Pre-discharge lightning arresting rod control method and device, medium and pre-discharge lightning arresting rod

Abstract

The invention relates to a pre-discharge lightning arresting rod control method and device, a medium and a pre-discharge lightning arresting rod. The pre-discharge lightning arresting rod control method comprises the steps that environment data collected by different types of sensors are obtained; taking the environment data collected at each collection moment in a first historical time period of a preset time length from the first collection moment as target environment data, and calculating a Pearson's correlation coefficient of each target environment data and historical recording environment data pre-stored by the same type of sensors; determining target historical environment data according to the Pearson correlation coefficient of each type of target environment data; if the strike result information stored in association with the target historical environment data represents that strike occurs in a second historical time period of the sequential preset duration, pre-discharging and receiving lightning in advance; and if the strike occurs in a second historical time period with the first collection moment as the starting point and the preset duration, pre-storing the target environment data as historical record environment data, and associatively storing the corresponding strike result information and the target environment data.

Description

Technical Field

[0001] The present disclosure relates to the technical field of lightning arresters, and in particular to a pre-discharge lightning rod control method, device, medium and pre-discharge lightning rod. Background Art

[0002] Prediction and protection against lightning activity are necessary means to ensure the safe operation of various facilities. Traditional lightning protection methods often rely on passive lightning arresters such as lightning rods and lightning strips. These devices are used to arrest lightning and introduce lightning current into the ground when lightning occurs, but they lack the ability to effectively predict lightning activity. In order to overcome this limitation, intelligent lightning warnings usually monitor lightning-related environmental parameters such as electric field strength, magnetic field strength, temperature, humidity, etc. by setting sensors in key areas. However, most of these rely only on a single type of sensor data for lightning warning, lacking comprehensive analysis and utilization of different types of sensor data, resulting in limited accuracy and reliability of warnings. In addition, existing data processing often uses simple threshold judgment or statistical analysis methods, which makes it difficult to accurately capture the complexity and uncertainty of lightning activities. Summary of the invention

[0003] The purpose of the present invention is to provide a pre-discharge lightning rod control method, device, medium and pre-discharge lightning rod, aiming to solve the technical problems that the accuracy and reliability of lightning activity warning are limited, and that it is difficult to accurately capture the complexity and uncertainty of lightning activity by using simple threshold judgment or statistical analysis methods.

[0004] In order to achieve the above-mentioned object, a first aspect of an embodiment of the present disclosure provides a pre-discharge lightning rod control method, the method comprising: Respectively obtain a plurality of different types of sensors arranged on the pre-discharge lightning rod to collect environmental data corresponding to the types; Taking the environmental data collected by the sensors of each type at each collection moment in the first historical time period of a preset length in reverse order from the first collection moment as target environmental data, and calculating the Pearson correlation coefficient between the target environmental data of each type and a plurality of historical record environmental data pre-stored by sensors of the same type; Determine target historical environment data from a plurality of pieces of historical environment data corresponding to the type according to the Pearson correlation coefficient corresponding to the target environment data of each type; When the lightning result information stored in association with the target historical environment data indicates that a lightning strike occurred within a second historical time period of the preset duration, the ion generator configured for the pre-discharge lightning rod is used to perform early pre-discharge lightning termination; If lightning occurs within the second historical time period of the preset duration starting from the first collection moment, the target environment data will be pre-stored as the historical record environment data, and the corresponding lightning result information will be stored in association with the target environment data, and the lightning result information will include the interval between each lightning occurrence and the first collection moment.

[0005] In a preferred embodiment, the calculating of the Pearson correlation coefficient between the target environment data of each type and a plurality of historical record environment data pre-stored by sensors of the same type includes: Calculate the average of the first collected values ​​corresponding to the target environment data of each type according to the collected values ​​collected at each collection time in the target environment data of each type, and calculate the average of the second collected values ​​corresponding to each piece of the historical record environment data pre-stored by the sensor of the same type according to the collected values ​​collected at each collection time in the multiple historical record environment data pre-stored by the sensor of the same type; Calculate, according to the mean of the first collected values ​​corresponding to each of the types and the collected values ​​collected at each of the collection moments in the target environment data of the corresponding type, a first deviation between each of the collected values ​​in the target environment data of each type and the mean of the first collected values; Calculate, according to the second collection value mean corresponding to each piece of the historical record environment data of each type and the collection value collected at each collection time in the piece of the historical record environment data, a second deviation between each collection value in the piece of the historical record environment data and the second collection value mean; According to the first deviation and the second deviation corresponding to each of the types, the product of the first deviation and the second deviation corresponding to the type is calculated and then summed to obtain the deviation product sum corresponding to the type, and the deviation square sum of the first deviations corresponding to each of the types is calculated to obtain the first deviation square sum, and the deviation square sum of the second deviations corresponding to each of the types is calculated to obtain the second deviation square sum; The Pearson correlation coefficient between the target environment data of each type and each piece of the historical record environment data pre-stored by the sensor of the same type is calculated according to the deviation product sum, the first deviation square sum and the second deviation square sum corresponding to each type.

[0006] In a preferred embodiment, the determining the target historical environment data from the plurality of historical record environment data corresponding to the type according to the Pearson correlation coefficient corresponding to the target environment data of each type includes: The historical record environment data of each type whose Pearson correlation coefficient is greater than or equal to a preset coefficient threshold of the corresponding type is used as candidate historical record environment data; Calculate the mean value of the Pearson correlation coefficient corresponding to the candidate historical record environment data of each type; Counting the number of candidate historical record environment data corresponding to each type; When the number of candidate historical record environment data corresponding to any of the types is greater than or equal to the preset number threshold of the corresponding type, the candidate historical record environment data of the type whose Pearson correlation coefficient is greater than or equal to the corresponding mean is determined as the target historical record environment data of the type.

[0007] In a preferred embodiment, the method further comprises: In the case that the number of the candidate historical record environment data corresponding to any of the types is less than the preset number threshold of the corresponding type, based on the voting method, the candidate historical record environment data corresponding to the type is used as the voting subject, and voting based on voting probability is performed, wherein the candidate historical record environment data corresponding to the type are all issued with the same number of tickets, and if the Pearson correlation coefficient corresponding to the number of the candidate historical record environments is greater than or equal to the corresponding mean, the voting probability of each of the candidate historical record environment data voting for it is randomly increased; if the Pearson correlation coefficient corresponding to the number of the candidate historical record environments is less than the corresponding mean, the voting probability of each of the candidate historical record environment data voting for it is randomly reduced; After all the tickets issued by the alternative historical record environment data corresponding to this type have been voted out, if the number of votes obtained by the alternative historical record environment data exceeds the number of tickets issued by any alternative historical record environment data, the alternative historical record environment data will be determined as the target historical record environment data of this type.

