A lightning locator fault self-detection method, device and storage medium

Abstract

The present invention discloses a lightning locator fault self-detection method, device and storage medium, belonging to the field of meteorological big data analysis technology, the method comprises the following steps: obtaining a two-dimensional lightning location data set, combining every three lightning locator nodes in the filtered lightning locator node set to form a lightning locator node group; respectively using a two-dimensional location algorithm to calculate the lightning distance data of the lightning locator nodes in each lightning locator node group to obtain a two-dimensional lightning location position, and all the obtained two-dimensional lightning location positions form a two-dimensional lightning location data set; classifying the two-dimensional lightning location data set based on a divergent point analysis algorithm, respectively calculating the chi-square distribution values ​​of different classified data sets; and judging whether there is a certain lightning locator node that has a fault. The present invention can timely discover the existence of a faulty lightning locator node, improve the accuracy and availability of lightning location data, and can save the labor cost of operation and maintenance.

Description

Technical Field

[0001] The invention relates to a lightning locator fault self-detection method, device and storage medium, and belongs to the technical field of meteorological big data analysis. Background Art

[0002] A lightning locator is a professional automated instrument mainly used to detect lightning. It records the time, intensity, direction and angle of the observed lightning relative to the instrument. The data recorded by the lightning locator is of great significance for the scientific research of severe convective meteorological activities, the analysis and judgment of the meteorological movement state at the observation time point, and the deduction and prediction of future meteorological development processes. In addition, lightning, as severe convective weather, can have a serious impact on human social activities, production, and personal safety. Therefore, lightning locators play an important role in the observation and collection of meteorological data.

[0003] The lightning locator uses an unattended automated observation and recording method. To avoid electromagnetic interference in the surrounding area, the locator and related equipment are installed in the field, requiring no obstruction and a wide field of vision. The field deployment method brings inconvenience to the operation status detection and maintenance of the lightning locator. Therefore, the lightning locator adopts periodic scheduled on-site inspection and maintenance.

[0004] It is difficult to detect faulty lightning locators in a timely manner through periodic inspection and maintenance. When a lightning locator fails, the lightning location data it collects will be biased, and the meteorological data processing center cannot detect the bias and correct it. And because it is impossible to determine the exact time when the lightning locator fails, once a lightning locator fails, it will cause a problem in the availability of lightning observation data for an entire period of time.

[0005] At present, most lightning locators are based on the radio multi-station networking positioning method. By detecting the VHF electromagnetic pulses generated during the lightning process, the direction and time of the VHF electromagnetic pulses reaching multiple locators are obtained, and then the spatial location of the lightning is determined based on a two-dimensional or three-dimensional positioning algorithm. These data are then sent to the meteorological data processing center for storage and analysis. Since lightning positioning is generated by the collaborative analysis of multi-locator node data, if a node in the lightning locator network fails, it will cause deviations in the final positioning data, thereby affecting subsequent meteorological analysis.

[0006] In the prior art, node fault inspection in the locator network generally adopts the method of manual periodic inspection and maintenance, which has the following defects: there is a lag, and the faulty node can only be determined after manual inspection, which reduces the accuracy and availability of lightning location data; and it requires high operating and maintenance labor costs. Summary of the invention

[0007] The technical problem to be solved by the present invention is to overcome the defects of the prior art and provide a lightning locator fault self-detection method, device and storage medium, which can timely detect the existence of faulty lightning locator nodes, improve the accuracy and availability of lightning location data, and save the labor costs of operation and maintenance.

[0008] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0009] In a first aspect, the present invention provides a lightning locator fault self-detection method, comprising the following steps:

[0010] Acquire a two-dimensional lightning location dataset, wherein the two-dimensional lightning location dataset includes lightning distance data between a lightning location device node that observes lightning activity in a lightning location network and a lightning occurrence location;

[0011] The lightning locator nodes corresponding to the lightning distance data exceeding the preset lightning distance threshold in the two-dimensional lightning location data set are filtered out to obtain a filtered lightning locator node set, and then every three lightning locator nodes in the filtered lightning locator node set are combined to form a lightning locator node group;

[0012] The lightning distance data of the lightning locator nodes in each lightning locator node group are respectively calculated using a two-dimensional positioning algorithm to obtain a two-dimensional lightning positioning position, and all the obtained two-dimensional lightning positioning positions are combined into a two-dimensional lightning positioning data set;

[0013] Based on the outlier analysis algorithm, the two-dimensional lightning location data set is classified to obtain different classification data sets, and the chi-square distribution values ​​of different classification data sets are calculated respectively;

[0014] Based on the chi-square distribution value, it is determined whether there is a lightning locator node with faults.

