Environmental magnetic field perception evaluation method and system based on space perception and MR technology

Abstract

The invention discloses an environmental magnetic field sensing evaluation method and system based on space sensing and MR technologies, and relates to the technical field of environmental magnetic field sensing. The method comprises the following steps: acquiring current magnetic field data and historical magnetic field data in a preset area; preprocessing the current magnetic field data to obtain first magnetic field data, and preprocessing the historical magnetic field data to obtain second magnetic field data; obtaining abnormal magnetic field data based on the first magnetic field data and the second magnetic field data; performing data fusion on the first magnetic field data and the three-dimensional model to generate initial three-dimensional magnetic field data; fusing the abnormal magnetic field data with the initial three-dimensional magnetic field data to obtain three-dimensional magnetic field distribution data; and transmitting the three-dimensional magnetic field distribution data to the MR equipment, so that the MR equipment visually displays the magnetic field distribution in the preset area. By implementing the technical scheme of the application, the magnetic field distribution data can be accurately captured and visualized, so that a user can intuitively understand and analyze the abnormal change of the magnetic field.

Description

Technical Field

[0001] The present application relates to the field of environmental magnetic field perception technology, and specifically to an environmental magnetic field perception evaluation method and system based on spatial perception and MR technology. Background Art

[0002] With the continuous advancement of smart devices and sensor technology, applications based on magnetic field distribution data have become increasingly important in areas such as industrial equipment monitoring, underground facility detection, and smart home positioning. In these applications, accurate capture and visualization of magnetic field distribution data within a preset area have become key requirements for technicians and users. In current technical scenarios, fixed or mobile magnetic field sensor networks are usually used to capture magnetic field data. These sensors are installed at various locations within a preset area, responsible for real-time monitoring of the strength and direction of the magnetic field, and transmitting the data to a data processing center via a wired or wireless network. The data processing center then analyzes the data to identify changes in the magnetic field and possible anomalies.

[0003] However, existing technologies have obvious limitations in capturing and processing magnetic field data. First, due to the limited density and position of the sensor layout, the collected magnetic field data has blind spots and errors. This layout cannot fully cover the preset area, affecting the overall accuracy of the data. Secondly, existing technologies usually only analyze and display magnetic field data in a two-dimensional plane, making it difficult to intuitively present the distribution of the magnetic field in three-dimensional space, limiting the user's comprehensive understanding of the magnetic field distribution. This two-dimensional display method cannot provide spatially perceived information display, making it difficult for users to fully and intuitively understand the true situation of the magnetic field.

[0004] Therefore, how to accurately capture and visualize magnetic field distribution data so that users can intuitively understand and analyze abnormal changes in the magnetic field has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] The present application provides an environmental magnetic field perception and assessment method, system, electronic device and storage medium based on spatial perception and MR technology, which can accurately capture and visualize magnetic field distribution data so that users can intuitively understand and analyze abnormal changes in the magnetic field.

[0006] In a first aspect, the present application provides an environmental magnetic field perception and assessment method based on spatial perception and MR technology, the method comprising: capturing geometric structure data within a preset area based on spatial perception technology, and generating a three-dimensional model corresponding to the preset area based on the geometric structure data; Acquiring current magnetic field data and historical magnetic field data within the preset area; Preprocessing the current magnetic field data to obtain first magnetic field data, and preprocessing the historical magnetic field data to obtain second magnetic field data; Obtaining abnormal magnetic field data based on the first magnetic field data and the second magnetic field data; Fusing the first magnetic field data with the three-dimensional model to generate preliminary three-dimensional magnetic field data; fusing the abnormal magnetic field data with the preliminary three-dimensional magnetic field data to obtain three-dimensional magnetic field distribution data; The three-dimensional magnetic field distribution data is transmitted to the MR device, so that the MR device can visualize the magnetic field distribution in the preset area.

[0007] By adopting the above technical solution, the geometric structure data in the preset area is captured based on the spatial perception technology, and the three-dimensional model corresponding to the preset area is generated based on the geometric structure data, so that the three-dimensional model of the preset area can be accurately constructed to provide detailed geometric information. By obtaining the current magnetic field data and the historical magnetic field data in the preset area, a complete data basis can be provided for the analysis of magnetic field changes. By preprocessing the current magnetic field data to obtain the first magnetic field data, and preprocessing the historical magnetic field data to obtain the second magnetic field data, the noise and error in the data can be eliminated, and the accuracy and reliability of the data can be improved. By comparing and analyzing the first magnetic field data and the second magnetic field data point by point, the abnormal magnetic field data is obtained, so that the abnormal changes in the magnetic field can be effectively identified. By fusing the first magnetic field data with the three-dimensional model, preliminary three-dimensional magnetic field data is generated, and the abnormal magnetic field data is fused with the preliminary three-dimensional magnetic field data to obtain three-dimensional magnetic field distribution data, so that the normal and abnormal magnetic field distribution conditions can be simultaneously displayed in the three-dimensional model. By transmitting the three-dimensional magnetic field distribution data to the MR device, the MR device visualizes the magnetic field distribution in the preset area, so that the magnetic field distribution data can be accurately captured and visualized, so that the user can intuitively understand and analyze the abnormal changes in the magnetic field.

[0008] Optionally, preprocessing the current magnetic field data to obtain first magnetic field data specifically includes: Performing filtering on the current magnetic field data to obtain filtered current magnetic field data; Performing denoising processing on the filtered current magnetic field data to obtain denoised current magnetic field data; Performing calibration processing on the denoised current magnetic field data to obtain calibrated current magnetic field data; The calibrated current magnetic field data is standardized to obtain the first magnetic field data.

[0009] By adopting the above technical scheme, the current magnetic field data is filtered to obtain the filtered current magnetic field data, thereby removing high-frequency noise and environmental interference, and improving the smoothness and stability of the magnetic field data. Next, the filtered current magnetic field data is denoised to obtain the denoised current magnetic field data, thereby further eliminating random noise and abnormal fluctuations, and improving the clarity and accuracy of the data. Then, the denoised current magnetic field data is calibrated to obtain the calibrated current magnetic field data, thereby eliminating the influence of sensor errors and environmental factors, and ensuring the accuracy and consistency of the magnetic field data. Finally, the calibrated current magnetic field data is standardized to obtain the first magnetic field data, thereby unifying the data to the same scale and range, which is convenient for subsequent data fusion and comparative analysis.

[0010] Optionally, the calibrating the denoised current magnetic field data to obtain calibrated current magnetic field data specifically includes: Acquire environmental data corresponding to the preset area, and acquire a preset sensor calibration curve; Based on the environmental data and the sensor calibration curve, obtaining a calibration factor corresponding to the sensor; The denoised current magnetic field data is calibrated based on the correction factor to obtain calibrated current magnetic field data.

