Electricity approaching risk early warning method and system based on electric field and magnetic field distribution

Abstract

The invention provides a near-electricity risk early warning method and system based on electric field and magnetic field distribution. The method comprises the following steps: acquiring three-dimensional space electromagnetic field data in a target scene every first preset time; preprocessing the three-dimensional space electromagnetic field data, and constructing an electric field distribution model and a magnetic field distribution model according to the preprocessed electric field data and magnetic field data; respectively extracting a field intensity gradient amplitude and an electromagnetic field energy density from the electric field distribution model and the magnetic field distribution model, calculating a risk index of each space point according to the field intensity gradient amplitude and the electromagnetic field energy density, and dividing a risk area according to the risk indexes; and acquiring the current position of the operator, judging whether the current position is in the risk area, and if so, sending out early warning information. According to the method, the near-electricity risk can be comprehensively and accurately evaluated, the accuracy and effectiveness of early warning are improved, and the safety of operators is effectively guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of near-electrical operation warning technology, and in particular to a near-electrical risk warning method and system based on electric field and magnetic field distribution. Background Art

[0002] In many fields involving electromagnetic environment, such as power facility operation and maintenance, electrical engineering construction, etc., the prevention and control of near-electrical risks is extremely important.

[0003] Existing common methods for warning near-electrical hazards primarily rely on presetting a fixed safety distance threshold. When the distance between the operator and live equipment falls below this threshold, an alert is triggered. However, this approach has significant limitations. For one thing, electromagnetic fields are not uniformly distributed. Even at the same distance, the strength and characteristics of the electromagnetic field can vary significantly across different locations and environments. For example, in complex power line intersections or industrial environments exposed to electromagnetic interference, a fixed safety distance cannot accurately reflect the actual level of near-electrical hazard risk. Furthermore, it fails to account for temporal variations in the electromagnetic field, making it less adaptable to dynamically changing electromagnetic environments, potentially leading to untimely or false warnings. Summary of the Invention

[0004] The purpose of the present invention is to provide a near-electric risk warning method and system based on the distribution of electric and magnetic fields, aiming to solve the problem that traditional technologies have poor adaptability due to relying on fixed thresholds for warning, resulting in untimely or false warnings.

[0005] In a first aspect, the present invention provides a method for early warning of near-electrical risk based on electric field and magnetic field distribution, the method comprising:

[0006] Collecting three-dimensional electromagnetic field data of the target scene at first preset intervals, wherein the three-dimensional electromagnetic field data includes electric field data and magnetic field data of each spatial point at a corresponding moment;

[0007] Preprocessing the three-dimensional electromagnetic field data, and constructing an electric field distribution model and a magnetic field distribution model based on the preprocessed electric field data and magnetic field data;

[0008] extracting the field intensity gradient amplitude and the electromagnetic field energy density from the electric field distribution model and the magnetic field distribution model, respectively, calculating a risk index for each spatial point based on the field intensity gradient amplitude and the electromagnetic field energy density, and dividing the risk area based on the risk index;

[0009] The operator's current location is obtained, and it is determined whether the current location is within the risk area. If so, an early warning message is issued.

[0010] In summary, the aforementioned near-electric shock risk warning method based on electric and magnetic field distribution constructs accurate electric and magnetic field distribution models by acquiring real-time three-dimensional electromagnetic field data in the target scenario. During model construction, the data is corrected to eliminate errors and interference. Radial basis functions are used to flexibly fit the electromagnetic field distribution, and model accuracy is improved by rationally calculating weight coefficient vectors. Key features such as the field intensity gradient amplitude and electromagnetic field energy density are extracted from the model. By comprehensively considering the intensity variation and energy distribution of the electromagnetic field, risk indicators are calculated and risk areas are divided. Finally, the operator's current location is determined to determine whether they are within the risk area, and targeted warning information is issued in a timely manner. This method can comprehensively and accurately assess near-electric shock risks, improve the accuracy and effectiveness of warnings, and effectively ensure operator safety, offering significant advantages over traditional methods.

