(1-α)·pL

[0077] Where w′E i represents the adjusted standard weight of the i-th environmental influencing factor, wE i represents the standard weight of the i-th environmental influencing factor, w′L j represents the adjusted standard weight of the jth line influencing factor, wL j represents the standard weight of the j-th line influencing factor, and α represents the adjustment factor, which is used to balance the influence between the original weight and the normalized weight.

[0078] When the number of historical inspection fault results reaches a certain level, the historical inspection fault results are unified and normalized again, the normalized weights are updated, and the standard weights are adjusted according to the normalized weights, so that the standard weights of various influencing factors can more accurately reflect the degree of influence on the fault type, so that the fault scoring result of the fault type, that is, the scoring threshold, is more accurate. By improving the accuracy and rationality of the scoring threshold, the accuracy of mountain line fault warning is further improved.

[0079] The present embodiment provides a method for detecting and warning line faults in mountainous areas. The present invention realizes accurate positioning of the fault point of the line in mountainous areas by detecting the fault traveling wave signal, and realizes judgment of the degree of external influence on the line fault by performing comprehensive scoring calculation on the environmental influence factors and line influence factors when the line fault occurs. The comprehensive scoring is used to perform fault warning judgment, thereby realizing accurate line fault warning in mountainous areas, thereby effectively reducing the occurrence frequency of line faults in mountainous areas.

[0080] See also Figure 2 Based on the same inventive concept, a second embodiment of the present invention provides a mountain line fault detection and early warning system, comprising:

[0081] The fault location module 10 is used to segment the mountain power grid line, obtain the fault traveling wave signal on the segmented line, and locate the fault point according to the fault traveling wave signal to obtain the line fault point;

[0082] The data collection module 20 is used to collect the on-site data of the line fault point through the inspection robot, and determine the fault type and the corresponding influencing factor set according to the on-site data;

[0083] A score calculation module 30, configured to perform a weighted summation on each influencing factor in the influencing factor set according to a preset standard weight to obtain a first fault score;

[0084] The fault warning module 40 is used to perform segmented inspection on the power grid line by using the inspection robot to obtain inspection data of each segmented line, extract the influencing factors from the inspection data according to the influencing factor set, and perform weighted summation according to the standard weight to obtain a second fault score;

[0085] The second fault score is compared with the first fault score, and in response to the second fault score being greater than the first fault score, a fault early warning reminder is issued.

[0086] The technical features and technical effects of the mountain line fault detection and early warning system proposed in the embodiment of the present invention are the same as those of the method proposed in the embodiment of the present invention, and are not described in detail here. Each module in the above-mentioned mountain line fault detection and early warning system can be implemented in whole or in part by software, hardware, and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0087] In addition, an embodiment of the present invention further provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0088] See also Figure 3 , an internal structure diagram of a computer device in one embodiment, the computer device can specifically be a terminal or a server. The computer device includes a processor, a memory, a network interface, a display and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for detecting and warning faults in mountainous areas is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0089] It can be understood by those skilled in the art that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computing device may include more or fewer components than shown in the figure, or combine certain components, or have the same component arrangement.

[0090] In addition, an embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0091] In summary, the embodiments of the present invention propose a method, system, device and storage medium for detecting and warning faults in mountainous areas. The method divides the power grid lines in mountainous areas into sections, obtains fault traveling wave signals on the sectioned lines, and locates the fault points according to the fault traveling wave signals to obtain the line fault points; collects field data of the line fault points by means of a patrol robot, and determines the fault type and the corresponding set of influencing factors according to the field data; performs weighted summation on each influencing factor in the set of influencing factors according to preset standard weights to obtain a first fault score corresponding to the fault type; performs sectioned inspections on the power grid lines by means of a patrol robot to obtain inspection data of each sectioned line, extracts influencing factors from the patrol data according to the set of influencing factors, and performs weighted summation according to the standard weights to obtain a second fault score; compares the second fault score with the first fault score, and issues a fault warning reminder in response to the second fault score being greater than the first fault score. The present invention realizes accurate positioning of the fault point of the line in mountainous area by detecting the fault traveling wave signal, realizes judgment of the degree of external influence on the line fault by performing comprehensive scoring calculation on the environmental influence factors and line influence factors when the line fault occurs, and performs fault early warning judgment through comprehensive scoring, thereby realizing accurate early warning of line faults in mountainous area, thereby effectively reducing the occurrence frequency of line faults in mountainous area.

[0092] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above-mentioned embodiments are not 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.

[0093] The above-mentioned embodiments only express several preferred implementation modes of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in the technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be regarded as the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be based on the protection scope of the claims.