[0011] By adopting the above technical solution, by obtaining the environmental data corresponding to the preset area and obtaining the preset sensor calibration curve, it is possible to fully understand the specific conditions of the environment where the sensor is located and the response characteristics of the sensor in different environments. Based on the environmental data and the sensor calibration curve, the correction factor corresponding to the sensor is obtained, thereby realizing the quantification and correction of the sensor error. Based on the correction factor, the current magnetic field data after denoising is calibrated to obtain the calibrated current magnetic field data, so that the magnetic field data is more accurate and consistent.

[0012] Optionally, obtaining abnormal magnetic field data based on the first magnetic field data and the second magnetic field data specifically includes: Comparing the first magnetic field data and the second magnetic field data point by point to obtain a plurality of difference data, wherein the difference data is a difference between a single data point in the first magnetic field data and the second magnetic field data; Determine the threshold range of magnetic field anomaly; Compare each of the difference data with the magnetic field anomaly threshold range, and mark abnormal data points that exceed the magnetic field anomaly threshold range; The abnormal data points are marked and clustered to obtain the abnormal magnetic field data.

[0013] By adopting the above technical solution, by comparing the first magnetic field data and the second magnetic field data point by point, a plurality of difference data are obtained, so that the change between the current magnetic field data and the historical magnetic field data can be accurately identified. Next, by determining the magnetic field anomaly threshold range, it is possible to distinguish between normal magnetic field changes and abnormal magnetic field changes. Then, by comparing each difference data with the magnetic field anomaly threshold range, marking the abnormal data points that exceed the magnetic field anomaly threshold range, it is possible to quickly and accurately locate the abnormal magnetic field change points. Finally, by marking and clustering the abnormal data points, the abnormal magnetic field data is obtained, so that the specific pattern and distribution of the abnormal magnetic field can be identified and presented.

[0014] Optionally, fusing the first magnetic field data with the three-dimensional model to generate preliminary three-dimensional magnetic field data specifically includes: Determine a first spatial coordinate system and a first coordinate corresponding to the first magnetic field data, and determine a second spatial coordinate system corresponding to the three-dimensional model; Based on the first spatial coordinate system and the second spatial coordinate system, transform the first coordinate to obtain a transformed first coordinate; Determining a first grid point corresponding to the converted first coordinate on the three-dimensional model; performing interpolation processing on the first magnetic field data at the first grid points to obtain interpolated first magnetic field data; The interpolated first magnetic field data is integrated with the three-dimensional model to generate the preliminary three-dimensional magnetic field data.

[0015] By adopting the above technical solution, by determining the first spatial coordinate system and the first coordinate corresponding to the first magnetic field data, and determining the second spatial coordinate system corresponding to the three-dimensional model, the foundation for data fusion is laid. Next, by converting the first coordinate based on the first spatial coordinate system and the second spatial coordinate system, the converted first coordinate is obtained, thereby ensuring that the magnetic field data is consistent with the coordinate system of the three-dimensional model. Then, by determining the first grid point corresponding to the converted first coordinate on the three-dimensional model, the specific position mapping of the magnetic field data in the three-dimensional model is realized. Next, by interpolating the first magnetic field data at the first grid point, the interpolated first magnetic field data is obtained, thereby smoothing the discrete magnetic field data into a continuous magnetic field distribution. Finally, by integrating the interpolated first magnetic field data with the three-dimensional model, preliminary three-dimensional magnetic field data is generated, thereby realizing the fusion of magnetic field data and the three-dimensional model.

[0016] Optionally, fusing the abnormal magnetic field data with the preliminary three-dimensional magnetic field data to obtain three-dimensional magnetic field distribution data specifically includes: Determine a third space coordinate system and a second coordinate corresponding to the abnormal magnetic field data; Based on the third space coordinate system and the second space coordinate system, transform the second coordinate to obtain transformed second coordinate; determining, in the preliminary three-dimensional magnetic field data, a second grid point corresponding to the converted second coordinate; performing interpolation processing on the abnormal magnetic field data at the second grid points to obtain interpolated abnormal magnetic field data; The interpolated abnormal magnetic field data is integrated with the preliminary three-dimensional magnetic field data to generate the three-dimensional magnetic field distribution data.

[0017] By adopting the above technical solution, by determining the third spatial coordinate system and the second coordinate corresponding to the abnormal magnetic field data, the foundation for the spatial positioning of the abnormal magnetic field data is laid. Next, by converting the second coordinate based on the third spatial coordinate system and the second spatial coordinate system, the converted second coordinate is obtained, thereby ensuring that the coordinate system of the abnormal magnetic field data is consistent with that of the preliminary three-dimensional magnetic field data. Then, by determining the second grid point corresponding to the converted second coordinate in the preliminary three-dimensional magnetic field data, the specific position mapping of the abnormal magnetic field data in the preliminary three-dimensional magnetic field data is realized. Next, by interpolating the abnormal magnetic field data at the second grid point, the interpolated abnormal magnetic field data is obtained, thereby smoothing the discrete abnormal data points into a continuous abnormal magnetic field distribution. Finally, by integrating the interpolated abnormal magnetic field data with the preliminary three-dimensional magnetic field data, the three-dimensional magnetic field distribution data is generated, thereby realizing the fusion of the abnormal magnetic field data with the overall magnetic field distribution.

[0018] Optionally, transmitting the three-dimensional magnetic field distribution data to an MR device specifically includes: Determining the format and structure corresponding to the three-dimensional magnetic field distribution data; Based on the format and the structure, determining a rendering engine corresponding to the MR device; Determine connection parameters corresponding to the MR device, wherein the connection parameters include an IP address, a port number, a communication protocol, and authentication information; Initializing the MR device based on the rendering engine, and establishing a data transmission channel based on the connection parameters; Based on the data transmission channel, the three-dimensional magnetic field distribution data is transmitted to the initialized MR device.

[0019] In a second aspect of the present application, a system for environmental magnetic field perception and assessment based on spatial perception and MR technology is provided, the system comprising: a processing module, an acquisition module and a transmission module; The processing module is used to capture geometric structure data within a preset area based on a spatial perception technology, and generate a three-dimensional model corresponding to the preset area based on the geometric structure data; The acquisition module is used to acquire current magnetic field data and historical magnetic field data in the preset area; The processing module is further used to pre-process the current magnetic field data to obtain first magnetic field data, and to pre-process the historical magnetic field data to obtain second magnetic field data; The processing module is further used to obtain abnormal magnetic field data based on the first magnetic field data and the second magnetic field data; The processing module is further used to fuse the first magnetic field data with the three-dimensional model to generate preliminary three-dimensional magnetic field data; The processing module is further used to fuse the abnormal magnetic field data with the preliminary three-dimensional magnetic field data to obtain three-dimensional magnetic field distribution data; The transmission module is used to transmit the three-dimensional magnetic field distribution data to the MR device, so that the MR device can visualize the magnetic field distribution in the preset area.