[0011] Furthermore, the step of collecting three-dimensional electromagnetic field data of the target scene at first preset time intervals, wherein the three-dimensional electromagnetic field data includes electric field data and magnetic field data of each spatial point at a corresponding moment, comprises:

[0012] Constructing a field strength sample set based on three-dimensional electric and magnetic field data in:

[0013] F(A i , t) = {E x (A i ,t),E y (A i ,t),E z (A i ,t),H x (A i ,t),H y (A i ,t),H z (A i , t)}

[0014] Among them, F(A i , t) represents the three-dimensional electromagnetic field data of the i-th spatial point, E x (A i ,t),E y (A i ,t),E z (A i , t) represent the electric field components of the ith spatial point in the x, y, and z directions, respectively, H x (A i ,t),H y (A i ,t),H z (A i, t) represent the magnetic field components of the i-th spatial point in the x-direction, y-direction, and z-direction, respectively, and K is the total number of spatial points in the target scene.

[0015] Furthermore, the step of preprocessing the three-dimensional electromagnetic field data includes:

[0016] Correcting the three-dimensional electromagnetic field data:

[0017] F(A i , t), = F(A i ,t)·D i -α

[0018] Among them, F(A i , t), represents the preprocessed three-dimensional electromagnetic field data, D i is the calibration coefficient matrix, and α is the environmental compensation term.

[0019] Furthermore, the step of constructing an electric field distribution model and a magnetic field distribution model based on the preprocessed electric field data and magnetic field data includes:

[0020] The electric field distribution model is constructed according to the following formula:

[0021]

[0022] The magnetic field distribution model is constructed according to the following formula:

[0023]

[0024] Where E(A, t) represents the continuous electric field distribution of the target space point at time t, ‖AA i ‖ represents the Euclidean distance between the target spatial point and the i-th spatial point, W e Represents the weight coefficient vector of the electric field, W h represents the weight coefficient vector of the magnetic field, σ represents the width parameter of the radial basis function, and φ represents the radial basis function.

[0025] Furthermore, the weight coefficient vector of the electric field or magnetic field is calculated according to the following formula:

[0026]

[0027] Among them, argmin represents the minimum function, E(A i , t), represents the pre-processed electric field data, H(A i , t), represents the preprocessed magnetic field data.

[0028] Furthermore, the step of extracting the field intensity gradient amplitude and the electromagnetic field energy density from the electric field distribution model and the magnetic field distribution model respectively includes:

[0029] The field intensity gradient amplitude includes the electric field gradient amplitude and the magnetic field gradient amplitude, and the field intensity gradient amplitude is extracted according to the following formula:

[0030]

[0031] The electromagnetic field energy density is extracted according to the following formula:

[0032]

[0033] Among them, w e 、w h are the electric field and magnetic field energy densities, β1 and β2 are the dielectric constant and magnetic permeability, respectively.

[0034] Furthermore, the step of calculating the risk index of each spatial point according to the field intensity gradient amplitude and the electromagnetic field energy density, and dividing the risk area according to the risk index includes:

[0035] The risk index is calculated according to the following formula:

[0036]

[0037] Among them, ∈ represents the risk indicator;

[0038] If the risk index of a spatial point is greater than or equal to a first preset threshold, the spatial point is classified as a first-level risk area;

[0039] If the risk index of a spatial point is greater than or equal to the second preset threshold and less than the first preset threshold, the spatial point is classified as a secondary risk area;

[0040] If the risk index of a spatial point is greater than or equal to the third preset threshold and less than the first preset threshold, the spatial point will be classified as a third-level risk area, and all other cases are safe areas.

[0041] Furthermore, the step of obtaining the current location of the operator and determining whether the current location is within the risk area, and if so, issuing an early warning message includes:

[0042] The name of the area where the operator is located is obtained according to the current position. If the area name is a risk area, corresponding warning information is issued according to the level of the risk area.

[0043] In a second aspect, the present invention provides a near-electric risk identification system based on electric field and magnetic field distribution, the system comprising:

[0044] A data acquisition module is used to collect three-dimensional electromagnetic field data in the target scene at a first preset time interval, wherein the three-dimensional electromagnetic field data includes electric field data and magnetic field data of each spatial point at a corresponding time;

[0045] A model building module, used to preprocess the three-dimensional electromagnetic field data and build an electric field distribution model and a magnetic field distribution model based on the preprocessed electric field data and magnetic field data;

[0046] a risk area division module, configured to extract the field intensity gradient amplitude and the electromagnetic field energy density from the electric field distribution model and the magnetic field distribution model, respectively, calculate the risk index of each spatial point based on the field intensity gradient amplitude and the electromagnetic field energy density, and divide the risk area according to the risk index;

[0047] The near-electricity risk warning module is used to obtain the current location of the operator and determine whether the current location is within the risk area. If so, a warning message is issued.

