A lightning observation method and system based on optical fiber sensor

Abstract

The present invention discloses a lightning observation method and system based on optical fiber sensors, which relates to the field of lightning monitoring technology, including deploying sensor nodes to form a sensor network, and collecting lightning signals through sensor nodes; establishing a set of equations based on the coordinates of sensor nodes to solve the lightning position and lightning intensity, identifying the lightning type according to the lightning signal and lightning intensity, and generating a lightning observation report; and displaying and storing the lightning observation report in a database. The present invention collects lightning information through optical fiber sensors, and solves lightning coordinates and lightning intensity according to the position of optical fiber sensor nodes and lightning information, thereby improving the accuracy of lightning observation, realizing the precise positioning of lightning events, identifying lightning types according to lightning signals and lightning intensity, generating lightning observation reports, realizing automatic identification of lightning, and providing effective support for lightning early warning.

Description

Technical Field

[0001] The invention relates to the technical field of lightning monitoring, and in particular to a lightning observation method and system based on optical fiber sensors. Background Art

[0002] In recent years, with the rapid development of meteorological science and technology, lightning observation technology has become an important part of early warning and disaster prevention and mitigation. Traditional lightning observation technology is mostly based on the principles of electromagnetic waves and optical imaging. It records lightning events by detecting changes in electric and magnetic fields through ground receiving devices. Although these methods can detect lightning position and intensity to a certain extent, they are significantly insufficient in measurement accuracy and real-time performance due to the influence of factors such as equipment distribution density and environmental noise. At the same time, the rapid development of fiber optic sensor technology in recent years has gradually made it a new type of lightning observation method. Fiber optic sensors have the advantages of high voltage resistance, anti-interference and high sensitivity. They are especially suitable for precise measurement in areas with frequent lightning activities and strong electromagnetic interference. Compared with traditional electromagnetic methods, fiber optic sensors can achieve higher resolution signal acquisition and more accurate timing synchronization, and have broad application prospects. However, the existing lightning observation technology lacks high-precision lightning coordinate positioning and intensity analysis methods, making it difficult to comprehensively and effectively realize the accurate identification of lightning events and meet the needs of accurate observation of complex lightning events. Summary of the invention

[0003] In view of the problems existing in the above-mentioned existing lightning observation methods and systems based on optical fiber sensors, the present invention is proposed.

[0004] Therefore, the problem to be solved by the present invention is that the existing lightning observation technology lacks high-precision lightning coordinate positioning and intensity analysis means, making it difficult to fully and effectively realize the accurate identification of lightning events.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: a lightning observation method based on optical fiber sensors, which includes deploying sensor nodes to form a sensor network and collecting lightning signals through the sensor nodes; establishing a set of equations based on the coordinates of the sensor nodes to solve the lightning position and lightning intensity, identifying the lightning type according to the lightning signal and lightning intensity and generating a lightning observation report; and displaying the lightning observation report and storing it in a database.

[0006] As a preferred solution of the lightning observation method based on optical fiber sensors described in the present invention, the deployment of sensor nodes to form a sensor network refers to the deployment of sensor nodes in the lightning occurrence area through a grid deployment method, the sensor nodes include optical fiber sensors and data transmission components, the GPS clock is used to synchronize the optical fiber sensors, and all sensor nodes are connected to the data center through a wired method to form a sensor network.

[0007] As a preferred solution of the lightning observation method based on optical fiber sensors described in the present invention, wherein: the collection of lightning signals through optical fiber sensors refers to collecting optical signals and electromagnetic signals of lightning through distributed optical fiber sensors, and obtaining timestamps to generate lightning signals and transmit them to a data center, using a bandpass filter to filter noise on the lightning signals, applying a Kalman filter algorithm to dynamically correct the lightning signals after noise filtering, and aligning the timestamp sequences of the lightning signals after dynamic correction of each sensor node.