[0020] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes any method as described in the first aspect of the present application.

[0021] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program that can be loaded by a processor and execute the method as described in any one of the first aspects of the present application.

[0022] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: By capturing the geometric structure data in the preset area based on the spatial perception technology, and generating a three-dimensional model corresponding to the preset area based on the geometric structure data, the three-dimensional model of the preset area can be accurately constructed to provide detailed geometric information. By obtaining the current magnetic field data and the historical magnetic field data in the preset area, a complete data basis can be provided for the analysis of magnetic field changes. By preprocessing the current magnetic field data to obtain the first magnetic field data, and preprocessing the historical magnetic field data to obtain the second magnetic field data, the noise and error in the data can be eliminated, and the accuracy and reliability of the data can be improved. By comparing and analyzing the first magnetic field data and the second magnetic field data point by point, the abnormal magnetic field data can be obtained, so that the abnormal changes in the magnetic field can be effectively identified. By fusing the first magnetic field data with the three-dimensional model, preliminary three-dimensional magnetic field data is generated, and the abnormal magnetic field data is fused with the preliminary three-dimensional magnetic field data to obtain three-dimensional magnetic field distribution data, so that the normal and abnormal magnetic field distribution conditions can be simultaneously displayed in the three-dimensional model. By transmitting the three-dimensional magnetic field distribution data to the MR device, the MR device visualizes the magnetic field distribution in the preset area, so that the magnetic field distribution data can be accurately captured and visualized, so that the user can intuitively understand and analyze the abnormal changes in the magnetic field. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is one of the flow charts of an environmental magnetic field perception and evaluation method based on spatial perception and MR technology provided in an embodiment of the present application; Figure 2 This is the second flow chart of an environmental magnetic field perception and evaluation method based on spatial perception and MR technology provided in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an environmental magnetic field perception and evaluation system based on space perception and MR technology provided in an embodiment of the present application; Figure 4 It is a structural schematic diagram of an electronic device disclosed in an embodiment of the present application.

[0024] Explanation of the reference numerals: 1. Processing module; 2. Acquisition module; 3. Transmission module; 400. Electronic device; 401. Processor; 402. Communication bus; 403. User interface; 404. Network interface; 405. Memory. DETAILED DESCRIPTION

[0025] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0026] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0027] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" can explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0028] This application provides an environmental magnetic field perception and evaluation method based on spatial perception and MR technology. Figure 1 , which shows one of the flow diagrams of an environmental magnetic field perception and assessment method based on spatial perception and MR technology provided in an embodiment of the present application. The method includes steps S1-S7, which are as follows: Step S1: Capture geometric structure data within a preset area based on spatial perception technology, and generate a three-dimensional model corresponding to the preset area based on the geometric structure data.

[0029] In the environmental magnetic field perception assessment method, it is first necessary to capture the geometric structure data in the preset area based on spatial perception technology, and generate a three-dimensional model corresponding to the preset area based on these geometric structure data. The main purpose of this step is to provide an accurate spatial basis for the subsequent fusion and visualization of magnetic field data. Only after the geometric structure of the preset area is clear can the accurate mapping of magnetic field data in three-dimensional space be ensured.

[0030] Specifically, the preset area is fully scanned through spatial perception technology (such as LiDAR, structured light scanning or photogrammetry) to capture its internal and external geometric structure data. These technologies can be used to obtain high-precision 3D point cloud data, which records the spatial coordinates and structural features of each position in the preset area in detail. After obtaining the complete point cloud data, these data are processed and reconstructed through professional 3D modeling software (such as Autodesk ReCap, BentleyContextCapture, etc.) to generate a 3D model of the preset area.

[0031] This 3D model can not only accurately reflect the spatial structure of the preset area, but also provide an accurate reference coordinate system for subsequent magnetic field data fusion. In this way, it can ensure that the magnetic field data captured later can be accurately aligned with the spatial structure of the preset area, avoiding data errors caused by inconsistent coordinate systems.

[0032] In addition, this spatial foundation based on the 3D model can also be efficiently rendered and displayed on MR devices. MR devices can use 3D models to intuitively visualize the preset area, allowing users to interactively view and analyze in a virtual environment. This intuitive 3D display method can significantly improve users' understanding and analysis of magnetic field distribution.

[0033] In short, by capturing the geometric structure data within the preset area based on spatial perception technology and generating a three-dimensional model corresponding to the preset area based on these data, it can not only provide an accurate spatial basis for the magnetic field data, but also realize efficient three-dimensional visualization display, thereby greatly improving the accuracy of environmental magnetic field perception assessment and user experience.

[0034] Step S2: Acquire current magnetic field data and historical magnetic field data within a preset area.

[0035] In the environmental magnetic field perception and assessment method, step S2 is to obtain the current magnetic field data and historical magnetic field data in the preset area. The main purpose of this step is to provide the necessary data basis for subsequent magnetic field data analysis, anomaly detection and three-dimensional visualization. The current magnetic field data can reflect the real-time magnetic field conditions in the preset area, while the historical magnetic field data provides a reference benchmark, making comparative analysis and anomaly detection possible.

[0036] Specifically, in order to obtain the current magnetic field data, multiple high-precision magnetic field sensors can be arranged in a preset area. These sensors can be fixedly installed in key locations or portable sensors carried by mobile devices. These sensors monitor the magnetic field strength and direction in the area in real time and transmit the data to the data processing center via wired or wireless networks. In the data processing center, these real-time data will be received, stored, and preliminarily processed.

[0037] At the same time, in order to obtain historical magnetic field data, relevant data can be extracted from previous monitoring records. Historical data can be stored in a local database or cloud storage. By querying historical records, magnetic field data over a period of time can be obtained. These data also need to be transmitted to the data processing center for comparison and analysis with current magnetic field data.

[0038] By acquiring current magnetic field data and historical magnetic field data, sufficient raw data can be provided for subsequent magnetic field data preprocessing. These data can help identify and correct possible sensor errors and improve data accuracy. In addition, historical data can also provide a reference benchmark for comparative analysis of current data, making it easier to identify abnormal magnetic field changes.

[0039] Step S3: preprocessing the current magnetic field data to obtain first magnetic field data, and preprocessing the historical magnetic field data to obtain second magnetic field data.

