Electromagnetic field near-field visualization method and device fusing machine vision and electromagnetic measurement

Abstract

The invention discloses an electromagnetic field near-field visualization method and device fusing machine vision and electromagnetic measurement, and relates to the technical field of electromagnetic compatibility test and electromagnetic environment perception. The method comprises the following steps: firstly, acquiring an image of the surface of a measured object, identifying and positioning a probe by using a model, and performing grid division on a view field area; meanwhile, electromagnetic field intensity data are obtained, and a representative typical field intensity value is extracted. When the probe is identified to be located in a certain grid unit, if the grid enters for the first time, creating the grid and writing the typical value of the current area; if yes, the typical value of the grid is updated on the existing basis, and two-dimensional and three-dimensional electromagnetic field patterns which are continuously distributed are formed. And carrying out fusion rendering on an electromagnetic field interpolation result and a video image to realize visual output of electromagnetic field distribution in a real scene. According to the method, the electromagnetic field distribution, the equipment three-dimensional structure and the actual environment can be visually presented while the measurement precision is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electromagnetic compatibility testing and electromagnetic environment perception, and particularly relates to an electromagnetic field near-field visualization method fusing machine vision and electromagnetic measurement. BACKGROUND

[0002] With the development of high-speed, miniaturization and high integration of electronic devices, the working frequency of circuits is continuously increasing, the device spacing is continuously reducing, and the electromagnetic coupling effect of the system is dramatically enhanced. Electromagnetic compatibility (EMC) problems show a multi-source and complex trend, which brings severe challenges to the stable operation and electromagnetic protection of electronic systems. Various high-frequency and high-speed circuits, wireless communication modules and power electronic devices will produce electromagnetic interference with different intensities and wide frequency bands during operation. If not effectively controlled, it may interfere with surrounding sensitive devices and systems, and even threaten the safe operation of the equipment and system.

[0003] In order to accurately evaluate the electromagnetic leakage intensity and potential interference source distribution of electronic devices, electromagnetic field near-field scanning technology has become an indispensable important means in electromagnetic compatibility testing and design optimization. By scanning the near-field region on the surface of the measured object, the amplitude, phase and direction distribution information of the electromagnetic field can be obtained, so as to realize the accurate positioning of the electromagnetic interference source and the coupling path, and provide quantitative basis for subsequent structure optimization and interference suppression design.

[0004] However, although the traditional near-field scanning technology can obtain local electromagnetic field data, the results are mostly presented in the form of amplitude-frequency characteristics or two-dimensional discrete graphs, and lack of association with the three-dimensional structure of the measured object and the actual spatial environment. The test data stays at the abstract numerical level, and the engineers need to repeatedly compare between the electromagnetic distribution graph and the actual device when analyzing, which is difficult to intuitively judge the spatial position and cause of electromagnetic anomalies. In addition, the existing visualization means are mostly completed in the post-processing stage, and cannot realize the synchronous fusion of measurement data and actual scene information, making it difficult to reveal the corresponding relationship between electromagnetic field characteristics and structure morphology, and limiting the application value of test results in design optimization and electromagnetic protection analysis. SUMMARY

[0005] The technical problem to be solved by the present application is to provide an electromagnetic field near-field visualization method fusing machine vision and electromagnetic measurement, which can intuitively present the electromagnetic field distribution, the three-dimensional structure of the device and the actual environment while ensuring measurement accuracy.