[0008] As a preferred solution of the lightning observation method based on optical fiber sensors described in the present invention, wherein: the establishment of a set of equations based on sensor node coordinates to solve the lightning position and lightning intensity refers to sorting the sensor nodes from early to late according to the timestamp of receiving the lightning signal, selecting nodes from front to back according to the sorting, and if the top three nodes in the sorting cannot form a geometrically stable triangle, then replacing the nodes from front to back until the nodes can form a triangle, taking the nodes as the vertices of the triangle, and extracting the node coordinates as the node coordinate vector ;

[0009] Set the lightning position coordinate vector to ,initialization , respectively calculate The square of the distance is used to construct the CMD matrix:

[0010] ;

[0011] in is the CMD matrix, is the node coordinate vector The square of the distance between them;

[0012] According to the CMD matrix, we calculate Weight :

[0013] ;

[0014] ;

[0015] The weight determines the influence of each node in the estimation of the lightning position. The combination of CMD matrix and weight calculation ensures that the spatial position and influence of each node in the triangulation positioning process are accurately reflected, thereby improving the solution accuracy of lightning positioning.

[0016] Calculate the node coordinate vector Direction vector to other nodes :

[0017] ;

[0018] Using weights based on triangulation positioning optimization formula Solve for the new lightning position coordinate vector :

[0019] ;

[0020] The new lightning position coordinate vector Update CMD matrix and weights , and iteratively calculate the new lightning position coordinate vector, calculate the change value of the lightning position coordinate vector after each iteration, stop the iteration when the change value is lower than the set threshold, output the final lightning position coordinate vector and extract the lightning position therein;

[0021] Under the initial geometric framework provided by the CMD matrix, the triangulated positioning optimization formula is used to solve the new lightning position coordinate vector through the node coordinate direction vector and weight. The triangulated positioning optimization formula gradually approaches the true position of the lightning event through multiple iterations. After each iteration, the CMD matrix and each weight are updated using the new lightning position coordinates, and the optimization formula is applied again for the next iterative calculation until the change value of the lightning position coordinates is lower than the set accuracy threshold. The iteration is stopped and the final lightning position coordinate vector is output. The iterative process of this optimization formula helps to eliminate position deviation and system error and achieve high-precision lightning event positioning.

[0022] Calculate the distance between the lightning location and any sensor node , according to the electromagnetic signal strength received by the sensor node Calculating lightning intensity :

[0023] ;

[0024] in For additional noise, is the signal gain factor;

[0025] The lightning intensity is calculated for all sensor nodes and the average lightning intensity of all sensor nodes is calculated as the final lightning intensity S.

[0026] As a preferred solution of the lightning observation method based on optical fiber sensor of the present invention, wherein: the identifying lightning type according to lightning signal and lightning intensity and generating lightning observation report comprises constructing SVM model, using support vector machine classifier for model training, and defining loss function for iterative optimization of SVM model;

[0027] The lightning signal and lightning intensity are input into the trained SVM model, the lightning type is output, and a lightning observation report is generated based on the lightning information and lightning type.

[0028] As a preferred solution of the lightning observation method based on optical fiber sensors described in the present invention, wherein: the display of the lightning observation report refers to visually displaying the lightning observation report in combination with the lightning intensity, and marking the lightning location on the map, to assist staff in analyzing lightning disasters in combination with the lightning observation report.

[0029] As a preferred solution of the lightning observation method based on optical fiber sensors described in the present invention, the storage in the database refers to storing lightning information and lightning observation reports in the database, and classifying them according to timestamps. The database regularly performs integrity checks on the stored data and synchronously uploads them to the cloud for storage data backup.

[0030] Another object of the present invention is to provide a lightning observation system based on optical fiber sensors, which comprises:

[0031] Data collection module, used to deploy sensor nodes to form a sensor network and collect lightning signals for pre-processing;

[0032] The lightning observation module is used to solve the lightning position according to the sensor node coordinates and calculate the lightning intensity based on the lightning position;

[0033] An identification module is used to identify the lightning type according to the lightning information and lightning intensity and generate a lightning observation report;

[0034] The display and storage module is used to display the lightning observation report and store it in the database.

[0035] A computer device comprises: a memory and a processor; the memory stores a computer program, and the processor implements the steps of the above-mentioned lightning observation method based on optical fiber sensors when executing the computer program.