[0008] In a preferred embodiment, the method further comprises: When the Pearson correlation coefficient of any of the types is less than the preset coefficient threshold corresponding to the type, the historical record environment data with the largest Pearson correlation coefficient corresponding to the target environment data of the type is determined as the target historical environment data of the type.

[0009] In a preferred embodiment, the method further comprises: Recording the storage time of pre-storing the target environment data as the historical record environment data, determining the storage duration of each target environment data according to the storage time, and recording the determination time when the target environment data is determined as the target historical environment data; Determine, based on the determination time when the target environment data was most recently determined as the target historical environment data, the interval duration when the target environment data was most recently determined as the target historical environment data, and, if the interval duration when the target environment data was most recently determined as the target historical environment data reaches a preset interval duration threshold, determine the number of times the target environment data was determined as the target historical environment data when it was the historical record environment data; If the number of times that the target environment data is determined as the target historical environment data does not reach a preset number, the target environment data is removed from the historical record environment data.

[0010] In a preferred embodiment, the sensor includes at least two of the following: a magnetic field sensor, an electric field sensor, a humidity sensor, an air pressure sensor, and a wind speed sensor.

[0011] A second aspect of the embodiments of the present disclosure provides a pre-discharge lightning rod control device, comprising: An acquisition module is configured to respectively acquire environmental data corresponding to the type collected by a plurality of different types of sensors arranged on the pre-discharge lightning rod; a calculation module configured to use the environmental data collected by the sensors of each type at each collection moment in a first historical time period of a preset length in reverse order from a first collection moment as target environmental data, and calculate a Pearson correlation coefficient between the target environmental data of each type and a plurality of historical record environmental data pre-stored by sensors of the same type; a determination module configured to determine target historical environment data from a plurality of pieces of historical environment data corresponding to the type according to the Pearson correlation coefficient corresponding to the target environment data of each type; A lightning arrester module is configured to perform early pre-discharge lightning arrester through an ion generator configured on the pre-discharge lightning arrester rod when the lightning result information stored in association with the target historical environment data indicates that a lightning strike occurs within a second historical time period of the preset duration; The storage module is configured to pre-store the target environment data as the historical record environment data if a lightning strike occurs within the second historical time period of the preset duration starting from the first collection moment, and to store the corresponding lightning result information in association with the target environment data, wherein the lightning result information includes the interval duration between each lightning strike and the first collection moment.

[0012] According to a third aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the steps of any one of the methods described in the first aspect are implemented.

[0013] A fourth aspect of the embodiments of the present disclosure provides a pre-discharge lightning rod, comprising: a memory having a computer program stored thereon; A processor is used to execute the computer program in the memory to implement the steps of any one of the methods in the first aspect.

[0014] The present invention provides a pre-discharge lightning rod control method, device, medium and pre-discharge lightning rod. Compared with the prior art, it has the following beneficial effects: By setting up multiple different types of sensors on the pre-discharge lightning rod, comprehensive lightning-related environmental data are collected, and multi-type data provides a richer and more accurate source of information for lightning warning. The Pearson correlation coefficient is used to evaluate the correlation between the target environmental data and the historical record environmental data, so as to screen out the historical records that are most similar to the target environmental data from a large amount of historical data. When the lightning result information stored in these historical records indicates that lightning has occurred within the second historical time period of the sequential preset time length, the ion generator configured on the pre-discharge lightning rod is used to pre-discharge the lightning rod in advance, effectively avoiding lightning from directly hitting the facility.

[0015] In addition, according to the results of whether lightning occurred in the second historical time period, the historical record environment database is updated and enriched in real time, so that the early warning capability is continuously improved with the accumulation of data. This dynamic learning and adaptation mechanism further enhances the stability and reliability of the system.

[0016] In summary, by comprehensively utilizing data from multiple types of sensors, using the Pearson correlation coefficient for data analysis, and realizing advance pre-discharge lightning protection, the accuracy and reliability of lightning warnings have been significantly improved, providing more effective lightning protection measures for various facilities.

[0017] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings: Figure 1 The present invention is a process of a pre-discharge lightning rod control method according to an embodiment of the specification.

[0019] Figure 2 The present invention is a schematic diagram of the plane dimensions of a pre-discharge lightning rod according to an embodiment of the specification.

[0020] Figure 3It is a block diagram of a pre-discharge lightning rod control device shown in an embodiment of the specification.

[0021] Figure 4 It is a block diagram of another pre-discharge lightning rod control device shown in the embodiment of the specification. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0023] The specific implementation of the present disclosure is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the present disclosure, and is not used to limit the present disclosure.

[0024] The present invention provides a method for controlling a pre-discharge lightning rod. Figure 1 The present invention is a flow chart of a method for controlling a pre-discharge lightning rod according to an embodiment. Specifically, the method includes: In step S11, a plurality of different types of sensors arranged on the pre-discharge lightning rod are respectively acquired to collect environmental data corresponding to the type; Among them, the pre-discharge lightning rod is a device used to guide lightning discharge in advance to protect buildings or other facilities from lightning strikes. Figure 2 The figure shows the planar dimensions of the pre-discharge lightning rod, wherein the pre-discharge lightning rod is divided into multiple parts from top to bottom, wherein the diameter of the needle tip is 19.5mm and the length is 150mm, the diameter of the part connected to the needle tip is 51mm and the length is 165mm, and the diameters and lengths of the following multiple parts are: the first connecting part with a diameter of 38mm and a length of 65mm, the clamping part with a diameter of 50mm and a length of 25mm, and the second connecting part with diameters of 49mm and 26mm respectively and a total length of 50mm. The lightning flux of the pre-discharge lightning rod is ≥100kA (10 / 350us), or ≥200kA (8 / 20us), the wind resistance is 40m / s, the weight is 2.2±0.2kg, the total height is 455mm±0.5mm, and the adaptability environment is -40 to +70℃.

[0025] A sensor is a device or apparatus that can sense a specified quantity and convert it into a usable signal according to certain rules. It is used to collect environmental data. Environmental data refers to information collected by sensors that reflects the state of the surrounding environment, such as temperature, humidity, wind speed, etc.

[0026] In the disclosed embodiment, a plurality of different types of sensors are installed on the pre-discharge lightning rod, and these sensors can monitor the changes in the surrounding environment in real time and transmit the collected data to the control. These sensors may include temperature sensors, humidity sensors, wind speed sensors, etc., which are used to comprehensively monitor various environmental factors that may affect lightning activities. For example: it is assumed that a temperature sensor and a humidity sensor are installed on the pre-discharge lightning rod. The temperature sensor collects environmental temperature data in real time, while the humidity sensor collects environmental humidity data in real time.

