Passenger compartment electromagnetic interference assessment method and device and computer equipment

Abstract

The invention relates to the technical field of vehicles, and discloses a passenger compartment electromagnetic interference assessment method and device and computer equipment, and the method comprises the steps: employing electromagnetic field measurement equipment to carry out the sampling of a plurality of common equipment in a plurality of working states, and carrying out the calculation based on the sampling data, and obtaining reference data; determining a test point in the internal space of the passenger compartment of the to-be-tested vehicle based on the preset side length; electromagnetic field measurement equipment is used for sampling the test points under various whole vehicle working conditions of the to-be-tested vehicle, and comprehensive data of each planar grid on the grid surface is calculated based on the sampled data; based on the reference data and the comprehensive data of each planar grid on the grid surface, calculating to obtain an evaluation score of each planar grid on the grid surface; and generating a hotspot map of the grid surface based on the evaluation score. The method has the beneficial effect that the evaluation effectiveness of electromagnetic field interference is improved.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a method, apparatus and computer equipment for assessing electromagnetic interference in a passenger compartment. Background Technology

[0002] With the booming development of the new energy electric vehicle market, the degree of electrical integration is constantly increasing, and electronic and electrical systems are becoming increasingly complex. The resulting electromagnetic compatibility issues have attracted much attention, especially the assessment and evaluation technology for the impact of electromagnetic fields generated by electronic and electrical systems on human health has become crucial.

[0003] Currently, the assessment and evaluation of electromagnetic interference is mainly based on the test results of key points, which analyze the electromagnetic interference situation by collecting test data at specific locations.

[0004] However, this method of analysis is cumbersome and has low visualization, making it unable to provide systematic design references for R&D or convenient for presentation to customers, and it suffers from poor effectiveness in evaluating electromagnetic interference. Summary of the Invention

[0005] This application provides a method, apparatus, and computer equipment for assessing electromagnetic interference in a crew cabin, which solves the technical problem of poor effectiveness in assessing electromagnetic interference and improves the technical effect of assessing electromagnetic interference.

[0006] To achieve the above objectives, the main technical solutions adopted in this application include: In a first aspect, embodiments of this application provide a method for assessing electromagnetic interference in a passenger cabin, the method comprising: Electromagnetic field measurement equipment is used to sample multiple commonly used devices under various operating conditions, and reference data is calculated based on the sampled data; wherein, the reference data is a feature vector composed of reference field strengths on a preset frequency sequence; Based on a preset side length, a grid surface composed of multiple planar grids parallel to the chassis direction of the vehicle under test is determined in the passenger compartment interior space of the vehicle under test; and based on the grid surface and the preset side length, multiple three-dimensional grids are divided in the direction perpendicular to the chassis of the vehicle under test; and the grid points of the three-dimensional grids are determined as test points. Using an electromagnetic field measurement device, the test points are sampled under various vehicle operating conditions of the vehicle under test, and the comprehensive data of each planar grid on the grid surface is calculated based on the sampled data; the comprehensive data is a feature vector composed of the comprehensive field strength on a preset frequency sequence. Based on the baseline data and the comprehensive data of each planar grid on the grid surface, the evaluation score of each planar grid on the grid surface is calculated; and based on the evaluation score, a heat map of the grid surface is generated.

[0007] Secondly, embodiments of this application provide a passenger cabin electromagnetic interference assessment device, the device comprising: The reference module is used to sample multiple commonly used devices under various operating conditions using electromagnetic field measurement equipment, and calculate reference data based on the sampled data; wherein, the reference data is a feature vector composed of reference field strengths on a preset frequency sequence; The sampling module is used to determine a grid surface composed of multiple planar grids parallel to the chassis direction of the vehicle under test in the passenger compartment interior space of the vehicle under test, based on a preset side length; and to divide multiple three-dimensional grids in the direction perpendicular to the chassis direction of the vehicle under test based on the grid surface and the preset side length; to determine the grid points of the three-dimensional grids as test points; to sample the test points under various vehicle operating conditions using an electromagnetic field measurement device; and to calculate the comprehensive data of each planar grid on the grid surface based on the sampled data; the comprehensive data is a feature vector composed of comprehensive field strength on a preset frequency sequence. The evaluation module is used to calculate the evaluation score of each planar grid on the grid surface based on the benchmark data and the comprehensive data of each planar grid on the grid surface; and to generate a heat map of the grid surface based on the evaluation score.