Claims

1. A method for detecting and warning line faults in mountainous areas, characterized in that: include: Divide the mountain power grid lines into sections, obtain the fault traveling wave signals on the sectioned lines, and locate the fault points according to the fault traveling wave signals to obtain the line fault points; Collecting on-site data of line fault points through inspection robots, and determining the fault type and the corresponding set of influencing factors based on the on-site data; Performing a weighted summation on each influencing factor in the influencing factor set according to a preset standard weight, to obtain a first fault score corresponding to the fault type; Performing segmented inspections on the power grid lines by using the inspection robot to obtain inspection data of each segmented line, extracting influencing factors from the inspection data according to the influencing factor set, and performing weighted summation according to the standard weights to obtain a second fault score; The second fault score is compared with the first fault score, and in response to the second fault score being greater than the first fault score, a fault early warning reminder is issued.

2. The method for detecting and warning line faults in mountainous areas according to claim 1, characterized in that: The steps of segmenting the mountain power grid line, obtaining the fault traveling wave signal on the segmented line, and locating the fault point according to the fault traveling wave signal to obtain the line fault point include: The mountain power grid lines are segmented according to the line material, and the fault traveling wave signals on the segmented lines are obtained through traveling wave sensors; A traveling wave feature is extracted from the fault traveling wave signal, and based on the traveling wave feature, a distance from the fault point to the traveling wave sensor is calculated to obtain the line fault point.

3. The method for detecting and warning line faults in mountainous areas according to claim 2, characterized in that: The distance from the fault point to the traveling wave sensor is expressed by the following formula: D=V i ×t i Where V i is the propagation speed of the traveling wave in the i-th line, t i is the time from the occurrence of the fault in the i-th line to the receipt of the fault traveling wave signal by the traveling wave sensor, and D is the distance from the fault point to the traveling wave sensor.

4. The method for detecting and warning line faults in mountainous areas according to claim 1, characterized in that: The step of collecting on-site data of the line fault point by the inspection robot and determining the fault type and the corresponding influencing factor set according to the on-site data includes: Collecting on-site data of line fault points by inspection robots, wherein the on-site data includes fault point data, environmental data and line data; Determine the fault type according to the fault point data, and extract environmental influencing factors and line influencing factors from the environmental data and the line data respectively; The various environmental influencing factors are combined into an environmental influencing factor set, and the various line influencing factors are combined into a line influencing factor set.

5. The method for detecting and warning line faults in mountainous areas according to claim 4, characterized in that: The first fault score is expressed by the following formula: Where, wE i represents the standard weight of the i-th environmental influencing factor, DE i represents the i-th environmental influencing factor, n represents the total number of environmental influencing factors, wL j represents the standard weight of the jth line influencing factor, DL j represents the jth line influencing factor, and m represents the total number of line influencing factors.

6. The method for detecting and warning faults in mountainous areas according to claim 4, characterized in that: Before the step of weighted summing the influencing factors in the influencing factor set according to the preset standard weight, the method further includes: Extracting a set of historical influencing factors corresponding to the fault type from the historical inspection fault results, and normalizing the set of historical influencing factors according to the types and quantities of the influencing factors to obtain normalized weights; The standard weight is dynamically adjusted according to the normalized weight to obtain the adjusted standard weight.

7. The method for detecting and warning line faults in mountainous areas according to claim 5, characterized in that: The normalized weight is expressed by the following formula: Where pE i represents the normalized weight of the i-th historical environmental influencing factor, cE i represents the i-th historical environmental influencing factor, n1 represents the total number of historical environmental influencing factors, and pL j represents the normalized weight of the influencing factor of the jth historical route, cL j represents the jth historical route influencing factor, and m1 represents the total number of historical route influencing factors; The adjusted standard weights are expressed using the following formula: w′E i =α·wE i +(1−α)·pE i w′L j =α·wL j +(1-α)·pLj Where w′E i represents the adjusted standard weight of the i-th environmental influencing factor, wE i represents the standard weight of the i-th environmental influencing factor, w′L j represents the adjusted standard weight of the jth line influencing factor, wL j represents the standard weight of the j-th line influencing factor, and α represents the adjustment factor.

8. A mountain line fault detection and early warning system, characterized in that: include: The fault location module is used to segment the mountain power grid line, obtain the fault traveling wave signal on the segmented line, and locate the fault point according to the fault traveling wave signal to obtain the line fault point; A data acquisition module, used to collect on-site data of line fault points through an inspection robot, and determine the fault type and a corresponding set of influencing factors based on the on-site data; A scoring calculation module, used for performing weighted summation on each influencing factor in the influencing factor set according to a preset standard weight to obtain a first fault score; a fault warning module, configured to perform segmented inspections on power grid lines by using an inspection robot, obtain inspection data of each segmented line, extract influencing factors from the inspection data according to the influencing factor set, and perform weighted summation according to the standard weights to obtain a second fault score; The second fault score is compared with the first fault score, and in response to the second fault score being greater than the first fault score, a fault early warning reminder is issued.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images