[0040] In one possible implementation, refer to Figure 2 , which shows the second flow diagram of an environmental magnetic field perception and assessment method based on spatial perception and MR technology provided in an embodiment of the present application. In step S3, the preprocessing step specifically includes steps S31-S34, and steps S31-S34 are to preprocess the current magnetic field data to obtain the first magnetic field data for specific description.

[0041] Step S31: Filter the current magnetic field data to obtain filtered current magnetic field data.

[0042] Step S32: performing denoising processing on the filtered current magnetic field data to obtain denoised current magnetic field data.

[0043] Step S33: calibrating the denoised current magnetic field data to obtain calibrated current magnetic field data.

[0044] Step S34: performing standardization processing on the calibrated current magnetic field data to obtain first magnetic field data.

[0045] In the environmental magnetic field perception and assessment method, step S3 is to preprocess the current magnetic field data to obtain the first magnetic field data, and preprocess the historical magnetic field data to obtain the second magnetic field data. The main purpose of this step is to eliminate noise and errors in the data, improve the accuracy and reliability of the data, and provide a high-quality data foundation for subsequent anomaly detection and three-dimensional visualization.

[0046] Specifically, the preprocessing of the current magnetic field data includes the following steps: First, filtering is performed. The current magnetic field data may be interfered by various noises during the acquisition process, such as electromagnetic interference and environmental noise. Through filtering, these high-frequency noises can be effectively removed, thereby obtaining smoother and more stable magnetic field data. Commonly used filtering methods include low-pass filters and band-pass filters.

[0047] Secondly, denoising is performed. Although filtering can remove most of the noise, some random noise may still exist. Through denoising, the random noise in the data can be further reduced and the clarity and accuracy of the data can be improved. Common denoising methods include wavelet denoising and adaptive noise suppression.

[0048] Then, calibration is performed. The current magnetic field data may be affected by sensor deviations during the acquisition process, resulting in inaccurate data. Through calibration, these sensor deviations can be eliminated and the accuracy of the data can be improved.

[0049] Finally, standardization is performed. Different magnetic field sensors may have different ranges and units. Through standardization, the data of different sensors can be converted to a unified standard range and unit, which facilitates subsequent data fusion and comparative analysis.

[0050] After the preprocessing in the above steps, the first magnetic field data can be obtained, which has eliminated most of the noise and errors and has high accuracy and reliability. Similarly, the same preprocessing is performed on the historical magnetic field data to obtain the second magnetic field data.

[0051] In a possible implementation, step S33 specifically includes the following steps: Obtain environmental data corresponding to a preset area and obtain a preset sensor calibration curve.

[0052] Based on the environmental data and the sensor calibration curve, the calibration factor corresponding to the sensor is obtained.

[0053] The denoised current magnetic field data is calibrated based on the correction factor to obtain calibrated current magnetic field data.

[0054] Specifically, step S33 includes the following sub-steps: First, obtain the environmental data corresponding to the preset area and obtain the preset sensor calibration curve. The environmental data includes various factors that affect the sensor readings, such as temperature, humidity, and pressure. The sensor calibration curve is pre-calibrated according to the response characteristics of the sensor under different environmental conditions. By obtaining these data, necessary reference information can be provided for subsequent calibration processing.

[0055] Next, the sensor's correction factor is calculated based on the environmental data and the sensor calibration curve. The correction factor reflects the degree of error of the sensor under the current environmental conditions. Specifically, the environmental data is used to find the corresponding correction value in the sensor calibration curve, or the correction factor under the current environmental conditions is calculated by interpolation. The purpose of this step is to quantify the sensor's error so that it can be corrected in subsequent data processing.

[0056] Finally, the denoised current magnetic field data is calibrated based on the correction factor. Specifically, the denoised magnetic field data is calibrated with the correction factor to correct the deviation of the sensor reading, thereby obtaining the calibrated current magnetic field data. This step ensures the accuracy of the magnetic field data and makes the corrected data closer to the actual magnetic field value.

[0057] Step S4: Obtain abnormal magnetic field data based on the first magnetic field data and the second magnetic field data.

[0058] In the environmental magnetic field perception and evaluation method, step S4 is to obtain abnormal magnetic field data based on the first magnetic field data and the second magnetic field data. The main purpose of this step is to identify abnormal changes in the magnetic field and provide key data support for subsequent three-dimensional visualization and analysis. By comparing the current magnetic field data with the historical magnetic field data, abnormal magnetic field fluctuations can be effectively detected, thereby realizing the monitoring and evaluation of the environmental magnetic field.

[0059] In a possible implementation, step S4 specifically includes the following steps: The first magnetic field data and the second magnetic field data are compared point by point to obtain a plurality of difference data, wherein the difference data is the difference between a single data point in the first magnetic field data and the second magnetic field data.

[0060] Specifically, the first magnetic field data is the current magnetic field data obtained after preprocessing, and the second magnetic field data is the historical magnetic field data obtained after preprocessing. In order to identify the difference between the current magnetic field and the historical magnetic field, the two sets of data need to be compared point by point. In the specific implementation, it is first necessary to ensure that the spatial coordinate systems of the two sets of data are consistent, that is, the position of each data point in space corresponds one to one. In this way, the accuracy of the comparison can be ensured.

[0061] In the data comparison process, for each data point, the difference between the first magnetic field data and the second magnetic field data is calculated. Assuming that the first magnetic field data point is M1(i,j,k) and the second magnetic field data point is M2(i,j,k), the difference data D(i,j,k) can be expressed as: D(i,j,k)=M1(i,j,k)−M2(i,j,k).

[0062] This point-by-point comparison method can carefully reflect the difference between the current magnetic field and the historical magnetic field. Each difference data D(i,j,k) represents the specific changes in the current magnetic field and the historical magnetic field at a specific location (i,j,k). By calculating the difference of all data points, a complete difference data set can be obtained, which records the changes in the magnetic field in the entire preset area in detail.

[0063] Determine the magnetic field anomaly threshold range.

[0064] Specifically, the determination of the magnetic field anomaly threshold range can be performed by the following steps: First, statistical analysis is performed based on the second magnetic field data. The second magnetic field data is historical magnetic field data after preprocessing, and these data can reflect the normal changes of the magnetic field in the preset area. By performing statistical analysis on these historical data, statistical characteristics such as the mean and standard deviation of the magnetic field data can be understood. These statistical characteristics provide a basis for setting the anomaly threshold range.

[0065] Next, the threshold range is set according to the statistical analysis results. Assume that the mean of the historical magnetic field data is μ and the standard deviation is σ. In order to identify abnormal data points, the threshold range can be set as: LowerThreshold=μ−k⋅σ; UpperThreshold=μ+k⋅σ.