[0036] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned lightning observation method based on optical fiber sensors.

[0037] The beneficial effects of the present invention are as follows: the present invention collects lightning information through optical fiber sensors, and solves lightning coordinates and lightning intensity according to the optical fiber sensor node positions and lightning information, thereby improving the accuracy of lightning observation, realizing the precise positioning of lightning events, and identifying lightning types according to lightning signals and lightning intensity to generate lightning observation reports, thereby realizing automatic identification of lightning and providing effective support for lightning early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0039] Figure 1 Schematic diagram of the flow of lightning observation method based on optical fiber sensor.

[0040] Figure 2 This is a structural diagram of the lightning observation method based on optical fiber sensors. DETAILED DESCRIPTION

[0041] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0042] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0043] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments. Example

[0044] Reference Figure 1 , which is the first embodiment of the present invention, and which provides a lightning observation method based on an optical fiber sensor. The lightning observation method based on an optical fiber sensor includes:

[0045] S1, deploy sensor nodes to form a sensor network, and collect lightning signals through sensor nodes;

[0046] Specifically, deploying sensor nodes to form a sensor network refers to deploying sensor nodes in the lightning occurrence area through a grid layout method. The sensor nodes include optical fiber sensors and data transmission components. The optical fiber sensors are synchronized using a GPS clock. All sensor nodes are connected to a data center through a wired method to form a sensor network.

[0047] By deploying sensor nodes in areas prone to lightning through grid layout, it can not only ensure comprehensive coverage of the area, but also ensure that lightning events are observed by multiple nodes at the same time. Fiber optic sensors have the advantages of high sensitivity, high voltage resistance and anti-electromagnetic interference. Due to the high intensity of electromagnetic waves generated by lightning, traditional electromagnetic sensors may have reduced accuracy or even data loss in a strong interference environment, while fiber optic sensors can still maintain high-precision data acquisition in a strong interference environment. The sensitive response of fiber optic sensors to optical signals ensures the integrity of the signal waveform of lightning events, which is helpful for subsequent signal processing and intensity analysis. GPS clock synchronization technology is used to calibrate the time of sensor nodes to ensure that the time error of all nodes is kept at the microsecond level. The key advantage of this method is that it can provide accurate time difference data for subsequent lightning positioning. For lightning event positioning, a small time difference may cause a large positioning error. Therefore, high-precision time synchronization is achieved through GPS clock synchronization technology, which can effectively improve the accuracy of event positioning.

[0048] Furthermore, collecting lightning signals through optical fiber sensors refers to collecting optical signals and electromagnetic signals of lightning through distributed optical fiber sensors, obtaining timestamps to generate lightning signals and transmitting them to a data center, using a bandpass filter to filter noise on the lightning signals, applying a Kalman filter algorithm to dynamically correct the lightning signals after noise filtering, and aligning the timestamp sequences of the lightning signals after dynamic correction at each sensor node.

[0049] In the observation of lightning events, the use of fiber optic sensors to collect optical and electromagnetic signals of lightning can effectively cope with high-intensity electromagnetic interference environments. Fiber optic sensors have excellent anti-electromagnetic interference performance, avoiding the influence of electromagnetic interference in lightning events on signal acquisition accuracy. Compared with traditional electronic sensors, fiber optic sensors show higher sensitivity and data stability when collecting high-frequency lightning signals, providing a more reliable data basis for subsequent data analysis and lightning positioning. Using a bandpass filter to filter the noise of the collected lightning signals can significantly improve the clarity of the signal. Lightning signals usually contain rich high-frequency components, while low-frequency noise and high-frequency interference in the environment may reduce the observation accuracy. The validity of the data, the bandpass filter filters out the interference components outside the target frequency by setting a specific frequency range (i.e., the main frequency band of the lightning signal), thereby achieving effective purification of the lightning signal. On the basis of the bandpass filter, the Kalman filter algorithm is further applied to dynamically correct the lightning signal, which helps to eliminate random fluctuations and instantaneous interference in the signal. The timestamp sequence alignment process ensures the consistency of the observation data of all sensor nodes in the time dimension, avoiding positioning errors caused by time differences. This process aligns the dynamically corrected timestamps of each node, enabling the system to accurately calculate the time difference between the lightning signal reaching each node, thereby achieving high-precision event positioning.