[0015] The filtered lightning locator node set is represented as: {d1,d2,...,d n}, where d1, d2, ..., d n Represent the first, second, ... nth lightning locator nodes respectively; the longitude and latitude data Di of the i-th lightning locator node group in the two-dimensional lightning location data set is expressed as Di= <x i ,y i >, where x i and i They represent the longitude and latitude of the lightning occurrence location corresponding to the i-th lightning locator node group.

[0016] The classification of the two-dimensional lightning location data set based on the outlier analysis algorithm includes: looping through each lightning locator node m in the two-dimensional lightning location data set, and dividing the lightning locator node group in the two-dimensional lightning location data set into a classification data set S1 and a classification data set S2, wherein the two-dimensional lightning location data in the classification data set S1 is generated by the lightning locator node group including the lightning locator node m, and the classification data set S2 is generated by the lightning locator node group not including the lightning locator node m.

[0017] The calculating of the chi-square distribution values ​​of different classification data sets respectively includes:

[0018] Traverse the longitude and latitude data of the lightning locator node group in the classified data set, and find the longitude and latitude data D of the average positioning mean point of the classified data set x =<X,Y> , X and Y represent the longitude and latitude of the average positioning mean point, respectively, where , , k represents the number of lightning locator groups in the classification data set;

[0019] The chi-square distribution calculation formula is used for the classification data set S1 and the classification data set S2 respectively. According to the chi-square distribution value calculation formula, the chi-square distribution values ​​of the classification data sets are calculated and recorded as X(S1) and X(S2) respectively.

[0020] The chi-square distribution value calculation formula is as follows:

[0021]

[0022] in: is the square of the Euclidean distance between the location value corresponding to the i-th lightning locator group and the average location mean point of the classification data set, R(Di) is the Euclidean norm of the longitude and latitude of the lightning detected by the i-th lightning locator group, .

[0023] The method of judging whether there is a lightning locator node with a fault based on the chi-square distribution value includes:

[0024] Calculate the absolute value d of the difference in chi-square values ​​between the two classification data sets formed by the lightning locator node m m , that is, d m = |X(S1)-X(S2)|;

[0025] The d corresponding to each lightning locator node m is obtained m The values ​​are sorted from largest to smallest;

[0026] Select the largest absolute value of the chi-square difference d m The corresponding lightning locator node m is judged. If the fault node judgment condition is met, the lightning locator node m is faulty.

[0027] The fault node judgment condition is specifically as follows: based on the degree of freedom being 2 and the significance level being 0.05, the chi-square distribution table is searched to obtain a distribution value of r; the chi-square distribution value obtained from one of the classification data sets S1 and the classification data set S2 is greater than r, while the chi-square distribution value of the other classification data set is less than r.

[0028] The lightning locator nodes are deployed in one of five ways: Y-type, rectangular, diamond-shaped, T-type and ladder-shaped.

[0029] The lightning distance data is calculated using the time difference positioning algorithm built into the lightning locator node.

[0030] In a second aspect, the present invention provides a lightning locator fault self-detection device, comprising:

[0031] A two-dimensional lightning location data set acquisition module is used to acquire a two-dimensional lightning location data set, wherein the two-dimensional lightning location data set includes lightning distance data between a lightning location device node that observes lightning activity in a lightning location network and a lightning occurrence location;

[0032] A lightning locator node group generation module is used to filter out lightning locator nodes corresponding to lightning distance data exceeding a preset lightning distance threshold in the two-dimensional lightning location data set to obtain a filtered lightning locator node set, and then combine every three lightning locator nodes in the filtered lightning locator node set to form a lightning locator node group;

[0033] A two-dimensional lightning location data set generation module is used to calculate the lightning distance data of the lightning location device nodes in each lightning location device node group using a two-dimensional location algorithm to obtain a two-dimensional lightning location position, and to form a two-dimensional lightning location data set with all the obtained two-dimensional lightning location positions;

[0034] A chi-square distribution value calculation module is used to classify the two-dimensional lightning location data set based on the outlier analysis algorithm to obtain different classified data sets, and respectively calculate the chi-square distribution values ​​of the different classified data sets;

[0035] The fault determination module is used to determine whether there is a lightning locator node with a fault based on the chi-square distribution value.