[0027] In step S12, the environmental data collected by the sensors of each type at each collection moment in the first historical time period of a preset time length in reverse order from the first collection moment is taken as target environmental data, and the Pearson correlation coefficient between the target environmental data of each type and a plurality of historical record environmental data pre-stored by the sensors of the same type is calculated; Among them, Pearson correlation coefficient: a statistic used to measure the degree of linear correlation between two variables, with a value range between -1 and 1. 1 indicates a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates no correlation. Historical time period: refers to a period of time traced back from the current collection moment. Target environmental data: environmental data collected by sensors in a specific historical time period. Historical record environmental data: environmental data collected and stored by sensors in the past period of time.

[0028] In the disclosed embodiment, a first historical time period of a preset reverse time length is first determined, and then the target environment data collected by the sensor is extracted from each collection moment within this time period. Next, the Pearson correlation coefficient between these target environment data and multiple historical record environment data pre-stored by the sensor is calculated. In this way, the historical environment data that is most similar to the target environment data can be found for subsequent analysis and prediction.

[0029] For example, assume that the temperature sensor collects data every 30 milliseconds in the last hour (i.e., the first historical time period). First, extract these target environmental data, and then compare them with the historical environmental data of the same time period in the past few days to calculate the Pearson correlation coefficient. If the correlation coefficient between a historical environmental data and the current target environmental data is very high (close to 1), then this historical environmental data may be considered to be the most similar to the target environmental data.

[0030] In step S13, according to the Pearson correlation coefficient corresponding to the target environment data of each type, target historical environment data is determined from a plurality of historical record environment data corresponding to the type; In the disclosed embodiment, according to the calculated Pearson correlation coefficient, the data most similar to the target environmental data are screened out from the multiple historical environmental data corresponding to the sensor type, namely the target historical environmental data. These data will be used for subsequent lightning activity prediction and formulation of lightning rod control strategy.

[0031] Continuing with the above example, if the temperature sensor's target environmental data in the last hour has the highest correlation coefficient with the historical environmental data in the same time period on a certain day in the past, then this historical environmental data is regarded as the target historical environmental data. The lightning activity corresponding to this target historical environmental data can be further analyzed to formulate a more accurate lightning rod control strategy.

[0032] In step S14, when the lightning result information stored in association with the target historical environment data indicates that a lightning strike occurs within the second historical time period of the preset duration, the ion generator configured for the pre-discharge lightning rod is used to perform early pre-discharge lightning termination; Among them, lightning result information refers to information associated with historical environmental data that characterizes whether lightning (i.e., lightning discharge) occurs within a specific time period. An ion generator is a device that can generate and release ions. It is used on the pre-discharge lightning rod to generate an ion cloud in advance to guide lightning discharge. In the case of an impending lightning discharge, the pre-discharge lightning rod is equipped with an ion generator to generate an ion cloud in advance to guide lightning discharge and reduce the impact on the protected object.

[0033] In the disclosed embodiment, the lightning result information stored in association with the target historical environmental data is first checked. If this information indicates that lightning has occurred in the second historical time period of a sequentially preset duration, it is considered that the current environmental conditions are similar to the environmental conditions where lightning has occurred in the past, and therefore lightning may occur again in the near future. In this case, the ion generator configured by the pre-discharge lightning rod is used to perform pre-discharge lightning termination in advance to reduce the potential threat of lightning to the protected object.

[0034] For example: Assume that the lightning result information stored in the target historical environmental data shows that lightning did occur during the same time period on a certain day in the past. Then it is considered that the current environmental conditions are similar to that day, so there is a possibility that lightning will occur again in the near future. Therefore, the ion generator on the pre-discharge lightning rod releases the ion cloud in advance to guide the lightning discharge and reduce the impact on the building.

[0035] In step S15, if lightning occurs within the second historical time period of the preset duration starting from the first collection moment, the target environment data is pre-stored as the historical record environment data, and the corresponding lightning result information is stored in association with the target environment data, and the lightning result information includes the interval duration between each lightning occurrence and the first collection moment.

[0036] Among them, the historical record environment data update adds new environment data to the historical record database to replace or supplement the old data. The lightning result information update: the new lightning result information (including the interval between the time when the lightning occurred and the first collection time) is associated and stored with the corresponding environment data.

[0037] In the disclosed embodiment, if lightning does occur within the second historical time period of a preset duration starting from the first acquisition moment, the current target environment data is pre-stored as new historical record environment data. At the same time, the corresponding lightning result information (including the interval between each lightning occurrence moment and the first acquisition moment) is stored in association with the target environment data. In this way, more historical data and lightning result information can be accumulated over time, thereby further improving the accuracy and reliability of lightning prediction.

[0038] For example: Assume that lightning does occur in the second historical time period of the preset length from the first collection moment. Then the current target environmental data (such as temperature, humidity, wind speed, etc.) is stored as new historical record environmental data. At the same time, the interval between the time when the lightning occurred and the first collection moment is recorded, and this information is associated with the target environmental data. In this way, when similar environmental conditions are encountered again in the future, the occurrence of lightning can be predicted more accurately and corresponding protective measures can be taken.

[0039] The above technical solution comprehensively collects lightning-related environmental data by setting up multiple different types of sensors on the pre-discharge lightning rod. The multi-type data provides a richer and more accurate source of information for lightning warning. The Pearson correlation coefficient is used to evaluate the correlation between the target environmental data and the historical record environmental data, so as to screen out the historical records that are most similar to the target environmental data from a large amount of historical data. When the lightning result information stored in association with these historical records indicates that lightning has occurred within the second historical time period of a sequentially preset time length, the ion generator configured on the pre-discharge lightning rod is used to perform pre-discharge lightning termination in advance, effectively preventing lightning from directly hitting the facility.

[0040] In addition, according to the results of whether lightning occurred in the second historical time period, the historical record environment database is updated and enriched in real time, so that the early warning capability is continuously improved with the accumulation of data. This dynamic learning and adaptation mechanism further enhances the stability and reliability of the system.

[0041] In summary, by comprehensively utilizing data from multiple types of sensors, using the Pearson correlation coefficient for data analysis, and realizing advance pre-discharge lightning protection, the accuracy and reliability of lightning warnings have been significantly improved, providing more effective lightning protection measures for various facilities.