[0006] To solve the above technical problems, the technical solution adopted by the present application is: an electromagnetic field near-field visualization method fusing machine vision and electromagnetic measurement, comprising the following steps:

[0007] The image acquisition device continuously acquires the image of the surface of the measured object, the target detection model is used for identifying and positioning the probe, and the field of view area is divided into grid units;

[0008] The API of the electromagnetic field strength measurement device is called to acquire the electromagnetic field strength data of the measured object in real time, and the historical data is accumulated to extract representative field strength typical values;

[0009] When the probe is identified as being located in a certain grid unit, if the grid unit is entered for the first time, the grid is created and the field strength typical value of the current area is written; if it already exists, the field strength typical value of the grid is updated based on the existing grid;

[0010] After the field strength data of the grid unit is updated, the laser ranging module is called to acquire the distance information between the probe and the measured object, and the field strength is normalized according to the distance compensation model;

[0011] After the data collection of each grid unit is completed, the inverse distance weighted interpolation algorithm is used to combine the smoothing constraint model to reconstruct the spatial field strength, and the continuous distribution of two-dimensional and three-dimensional electromagnetic field maps is formed;

[0012] The electromagnetic field interpolation result is fused and rendered with the video image to realize the visualization output of the electromagnetic field distribution in the real scene.

[0013] The application further discloses an electromagnetic field near-field visualization device fusing machine vision and electromagnetic measurement, the device uses the electromagnetic field near-field visualization method, and the device comprises:

[0014] The probe identification unit is used for acquiring the surface image of the measured object through the camera and positioning the near-field probe by using the target detection algorithm;

[0015] The grid generation and data bearing unit is used for dynamically generating regularly arranged grid regions according to the probe identification result, each grid region is used as a data container for storing, managing and bearing the subsequent collected electromagnetic field test data, and the adaptive update of spatial division is realized;

[0016] The data acquisition and bearing unit is used for calling the Python interface to access the API of the electromagnetic field measuring instrument, continuously receiving the magnetic field amplitude data of the corresponding position, and storing the data in the data container of the grid unit in real time;

[0017] The statistical modeling and feature value establishing unit is used for performing distribution analysis on the discrete magnetic field data set recorded in each grid unit, and extracting a representative data center value based on a normal model;

[0018] The probe distance measuring unit is used for acquiring the instantaneous distance between the probe and the surface of the measured object in real time through the integrated laser range finder, and binding and storing the distance with the corresponding magnetic field amplitude data one by one.

[0019] The amplitude normalization compensation unit is used for performing amplitude normalization operation on each piece of collected data based on the frequency-dependent power law decay model obtained through calibration, so as to make it equivalent to be mapped to a unified reference height.

[0020] The spatial interpolation modeling unit is used for taking the dB amplitude in each grid region as input, and performing continuous field reconstruction according to the spatial topological relationship.

[0021] The visual fusion rendering unit is used for mapping the interpolated electromagnetic distribution matrix to the surface image obtained by the real camera, or superimposing it in the three-dimensional grid model of the measured object, to complete the near-field visualization of the electromagnetic field.

[0022] The beneficial effects produced by the above technical solutions are that the method described in the application can realize the near-field scanning visualization technology of the fusion of electromagnetic data and spatial visual information, can intuitively present the electromagnetic field distribution and the three-dimensional structure of the equipment and the actual environment while ensuring the measurement accuracy, and provides reliable and easy-to-understand technical support for electromagnetic compatibility testing, electromagnetic interference tracing and engineering design optimization. BRIEF DESCRIPTION OF DRAWINGS

[0023] The application will be described in further detail below with reference to the drawings and specific embodiments.

[0024] Figure 1 is a flowchart of the method described in the embodiments of the application;

[0025] Figure 2 is a principle diagram of laser ranging in the method described in the embodiments of the application;

[0026] Figure 3 is a scanning effect diagram in the method described in the embodiments of the application;

[0027] Figure 4 is a module composition structure diagram of the device described in the embodiments of the application. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the application.

[0029] In the following description, a lot of specific details are set forth in order to provide a thorough understanding of the present application, however, the present application can be practiced in other manners different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the present application, therefore, the present application is not limited to the specific embodiments disclosed below.