[0036] In a third aspect, the present invention provides a computer-readable storage medium having a computer program / instruction stored thereon. When the computer program / instruction is executed by a processor, the lightning locator fault self-detection method is implemented.

[0037] Beneficial effects of the present invention: The present invention provides a lightning locator fault self-detection method, device and storage medium, which combines lightning locators at different deployment positions in a locator network, obtains lightning perception data collected by the lightning locators in each combination, and calculates lightning locating data in a two-dimensional plane formed by the combination, and then based on the two-dimensional plane lightning locating data set formed by multiple locators, applies a lightning locating divergence point discrimination algorithm to perform binary classification on the data set, so as to judge and find out the possible fault locators in the current lightning locator network, and achieve the goal of self-inspection of lightning locator faults. The whole process does not require human participation, which can save the labor and time costs of the lightning locator. In addition, the self-detection method of the present application also enables the meteorological data center to promptly discover the existence of faulty lightning locators, thereby further improving the accuracy and availability of lightning locating data, and providing effective support for meteorological observation, research and prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 The present invention is a flowchart of a lightning locator fault self-detection method. DETAILED DESCRIPTION

[0039] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the protection scope of the present invention.

[0040] Example 1

[0041] like Figure 1 As shown, the present invention discloses a lightning locator fault self-detection method, comprising the following steps:

[0042] Step 1: obtaining a two-dimensional lightning location dataset, wherein the two-dimensional lightning location dataset includes lightning distance data between a lightning location device node that observes lightning activity in a lightning location network and a lightning occurrence location;

[0043] Step 2: Filter out the lightning locator nodes corresponding to the lightning distance data exceeding the preset lightning distance threshold in the two-dimensional lightning location data set to obtain a filtered lightning locator node set, and then combine every three lightning locator nodes in the filtered lightning locator node set to form a lightning locator node group;

[0044] Step 3: using a two-dimensional positioning algorithm to calculate the lightning distance data of the lightning locator nodes in each lightning locator node group to obtain a two-dimensional lightning location position, and forming all the obtained two-dimensional lightning location positions into a two-dimensional lightning location data set;

[0045] Step 4: classify the two-dimensional lightning location data set based on the outlier analysis algorithm to obtain different classified data sets, and calculate the chi-square distribution values ​​of the different classified data sets respectively;

[0046] Step 5: Determine whether there is a lightning locator node with a fault based on the chi-square distribution value.

[0047] The entire process of the present invention does not require human intervention, which can save the labor and time costs of the lightning locator. In addition, the self-detection method of the present application also enables the meteorological data center to promptly discover the existence of faulty lightning locators, thereby further improving the accuracy and availability of lightning location data, and providing effective support for meteorological observation, research and prediction.

[0048] Example 2

[0049] The present invention needs to construct a lightning locator network before use. The lightning locator network is composed of three parts, which are divided into a front end, a back end and a data transmission network. The front end is composed of a plurality of lightning locators based on the time difference positioning principle, which are responsible for detecting the VHF electromagnetic pulse signal of lightning, and sensing the time difference of the pulse signal arriving at the locator to achieve the positioning goal. The detection accuracy of the lightning locator requires that in addition to meeting the normal lightning positioning requirements, the error distribution detected should be as consistent as possible to ensure the probabilistic significance of the chi-square distribution value of the positioning data set. At present, mainstream lightning locators such as the ADTD lightning positioning system are all implemented using the time difference positioning algorithm, so the algorithm of this application has a wide range of application scenarios. The back end is a meteorological data processing center, which is responsible for receiving and sending the lightning electromagnetic pulse sensing signal sent by the front-end lightning locator, and realizing the spatial location positioning of the lightning generation through a two-dimensional or three-dimensional positioning algorithm. The data transmission network is responsible for the transmission of the data generated by the front-end lightning locator, which can be completed using a variety of protocols, such as Ethernet, RS232 and bidirectional RS485.