[0042] In a preferred manner, in step S12, the calculating of the Pearson correlation coefficient between the target environment data of each type and a plurality of historical record environment data pre-stored by sensors of the same type includes: In step S121, according to the collected values ​​collected at each collection moment in the target environment data of each type, the average of the first collected values ​​corresponding to the type of the target environment data of each type is calculated, and according to the collected values ​​collected at each collection moment in multiple historical record environment data pre-stored by the sensor of the same type, the average of the second collected values ​​corresponding to each of the historical record environment data pre-stored by the sensor of the same type is calculated; In the disclosed embodiment, first, for each type of sensor, the first collected value mean of the collected values ​​collected at each collection time in the target environmental data is calculated. This mean reflects the average level of the environmental parameters monitored by this type of sensor in the target historical time period. At the same time, the second collected value mean of the collected values ​​collected at each collection time in each historical record environmental data pre-stored by the same type of sensor is also calculated, and these means represent the average level of the same type of environmental parameters in different time periods in the past.

[0043] For example: Assume that there are two types of sensors: temperature sensor and humidity sensor. For the temperature sensor, collect temperature data every 10 minutes during the target historical time period (such as the last hour) to obtain a series of temperature collection values. First, calculate the average of these temperature collection values ​​as the first collection value average. Then, for each pre-stored historical record environmental data (such as temperature data in the same time period in the past few days), calculate the average of its collection values ​​as the second collection value average. For the humidity sensor, the operation is the same.

[0044] In step S122, according to the mean value of the first collected values ​​corresponding to each type and the collected values ​​collected at each collection moment in the target environment data of the corresponding type, a first deviation between each collected value in the target environment data of each type and the mean value of the first collected values ​​is calculated; In the embodiment of the present disclosure, the first deviation between each collected value in the target environment data and the first collected value average is calculated based on the first collected value average corresponding to each type and the collected values ​​collected at each collection time in the target environment data. This deviation reflects the degree of deviation of the environmental parameters monitored by the sensor of this type at each collection time from the average level during the target historical time period.

[0045] Continuing with the above example, for a temperature sensor, if the temperature value at a certain collection moment is 25°C, and the first collection value average is 24°C, then the first deviation at this collection moment is 1°C. For each collection moment in the target environment data, the corresponding first deviation is calculated. The same applies to humidity sensors.

[0046] In step S123, according to the second collection value mean corresponding to each piece of the historical record environment data of each type and the collection value collected at each collection time in the piece of the historical record environment data, a second deviation between each collection value in the piece of the historical record environment data and the second collection value mean is calculated; In the embodiment of the present disclosure, according to the second collection value mean corresponding to each historical record environmental data of each type and the collection values ​​collected at each collection time in the historical record environmental data, the second deviation between each collection value in the historical record environmental data and the second collection value mean is calculated. This deviation reflects the degree of deviation of the same type of environmental parameters at each collection time from the average level in different time periods in the past.

[0047] For example: For a certain historical record environmental data pre-stored by the temperature sensor (such as the temperature data of a certain day in the past), first calculate the average of its second collection value. Then, for each collection moment in this data, calculate its corresponding second deviation. For example, if the temperature collection value at a certain collection moment is 26°C, and the average of the second collection value of this data is 25°C, then the second deviation of this collection moment is 1°C. For each pre-stored historical record environmental data, calculate its corresponding second deviation. For the humidity sensor, the operation is the same.

[0048] Through the above steps, not only the mean of the target environmental data and the historical record environmental data are calculated, but also the deviation of their respective collected values ​​from the mean is calculated. These mean and deviation information will be used for subsequent analysis and comparison to find the historical record environmental data that is most similar to the target environmental data, and formulate the pre-discharge lightning protection strategy accordingly.

[0049] In step S124, according to the first deviation and the second deviation corresponding to each type, the product of the first deviation and the second deviation corresponding to the type is calculated and then summed to obtain the deviation product sum corresponding to the type, and the deviation square sum of the first deviations corresponding to each type is calculated to obtain the first deviation square sum, and the deviation square sum of the second deviations corresponding to each type is calculated to obtain the second deviation square sum; In the disclosed embodiment, first, based on the first deviation and the second deviation corresponding to each type, the product of the first deviation and the second deviation corresponding to the type is calculated and then summed to obtain the deviation product sum corresponding to the type. This product sum reflects the overall similarity between the target environment data and each historical record environment data in terms of deviation. At the same time, the square sum of the deviation of the first deviation corresponding to each type (i.e., the square sum of the first deviation) and the square sum of the deviation of the second deviation (i.e., the square sum of the second deviation) are also calculated. These square sums reflect the degree of discreteness of the internal deviations of the target environment data and the historical record environment data, respectively.

[0050] For example: suppose there is a temperature sensor, whose target environment data and a certain historical environment data respectively contain temperature collection values ​​at N collection times. For these two data sets, first calculate their respective first deviation and second deviation, and then calculate the deviation product sum (that is, multiply the first deviation and second deviation at the corresponding collection time and sum them up), and get the deviation product sum of the temperature sensor. At the same time, the deviation square sum of the first deviation (that is, square the first deviation of the target environment data and sum them up) and the deviation square sum of the second deviation (that is, square the second deviation of the historical environment data and sum them up) are also calculated. For the humidity sensor, the operation is the same.

[0051] In step S125, the Pearson correlation coefficient between the target environment data of each type and each piece of the historical record environment data pre-stored by the same type of sensor is calculated based on the deviation product sum, the first deviation square sum and the second deviation square sum corresponding to each type.

[0052] In the embodiment of the present disclosure, the Pearson correlation coefficient between each type of target environment data and each piece of historical record environment data pre-stored by the same type of sensor is calculated based on the sum of deviation products, the first sum of squares of deviations, and the second sum of squares of deviations corresponding to each type. The Pearson correlation coefficient is a statistic that measures the degree of linear correlation between two variables, and its value range is between -1 and 1. In this step, the correlation coefficient is calculated using the sum of deviation products, the first sum of squares of deviations, and the second sum of squares of deviations to evaluate the similarity between the target environment data and each piece of historical record environment data.

[0053] Continuing with the above example, for the temperature sensor, the sum of deviation products, the first square sum of deviations, and the second square sum of deviations between the target environmental data and a certain historical environmental data have been calculated. Then, these values ​​are used to calculate the Pearson correlation coefficient. If the correlation coefficient is close to 1, it means that the target environmental data and this historical environmental data have a high similarity in deviation; if the correlation coefficient is close to -1, it means that they have a high negative correlation; if the correlation coefficient is close to 0, it means that there is no significant linear relationship between them. For the humidity sensor, the correlation coefficient with each historical environmental data is also calculated.

[0054] Through the above steps, the similarity between the target environmental data and each piece of historical environmental data can be quantified, providing an important basis for the subsequent formulation of lightning protection strategies, so as to formulate accurate pre-discharge lightning protection strategies.