[0008] Thirdly, embodiments of this application provide a computer device, including: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the method described in any of the above embodiments.

[0009] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer instructions, which are used to cause a computer to perform the method described in any one of the above embodiments.

[0010] Fifthly, embodiments of this application provide a computer program product, including computer instructions, which are used to cause a computer to perform the method described in any of the above embodiments. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0012] Figure 1A flowchart illustrating an electromagnetic interference assessment method for a crew cabin provided in this application embodiment; Figure 2 A schematic diagram of reference data provided in an embodiment of this application; Figure 3 A schematic diagram of a grid surface inside a passenger compartment provided in an embodiment of this application; Figure 4 A flowchart illustrating an electromagnetic interference assessment method for a crew cabin provided in this application embodiment; Figure 5 A schematic diagram of a heat map provided in an embodiment of this application; Figure 6 A block diagram of an electromagnetic interference assessment device for a crew cabin provided in an embodiment of this application; Figure 7 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0014] Compared to traditional cars, new energy electric vehicles have a higher degree of electrical integration and more electronic and electrical systems. This leads to more electromagnetic compatibility (EMC) issues for new energy electric vehicles. The impact of these electronic components on human health is of particular concern to people both within and outside the industry. Furthermore, with the rapid development and increasing market share of new energy vehicles, the electromagnetic fields generated by the electronic components within these vehicles are becoming increasingly important in influencing human health.

[0015] Currently, the evaluation methods for electromagnetic interference are mainly based on test results at key locations. Furthermore, existing methods suffer from complex test data and low visualization capabilities. This further prevents current evaluation methods for electromagnetic interference in electric vehicles from providing a systematic and intuitive design reference for research and development.

[0016] Furthermore, due to the complexity and highly specialized nature of the data, it's difficult to effectively showcase the professional design level of electromagnetic safety during vehicle launches and promotions. This results in current electromagnetic interference assessments being unable to be presented to the general public in a simple and efficient manner. Consequently, without consumer understanding or awareness, these assessments fail to alleviate users' concerns about the health and safety of electric vehicles due to electromagnetic interference. This situation further contributes to a higher number of customer complaints in the market.

[0017] To address the above issues, this application proposes a method for evaluating the electromagnetic interference intensity of electric vehicles. This method provides a more intuitive and visual assessment of electromagnetic interference, simply and efficiently demonstrating the overall electromagnetic protection level of the vehicle. Furthermore, the results can provide a more effective reference for research and development. Additionally, these results can alleviate consumer concerns and reduce subsequent customer complaints and other market issues.

[0018] First, this application collected signals from the entire vehicle interior. Based on these signals, it can comprehensively assess the electromagnetic field distribution within the vehicle, providing a more complete visual evaluation of the electromagnetic field. Second, this application visualizes the strength of electromagnetic interference throughout the entire interior of the electric vehicle, offering more intuitive R&D support and optimization directions for subsequent improvements in human electromagnetic field protection for electric vehicles. Finally, this application uses commonly used electronic devices as a reference to assess the severity of electromagnetic interference within the electric vehicle's cabin. This assessment allows ordinary people to more intuitively evaluate the electromagnetic safety characteristics of electric vehicles, thereby alleviating concerns about health and safety.

[0019] According to an embodiment of this application, an embodiment of a method for assessing electromagnetic interference in a crew cabin is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed on a computer device via a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here. The computer device can be a mobile terminal, a personal computer, a server, etc.

[0020] Figure 1 A flowchart of a method for assessing electromagnetic interference in a crew cabin, as provided in this application embodiment, is shown below. Figure 1 As shown, with a computer device as the execution subject, the process includes the following steps: S101. Using an electromagnetic field measurement device, samples are taken from multiple commonly used devices under various operating conditions, and reference data is calculated based on the sampled data. The reference data is a feature vector composed of reference field strengths on a preset frequency sequence.

[0021] For example, the computer device can first acquire sampling data obtained by the electromagnetic field measuring device from sampling operations performed on multiple commonly used devices under various operating conditions. Then, the computer device can perform calculations based on this sampling data to obtain reference data.