[0030] Embodiment one

[0031] As shown in the figure, the embodiment of the present application discloses an electromagnetic field near-field visualization method combining machine vision and electromagnetic measurement, including the following steps: Figure 1 The image acquisition device continuously acquires the image of the surface of the measured object, the target detection model is used to identify and locate the probe, and the field of view area is divided into grid units;

[0032] The API of the electromagnetic field intensity measuring device is called to acquire the electromagnetic field intensity data of the measured object in real time, and the historical data is accumulated to extract representative field intensity typical values;

[0033] When the probe is identified as being located in a certain grid unit, if the grid unit is entered for the first time, the grid is created and the field intensity typical value of the current area is written; if it already exists, the field intensity typical value of the grid is updated based on the existing grid;

[0034] After the field intensity data of the grid unit is updated, the laser ranging module is called to acquire the distance information between the probe and the measured object, and the field intensity is normalized according to the distance compensation model;

[0035] After the data collection of each grid unit is completed, the inverse distance weighted interpolation algorithm is used to combine the smooth constraint model to reconstruct the spatial field intensity, forming a continuous distribution of two-dimensional and three-dimensional electromagnetic field diagram;

[0036] The electromagnetic field interpolation result is fused and rendered with the video image to realize the visualization output of the electromagnetic field distribution in the real scene.

[0037] The above steps will be described in detail in combination with specific contents:

[0038] Near-field scanning surface gridding area division based on visual recognition:

[0039] The present application provides a near-field scanning surface gridding area division method based on visual recognition, which is used in an electromagnetic field near-field scanning device, realizes automatic grid division of the surface of the measured object, and provides a unified data container for subsequent test data, mainly including probe identification, grid generation and data bearing two parts.

[0040]

[0041] ​Probe recognition: The surface image of the measured object is collected by the camera, and the improved YOLOv8 target detection algorithm is used for near-field probe positioning. For small size targets and complex scenes, this algorithm introduces feature adaptive extraction and center region constraint strategy to improve the accuracy and robustness of the spatial position of the probe.

[0042] Specifically, for probe recognition, the application introduces a lightweight dynamic convolution module in the YOLOv8 backbone network to adaptively extract features of different scales, and embeds a channel and spatial joint attention mechanism in the feature fusion stage, thereby enhancing the recognition ability of the probe in complex backgrounds. At the same time, in the detection output stage, only the center region of the bounding box is taken as the spatial representation point to reduce the influence of edge noise and occlusion interference on probe positioning, so that the probe position remains high precision and stability in a dynamic scanning environment.

[0043] Grid generation and data carrying: According to the probe recognition results, regularly arranged grid regions are dynamically generated, and each grid region is used as a data container to store, manage and carry the subsequent collected electromagnetic field test data, realizing adaptive update of spatial division.

[0044] Specifically, for grid generation and data carrying, the application continuously collects probe center coordinates in video frames and updates grid regions in real time according to position changes, realizing dynamic spatial unit division of the entire scanning surface. Each grid unit corresponds to a unique probe sampling position, providing a stable foundation for subsequent electromagnetic field data filling, management and visualization. This mechanism not only automatically maps the spatial position of the probe to the scanning surface, but also suppresses errors caused by unstable probe movement or detection boundary drift, improving the continuity and reliability of grid division while maintaining system real-time performance to meet the requirements of rapid scanning.

[0045] Through the grid region division of the near-field scanning surface based on visual recognition of the application, the following can be achieved:

[0046] (1) Rapid and automatic positioning of near-field probes on the surface of the measured object without manual intervention;

[0047] (2) The regularly generated grid regions provide a unified data container for subsequent electromagnetic field test data, realizing systematic storage and management of data and providing a foundation for rapid scanning, analysis and visualization;

[0048] (3) Preliminary fusion of electromagnetic field distribution information and actual visual scene improves the intuitiveness of test results and engineering application value, significantly improving the efficiency and operational convenience of near-field scanning.