[0055] In a preferred manner, in step S13, determining the target historical environment data from the plurality of historical record environment data corresponding to the type according to the Pearson correlation coefficient corresponding to the target environment data of each type includes: In step S131, the historical record environment data of each type whose Pearson correlation coefficient is greater than or equal to a preset coefficient threshold of the corresponding type is taken as candidate historical record environment data; In the disclosed embodiment, for each type of target environment data, the Pearson correlation coefficient between it and all historical record environment data is first calculated. Then, these correlation coefficients are compared with a preset coefficient threshold. If the correlation coefficient of a certain historical record environment data is greater than or equal to the threshold, it is regarded as candidate historical record environment data, which means that these data have a statistically significant linear relationship with the target environment data.

[0056] For example, suppose there are three types of environmental data: temperature, humidity, and wind speed. For the temperature type, the Pearson correlation coefficient between it and all historical temperature records is calculated, and the coefficient threshold is set to 0.7. If the correlation coefficient of a historical temperature record is 0.75, then this record is selected as the candidate historical environmental data for temperature.

[0057] In step S132, the mean value of the Pearson correlation coefficient corresponding to each type of the candidate historical record environment data is calculated; In the embodiment of the present disclosure, for each type, the mean value of the correlation coefficients of all candidate historical record environment data is calculated, which reflects the average level of linear correlation between the candidate data of the type and the target environment data.

[0058] For example, if there are 5 candidate historical record environment data of the temperature type and their correlation coefficients are 0.75, 0.78, 0.72, 0.80 and 0.76 respectively, then the mean of these correlation coefficients is (0.75+0.78+0.72+0.80+0.76) / 5 = 0.762.

[0059] In step S133, the number of candidate historical record environment data corresponding to each type is counted; In the embodiment of the present disclosure, for each type, the number of candidate historical record environment data that meets the condition (ie, the correlation coefficient is greater than or equal to the preset coefficient threshold) is counted. This is to determine whether there is enough data to support further analysis.

[0060] For example, taking humidity as an example, assuming that 10 candidate historical record environmental data are screened out, this means that there are 10 historical humidity records that have a high correlation with the target humidity data.

[0061] In step S134, when the number of the alternative historical record environment data corresponding to any of the types is greater than or equal to the preset number threshold of the corresponding type, the alternative historical record environment data of the type whose Pearson correlation coefficient is greater than or equal to the corresponding mean is determined as the target historical environment data of the type.

[0062] In the disclosed embodiment, for each type, if the number of candidate historical record environment data is greater than or equal to a preset number threshold (indicating that there is enough data for reliable analysis), then those data with correlation coefficients greater than or equal to the mean are further screened out as target historical environment data of that type. Such a screening process ensures that the selected historical data not only has a high correlation with the target data, but also has sufficient quantity to support analysis.

[0063] For example, take wind speed as an example. Assume that the threshold number is set to 5, and there are 8 candidate historical environmental data of wind speed type, and the mean value calculated above is 0.65. Among these 8 data, 4 have a correlation coefficient greater than or equal to 0.65, so these 4 data are determined as the target historical environmental data of wind speed type.

[0064] The above technical solution can effectively filter out historical environmental data that is closest to the target environmental data from historical records, providing strong support for subsequent analysis and prediction.

[0065] In a preferred embodiment, the method further comprises: In step S135, when the number of the candidate historical record environment data corresponding to any of the types is less than the preset number threshold of the corresponding type, based on the voting method, the candidate historical record environment data corresponding to the type is used as the voting subject, and voting based on voting probability is performed, wherein the candidate historical record environment data corresponding to the type are all issued with the same number of tickets, and if the Pearson correlation coefficient corresponding to the number of the candidate historical record environments is greater than or equal to the corresponding mean, the voting probability of each of the candidate historical record environment data voting for it is randomly increased, and if the Pearson correlation coefficient corresponding to the number of the candidate historical record environments is less than the corresponding mean, the voting probability of each of the candidate historical record environment data voting for it is randomly reduced; In the embodiment of the present disclosure, when the number of candidate historical record environment data of a certain type is insufficient to meet the preset number threshold, a voting method is used to further screen the target historical environment data. This method weights the importance of the candidate data by adjusting the voting probability, thereby more reasonably selecting the historical data that is closest to the target data.

[0066] Ticket allocation: First, all candidate historical records of each type are assigned the same number of tickets, which represent their rights to participate in the voting.

[0067] Voting probability adjustment: If the Pearson correlation coefficient of a candidate historical environmental data is greater than or equal to the mean of the type, the probability of voting for itself is randomly increased. This means that data with stronger correlation has a greater chance of being selected as the target historical environmental data.

[0068] On the contrary, if the Pearson correlation coefficient of a candidate historical record environment data is smaller than the mean of that type, the probability of voting for itself is randomly reduced. This reduces the possibility of data with weak correlation being selected.

[0069] Voting process: Based on the adjusted voting probability, the voting process is simulated. Each candidate historical record environment data randomly chooses whether to vote for itself based on its voting probability.

[0070] For example, suppose there are 3 candidate historical environmental data of humidity type (A, B, C), and the preset number threshold is 5. 10 tickets are allocated to A, B, and C respectively.

[0071] Assuming that A's Pearson correlation coefficient is 0.7 (greater than the mean 0.65), A's voting probability is randomly increased, for example, to 0.8.

[0072] Assuming that B's Pearson correlation coefficient is 0.6 (less than the mean 0.65), B's voting probability is randomly reduced, for example, to 0.6.

[0073] Assuming that C's Pearson correlation coefficient is 0.68 (greater than the mean 0.65), C's voting probability is also randomly increased, for example, to 0.75.

[0074] Based on these probabilities, a simulated vote was conducted. The possible results are that A received 8 votes, B received 4 votes, and C received 7 votes.

[0075] In step S136, after all the tickets issued by the alternative historical record environment data corresponding to this type have been voted out, if the number of votes obtained by the alternative historical record environment data exceeds the number of tickets issued by any alternative historical record environment data, the alternative historical record environment data is determined as the target historical record environment data of this type.

[0076] In the disclosed embodiment, after all the tickets issued by the candidate historical record environment data are voted out, the number of votes for each candidate data is checked.

[0077] If the number of votes for a candidate historical record environment data exceeds the number of tickets issued by any candidate data (ie, the number of tickets initially allocated), it will be determined as the target historical environment data of this type.

[0078] Continuing with the above example, let's assume that the number of tickets initially allocated is 10. After the voting is over, A received 19 votes, B received 4 votes, and C received 7 votes. A received more than 10 votes (the number of tickets initially allocated).