[0048] In a third aspect, the present invention provides a readable storage medium, which stores one or more programs, and when the program is executed by a processor, it implements the above-mentioned near-electric risk warning method based on electric field and magnetic field distribution.

[0049] In a fourth aspect, the present invention provides an electronic device, comprising a memory and a processor, wherein:

[0050] The memory is used to store computer programs;

[0051] When the processor is used to execute the computer program stored in the memory, the above-mentioned electric field and magnetic field distribution-based near-electric risk warning method is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a flow chart of a method for early warning of electric risk based on electric and magnetic field distributions proposed in one embodiment of the present invention;

[0053] Figure 2 This is a schematic structural diagram of a near-electric risk warning system based on electric and magnetic field distributions proposed in one embodiment of the present invention.

[0054] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. Unless otherwise defined, the technical terms or scientific terms used herein should be the common meanings understood by people with ordinary skills in the field to which the invention belongs. The words "including" and similar words used in this article mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0056] like Figure 1 As shown, an embodiment of the present invention provides a method for early warning of electric risk based on electric field and magnetic field distribution, the method comprising steps S101 to S104, wherein:

[0057] Step S101: collecting three-dimensional electromagnetic field data of a target scene at first preset time intervals, wherein the three-dimensional electromagnetic field data includes electric field data and magnetic field data of each spatial point at a corresponding time;

[0058] It should be noted that the first preset time is set in order to continuously collect data in real time. The target scene refers to a high-voltage operating environment, such as the working space around high-voltage equipment.

[0059] Specifically, in some embodiments, a field strength sample set is constructed based on three-dimensional electric and magnetic field data. in:

[0060] F(A i , t) = {E x (A i ,t),E y (A i ,t),E z (A i ,t),H x (A i ,t),H y (A i ,t),H z (A i , t)}

[0061] Among them, F(A i , t) represents the three-dimensional electromagnetic field data of the i-th spatial point, E x (A i ,t),E y (A i ,t),E z (Ai , t) represent the electric field components of the ith spatial point in the x, y, and z directions, respectively, H x (A i ,t),H y (A i ,t),H z (A i , t) represent the magnetic field components of the i-th spatial point in the x-direction, y-direction, and z-direction, respectively, and K is the total number of spatial points in the target scene.

[0062] By constructing a field intensity sample set, three-dimensional electromagnetic field data is organized by spatial points and directions, facilitating subsequent data processing and model building. This structured data representation helps improve the efficiency and accuracy of data processing and provides data support for building accurate electromagnetic field distribution models.

[0063] Step S102: preprocessing the three-dimensional electromagnetic field data, and constructing an electric field distribution model and a magnetic field distribution model based on the preprocessed electric field data and magnetic field data;

[0064] It should be pointed out that in order to overcome the errors caused by the sensor itself or environmental interference during the data acquisition process, the three-dimensional electromagnetic field data needs to be corrected. By using the calibration coefficient matrix and environmental compensation terms, the systematic errors and environmental interference in the measurement process can be eliminated, and the accuracy and reliability of the data can be improved, thereby ensuring the accuracy of the electromagnetic field distribution model constructed subsequently. Specifically, the three-dimensional electromagnetic field data is corrected according to the following formula:

[0065] F(A i , t), = F(A i ,t)·D i -α

[0066] Among them, F(A i , t), represents the preprocessed three-dimensional electromagnetic field data, D i is the calibration coefficient matrix, and α is the environmental compensation term.

[0067] Furthermore, in some embodiments, the electric field distribution model is constructed according to the following formula:

[0068]

[0069] The magnetic field distribution model is constructed according to the following formula:

[0070]

[0071] Where E(A, t) represents the continuous electric field distribution of the target space point at time t, ‖AA i‖ represents the Euclidean distance between the target spatial point and the i-th spatial point, W e Represents the weight coefficient vector of the electric field, W h represents the weight coefficient vector of the magnetic field, σ represents the width parameter of the radial basis function, and φ represents the radial basis function.