[0050] The system implemented in this application is constructed as follows:

[0051] 1) System deployment scope and lightning locator node number planning: According to the topography and landforms of the project site and the detection requirements of lightning location, the deployment area of ​​the lightning locator network is determined, so as to determine the number of nodes of the front-end lightning location network and the deployment location of each node;

[0052] 2) According to the planning, determine the lightning locator used in the system: As mentioned above, this patent is applicable to lightning locators based on the time difference positioning principle. And determine whether the parameters of the specific model of lightning locator meet the above requirements.

[0053] 3) Networking and testing: Deploy the lightning locators according to the planned deployment locations and connect them to the meteorological data center of this system. Furthermore, according to the specifications of the lightning locators, test the nodes through the meteorological data center to determine whether each locator node meets the above performance requirements.

[0054] After the application system is built according to the above steps, it enters the daily lightning detection and fault node self-checking workflow, which is generally divided into two stages:

[0055] 1) The first stage: Assuming that the probability of failure of each locator node in the system follows the Poisson distribution, it can be assumed that the detection function of the locator nodes of the entire system is normal during the partial time period when the system starts working, that is, the error distribution of the lightning location data generated by each node detection is within the normal range; in this stage, the collected location data is used to generate a locator group according to the aforementioned algorithm, and the chi-square distribution value of the location data set generated by each group under normal circumstances is determined as a reference value for fault node judgment in the next stage;

[0056] 2) The second stage: In this stage, according to the probability distribution of the normal operation of the nodes, there may be faulty nodes in the front-end lightning positioning network. At this time, the algorithm proposed in this application is applied to analyze and judge the faulty nodes. Once a faulty node is found, it is necessary to immediately inspect and troubleshoot the faulty node. Because if there are two or more faulty nodes in the lightning positioning network, it will seriously affect the accuracy of the judgment of the faulty node by the algorithm of this application.

[0057] like Figure 1 As shown, the lightning locator fault self-detection method of the present invention has the function of self-detecting a faulty instrument node in a lightning locator network, and specifically comprises the following steps:

[0058] Step 1: Obtain a two-dimensional lightning location data set. The two-dimensional lightning location data set includes lightning distance data between the lightning location device node that observes lightning activity in the lightning location network and the lightning location. The number of nodes included in the lightning location network should be as large as possible within a unit range (such as every ten kilometers) under the premise of cost feasibility. The determination of the unit range should be determined by the detection range of the nodes included in the location network. The deployment location requirements of the lightning location device, according to the lightning location device's location principle for lightning, require that the deployment locations of each node of the lightning location device be evenly distributed, and should not form an excessively large location blind area. According to the currently commonly used distribution methods of lightning location devices, five methods can be adopted: Y-type, rectangular, diamond, T-type and ladder-type. When multiple lightning location device nodes in the lightning location device network observe lightning activity at the same time, the distance of each node from the lightning location is calculated according to the built-in time difference location algorithm of the lightning location device, and the distance data is sent to the meteorological data center. The networked lightning location devices should maintain a high degree of consistency in the global clock, so as to further ensure the accuracy of the perception time of the lightning electromagnetic pulse.

[0059] Step 2: After receiving the two-dimensional lightning location data set, the meteorological data center first filters out nodes that are too far away and prone to errors based on the specifications of the lightning locator. Specifically, the lightning locator nodes corresponding to the lightning distance data in the two-dimensional lightning location data set that exceeds the preset lightning distance threshold are filtered out to obtain the filtered lightning locator node set. The filtered lightning locator node set is represented as: {d1, d2, ..., d n}, where d1, d2, ..., d n Respectively represent the 1st, 2nd, ... nth lightning locator nodes. Then, every three lightning locator nodes in the filtered lightning locator node set are combined to form a lightning locator node group. The deployment range of the nodes in the lightning locator node group must at least ensure that four nodes are within the effective detection range of the nodes. Because the positioning algorithm based on the two-dimensional plane of the present application requires the participation of at least three nodes to be generated, and to realize the division of the locator group, at least four nodes are required to form four groups in order to execute the method of the present application.