[0050] S2, based on the coordinates of the sensor nodes, establish a set of equations to solve the lightning position and lightning intensity, identify the lightning type according to the lightning signal and lightning intensity, and generate a lightning observation report;

[0051] Specifically, the equations for solving the lightning position and lightning intensity based on the coordinates of the sensor nodes are established, which means that the sensor nodes are sorted from early to late according to the timestamps of the received lightning signals, and the nodes are selected from front to back according to the sorting. If the top three nodes cannot form a geometrically stable triangle, the nodes are replaced from front to back until the nodes can form a triangle, and the nodes are used as the vertices of the triangle, and the node coordinates are extracted as the node coordinate vector. ;

[0052] The sensor nodes are sorted by the timestamp of the received lightning signal to ensure that the node that receives the lightning signal earliest participates in the positioning analysis first. This sorting method helps to improve the accuracy of the initial node combination. Then, for the sorted nodes, the first three nodes are gradually selected to try to form a geometrically stable triangle. The geometric stability of the triangle is crucial for accurate positioning, because a stable geometric structure can reduce errors in calculations. If the initial three nodes cannot form a stable triangle, continue to select and replace the sorted nodes until a stable triangle is obtained, thereby providing a reliable basis for the CMD matrix and positioning solution. This screening process optimizes the node selection mechanism while ensuring positioning accuracy.

[0053] Set the lightning position coordinate vector to ,initialization , respectively calculated The square of the distance is used to construct the CMD matrix:

[0054] ;

[0055] in is the CMD matrix, is the node coordinate vector The square of the distance between them;

[0056] According to the CMD matrix, we calculate Weight :

[0057] ;

[0058] ;

[0059] The weight determines the influence of each node in the estimation of the lightning position. The combination of CMD matrix and weight calculation ensures that the spatial position and influence of each node in the triangulation positioning process are accurately reflected, thereby improving the solution accuracy of lightning positioning.

[0060] Calculate the node coordinate vector Direction vector to other nodes :

[0061] ;

[0062] Using weights based on triangulation positioning optimization formula Solve for the new lightning position coordinate vector :

[0063] ;

[0064] The new lightning position coordinate vector Update CMD matrix and weights , and iteratively calculate the new lightning position coordinate vector, calculate the change value of the lightning position coordinate vector after each iteration, stop the iteration when the change value is lower than the set threshold, output the final lightning position coordinate vector and extract the lightning position therein;

[0065] Under the initial geometric framework provided by the CMD matrix, the triangulated positioning optimization formula is used to solve the new lightning position coordinate vector through the node coordinate direction vector and weight. The triangulated positioning optimization formula gradually approaches the true position of the lightning event through multiple iterations. After each iteration, the CMD matrix and each weight are updated using the new lightning position coordinates, and the optimization formula is applied again for the next iterative calculation until the change value of the lightning position coordinates is lower than the set accuracy threshold. The iteration is stopped and the final lightning position coordinate vector is output. The iterative process of this optimization formula helps to eliminate position deviation and system error and achieve high-precision lightning event positioning.

[0066] Calculate the distance between the lightning location and any sensor node , according to the electromagnetic signal strength received by the sensor node Calculating lightning intensity :

[0067] ;

[0068] in For additional noise, is the signal gain factor;

[0069] The lightning intensity is calculated for all sensor nodes and the average lightning intensity of all sensor nodes is calculated as the final lightning intensity S.