[0079] In a preferred embodiment, the method further comprises: In step S137, when the Pearson correlation coefficient of any of the types is less than the preset coefficient threshold corresponding to the type, the historical record environment data with the largest Pearson correlation coefficient corresponding to the target environment data of the type is determined as the target historical record environment data of the type.

[0080] In the disclosed embodiment, for a certain type of target environment data, the Pearson correlation coefficient between the target environment data and all historical record environment data is less than the preset coefficient threshold of the type. This means that in the historical data of this type, no record has a significant linear correlation with the target data. In this case, a different strategy needs to be adopted to determine the target historical environment data.

[0081] Comparing Pearson correlation coefficients: Although all correlation coefficients are less than the preset threshold, the magnitudes of these correlation coefficients can still be compared.

[0082] Select the maximum value: From all correlation coefficients, select the one with the largest value. The historical environmental data corresponding to it is considered to be closest to the target environmental data (although this proximity may not be statistically significant).

[0083] Determine as target historical environment data: determine the historical record environment data selected above as target historical environment data of this type.

[0084] For example, suppose you are processing wind speed data and set a high Pearson correlation coefficient threshold (such as 0.8) in the hope of finding historical records that are highly correlated with the target wind speed data. However, after calculation, it is found that the correlation coefficient between the target wind speed data and all historical wind speed records is less than 0.8.

[0085] Specifically, there may be 5 historical records, and their correlation coefficients with the target wind speed data are 0.75, 0.72, 0.68, 0.78, and 0.70 respectively. Although these correlation coefficients are all lower than 0.8, it is noted that one of the records has a correlation coefficient of 0.78, which is the highest among all records. Therefore, the historical record environmental data with a correlation coefficient of 0.78 is selected as the target historical environmental data of the wind speed type.

[0086] In a preferred embodiment, the method further comprises: In step S16, the storage time of pre-storing the target environment data as the historical environment data is recorded, and the storage time of each target environment data is determined according to the storage time, and the determination time when the target environment data is determined as the target historical environment data is recorded; In the embodiment of the present disclosure, the pre-stored storage time is: when the target environment data is first stored as historical record environment data, this time point is recorded. This time point indicates that the data officially becomes part of the historical record and can be used for subsequent analysis and comparison.

[0087] Determination time: Whenever the target environment data is determined as a certain type of target historical environment data, the determination time point is recorded. This information is crucial for tracking the usage history of the data and evaluating its importance. By recording these time points, the life cycle of historical data can be effectively managed, including the storage duration and update frequency of the data.

[0088] For example: Assume that at a certain time T1, new temperature data T_new is collected and stored as historical environmental data. At this time, T1 is recorded as the pre-storage storage time of T_new. Subsequently, at time T2, it is found that T_new is highly correlated with the historical temperature data in a certain time period, so it is determined as the target historical environmental data in this time period. T2 is recorded as the determination time of T_new.

[0089] In step S17, according to the determination time when the target environment data was last determined as the target historical environment data, the interval duration when the target environment data was last determined as the target historical environment data is determined, and when the interval duration when the target environment data was last determined as the target historical environment data reaches a preset interval duration threshold, the number of times the target environment data was determined as the target historical environment data when it was the historical record environment data is determined; In the disclosed embodiment, the interval duration is calculated based on the time when the target environment data was last determined as the target historical environment data. This interval duration refers to the time from when the target environment data was last determined as the target historical environment data to now. By comparing this interval duration with a preset interval duration threshold, the timeliness and activity of the data can be evaluated.

[0090] If the interval reaches a preset threshold, the number of times the target environment data is determined as the target historical environment data is further counted. This number reflects the importance and usage frequency of the data in the historical records.

[0091] Continuing with the above example, assume that after T_new is first identified as the target historical environment data at time T2, it has never been identified again (until some future time T3, but T3 is not considered here). Set an interval time threshold, such as one year. If the time from T2 to now exceeds this threshold, the number of times T_new is identified as the target historical environment data will be checked. In this example, since T_new has only been identified once, this number is 1.

[0092] In step S18, if the number of times that the target environment data is determined as the target historical environment data does not reach a preset number, the target environment data is removed from the historical record environment data.

[0093] In the disclosed embodiment, a decision is made based on the number of times the target environment data is determined as the target historical environment data. If this number is lower than a preset number threshold, it is considered that the data is not active enough in the historical records and may no longer be sufficiently representative or important. Therefore, it is removed from the historical record environment data to save storage space and maintain the refinement of the historical data.

[0094] Continuing with the above example, if the threshold is set to 3 (meaning that a piece of data needs to be identified as the target historical environment data at least 3 times to maintain its historical status), then T_new will be considered inactive data because it has only been identified once. After checking this point, T_new will be removed from the historical environment data.

[0095] It should be noted that this removal process should be reversible or configurable to avoid accidentally deleting important data. For example, a protection mechanism can be set up to allow manual review or confirmation before the data is removed. In addition, the removed data can also be stored in a backup or archive system so that it can be restored when needed.

[0096] In a preferred embodiment, the sensor includes at least two of the following: a magnetic field sensor, an electric field sensor, a humidity sensor, an air pressure sensor, and a wind speed sensor.

[0097] According to the test, the pre-discharge lightning rod control method disclosed in the present invention can achieve a pre-discharge lightning termination rate of more than 95%, and the advance time is 60us, thereby achieving accurate and reliable early warning lightning termination in complex environments and improving the accuracy of capturing lightning activities.

[0098] The present disclosure also provides a pre-discharge lightning rod control device, see Figure 3 As shown, including: The acquisition module 310 is configured to respectively acquire environmental data corresponding to the type collected by multiple different types of sensors arranged on the pre-discharge lightning rod; The calculation module 320 is configured to use the environmental data collected by the sensors of each type at each collection moment in the first historical time period of a preset time length in reverse order from the first collection moment as target environmental data, and calculate the Pearson correlation coefficient between the target environmental data of each type and a plurality of historical record environmental data pre-stored by the sensors of the same type; The determination module 330 is configured to determine target historical environment data from a plurality of historical record environment data corresponding to the type according to the Pearson correlation coefficient corresponding to the target environment data of each type; The lightning termination module 340 is configured to perform early pre-discharge lightning termination through the ion generator configured on the pre-discharge lightning termination rod when the lightning result information stored in association with the target historical environment data indicates that a lightning strike occurs within a second historical time period of the preset duration; The storage module 350 is configured to pre-store the target environment data as the historical record environment data if a lightning strike occurs within the second historical time period of the preset duration starting from the first collection moment, and to store the corresponding lightning result information in association with the target environment data, wherein the lightning result information includes the interval duration between each lightning strike and the first collection moment.