[0060] Step 3: Use a two-dimensional positioning algorithm to calculate the lightning distance data of the lightning locator nodes in each lightning locator node group to obtain a two-dimensional lightning location position, and combine all the obtained two-dimensional lightning location positions into a two-dimensional lightning location data set. The longitude and latitude data Di of the i-th lightning locator node group in the two-dimensional lightning location data set is expressed as D= <x i ,y i >,

[0061] where x i and i They represent the longitude and latitude of the lightning occurrence location corresponding to the i-th lightning locator node group.

[0062] Step four, classify the two-dimensional lightning location dataset based on the outlier analysis algorithm to obtain different classified datasets, specifically: loop through each lightning locator node m in the two-dimensional lightning location dataset, and divide the lightning locator node group in the two-dimensional lightning location dataset into a classified dataset S1 and a classified dataset S2, wherein the two-dimensional lightning location data in the classified dataset S1 is generated by the lightning locator node group including the lightning locator node m, and the classified dataset S2 is generated by the lightning locator node group not including the lightning locator node m.

[0063] Calculate the chi-square distribution values ​​of different classification data sets respectively, specifically: traverse the longitude and latitude data of the lightning locator node group in the classification data set, and find the longitude and latitude data D of the average positioning mean point of the classification data set x =<X,Y> , X and Y represent the longitude and latitude of the average positioning mean point, respectively, where , , k represents the number of lightning locator groups in the classification dataset.

[0064] The chi-square distribution calculation formula is used for the classification data set S1 and the classification data set S2 respectively. According to the chi-square distribution value calculation formula, the chi-square distribution values ​​of the classification data sets are calculated and recorded as X(S1) and X(S2) respectively.

[0065] The chi-square distribution value calculation formula is as follows:

[0066]

[0067] Where: Di represents the longitude and latitude of the lightning location generated by the i-th lightning locator group in the classification data set, is the square of the Euclidean distance between the location value of this group and the average location mean point of the classification data set, R(Di) is the Euclidean norm of the longitude and latitude of the lightning detected by the i-th lightning locator group, .

[0068] Step 5: Based on the chi-square distribution value, determine whether there is a lightning locator node with a fault. Specifically, calculate the absolute value d of the chi-square value difference between the two classification data sets formed by the lightning locator node m. m , that is, d m = | X (S1) - X (S2) | ; the d corresponding to each lightning locator node m is obtained m The values ​​are sorted from large to small; the largest absolute value of the chi-square difference d is selected m The corresponding lightning locator node m is judged. If the fault node judgment condition is met, the lightning locator node m may be faulty. The fault node judgment condition is as follows: according to the degree of freedom of 2 and the significance level of 0.05, the chi-square distribution table is searched and the distribution value is r; the chi-square distribution value of one of the classification data sets S1 and S2 is greater than r, while the chi-square distribution value of the other classification data set is less than r.

[0069] Example 3

[0070] This embodiment discloses a lightning locator fault self-detection device, comprising:

[0071] A two-dimensional lightning location data set acquisition module is used to acquire a two-dimensional lightning location data set, wherein the two-dimensional lightning location data set includes lightning distance data between a lightning location device node that observes lightning activity in a lightning location network and a lightning occurrence location;

[0072] A lightning locator node group generation module is used to filter out lightning locator nodes corresponding to lightning distance data exceeding a preset lightning distance threshold in the two-dimensional lightning location data set to obtain a filtered lightning locator node set, and then combine every three lightning locator nodes in the filtered lightning locator node set to form a lightning locator node group;

[0073] A two-dimensional lightning location data set generation module is used to calculate the lightning distance data of the lightning location device nodes in each lightning location device node group using a two-dimensional location algorithm to obtain a two-dimensional lightning location position, and to form a two-dimensional lightning location data set with all the obtained two-dimensional lightning location positions;

[0074] A chi-square distribution value calculation module is used to classify the two-dimensional lightning location data set based on the outlier analysis algorithm to obtain different classified data sets, and respectively calculate the chi-square distribution values ​​of the different classified data sets;

[0075] The fault determination module is used to determine whether there is a lightning locator node with a fault based on the chi-square distribution value.

[0076] Example 4

[0077] This embodiment discloses a computer-readable storage medium having a computer program / instruction stored thereon. When the computer program / instruction is executed by a processor, the method for self-detection of faults of a lightning locator is implemented.