[0072] Using radial basis functions to construct electric and magnetic field distribution models can flexibly adapt to electromagnetic field distributions of varying shapes and complexities. By introducing weight coefficient vectors and width parameters, it can better fit actual electromagnetic field data, improving the model's fitting accuracy and generalization capabilities.

[0073] Step S103: extracting the field intensity gradient amplitude and the electromagnetic field energy density from the electric field distribution model and the magnetic field distribution model respectively, calculating the risk index of each spatial point according to the field intensity gradient amplitude and the electromagnetic field energy density, and dividing the risk area according to the risk index;

[0074] In this step, the weight coefficient vector of the electric field or magnetic field is calculated according to the following formula:

[0075]

[0076] Among them, argmin represents the minimum function, E(A i , t), represents the pre-processed electric field data, H(A i , t), represents the preprocessed magnetic field data. Calculating the weight coefficient vector of the electric or magnetic field using the minimum function comprehensively considers the preprocessed electric and magnetic field data, making the determination of the weight coefficient more reasonable. This helps improve the accuracy of the electromagnetic field distribution model and, in turn, enhances the reliability of near-field electrical risk assessment.

[0077] In addition, in some embodiments, extracting the electric field gradient amplitude and the magnetic field gradient amplitude can reflect the rate of change of the electromagnetic field intensity in space, which is of great significance for identifying areas where the electromagnetic field changes dramatically. Calculating the electromagnetic field energy density can quantify the energy distribution of the electromagnetic field. The extraction of these features provides a key basis for calculating risk indicators and helps to more accurately assess near-electrical risks. Specifically, the field strength gradient amplitude includes the electric field gradient amplitude and the magnetic field gradient amplitude, and the field strength gradient amplitude is extracted according to the following formula:

[0078]

[0079] The electromagnetic field energy density is extracted according to the following formula:

[0080]

[0081] Among them, w e 、w hare the electric field and magnetic field energy densities, β1 and β2 are the dielectric constant and magnetic permeability, respectively.

[0082] The risk index is calculated according to the following formula:

[0083]

[0084] Among them, ∈ represents the risk indicator;

[0085] If the risk index of a spatial point is greater than or equal to a first preset threshold, the spatial point is classified as a first-level risk area;

[0086] If the risk index of a spatial point is greater than or equal to the second preset threshold and less than the first preset threshold, the spatial point is classified as a secondary risk area;

[0087] If the risk index of a spatial point is greater than or equal to the third preset threshold and less than the first preset threshold, the spatial point will be classified as a third-level risk area, and all other cases are safe areas.

[0088] In summary, using field intensity gradient amplitude and electromagnetic field energy density to calculate risk indicators and classify risk zones into different levels enables quantitative assessment and tiered management of near-electrical hazards. Differentiating risk zones into different early warning strategies helps improve the relevance and effectiveness of early warnings and ensure operator safety.

[0089] In this embodiment, the first preset time, the first preset threshold, the second preset threshold, and the third preset threshold are all determined by actual scenarios and their security requirements, and are not limited in detail in this embodiment.

[0090] Step S104: obtaining the current location of the operator and determining whether the current location is within the risk area; if so, issuing a warning message.

[0091] Specifically, the name of the area where the operator is located is obtained based on the current location. If the area name is a risk area, corresponding warning information is issued according to the level of the risk area. The warning information can be sent directly to the operator or forwarded to the operator by the monitoring platform, so that the operator can take corresponding safety measures in time according to the warning information to reduce the risk of proximity to electric shock.

[0092] In summary, the aforementioned near-electric shock risk warning method based on electric and magnetic field distribution constructs accurate electric and magnetic field distribution models by acquiring real-time three-dimensional electromagnetic field data in the target scenario. During model construction, the data is corrected to eliminate errors and interference. Radial basis functions are used to flexibly fit the electromagnetic field distribution, and model accuracy is improved by rationally calculating weight coefficient vectors. Key features such as the field intensity gradient amplitude and electromagnetic field energy density are extracted from the model. By comprehensively considering the intensity variation and energy distribution of the electromagnetic field, risk indicators are calculated and risk areas are divided. Finally, the operator's current location is determined to determine whether they are within the risk area, and targeted warning information is issued in a timely manner. This method can comprehensively and accurately assess near-electric shock risks, improve the accuracy and effectiveness of warnings, and effectively ensure operator safety, offering significant advantages over traditional methods.