[0070] The calculated lightning intensity data provides the energy characteristics of lightning events, providing a basis for analyzing the destructive power and scope of lightning events. This method uses the mean value of the intensity calculated by each node to obtain a systematic lightning intensity measurement value, making the observation data more reliable. The lightning intensity data calculated by all nodes are averaged to improve the stability and accuracy of the overall observation data. This process eliminates the deviation caused by individual node observation errors or noise interference on the data, making the intensity measurement results more reliable. The lightning intensity mean provides a global lightning energy feature, which can be used for the subsequent lightning disaster prediction and early warning system intensity level division. Through the intensity mean solution, the output data of the observation system is more scientific and consistent, and can provide accurate data support for disaster prevention and mitigation and lightning research.

[0071] Furthermore, identifying the lightning type according to the lightning signal and lightning intensity and generating a lightning observation report includes constructing an SVM model, using a support vector machine classifier to train the model, and defining a loss function to iteratively optimize the SVM model;

[0072] The lightning signal and lightning intensity are input into the trained SVM model, the lightning type is output, and a lightning observation report is generated based on the lightning information and lightning type.

[0073] In the process of lightning type identification, a SVM model specially used for lightning classification is constructed to realize the automatic classification of different types of lightning events. The SVM model has the ability to process complex data in high-dimensional space, and can effectively utilize lightning signal characteristics and intensity characteristics to classify complex and diverse lightning signal data. Through the hyperplane constructed by the SVM model, lightning data can be accurately divided into different types of categories, such as cloud-to-ground lightning, cloud-to-cloud lightning, etc. This step not only ensures the accuracy of classification, but also enhances the intelligence and automation level of the lightning observation system, laying a solid classification foundation for the subsequent generation of lightning observation reports. After the type identification is completed, the system will generate a detailed lightning observation report based on the lightning information (such as signal characteristics, intensity, and type). The report content includes: key information such as the time, location, intensity, and type of lightning, forming a structured and systematic data file. The report is generated in real time by the data center, which not only facilitates users to quickly grasp the basic information of lightning events, but also provides strong data support for subsequent meteorological analysis, disaster assessment, and disaster prevention and mitigation. The automatic generation function of lightning observation reports improves the efficiency and automation level of the system, greatly shortens the observation and recording time of lightning events, and provides strong data support for the real-time early warning system.

[0074] S3, displaying the lightning observation report and storing it in the database;

[0075] Specifically, displaying the lightning observation report refers to visually displaying the lightning observation report in combination with the lightning intensity, marking the lightning location on the map, and assisting staff in analyzing the lightning disaster in combination with the lightning observation report.

[0076] Furthermore, storing in a database means storing lightning information and lightning observation reports in the database and classifying them according to timestamps. The database periodically performs integrity checks on the stored data and synchronously uploads them to the cloud for storage data backup. Example

[0077] Reference Figure 2 , which is the second embodiment of the present invention, and which is different from the previous embodiment, provides a lightning observation system based on an optical fiber sensor, which includes:

[0078] Data collection module, used to deploy sensor nodes to form a sensor network and collect lightning signals for pre-processing;

[0079] The lightning observation module is used to solve the lightning position according to the sensor node coordinates and calculate the lightning intensity based on the lightning position;

[0080] An identification module is used to identify the lightning type according to the lightning information and lightning intensity and generate a lightning observation report;

[0081] The display and storage module is used to display the lightning observation report and store it in the database.