[0049] Electromagnetic test data statistics and typical value extraction:

[0050] After completing the gridding space division, the application proposes electromagnetic test data statistics and typical value extraction, which is used for inductive and statistical analysis of the electromagnetic amplitude data collected multiple times in the same grid area, so as to obtain the representative regional electromagnetic typical value with noise resistance. This step includes two parts of data collection bearing and statistical analysis and feature value extraction, the former is used for centralized storage and management of the data collected multiple times, and the latter extracts the typical value capable of representing the electromagnetic characteristics of the region by processing the data through a statistical method.

[0051] Data collection bearing: by calling the Python interface to access the Hidroquimica spectrum analyzer API, the magnetic field amplitude data corresponding to the position is continuously received during the probe resides in a certain one-ninth center frame area, and is stored in the data container of the grid unit in real time. Since the residence time of the probe in different areas is not fixed, the sampling data in the same area is distributed in quantity and amplitude. This unit adopts a full-cumulative recording strategy, does not directly judge or cut off the instantaneous data, and reserves the complete data basis for subsequent modeling.

[0052] Statistical modeling and feature value establishment: distribution analysis is performed on the discrete magnetic field data set recorded in each grid unit, and a representative data center value is extracted based on the normal model:

[0053] Suppose that m magnetic field amplitude data are collected in the nth spatial area, denoted as {x1,x2,…,x m}. First, the frequency statistics of the set is performed to construct the empirical distribution function f(x), and then the probability density function fitting is performed on the frequency distribution curve to obtain the approximate probability distribution function p(x), which can be expressed as:

[0054] (1)

[0055] Where p(x) is the probability density function of the field strength distribution; m is the total number of collected magnetic field amplitude data; x i represents the electromagnetic field strength observation value collected for the th time; represents the Dirac function.

[0056] By calculating the cumulative probability of the distribution, the cumulative distribution function can be obtained:

[0057] (2)

[0058] Where is the cumulative distribution function used to describe the probability that the magnetic field strength is less than or equal to a certain threshold ; is the probability density function of the field strength distribution.

[0059] The present application takes the quantile value x of the cumulative probability of 90% 0.9 As a typical representative value of the region, the data container for filling the current grid region. This selection method effectively avoids the influence of low amplitude noise, while suppressing the deviation caused by accidental peaks, so that each spatial region has a stable statistical significance field strength marker. That is:

[0060] (3)

[0061] Wherein is the typical field strength representative value of the region; is the 90% quantile function.

[0062] Through the electromagnetic test data statistical modeling and dynamic typical value extraction module of the present application, the following can be achieved:

[0063] (1) Standardized expression of electromagnetic amplitude in space, so that each grid element has clear and reproducible representative data, providing a consistent numerical benchmark for subsequent distance compensation, interpolation modeling and visualization rendering.

[0064] (2) Dynamic coverage update mechanism, when the probe passes through the same region again in the subsequent scanning process, the modeling process will be executed again with the newly collected data and the old value will be overwritten, avoiding the solidification of historical errors caused by early noise, device drift or environmental changes, so that the overall modeling system has long-term self-optimization capability.

[0065] Distance compensation and amplitude normalization calibration:

[0066] After completing the statistical establishment of electromagnetic typical values, the present application further eliminates the amplitude deviation caused by height fluctuation of the probe during scanning through distance compensation and amplitude normalization calibration, so that the measurement results at different times and different positions are established on a unified spatial scale. This step is completed through probe distance measurement and amplitude normalization compensation. The former is responsible for recording the instantaneous distance between the probe and the measured surface in real time, and the latter performs equivalent conversion and normalization processing on the original amplitude based on the distance information, thereby realizing consistent processing of data under height change conditions.

[0067] Probe distance measurement: as shown in Figure 2 , the instantaneous distance between the probe and the measured object surface is obtained in real time by integrating the laser range finder, and the distance and the corresponding magnetic field amplitude data are stored one by one. Figure 2 D1 and D2 in the formula represent the distance between the laser range finder at different positions and the measured object. Due to the high geometric sensitivity of near-field electromagnetic coupling, even millimeter-level displacement can significantly change the received amplitude, so this unit adopts a millisecond-level sampling strategy to ensure that the height recording and amplitude acquisition have strict time sequence consistency.