[0093] like Figure 2 As shown, an embodiment of the present invention further proposes a near-electric risk identification system based on electric field and magnetic field distribution, the system comprising:

[0094] A data acquisition module 10 is configured to acquire three-dimensional electromagnetic field data of a target scene at first preset intervals, wherein the three-dimensional electromagnetic field data includes electric field data and magnetic field data of each spatial point at a corresponding moment;

[0095] A model building module 20 is used to preprocess the three-dimensional electromagnetic field data and build an electric field distribution model and a magnetic field distribution model based on the preprocessed electric field data and magnetic field data;

[0096] a risk region division module 30 for extracting the field intensity gradient amplitude and the electromagnetic field energy density from the electric field distribution model and the magnetic field distribution model, respectively, calculating a risk index for each spatial point based on the field intensity gradient amplitude and the electromagnetic field energy density, and dividing the risk region based on the risk index;

[0097] The near-electricity risk warning module 40 is used to obtain the current location of the operator and determine whether the current location is within the risk area. If so, a warning message is issued.

[0098] On the other hand, the present invention further proposes a readable storage medium having one or more programs stored thereon, which, when executed by a processor, implements the above-mentioned near-electric risk warning method based on electric field and magnetic field distribution.

[0099] On the other hand, the present invention also proposes an electronic device, including a memory and a processor, wherein the memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory to implement the above-mentioned near-electric risk warning method based on electric field and magnetic field distribution.

[0100] Those skilled in the art will appreciate that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device), or in conjunction with such instruction execution system, apparatus, or device. For 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, apparatus, or device, or in conjunction with such instruction execution system, apparatus, or device.

[0101] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be 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 processing in another suitable manner as necessary, and then stored in a computer memory.

[0102] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement the hardware: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0103] While the embodiments of the present invention have been described in detail above, it will be apparent to those skilled in the art that various modifications and variations of these embodiments are possible. However, it should be understood that such modifications and variations are within the scope and spirit of the present invention as set forth in the claims. Furthermore, the invention described herein is susceptible to other embodiments and may be practiced or implemented in a variety of ways.

Claims

1. A method for early warning of near-electricity risk based on electric field and magnetic field distribution, characterized in that: The method comprises: Collecting three-dimensional electromagnetic field data of the target scene at first preset intervals, wherein the three-dimensional electromagnetic field data includes electric field data and magnetic field data of each spatial point at a corresponding moment; Preprocessing the three-dimensional electromagnetic field data, and constructing an electric field distribution model and a magnetic field distribution model based on the preprocessed electric field data and magnetic field data; extracting the field intensity gradient amplitude and the electromagnetic field energy density from the electric field distribution model and the magnetic field distribution model, respectively, calculating a risk index for each spatial point based on the field intensity gradient amplitude and the electromagnetic field energy density, and dividing the risk area based on the risk index; The operator's current location is obtained, and it is determined whether the current location is within the risk area. If so, an early warning message is issued.

2. The method for early warning of near-electricity risk based on electric field and magnetic field distribution according to claim 1, characterized in that: The step of collecting three-dimensional electromagnetic field data of the target scene at first preset time intervals, wherein the three-dimensional electromagnetic field data includes electric field data and magnetic field data of each spatial point at a corresponding moment, comprises: Constructing a field strength sample set based on three-dimensional electric and magnetic field data in: F(A i ,t)={E x (A i ,t),E y (A i ,t),E z (A i ,t),H x (A i ,t),H y (A i ,t),H z (A i ,t)} Among them, F(A i , t) represents the three-dimensional electromagnetic field data of the i-th spatial point, E x (A i ,t),E y (A i ,t),E z (A i , t) represent the electric field components of the ith spatial point in the x, y, and z directions, respectively, H x (A i ,t),H y (A i ,t),H z (A i , t) represent the magnetic field components of the i-th spatial point in the x-direction, y-direction, and z-direction, respectively, and K is the total number of spatial points in the target scene.