[0082] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0083] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0084] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0085] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit with a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit with a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0086] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A lightning observation method based on optical fiber sensor, characterized in that: include: Deploy sensor nodes to form a sensor network and collect lightning signals through the sensor nodes; Based on the coordinates of the sensor nodes, a set of equations is established to solve the lightning position and lightning intensity. According to the lightning signal and lightning intensity, the lightning type is identified and a lightning observation report is generated. Display lightning observation reports and store them in the database; Deploying sensor nodes to form a sensor network refers to deploying sensor nodes in a lightning-occurring area by means of a grid-based deployment method, wherein the sensor nodes include optical fiber sensors and data transmission components, and the optical fiber sensors are synchronized by using a GPS clock, and all sensor nodes are connected to a data center by wire to form a sensor network; Collecting lightning signals through optical fiber sensors means collecting optical and electromagnetic signals of lightning through distributed optical fiber sensors, obtaining timestamps to generate lightning signals and transmitting them to the data center. A bandpass filter is used to filter the noise of the lightning signals, and the Kalman filter algorithm is used to dynamically correct the lightning signals after noise filtering, and the timestamp sequence of the lightning signals after dynamic correction of each sensor node is aligned. The method of establishing a set of equations based on the coordinates of the sensor nodes to solve the lightning position and lightning intensity refers to sorting the sensor nodes from early to late according to the timestamps of receiving the lightning signals, selecting nodes from front to back according to the sorting, and if the top three nodes cannot form a geometrically stable triangle, replacing the nodes from front to back until the nodes can form a triangle, taking the nodes as the vertices of the triangle, and extracting the node coordinates as the node coordinate vector ; Set the lightning position coordinate vector to ,initialization , respectively calculated The square of the distance is used to construct the CMD matrix: ； in is the CMD matrix, is the node coordinate vector and The square of the distance between them; According to the CMD matrix, we calculate Weight ; ； Calculate the node coordinate vector Direction vector to other nodes ; ； Using weights based on triangulation positioning optimization formula Solve for the new lightning position coordinate vector ; ； The new lightning position coordinate vector Update CMD matrix and weights , and iteratively calculate the new lightning position coordinate vector, calculate the change value of the lightning position coordinate vector after each iteration, stop the iteration when the change value is lower than the set threshold, output the final lightning position coordinate vector and extract the lightning position therein; Under the initial geometric framework provided by the CMD matrix, the triangulated positioning optimization formula is used to solve the new lightning position coordinate vector through the node coordinate direction vector and weight. The triangulated positioning optimization formula gradually approaches the real position of the lightning event through multiple iterations. After each iteration, the CMD matrix and each weight are updated with the new lightning position coordinates, and the optimization formula is applied again for the next iterative calculation until the change value of the lightning position coordinates is lower than the set accuracy threshold. The iteration is stopped and the final lightning position coordinate vector is output. The iterative process of this optimization formula helps to eliminate position deviation and system error and realize high-precision lightning event positioning. Calculate the distance between the lightning location and any sensor node , according to the electromagnetic signal strength received by the sensor node Calculating lightning intensity : ； in For additional noise, is the signal gain factor; The lightning intensity is calculated for all sensor nodes and the average lightning intensity of all sensor nodes is calculated as the final lightning intensity S.

2. The lightning observation method based on optical fiber sensor according to claim 1, characterized in that: The identifying of lightning types according to lightning signals and lightning intensity and generating lightning observation reports include constructing an SVM model, using a support vector machine classifier to train the model, and defining a loss function to iteratively optimize the SVM model; The lightning signal and lightning intensity are input into the trained SVM model, the lightning type is output, and a lightning observation report is generated based on the lightning information and lightning type.

3. The lightning observation method based on optical fiber sensor according to claim 2, characterized in that: The display of the lightning observation report refers to visually displaying the lightning observation report in combination with the lightning intensity, marking the lightning location on the map, and assisting staff to analyze the lightning disaster in combination with the lightning observation report.

4. The lightning observation method based on optical fiber sensor according to claim 3, characterized in that: The storage in the database refers to storing lightning information and lightning observation reports in the database and classifying them according to timestamps. The database regularly performs integrity checks on the stored data and synchronously uploads them to the cloud for storage data backup.

5. A lightning observation system based on optical fiber sensors, characterized in that: The system is used to perform the method according to any one of claims 1 to 4, comprising: Data collection module, used to deploy sensor nodes to form a sensor network and collect lightning signals for pre-processing; The lightning observation module is used to solve the lightning position according to the sensor node coordinates and calculate the lightning intensity based on the lightning position; An identification module is used to identify the lightning type according to the lightning information and lightning intensity and generate a lightning observation report; The display and storage module is used to display the lightning observation report and store it in the database.

6. A computer device comprising: Memory and processor; The memory stores a computer program, wherein the processor implements the steps of the lightning observation method based on optical fiber sensors according to any one of claims 1 to 4 when executing the computer program.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the lightning observation method based on optical fiber sensors according to any one of claims 1 to 4 are implemented.

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