[0078] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A lightning locator fault self-detection method, characterized in that: The following steps are involved: Acquire a two-dimensional lightning location dataset, wherein the two-dimensional lightning location dataset includes lightning distance data between a lightning location device node that observes lightning activity in a lightning location network and a lightning occurrence location; The lightning locator nodes corresponding to the lightning distance data exceeding the preset lightning distance threshold in the two-dimensional lightning location dataset are filtered out to obtain a filtered lightning locator node set. Then, in the filtered lightning locator node set, every three lightning locator nodes are combined to form a lightning locator node group. The filtered lightning locator node set is represented as {d1, d2, ..., d n }, where d1, d2, ..., d n Represent the first, second, ... nth lightning locator nodes respectively; the longitude and latitude data Di of the i-th lightning locator node group in the two-dimensional lightning location data set is expressed as Di= <x i ,y i >, where x i and i Respectively represent the longitude and latitude of the lightning occurrence location corresponding to the i-th lightning locator node group; The lightning distance data of the lightning locator nodes in each lightning locator node group are respectively calculated using a two-dimensional positioning algorithm to obtain a two-dimensional lightning positioning position, and all the obtained two-dimensional lightning positioning positions are combined into a two-dimensional lightning positioning data set; Based on the outlier analysis algorithm, the two-dimensional lightning location data set is classified to obtain different classification data sets, and the chi-square distribution values ​​of different classification data sets are calculated respectively. The classifying of the two-dimensional lightning location data set based on the outlier analysis algorithm includes: looping through each lightning locator node m in the two-dimensional lightning location data set, dividing the lightning locator node group in the two-dimensional lightning location data set into a classification data set S1 and a classification data set S2, wherein the two-dimensional lightning location data in the classification data set S1 is generated by the lightning locator node group including the lightning locator node m, and the classification data set S2 is generated by the lightning locator node group not including the lightning locator node m, The calculating of the chi-square distribution values ​​of different classification data sets respectively includes: Traverse the longitude and latitude data of the lightning locator node group in the classified data set, and find the longitude and latitude data D of the average positioning mean point of the classified data set x =<X,Y> , X and Y represent the longitude and latitude of the average positioning mean point, respectively, where , , k represents the number of lightning locator groups in the classification data set; The chi-square distribution calculation formula is used for the classification data set S1 and the classification data set S2 respectively. According to the chi-square distribution value calculation formula, the chi-square distribution values ​​of the classification data sets are calculated and recorded as X(S1) and X(S2) respectively. The chi-square distribution value calculation formula is as follows: ； in: is the square of the Euclidean distance between the location value corresponding to the i-th lightning locator group and the average location mean point of the classification data set, R(Di) is the Euclidean norm of the longitude and latitude of the lightning detected by the i-th lightning locator group, ; Based on the chi-square distribution value, determine whether there is a lightning locator node with a fault. The method of judging whether there is a lightning locator node with a fault based on the chi-square distribution value includes: Calculate the absolute value d of the difference in chi-square values ​​between the two classification data sets formed by the lightning locator node m m , that is, d m = |X(S1)-X(S2)|; The d corresponding to each lightning locator node m is obtained m The values ​​are sorted from largest to smallest; Select the largest absolute value of the chi-square difference d m The corresponding lightning locator node m is judged. If the fault node judgment condition is met, the lightning locator node m is faulty. The fault node judgment condition is specifically as follows: based on the degree of freedom being 2 and the significance level being 0.05, the chi-square distribution table is searched to obtain a distribution value of r; the chi-square distribution value obtained from one of the classification data sets S1 and the classification data set S2 is greater than r, while the chi-square distribution value of the other classification data set is less than r.

2. The lightning locator fault self-detection method according to claim 1, characterized in that: The lightning locator nodes are deployed in one of five ways: Y-type, rectangular, diamond-shaped, T-type and ladder-shaped.

3. The lightning locator fault self-detection method according to claim 1, characterized in that: The lightning distance data is calculated using the time difference positioning algorithm built into the lightning locator node.

4. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instruction is executed by a processor, the lightning locator fault self-detection method described in any one of claims 1-3 is implemented.

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