[0066] Among them, k is a coefficient, usually 2 or 3, which can ensure that the threshold range covers most of the normal data and can effectively identify abnormal data. By setting the upper and lower thresholds, the magnetic field data can be divided into a normal range and an abnormal range.

[0067] Then, adjust it based on expert experience. In some cases, the threshold range set based solely on statistical analysis may not fully adapt to the actual situation. In this case, the threshold range can be adjusted based on the experience of field experts. For example, in some specific environments or application scenarios, the fluctuation of the magnetic field may have a special pattern. Experts can optimize the threshold range based on experience to improve the accuracy of anomaly detection.

[0068] Each difference data is compared with the magnetic field anomaly threshold range, and abnormal data points that exceed the magnetic field anomaly threshold range are marked.

[0069] The specific comparison process is as follows: For each difference data point D(i,j,k), check whether it is within the preset threshold range. Assuming the threshold range is [LowerThreshold, UpperThreshold], the comparison condition can be expressed as: LowerThreshold≤D(i,j,k)≤UpperThreshold. If the difference data point D(i,j,k) exceeds this range, the data point is marked as an abnormal data point. This comparison method can check the magnetic field changes point by point and accurately identify those change points that significantly deviate from the normal range.

[0070] Then, this comparison and marking operation is performed on all difference data points. Data points that exceed the magnetic field anomaly threshold range are marked as abnormal data points, and these abnormal data points are recorded to form an abnormal data point set. The result of this step is a clear list of abnormal data points, recording all magnetic field change points that significantly deviate from the normal range.

[0071] Abnormal data points are marked and clustered to obtain abnormal magnetic field data.

[0072] Specifically, the process of executing step S44 is as follows: First, the abnormal data points marked in step S43 are further processed. These abnormal data points are obtained by comparing the difference data with the magnetic field abnormality threshold range, and represent those points that significantly deviate from the normal magnetic field variation range. These abnormal data points are extracted to form a preliminary abnormal data point set.

[0073] Next, cluster analysis is performed on the preliminary set of abnormal data points. The purpose of cluster analysis is to classify abnormal data points with similar characteristics into one category, so as to identify the pattern and range of abnormal magnetic fields. Commonly used clustering algorithms include K-means clustering and DBSCAN clustering. Select a suitable clustering algorithm to classify and aggregate abnormal data points based on their spatial location and magnetic field strength.

[0074] For example, using the DBSCAN clustering algorithm, you can effectively identify clusters of abnormal data points with high density. The specific operations are as follows: Determine the clustering parameters, such as the neighborhood range (epsilon) and the minimum number of samples (minPts). Perform DBSCAN clustering on the set of abnormal data points to identify clusters of abnormal data points with high density. Classify each abnormal data point according to the clustering results to form several clusters of abnormal data points.

[0075] The result of cluster analysis is multiple clusters of abnormal data points, each cluster represents an area of ​​abnormal magnetic field. These clusters not only reflect the spatial distribution of abnormal magnetic field, but also can identify the intensity and change pattern of abnormal magnetic field through further analysis.

[0076] By marking and clustering abnormal data points, more detailed and accurate abnormal magnetic field data can be obtained. These abnormal magnetic field data can not only intuitively display the distribution of abnormal magnetic fields, but also provide important references for subsequent three-dimensional visualization and detailed analysis.

[0077] Step S5: fusing the first magnetic field data with the three-dimensional model to generate preliminary three-dimensional magnetic field data.

[0078] In the environmental magnetic field perception assessment method, step S5 is to fuse the first magnetic field data with the three-dimensional model to generate preliminary three-dimensional magnetic field data. The main purpose of this step is to map the pre-processed magnetic field data to the three-dimensional model of the preset area, realize the spatial visualization of the magnetic field data, and provide a basis for subsequent three-dimensional magnetic field distribution data.

[0079] In a possible implementation, step S5 specifically includes the following steps: A first spatial coordinate system and a first coordinate corresponding to the first magnetic field data are determined, and a second spatial coordinate system corresponding to the three-dimensional model is determined.

[0080] Specifically, first of all, it is necessary to clarify the first spatial coordinate system corresponding to the first magnetic field data. The first magnetic field data is obtained after preprocessing and contains the spatial position and magnetic field strength information of each data point. Usually, the spatial position of these data points is determined based on the position coordinates of the magnetic field sensor. Therefore, it is necessary to confirm what kind of spatial coordinate system (such as the global coordinate system or the local coordinate system) these coordinates are based on. In actual operation, the sensor data may have coordinate deviations due to different installation positions. The installation position and reference coordinate system of the sensor must be recorded in detail for accurate coordinate matching.

[0081] Next, determine the second spatial coordinate system corresponding to the 3D model. The 3D model is generated based on the geometric structure data within the preset area captured by spatial perception technology (such as lidar scanning or photogrammetry), and contains the 3D coordinate information of each position in the preset area. The second spatial coordinate system is generally a global coordinate system or a specific coordinate system set according to the scanning device. This step involves extracting and building a 3D model from the captured raw point cloud data or scanned data. During this process, it is necessary to ensure that the coordinate system of the model is as consistent as possible with the coordinate system of the sensor data.

[0082] Then, compare and confirm the relationship between the two coordinate systems. If it is found that the first space coordinate system is inconsistent with the second space coordinate system, coordinate conversion is required. A common situation is that the first space coordinate system is not completely consistent with the second space coordinate system, and needs to be converted through a space transformation algorithm. Affine transformation, rigid body transformation or other coordinate transformation methods can be used to convert the coordinates of the first magnetic field data into the coordinate system of the three-dimensional model. This step ensures that all data points are represented in the same coordinate system, so that the subsequent fusion process can proceed smoothly.

[0083] Based on the first space coordinate system and the second space coordinate system, the first coordinate is transformed to obtain the transformed first coordinate.

[0084] The specific transformation process includes the following steps: First, determine the transformation parameters. These parameters can be obtained through known control points or calibration processes. Control points are known corresponding points in the first coordinate system and the second coordinate system, and the transformation matrix can be calculated through these points. Secondly, apply the transformation matrix. Multiply the coordinates of each data point in the first spatial coordinate system by the transformation matrix to obtain the transformed coordinates. This step usually requires matrix operations, and the specific transformation formula is: X′=T⋅X. Among them, X is the coordinate in the first spatial coordinate system, T is the transformation matrix, and X′ is the transformed coordinate.

[0085] In this way, all the first magnetic field data points can be converted into the second spatial coordinate system. This process ensures the spatial position consistency of the magnetic field data and the three-dimensional model, so that subsequent data fusion can be performed in a unified coordinate system.

[0086] A first grid point corresponding to the converted first coordinate is determined on the three-dimensional model.