[0068] Amplitude normalization compensation: based on the frequency-dependent power-law decay model obtained by calibration, the amplitude normalization operation is performed on each piece of collected data, so that it is equivalent to mapping to a unified reference height. According to classical electromagnetic theory, in the quasi-static region dominated by magnetic dipoles, the magnetic field intensity and the detection distance approximately satisfy the power-law decay relationship , where ideally , that is, the field strength at 1 cm is about 8 times that at 2 cm. However, in actual engineering scenarios, the measured target is often not a standard point dipole, but a complex structure composed of PCB boards, cables, motors, shielding shells, etc., and its near-field electromagnetic distribution characteristics are affected by multiple factors such as frequency, coupling path, reflected echo, etc., so that the power index presents frequency dependence, and may be between 1 and 3. This step does not simply use a fixed for compensation, but introduces a frequency response height decay model based on calibration.

[0069] Specifically, the instantaneous distance between the probe and the target surface is obtained in real time by a laser range finder , and the actually measured amplitude is normalized to the reference height , and its compensation model is represented as:

[0070] (4)

[0071] where is the normalized field strength value; is the measured field strength value; h is the vertical height of the probe from the measured object surface; f is the frequency; is the ratio of the measured height to the normalized reference height; is the propagation attenuation index.

[0072] The present application finally uses dB as a unit to represent the field strength, and the compensation form corresponds to:

[0073] (5)

[0074] where is the normalized field strength value in dB; is the measured field strength value in dB; is the height compensation term; f is the frequency; is the ratio of the measured height to the normalized reference height.

[0075] The compensation operation is completed before data is written into the grid unit, ensuring that the subsequent statistical modeling and typical value extraction stage only processes data that has been normalized to a unified height reference. When the probe passes through the same area again, this module will re-execute the same normalization compensation process on the new data to achieve long-term self-correcting ability.

[0076] Through the distance compensation and amplitude normalization calibration module of the application, the following can be achieved:

[0077] (1) Systematic amplitude deviation introduced by height fluctuation during probe scanning is eliminated, making the measurement results at different positions and different times have a unified reference scale;

[0078] (2) Each piece of electromagnetic test data completes amplitude normalization processing before being written into the grid area, avoiding mixed dimension or amplitude drift problems in subsequent statistical modeling;

[0079] (3) The normalization compensation strategy has adaptive updating ability. When the probe repeatedly passes through the same area, the compensation parameters can be automatically adjusted based on new data, thereby maintaining long-term modeling consistency and reproducibility;

[0080] (4) Provides a stable physical layer foundation for subsequent interpolation modeling and visualization rendering, making the data in each area have good lateral comparison and longitudinal traceability.

[0081] Interpolation reconstruction and scene visualization:

[0082] After completing the grid typical value establishment and distance compensation, the application constructs a continuous spatial distribution model based on the normalized electromagnetic amplitude data through the interpolation reconstruction and scene visualization module, and performs fusion rendering with the real visual scene to realize the intuitive presentation of the electromagnetic near field. Including spatial interpolation modeling and visual fusion rendering, the former is responsible for constructing a continuous electromagnetic distribution field according to the representative values in the discrete grid, and the latter maps the interpolation results to the real camera screen or three-dimensional model surface to realize the synchronous display of electromagnetic intensity and physical structure.

[0083] Spatial interpolation modeling: Take the dB amplitude in each grid area as input, and reconstruct the continuous field according to the spatial topological relationship. Considering the smoothness and direction consistency of the near-field magnetic field in the local area, the application selects the inverse distance weighting (IDW) model based on weighted distance to interpolate the representative values in each area. Its basic form is:

[0084] (6)

[0085] Where f i is the typical amplitude of the i-th grid unit, d iwhere p is the attenuation exponent, and d is the distance between the predicted position and the center of the cell.