3. The method for early warning of near-electricity risk based on electric field and magnetic field distribution according to claim 2, characterized in that: The step of preprocessing the three-dimensional electromagnetic field data includes: Correcting the three-dimensional electromagnetic field data: F(A i ,t),=F(A i ,t)·D i -α Among them, F(A i , t), represents the preprocessed three-dimensional electromagnetic field data, D i is the calibration coefficient matrix, and α is the environmental compensation term.

4. The method for early warning of near-electricity risk based on electric field and magnetic field distribution according to claim 3 is characterized in that: The step of constructing an electric field distribution model and a magnetic field distribution model based on the preprocessed electric field data and magnetic field data comprises: The electric field distribution model is constructed according to the following formula: The magnetic field distribution model is constructed according to the following formula: Where E(A, t) represents the continuous electric field distribution of the target space point at time t, ‖AA i ‖ represents the Euclidean distance between the target spatial point and the i-th spatial point, W e Represents the weight coefficient vector of the electric field, W h represents the weight coefficient vector of the magnetic field, σ represents the width parameter of the radial basis function, and φ represents the radial basis function.

5. The method for early warning of near-electricity risk based on electric field and magnetic field distribution according to claim 4 is characterized in that: The weight coefficient vector of the electric field or magnetic field is calculated according to the following formula: Among them, argmin represents the minimum function, E(A i , t), represents the pre-processed electric field data, H(A i , t), represents the preprocessed magnetic field data.

6. The method for early warning of electric risk based on electric field and magnetic field distribution according to claim 5, characterized in that: The step of extracting the field intensity gradient amplitude and the electromagnetic field energy density from the electric field distribution model and the magnetic field distribution model respectively comprises: The field intensity gradient amplitude includes the electric field gradient amplitude and the magnetic field gradient amplitude, and the field intensity gradient amplitude is extracted according to the following formula: The electromagnetic field energy density is extracted according to the following formula: Among them, w e 、w h are the electric field and magnetic field energy densities, β1 and β2 are the dielectric constant and magnetic permeability, respectively.

7. The method for early warning of near-electricity risk based on electric and magnetic field distribution according to claim 1, characterized in that: The step of calculating the risk index of each spatial point according to the field intensity gradient amplitude and the electromagnetic field energy density, and dividing the risk area according to the risk index comprises: The risk index is calculated according to the following formula: Among them, ∈ represents the risk indicator; If the risk index of a spatial point is greater than or equal to a first preset threshold, the spatial point is classified as a first-level risk area; If the risk index of a spatial point is greater than or equal to the second preset threshold and less than the first preset threshold, the spatial point is classified as a secondary risk area; If the risk index of a spatial point is greater than or equal to the third preset threshold and less than the first preset threshold, the spatial point will be classified as a third-level risk area, and all other cases are safe areas.

8. The method for early warning of electric risk based on electric field and magnetic field distribution according to claim 1, characterized in that: The step of obtaining the current location of the operator and determining whether the current location is within the risk area, and if so, issuing an early warning message includes: The name of the area where the operator is located is obtained according to the current position. If the area name is a risk area, corresponding warning information is issued according to the level of the risk area.

9. A near-electric risk identification system based on electric and magnetic field distribution, characterized in that: The system comprises: A data acquisition module is used to collect three-dimensional electromagnetic field data in the target scene at a first preset time interval, wherein the three-dimensional electromagnetic field data includes electric field data and magnetic field data of each spatial point at a corresponding time; A model building module, used to preprocess the three-dimensional electromagnetic field data and build an electric field distribution model and a magnetic field distribution model based on the preprocessed electric field data and magnetic field data; a risk area division module, configured to extract the field intensity gradient amplitude and the electromagnetic field energy density from the electric field distribution model and the magnetic field distribution model, respectively, calculate the risk index of each spatial point based on the field intensity gradient amplitude and the electromagnetic field energy density, and divide the risk area according to the risk index; The near-electricity risk warning module is used to obtain the current location of the operator and determine whether the current location is within the risk area. If so, a warning message is issued.

10. A readable storage medium, characterized in that: The readable storage medium stores one or more programs, which, when executed by a processor, implement the near-electric risk warning method based on electric field and magnetic field distribution according to any one of claims 1 to 8.

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