[0087] Specifically, a spatial index structure, such as a KD tree or an octree, can be established for the 3D model to speed up the query. These data structures can efficiently organize and query points in 3D space, making it fast and accurate to find the nearest neighbor points. Using these index structures, for each transformed first coordinate, the nearest neighbor search algorithm can be used to find the closest grid point in the 3D model. In this way, each transformed first coordinate can be matched with the nearest neighbor grid point found, and the corresponding relationship can be recorded.

[0088] Interpolation processing is performed on the first magnetic field data at the first grid point to obtain interpolated first magnetic field data.

[0089] First, the converted first coordinate has been accurately mapped to the grid points of the three-dimensional model in the previous step. However, since the data acquisition of the magnetic field sensor is discrete, the data points between different sensor positions may be unevenly distributed, which will cause discontinuity or incompleteness when displaying the magnetic field distribution in the three-dimensional model. Therefore, these discrete magnetic field data points must be interpolated to generate continuous magnetic field distribution data.

[0090] Specifically, interpolation can be done in a variety of ways, including linear interpolation, spline interpolation, Kriging interpolation, etc. The selection of an appropriate interpolation method depends on the characteristics of the data and the actual application requirements. For example, linear interpolation is suitable for situations where data changes relatively slowly, while spline interpolation can better handle situations where data changes dramatically.

[0091] In the specific operation, the spline interpolation method can be selected first. Spline interpolation is a commonly used interpolation method that can construct a smooth curve through multiple data points to achieve data smoothing. The steps of interpolation processing are as follows: First, based on the determined first grid point and the corresponding magnetic field data, a spline interpolation function is constructed. Then, the interpolation function is used to interpolate the area between the grid points to generate continuous magnetic field data. Finally, the first magnetic field data after interpolation is obtained, and these data have corresponding magnetic field values ​​at each grid point in the three-dimensional model, forming a continuous magnetic field distribution.

[0092] Through interpolation processing, discrete magnetic field data points can be smoothed to generate continuous magnetic field distribution data. The effect of this process is significant. First, interpolation processing can eliminate gaps and discontinuities in the data, making the distribution of magnetic field data in the three-dimensional model smoother and more continuous. Secondly, by selecting an appropriate interpolation method, it is possible to effectively handle situations where data changes dramatically or unevenly, and improve the accuracy and reliability of magnetic field data. Ultimately, the interpolated magnetic field data can form a continuous magnetic field distribution in the three-dimensional model, thereby significantly improving the visualization of the data.

[0093] The interpolated first magnetic field data is integrated with the three-dimensional model to generate preliminary three-dimensional magnetic field data.

[0094] Specifically, first, the first magnetic field data has been interpolated in the aforementioned steps to obtain continuous magnetic field distribution data. These interpolated magnetic field data have corresponding magnetic field values ​​at each grid point in the three-dimensional model, forming a complete magnetic field image. Next, these interpolated magnetic field data need to be integrated with the three-dimensional model.

[0095] In specific implementation, data integration can be performed using 3D modeling software or visualization tools (such as Unity, Unreal Engine or other professional software). The 3D model is imported into the visualization tool, and then the interpolated magnetic field data is mapped to the grid points of the 3D model as texture or color information. In this way, the magnetic field intensity of each grid point can be represented by color or transparency, thus forming an intuitive magnetic field distribution image in the 3D model.

[0096] During the integration process, the following points should be noted: First, ensure that the magnetic field data is accurately aligned with the grid points of the 3D model to avoid positional deviation. Second, choose a suitable color mapping method so that the changes in magnetic field intensity can be clearly reflected on the 3D model. Linear color mapping or heat map can be used to make magnetic field data of different intensities appear in different colors.

[0097] Step S6: Fusing the abnormal magnetic field data with the preliminary three-dimensional magnetic field data to obtain three-dimensional magnetic field distribution data.

[0098] In a possible implementation, step S6 specifically includes the following steps: Determine a third space coordinate system and a second coordinate corresponding to the abnormal magnetic field data.

[0099] Based on the third space coordinate system and the second space coordinate system, the second coordinate is transformed to obtain a transformed second coordinate.

[0100] A second grid point corresponding to the converted second coordinate is determined in the preliminary three-dimensional magnetic field data.

[0101] The abnormal magnetic field data are interpolated at the second grid points to obtain interpolated abnormal magnetic field data.

[0102] The interpolated abnormal magnetic field data are integrated with the preliminary three-dimensional magnetic field data to generate three-dimensional magnetic field distribution data.

[0103] The specific implementation of step S6 is similar to the specific implementation of step S5, so the specific implementation of step S6 can refer to the above embodiment, so it will not be described in detail here.

[0104] It should be noted that the abnormal magnetic field data after conversion and interpolation processing are integrated with the preliminary three-dimensional magnetic field data. During the integration process, the abnormal magnetic field data can be superimposed on the corresponding position of the preliminary three-dimensional magnetic field data by superposition. In order to highlight the abnormal area, different colors or transparency can be used to distinguish. For example, red or other highlight colors can be used to represent the abnormal magnetic field area, while the normal magnetic field area retains the original color. This color superposition method can make the abnormal magnetic field data clear at a glance in the three-dimensional model.

[0105] Step S7: transmitting the three-dimensional magnetic field distribution data to the MR device, so that the MR device can visualize the magnetic field distribution in the preset area.

[0106] In a possible implementation, step S7 specifically includes the following steps: Determine the format and structure corresponding to the three-dimensional magnetic field distribution data.

[0107] Based on the format and structure, determine the rendering engine corresponding to the MR device.

[0108] Determine the connection parameters corresponding to the MR device, which include IP address, port number, communication protocol and authentication information.

[0109] The MR device is initialized based on the rendering engine, and a data transmission channel is established based on the connection parameters.

[0110] Based on the data transmission channel, the three-dimensional magnetic field distribution data is transmitted to the initialized MR device.

[0111] In the environmental magnetic field perception and assessment method, step S7 is to transmit the three-dimensional magnetic field distribution data to the MR device so that the MR device can visualize the magnetic field distribution in the preset area. The main purpose of this step is to intuitively display the fused magnetic field data through the MR (mixed reality) device, so that the user can fully understand the distribution and abnormal conditions of the magnetic field in the virtual environment.

[0112] First, the format and structure of the 3D magnetic field distribution data need to be determined. To ensure that the data can be correctly read and rendered by the MR device, the 3D magnetic field distribution data needs to be in a standardized format, such as JSON, XML, or a specific binary format. These formats can contain spatial coordinates, magnetic field strength, and color information, so that the MR device can fully reconstruct the 3D magnetic field distribution.