[0086] To further improve continuity, the application introduces a local fitting smoothing factor to constrain the second-order gradient of the interpolation result, so that the predicted surface remains derivable at the grid boundary, avoiding obvious faults or mutations.

[0087] Visual fusion rendering: map the interpolated electromagnetic distribution matrix to the surface image obtained by the real camera, or superimpose it on the three-dimensional grid model of the measured object. Specifically, the application encodes different amplitude intervals using color gradient, maps field strength regions below the set threshold to cold colors, and maps field strength regions above the set threshold to warm colors. Through the Alpha channel, semi-transparent superimposition is achieved, allowing users to observe the actual shape structure while perceiving electromagnetic intensity differences. In addition, the system supports multi-frame superimposed display at key frequency points, that is, after generating independent heat maps for different frequency bands, it plays them in time sequence to show the distribution rule of field strength with frequency change.

[0088] In the test verification stage, the application selects a single-frequency antenna composed of a loop coil as the measured object, and performs near-field scanning on it through signal excitation, obtaining the scanning effect as shown in Figure 3 .

[0089] Through the interpolation reconstruction and scene visualization module of the application, the following can be achieved:

[0090] (1) Discrete grid data is converted into a continuous near-field distribution model, making the electromagnetic field strength present a spatial gradient and diffusion pattern with physical meaning;

[0091] (2) The interpolation result is fused and displayed with the real visual scene, making the electromagnetic distribution no longer stay on the abstract numerical level, but have structure correspondence and spatial intuitiveness;

[0092] (3) Provide visual decision basis for subsequent optimization layout, shielding rectification or interference positioning, significantly improving engineering application efficiency and interactive experience.

[0093] Embodiment Two

[0094] As shown in Figure 4 , the application embodiment discloses an electromagnetic field near-field visualization device integrating machine vision and electromagnetic measurement, which comprises:

[0095] Probe recognition unit 101: used for acquiring the surface image of the measured object through the camera, and positioning the near-field probe using the improved YOLOv8 target detection algorithm;

[0096] Mesh generation and data carrying unit 102: It is used to dynamically generate a grid area arranged according to the probe identification results. Each grid area serves as a data container for storing, managing, and carrying the electromagnetic field test data acquired subsequently, thereby realizing the adaptive update of the spatial division.

[0097] Data acquisition unit 103: It is used to access the Hydrologic spectrum analyzer API by calling the Python interface, continuously receive the magnetic field amplitude data corresponding to a certain one-ninth of the center frame area, and store it in the data container of the grid unit in real time.

[0098] Statistical modeling and eigenvalue establishment unit 104: used to perform distribution analysis on the discrete magnetic field dataset recorded in each grid cell and extract representative data center values ​​based on a normal model.

[0099] Probe distance measurement unit 105: used to acquire the instantaneous distance between the probe and the surface of the object being measured in real time through an integrated laser rangefinder, and to bind and store the distance with the corresponding magnetic field amplitude data one by one.

[0100] Amplitude normalization compensation unit 106: used to calibrate the obtained frequency-related power-law attenuation model, and to perform amplitude normalization operation on each piece of acquired data to make it equivalently mapped to a unified reference height.

[0101] Spatial interpolation modeling unit 107: used to reconstruct the continuous field based on the spatial topological relationship, using the dB amplitude of each grid region as input.

[0102] Visual fusion rendering unit 108: used to map the interpolated electromagnetic distribution matrix to the surface image obtained by the real camera, or to superimpose it on the three-dimensional mesh model of the object being measured.

[0103] It should be noted that the specific implementation methods of each unit in the device can refer to the specific implementation steps of the method described in Embodiment 1, and will not be repeated here.

[0104] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0105] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0106] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units, etc., and are not limited to these.