[0099] Optionally, the calculation module 320 is configured to: Calculate the average of the first collected values ​​corresponding to the target environment data of each type according to the collected values ​​collected at each collection time in the target environment data of each type, and calculate the average of the second collected values ​​corresponding to each piece of the historical record environment data pre-stored by the sensor of the same type according to the collected values ​​collected at each collection time in the multiple historical record environment data pre-stored by the sensor of the same type; Calculate, according to the mean of the first collected values ​​corresponding to each of the types and the collected values ​​collected at each of the collection moments in the target environment data of the corresponding type, a first deviation between each of the collected values ​​in the target environment data of each type and the mean of the first collected values; Calculate, according to the second collection value mean corresponding to each piece of the historical record environment data of each type and the collection value collected at each collection time in the piece of the historical record environment data, a second deviation between each collection value in the piece of the historical record environment data and the second collection value mean; According to the first deviation and the second deviation corresponding to each of the types, the product of the first deviation and the second deviation corresponding to the type is calculated and then summed to obtain the deviation product sum corresponding to the type, and the deviation square sum of the first deviations corresponding to each of the types is calculated to obtain the first deviation square sum, and the deviation square sum of the second deviations corresponding to each of the types is calculated to obtain the second deviation square sum; The Pearson correlation coefficient between the target environment data of each type and each piece of the historical record environment data pre-stored by the sensor of the same type is calculated according to the deviation product sum, the first deviation square sum and the second deviation square sum corresponding to each type.

[0100] Optionally, the determining module 330 is configured to: The historical record environment data of each type whose Pearson correlation coefficient is greater than or equal to a preset coefficient threshold of the corresponding type is used as candidate historical record environment data; Calculate the mean value of the Pearson correlation coefficient corresponding to the candidate historical record environment data of each type; Counting the number of candidate historical record environment data corresponding to each type; When the number of candidate historical record environment data corresponding to any of the types is greater than or equal to the preset number threshold of the corresponding type, the candidate historical record environment data of the type whose Pearson correlation coefficient is greater than or equal to the corresponding mean is determined as the target historical record environment data of the type.

[0101] Optionally, the determining module 330 is configured to: In the case that the number of the candidate historical record environment data corresponding to any of the types is less than the preset number threshold of the corresponding type, based on the voting method, the candidate historical record environment data corresponding to the type is used as the voting subject, and voting based on voting probability is performed, wherein the candidate historical record environment data corresponding to the type are all issued with the same number of tickets, and if the Pearson correlation coefficient corresponding to the number of the candidate historical record environments is greater than or equal to the corresponding mean, the voting probability of each of the candidate historical record environment data voting for it is randomly increased; if the Pearson correlation coefficient corresponding to the number of the candidate historical record environments is less than the corresponding mean, the voting probability of each of the candidate historical record environment data voting for it is randomly reduced; After all the tickets issued by the alternative historical record environment data corresponding to this type have been voted out, if the number of votes obtained by the alternative historical record environment data exceeds the number of tickets issued by any alternative historical record environment data, the alternative historical record environment data will be determined as the target historical record environment data of this type.

[0102] Optionally, the determining module 330 is configured to: When the Pearson correlation coefficient of any of the types is less than the preset coefficient threshold corresponding to the type, the historical record environment data with the largest Pearson correlation coefficient corresponding to the target environment data of the type is determined as the target historical environment data of the type.

[0103] Optionally, the device further comprises: a removal module configured to: Recording the storage time of pre-storing the target environment data as the historical record environment data, determining the storage duration of each target environment data according to the storage time, and recording the determination time when the target environment data is determined as the target historical environment data; Determine, based on the determination time when the target environment data was most recently determined as the target historical environment data, the interval duration when the target environment data was most recently determined as the target historical environment data, and, if the interval duration when the target environment data was most recently determined as the target historical environment data reaches a preset interval duration threshold, determine the number of times the target environment data was determined as the target historical environment data when it was the historical record environment data; If the number of times that the target environment data is determined as the target historical environment data does not reach a preset number, the target environment data is removed from the historical record environment data.

[0104] Optionally, the sensor includes at least two of the following: a magnetic field sensor, an electric field sensor, a humidity sensor, an air pressure sensor, and a wind speed sensor.

[0105] The present disclosure also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the method described in any one of the aforementioned embodiments are implemented.

[0106] The present disclosure also provides a pre-discharge lightning rod, comprising: a memory having a computer program stored thereon; A processor is used to execute the computer program in the memory to implement the steps of the method in any one of the aforementioned embodiments.

[0107] Figure 4 The pre-discharge lightning rod control device 100 shown includes: a processor 1001 and a memory 1003. The processor 1001 and the memory 1003 are connected, such as through a bus 1002. Optionally, the pre-discharge lightning rod control device 100 may also include a communication component 1004, which can be used for data interaction between the device 100 and other devices, such as data transmission and / or data reception. It should be noted that in actual scheduling, the communication component 1004 is not limited to one, and the structure of the pre-discharge lightning rod control device 100 does not constitute a limitation on the embodiments of the present application.

[0108] Processor 1001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. Processor 1001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0109] The bus 1002 may include a path to transmit information between the above components. The bus 1002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 1002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0110] The memory 1003 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), magnetic disk storage medium, other magnetic storage devices, or any other medium that can be used to carry or store program code and can be read by a computer, without limitation herein.

[0111] The memory 1003 is used to store program codes for executing the embodiments of the present disclosure, and the execution is controlled by the processor 1001. The processor 1001 is used to execute the program codes stored in the memory 1003 to implement the steps shown in the above-mentioned embodiment of the pre-discharge lightning rod control method.

[0112] The embodiment of the present disclosure also provides a computer-readable storage medium having program codes stored thereon. When the program codes are executed by a processor, the steps and corresponding contents of the aforementioned embodiment of the pre-discharge lightning rod control method can be implemented.

[0113] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings; however, the present disclosure is not limited to the specific details in the above embodiments; within the technical concept of the present disclosure, various changes, modifications, substitutions and variations may be made to these embodiments, and these changes, modifications, substitutions and variations all fall within the protection scope of the present disclosure.