[0113] Next, based on the data format and structure, determine the rendering engine corresponding to the MR device. Different MR devices may use different rendering engines, such as Unity, Unreal Engine, or the device's own dedicated engine. Selecting the right rendering engine can ensure that the three-dimensional magnetic field distribution data is efficiently rendered and displayed on the MR device.

[0114] Then, the connection parameters of the MR device need to be determined, including the IP address, port number, communication protocol, and authentication information. With these parameters, the data processing system can establish a reliable communication channel with the MR device to ensure smooth data transmission. Usually, the MR device is connected to the data processing system via Wi-Fi, Ethernet, or other wireless communication methods.

[0115] Initialize the MR device based on the rendering engine and establish a data transmission channel based on the connection parameters. In this process, first start the rendering engine on the MR device and ensure that it is in a state of waiting to receive data. Then, establish a communication channel with the MR device in the data processing system through the set connection parameters. WebSocket, HTTP or other suitable communication protocols can be used for data transmission.

[0116] Finally, the three-dimensional magnetic field distribution data is transmitted to the initialized MR device through the data transmission channel. During the transmission process, the integrity and real-time of the data need to be ensured. A data subpackaging and verification mechanism can be used to ensure that large amounts of three-dimensional magnetic field distribution data can be smoothly transmitted and correctly reassembled. After the MR device receives the data, the rendering engine will visualize the magnetic field distribution according to the data format and structure, and finally display the three-dimensional magnetic field distribution in the preset area on the MR device.

[0117] By transmitting the three-dimensional magnetic field distribution data to the MR device, the magnetic field distribution can be intuitively displayed. The effect of this display method is significant. Users can move and observe freely in the virtual environment to fully understand the magnetic field distribution and abnormal conditions in the preset area. This not only improves the visualization of magnetic field data, but also enhances the user's interactive experience and analysis capabilities.

[0118] Reference Figure 3, which shows a schematic diagram of the structure of an environmental magnetic field perception and evaluation system based on space perception and MR technology provided in an embodiment of the present application, the system includes: a processing module 1, an acquisition module 2 and a transmission module 3; Processing module 1, used to capture geometric structure data in a preset area based on space perception technology, and generate a three-dimensional model corresponding to the preset area based on the geometric structure data; Acquisition module 2, used to acquire current magnetic field data and historical magnetic field data in a preset area; The processing module 1 is also used to pre-process the current magnetic field data to obtain the first magnetic field data, and to pre-process the historical magnetic field data to obtain the second magnetic field data; The processing module 1 is further used to obtain abnormal magnetic field data based on the first magnetic field data and the second magnetic field data; The processing module 1 is further used to fuse the first magnetic field data with the three-dimensional model to generate preliminary three-dimensional magnetic field data; The processing module 1 is also used to fuse the abnormal magnetic field data with the preliminary three-dimensional magnetic field data to obtain three-dimensional magnetic field distribution data; The transmission module 3 is used to transmit the three-dimensional magnetic field distribution data to the MR device, so that the MR device can visualize the magnetic field distribution in a preset area.

[0119] In a possible implementation manner, the processing module 1 is further used to filter the current magnetic field data to obtain filtered current magnetic field data; The processing module 1 is also used to perform denoising on the filtered current magnetic field data to obtain denoised current magnetic field data; The processing module 1 is also used to calibrate the current magnetic field data after denoising to obtain calibrated current magnetic field data; The processing module 1 is also used to perform standardization processing on the calibrated current magnetic field data to obtain first magnetic field data.

[0120] In a possible implementation, the acquisition module 2 is further used to acquire environmental data corresponding to a preset area and to acquire a preset sensor calibration curve; The processing module 1 is also used to obtain a correction factor corresponding to the sensor based on the environmental data and the sensor calibration curve; The processing module 1 is further used to perform calibration processing on the denoised current magnetic field data based on the correction factor to obtain calibrated current magnetic field data.

[0121] In a possible implementation, the processing module 1 is further used to compare the first magnetic field data and the second magnetic field data point by point to obtain a plurality of difference data, wherein the difference data is the difference between a single data point in the first magnetic field data and the second magnetic field data; The processing module 1 is also used to determine the magnetic field anomaly threshold range; The processing module 1 is also used to compare each difference data with the magnetic field anomaly threshold range, and mark the abnormal data points that exceed the magnetic field anomaly threshold range; The processing module 1 is also used to mark and cluster analyze abnormal data points to obtain abnormal magnetic field data.

[0122] In a possible implementation, the processing module 1 is further used to determine a first spatial coordinate system and a first coordinate corresponding to the first magnetic field data, and to determine a second spatial coordinate system corresponding to the three-dimensional model; The processing module 1 is further used to transform the first coordinate based on the first space coordinate system and the second space coordinate system to obtain the transformed first coordinate; The processing module 1 is further used to determine a first grid point corresponding to the converted first coordinate on the three-dimensional model; The processing module 1 is further used to perform interpolation processing on the first magnetic field data at the first grid point to obtain the interpolated first magnetic field data; The processing module 1 is further used to integrate the interpolated first magnetic field data with the three-dimensional model to generate preliminary three-dimensional magnetic field data.

[0123] In a possible implementation, the processing module 1 is further used to determine a third space coordinate system and a second coordinate corresponding to the abnormal magnetic field data; The processing module 1 is further used to transform the second coordinate based on the third space coordinate system and the second space coordinate system to obtain the transformed second coordinate; The processing module 1 is further used to determine a second grid point corresponding to the converted second coordinate in the preliminary three-dimensional magnetic field data; The processing module 1 is further used to perform interpolation processing on the abnormal magnetic field data at the second grid point to obtain interpolated abnormal magnetic field data; The processing module 1 is also used to integrate the interpolated abnormal magnetic field data with the preliminary three-dimensional magnetic field data to generate three-dimensional magnetic field distribution data.

[0124] In a possible implementation manner, the transmission module 3 is further used to determine the format and structure corresponding to the three-dimensional magnetic field distribution data; The transmission module 3 is further used to determine the rendering engine corresponding to the MR device based on the format and structure; The transmission module 3 is also used to determine the connection parameters corresponding to the MR device, and the connection parameters include IP address, port number, communication protocol and authentication information; The transmission module 3 is further used to initialize the MR device based on the rendering engine and establish a data transmission channel based on the connection parameters; The transmission module 3 is further used to transmit the three-dimensional magnetic field distribution data to the initialized MR device based on the data transmission channel.

[0125] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0126] The present application also discloses an electronic device. Figure 4 , Figure 4 The electronic device 400 may include: at least one processor 401 , at least one network interface 404 , a user interface 403 , a memory 405 , and at least one communication bus 402 .

[0127] The communication bus 402 is used to realize the connection and communication between these components.

[0128] The user interface 403 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 403 may also include a standard wired interface and a wireless interface.