[0107] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0108] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for near-field visualization of electromagnetic fields that integrates machine vision and electromagnetic measurement, characterized in that... Includes the following steps: The image acquisition device continuously acquires images of the surface of the object being tested, uses a target detection model to identify and locate the probe, and divides the field of view into grid cells. Call the API of the electromagnetic field strength measurement device to obtain the electromagnetic field strength data of the object under test in real time, and accumulate historical data to extract representative typical field strength values. When the probe is identified as being located within a certain grid cell, if the grid cell is being entered for the first time, the grid is created and the typical field strength value of the current region is written; if it already exists, the typical field strength value of the grid is updated based on the existing grid. After the field strength data of the grid cells is updated, the laser ranging module is called to obtain the distance information between the probe and the object being measured, and the field strength is normalized according to the distance compensation model. After the data of each grid cell is collected, the spatial field strength is reconstructed by using the inverse distance weighted interpolation algorithm combined with the smooth constraint model, forming a continuously distributed two-dimensional and three-dimensional electromagnetic field map. By fusing and rendering the electromagnetic field interpolation results with video images, a visual output of the electromagnetic field distribution in a real scene can be achieved.

2. The electromagnetic field near-field visualization method integrating machine vision and electromagnetic measurement as described in claim 1, characterized in that, The method for identifying and locating the probe includes the following steps: A lightweight dynamic convolution module is introduced into the YOLOv8 backbone network, and a channel and spatial joint attention mechanism is embedded in the feature fusion stage. At the same time, in the detection output stage, only the center region of the bounding box is taken as the spatial representation point.

3. The electromagnetic field near-field visualization method integrating machine vision and electromagnetic measurement as described in claim 1, characterized in that, The method for dividing the field of view into grid cells includes the following steps: Based on the probe's identification results, a grid area is dynamically generated according to a set pattern. Each grid area serves as a data container for storing, managing, and carrying subsequent electromagnetic field test data, thus enabling adaptive updates of the spatial division.

4. The electromagnetic field near-field visualization method integrating machine vision and electromagnetic measurement as described in claim 1, characterized in that, The method for acquiring electromagnetic field strength data of the object under test in real time includes the following steps: By calling the Python interface to access the API of the electromagnetic field strength measurement device, while the probe is stationed in a certain central frame area, the magnetic field amplitude data corresponding to that location is continuously received and stored in the data container of that grid cell in real time.

5. The electromagnetic field near-field visualization method integrating machine vision and electromagnetic measurement as described in claim 4, characterized in that, The electromagnetic field strength measurement equipment uses a spectrum analyzer.

6. The electromagnetic field near-field visualization method integrating machine vision and electromagnetic measurement as described in claim 1, characterized in that, Distribution analysis was performed on the discrete magnetic field dataset recorded within each grid cell, and representative data center values ​​were extracted based on a normal model: In the nth spatial region, a total of m magnetic field amplitude data points were collected, denoted as {x1, x2, ..., x...} m First, frequency statistics are performed on the set to construct an empirical distribution function f(x). Then, the frequency distribution curve is fitted with a probability density function to obtain an approximate probability distribution function p(x), which is expressed in the form: (1) Where p(x) is the probability density function of the field strength distribution; m is the total number of magnetic field amplitude data collected so far; x i Indicates the first The electromagnetic field strength observations collected this time; Represents the Dirac function; By calculating the cumulative probability of this distribution, the cumulative distribution function is obtained: (2) in It is used to describe magnetic field strength values ​​that are less than or equal to a certain threshold. The cumulative distribution function of the probability; Let be the probability density function of the electric field strength distribution; Using the 90% quantile value x 0.9 As a representative value of the typical field strength in this region, it is used to fill the data container of the current grid area, so that each spatial region has a stable statistically significant field strength label. : (3) in This represents a typical field strength value for this region; This is a function to take the 90th percentile value.