[0114] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any appropriate manner without contradiction, and they should also be regarded as the contents disclosed in this disclosure. In order to avoid unnecessary repetition, this disclosure will not further describe various possible combinations. The technical scope of this application is not limited to the contents in the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A pre-discharge lightning rod control method, characterized in that: The method comprises: Respectively obtain a plurality of different types of sensors arranged on the pre-discharge lightning rod to collect environmental data corresponding to the types; Taking the environmental data collected by the sensors of each type at each collection moment in the first historical time period of a preset length in reverse order from the first collection moment as target environmental data, and calculating the Pearson correlation coefficient between the target environmental data of each type and a plurality of historical record environmental data pre-stored by sensors of the same type; Determine target historical environment data from a plurality of pieces of historical environment data corresponding to the type according to the Pearson correlation coefficient corresponding to the target environment data of each type; When the lightning result information stored in association with the target historical environment data indicates that a lightning strike occurred within a second historical time period of the preset duration, the ion generator configured for the pre-discharge lightning rod is used to perform early pre-discharge lightning termination; If lightning occurs within the second historical time period of the preset duration starting from the first collection moment, the target environment data will be pre-stored as the historical record environment data, and the corresponding lightning result information will be stored in association with the target environment data, and the lightning result information will include the interval between each lightning occurrence and the first collection moment.

2. The pre-discharge lightning rod control method according to claim 1, characterized in that: The calculating of the Pearson correlation coefficient between the target environment data of each type and a plurality of historical record environment data pre-stored by sensors of the same type comprises: Calculate the average of the first collected values ​​corresponding to the target environment data of each type according to the collected values ​​collected at each collection time in the target environment data of each type, and calculate the average of the second collected values ​​corresponding to each piece of the historical record environment data pre-stored by the sensor of the same type according to the collected values ​​collected at each collection time in the multiple historical record environment data pre-stored by the sensor of the same type; Calculate, according to the mean of the first collected values ​​corresponding to each of the types and the collected values ​​collected at each of the collection moments in the target environment data of the corresponding type, a first deviation between each of the collected values ​​in the target environment data of each type and the mean of the first collected values; Calculate, according to the second collection value mean corresponding to each piece of the historical record environment data of each type and the collection value collected at each collection time in the piece of the historical record environment data, a second deviation between each collection value in the piece of the historical record environment data and the second collection value mean; According to the first deviation and the second deviation corresponding to each of the types, the product of the first deviation and the second deviation corresponding to the type is calculated and then summed to obtain the deviation product sum corresponding to the type, and the deviation square sum of the first deviations corresponding to each of the types is calculated to obtain the first deviation square sum, and the deviation square sum of the second deviations corresponding to each of the types is calculated to obtain the second deviation square sum; The Pearson correlation coefficient between the target environment data of each type and each piece of the historical record environment data pre-stored by the sensor of the same type is calculated according to the deviation product sum, the first deviation square sum and the second deviation square sum corresponding to each type.

3. The pre-discharge lightning rod control method according to claim 1, characterized in that: Determining target historical environment data from a plurality of pieces of historical record environment data corresponding to the type according to the Pearson correlation coefficient corresponding to the target environment data of each type includes: The historical record environment data of each type whose Pearson correlation coefficient is greater than or equal to a preset coefficient threshold of the corresponding type is used as candidate historical record environment data; Calculate the mean value of the Pearson correlation coefficient corresponding to the candidate historical record environment data of each type; Counting the number of candidate historical record environment data corresponding to each type; When the number of candidate historical record environment data corresponding to any of the types is greater than or equal to the preset number threshold of the corresponding type, the candidate historical record environment data of the type whose Pearson correlation coefficient is greater than or equal to the corresponding mean is determined as the target historical record environment data of the type.

4. The pre-discharge lightning rod control method according to claim 3, characterized in that: The method further comprises: In the case that the number of the candidate historical record environment data corresponding to any of the types is less than the preset number threshold of the corresponding type, based on the voting method, the candidate historical record environment data corresponding to the type is used as the voting subject, and voting based on voting probability is performed, wherein the candidate historical record environment data corresponding to the type are all issued with the same number of tickets, and if the Pearson correlation coefficient corresponding to the number of the candidate historical record environments is greater than or equal to the corresponding mean, the voting probability of each of the candidate historical record environment data voting for it is randomly increased; if the Pearson correlation coefficient corresponding to the number of the candidate historical record environments is less than the corresponding mean, the voting probability of each of the candidate historical record environment data voting for it is randomly reduced; After all the tickets issued by the alternative historical record environment data corresponding to this type have been voted out, if the number of votes obtained by the alternative historical record environment data exceeds the number of tickets issued by any alternative historical record environment data, the alternative historical record environment data will be determined as the target historical record environment data of this type.

5. The pre-discharge lightning rod control method according to claim 3, characterized in that: The method further comprises: When the Pearson correlation coefficient of any of the types is less than the preset coefficient threshold corresponding to the type, the historical record environment data with the largest Pearson correlation coefficient corresponding to the target environment data of the type is determined as the target historical environment data of the type.

6. The pre-discharge lightning rod control method according to claim 1, characterized in that: The method further comprises: Recording the storage time of pre-storing the target environment data as the historical record environment data, determining the storage duration of each target environment data according to the storage time, and recording the determination time when the target environment data is determined as the target historical environment data; Determine, based on the determination time when the target environment data was most recently determined as the target historical environment data, the interval duration when the target environment data was most recently determined as the target historical environment data, and, if the interval duration when the target environment data was most recently determined as the target historical environment data reaches a preset interval duration threshold, determine the number of times the target environment data was determined as the target historical environment data when it was the historical record environment data; If the number of times that the target environment data is determined as the target historical environment data does not reach a preset number, the target environment data is removed from the historical record environment data.

7. The pre-discharge lightning rod control method according to any one of claims 1 to 6, characterized in that: The sensor includes at least two of the following: a magnetic field sensor, an electric field sensor, a humidity sensor, an air pressure sensor, and a wind speed sensor.

8. A pre-discharge lightning rod control device, characterized in that: include: An acquisition module is configured to respectively acquire environmental data corresponding to the type collected by a plurality of different types of sensors arranged on the pre-discharge lightning rod; a calculation module configured to use the environmental data collected by the sensors of each type at each collection moment in a first historical time period of a preset length in reverse order from a first collection moment as target environmental data, and calculate a Pearson correlation coefficient between the target environmental data of each type and a plurality of historical record environmental data pre-stored by sensors of the same type; a determination module configured to determine target historical environment data from a plurality of pieces of historical environment data corresponding to the type according to the Pearson correlation coefficient corresponding to the target environment data of each type; A lightning arrester module is configured to perform early pre-discharge lightning arrester through an ion generator configured on the pre-discharge lightning arrester rod when the lightning result information stored in association with the target historical environment data indicates that a lightning strike occurs within a second historical time period of the preset duration; The storage module is configured to pre-store the target environment data as the historical record environment data if a lightning strike occurs within the second historical time period of the preset duration starting from the first collection moment, and to store the corresponding lightning result information in association with the target environment data, wherein the lightning result information includes the interval duration between each lightning strike and the first collection moment.

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

10. A pre-discharge lightning rod, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the method according to any one of claims 1 to 7.

Images