[0129] The network interface 404 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0130] Among them, the processor 401 may include one or more processing cores. The processor 401 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 405, and calling data stored in the memory 405. Optionally, the processor 401 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 401 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processor (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 401, and it can be implemented separately through a chip.

[0131] Among them, the memory 405 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 405 includes a non-transitory computer-readable storage medium. The memory 405 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 405 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments, etc. The memory 405 may optionally be at least one storage device located away from the aforementioned processor 401. Refer to Figure 4 , the memory 405 as a computer-readable storage medium may include an operating system, a network communication module, a user interface module and an application program.

[0132] exist Figure 4In the electronic device 400 shown, the user interface 403 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 401 can be used to call an application stored in the memory 405, and when executed by one or more processors 401, the electronic device 400 executes one or more methods in the above-mentioned embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0133] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0134] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0135] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0136] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0137] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0138] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0139] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for environmental magnetic field perception and assessment based on spatial perception and MR technology, characterized in that: The method comprises: Capturing geometric structure data within a preset area based on spatial perception technology, and generating a three-dimensional model corresponding to the preset area based on the geometric structure data; Acquiring current magnetic field data and historical magnetic field data within the preset area; Preprocessing the current magnetic field data to obtain first magnetic field data, and preprocessing the historical magnetic field data to obtain second magnetic field data; Obtaining abnormal magnetic field data based on the first magnetic field data and the second magnetic field data; Fusing the first magnetic field data with the three-dimensional model to generate preliminary three-dimensional magnetic field data; fusing the abnormal magnetic field data with the preliminary three-dimensional magnetic field data to obtain three-dimensional magnetic field distribution data; The three-dimensional magnetic field distribution data is transmitted to the MR device, so that the MR device can visualize the magnetic field distribution in the preset area.

2. The method according to claim 1, characterized in that The preprocessing of the current magnetic field data to obtain the first magnetic field data specifically includes: Performing filtering on the current magnetic field data to obtain filtered current magnetic field data; Performing denoising processing on the filtered current magnetic field data to obtain denoised current magnetic field data; Performing calibration processing on the denoised current magnetic field data to obtain calibrated current magnetic field data; The calibrated current magnetic field data is standardized to obtain the first magnetic field data.

3. The method according to claim 2, characterized in that The calibrating the denoised current magnetic field data to obtain calibrated current magnetic field data specifically includes: Acquire environmental data corresponding to the preset area, and acquire a preset sensor calibration curve; Based on the environmental data and the sensor calibration curve, obtaining a calibration factor corresponding to the sensor; The denoised current magnetic field data is calibrated based on the correction factor to obtain calibrated current magnetic field data.

4. The method according to claim 1, characterized in that: The obtaining abnormal magnetic field data based on the first magnetic field data and the second magnetic field data specifically includes: Comparing the first magnetic field data and the second magnetic field data point by point to obtain a plurality of difference data, wherein the difference data is a difference between a single data point in the first magnetic field data and the second magnetic field data; Determine the threshold range of magnetic field anomaly; Compare each of the difference data with the magnetic field anomaly threshold range, and mark abnormal data points that exceed the magnetic field anomaly threshold range; The abnormal data points are marked and clustered to obtain the abnormal magnetic field data.

5. The method according to claim 1, characterized in that The fusing the first magnetic field data with the three-dimensional model to generate preliminary three-dimensional magnetic field data specifically includes: Determine a first spatial coordinate system and a first coordinate corresponding to the first magnetic field data, and determine a second spatial coordinate system corresponding to the three-dimensional model; Based on the first spatial coordinate system and the second spatial coordinate system, transform the first coordinate to obtain a transformed first coordinate; Determining a first grid point corresponding to the converted first coordinate on the three-dimensional model; performing interpolation processing on the first magnetic field data at the first grid points to obtain interpolated first magnetic field data; The interpolated first magnetic field data is integrated with the three-dimensional model to generate the preliminary three-dimensional magnetic field data.

6. The method according to claim 2, characterized in that The fusing of the abnormal magnetic field data with the preliminary three-dimensional magnetic field data to obtain three-dimensional magnetic field distribution data specifically includes: Determine a third space coordinate system and a second coordinate corresponding to the abnormal magnetic field data; Based on the third space coordinate system and the second space coordinate system, transform the second coordinate to obtain transformed second coordinate; determining, in the preliminary three-dimensional magnetic field data, a second grid point corresponding to the converted second coordinate; performing interpolation processing on the abnormal magnetic field data at the second grid points to obtain interpolated abnormal magnetic field data; The interpolated abnormal magnetic field data is integrated with the preliminary three-dimensional magnetic field data to generate the three-dimensional magnetic field distribution data.

7. The method according to claim 1, characterized in that The transmitting the three-dimensional magnetic field distribution data to the MR device specifically includes: Determining the format and structure corresponding to the three-dimensional magnetic field distribution data; Based on the format and the structure, determining a rendering engine corresponding to the MR device; Determine connection parameters corresponding to the MR device, wherein the connection parameters include an IP address, a port number, a communication protocol, and authentication information; Initializing the MR device based on the rendering engine, and establishing a data transmission channel based on the connection parameters; Based on the data transmission channel, the three-dimensional magnetic field distribution data is transmitted to the initialized MR device.

8. An environmental magnetic field perception and assessment system based on spatial perception and MR technology, characterized in that: The system comprises: a processing module, an acquisition module and a transmission module; The processing module is used to capture geometric structure data within a preset area based on a spatial perception technology, and generate a three-dimensional model corresponding to the preset area based on the geometric structure data; The acquisition module is used to acquire current magnetic field data and historical magnetic field data in the preset area; The processing module is further used to pre-process the current magnetic field data to obtain first magnetic field data, and to pre-process the historical magnetic field data to obtain second magnetic field data; The processing module is further used to obtain abnormal magnetic field data based on the first magnetic field data and the second magnetic field data; The processing module is further used to fuse the first magnetic field data with the three-dimensional model to generate preliminary three-dimensional magnetic field data; The processing module is further used to fuse the abnormal magnetic field data with the preliminary three-dimensional magnetic field data to obtain three-dimensional magnetic field distribution data; The transmission module is used to transmit the three-dimensional magnetic field distribution data to the MR device, so that the MR device can visualize the magnetic field distribution in the preset area.

9. An electronic device, characterized in that: The electronic device (400) comprises a processor (401), a memory (405), a user interface (403) and a network interface (404), wherein the memory (405) is used to store instructions, the user interface (403) and the network interface (404) are used to communicate with other devices, and the processor (401) is used to execute the instructions stored in the memory (405) so that the electronic device (400) executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is executed.

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