7. The electromagnetic field near-field visualization method integrating machine vision and electromagnetic measurement as described in claim 1, characterized in that, The method for normalizing the field strength based on the distance compensation model includes the following steps: The instantaneous distance between the probe and the target surface is obtained in real time using a laser rangefinder. and the actual measured amplitude Normalized to reference height Its compensation model is expressed as: (4) in This is the normalized field strength value; is the measured field strength value; h is the vertical height of the probe from the surface of the object being measured; f is the frequency; This is the ratio of the measured height to the normalized reference height. The propagation attenuation index; The electric field strength is expressed in dB, and the corresponding compensation method is as follows: (5) in This is the normalized field strength value in dB. The measured electric field strength value is expressed in dB. Here, f represents the height compensation term; f is the frequency. This is the ratio of the measured height to the normalized reference height. The compensation operation is completed before the data is written to the grid cell. When the probe passes through the same area again, the same normalization compensation process is repeated for the new data.

8. The electromagnetic field near-field visualization method integrating machine vision and electromagnetic measurement as described in claim 1, characterized in that, The inverse distance weighted interpolation algorithm includes the following steps: Using the dB amplitude within each grid region as input, a continuous field reconstruction is performed based on the spatial topological relationship. A weighted inverse distance interpolation model is then used to interpolate the representative values ​​for each region. Its basic form is as follows: (6) Among them, f i Let d be the typical field strength of the i-th grid cell. i The distance between the location to be predicted and the center of the cell is p, where p is the attenuation exponent. A local fitting smoothing factor is introduced to constrain the interpolation results with a second-order gradient, so that the predicted surface remains differentiable at the grid boundary.

9. The electromagnetic field near-field visualization method integrating machine vision and electromagnetic measurement as described in claim 1, characterized in that, The method for fusing and rendering electromagnetic field interpolation results with video images includes the following steps: The interpolated electromagnetic distribution matrix is ​​mapped onto the surface image acquired by a real camera, or superimposed on the 3D mesh model of the object under test. Different amplitude ranges are encoded using a color gradient method, mapping the field strength area below the set threshold to a cool color and the field strength area above the set threshold to a warm color. Semi-transparent superposition is achieved through the Alpha channel, allowing users to perceive the difference in electromagnetic intensity while observing the actual shape and structure. It also supports multi-frame superposition display at key frequency points. Independent heat maps are generated for different frequency bands and played in a time sequence to show the distribution law of field strength as frequency changes.

10. An electromagnetic field near-field visualization device integrating machine vision and electromagnetic measurement, wherein the device uses the electromagnetic field near-field visualization method as described in any one of claims 1-9, characterized in that... The device includes: Probe recognition unit: used to acquire images of the surface of the object being measured through a camera and to locate the near-field probe using a target detection algorithm; Mesh generation and data carrying unit: used to dynamically generate a grid area arranged according to the probe identification results. Each grid area serves as a data container to store, manage, and carry the electromagnetic field test data acquired subsequently, thereby achieving adaptive updates of the spatial division. Data acquisition unit: By calling the Python interface to access the API of the electromagnetic field measuring instrument, it continuously receives the magnetic field amplitude data at the corresponding location and stores it in the data container of the grid unit in real time; Statistical modeling and eigenvalue establishment unit: used to perform distribution analysis on the discrete magnetic field dataset recorded in each grid cell, and extract representative data center values ​​based on the normal model; Probe distance measurement unit: used to acquire the instantaneous distance between the probe and the surface of the object being measured in real time through an integrated laser rangefinder, and to bind and store the distance with the corresponding magnetic field amplitude data one by one; Amplitude normalization compensation unit: Based on the frequency-dependent power-law attenuation model obtained by calibration, it performs amplitude normalization operation on each piece of acquired data to make it equivalently mapped to a unified reference height; Spatial interpolation modeling unit: used to reconstruct the continuous field based on the spatial topological relationship, using the dB amplitude of each grid region as input; Visual fusion rendering unit: used to map the interpolated electromagnetic distribution matrix onto the surface image acquired by the real camera, or to overlay it onto the 3D mesh model of the object under test, to complete the near-field visualization of the electromagnetic field.

Images