[0022] In one implementation, the reference data is a feature vector. It consists of a series of reference field strengths along a preset frequency sequence. For example, using the preset frequency sequence as the horizontal axis and the field strength as the vertical axis, the reference data can be as follows: Figure 2 As shown by the blue line in the image. Among them, Figure 2 The red line in the image represents the preset upper limit value.

[0023] In one implementation, "multiple commonly used devices" refers to electronic and electrical devices that users frequently use daily. Examples include mobile phones, tablets, and computers. For instance, these multiple commonly used devices could be five different brands of mobile phones. Alternatively, they could include three different brands of mobile phones and three different brands of tablets.

[0024] In one implementation, the multiple operating states can be various common states specific to the commonly used device. For example, when the commonly used device is a mobile phone, the operating states could be making a call, phone in standby mode, phone playing music, phone transmitting data via Bluetooth, phone charging, etc.

[0025] In one implementation, a sampling point can be set for each frequently used device. This sampling point can be located on the side of the frequently used device facing the user. For example, when the frequently used device is a mobile phone, the sampling point can be located at the center of the mobile phone screen.

[0026] In one implementation, for a commonly used device operating in a certain state, the electromagnetic field measuring device can perform at least one sampling at a sampling point. During each sampling, the electromagnetic field measuring device can collect the field strength corresponding to each frequency point in the preset frequency sequence. This field strength is the electromagnetic field intensity corresponding to that frequency point.

[0027] In one implementation, the computer device can calculate the final reference data based on multiple sampled data uploaded by the electromagnetic field measuring device.

[0028] S102. Based on the preset side length, determine a grid surface composed of multiple planar grids parallel to the chassis direction of the vehicle under test in the passenger compartment interior space of the vehicle under test; and based on the grid surface and the preset side length, divide multiple three-dimensional grids in the direction perpendicular to the chassis of the vehicle under test; determine the grid points of the three-dimensional grids as test points.

[0029] For example, the computer device can first acquire a pre-stored preset side length. Then, the computer device can determine a cross-section parallel to the chassis direction of the vehicle under test within the passenger compartment interior space. Based on this cross-section, the computer device can determine multiple planar grids on the cross-section. The side length of this planar grid is the preset side length. These planar grids can form a grid surface on the cross-section. Optionally, this grid surface can be rectangular.

[0030] Furthermore, based on this grid surface, the computer equipment can continue to use the preset side length to divide the interior space of the passenger compartment into a three-dimensional grid, either upwards or downwards, along a direction perpendicular to the chassis of the vehicle under test. These three-dimensional grids can form a cuboid within the interior space of the passenger compartment. The grid points of these three-dimensional grids are the test points for measuring the electromagnetic field information within the interior space of the passenger compartment in subsequent steps.

[0031] In one implementation, the chassis of the vehicle under test is typically parallel to the ground; therefore, the cross-section is parallel to the ground. Furthermore, the direction perpendicular to the chassis of the vehicle under test is the same as the direction perpendicular to the ground.

[0032] In one implementation, when determining these grid points, the interior space of the passenger compartment of the vehicle under test can be simulated as an empty space, excluding fixed components such as the central control unit, seats, and armrest box.

[0033] In one implementation, after determining these grid points, the computer device can determine whether the test points corresponding to these grid points coincide with fixed components such as the central control unit, seats, and armrest boxes. If they coincide, the computer device can discard the test point.

[0034] In one implementation, the three-dimensional mesh can be a cubic mesh with the same length, width, and height. The length, width, and height are all preset side lengths. For example, the preset side length can be 10cm.

[0035] In another implementation, the computer device can have different preset side lengths for length, width, and height. For example, the length can be 10cm, the width 8cm, and the height 12cm. Or, the length can be 10cm, the width 10cm, and the height 12cm.

[0036] S103. Using electromagnetic field measurement equipment, sample test points under various vehicle operating conditions of the vehicle under test, and calculate the comprehensive data of each planar grid on the grid surface based on the sampled data. The comprehensive data is a feature vector composed of the comprehensive field strength on a preset frequency sequence.

[0037] For example, a computer device can acquire sampling data obtained by an electromagnetic field measuring device at test points on the vehicle under test. After sampling, the computer device can calculate the comprehensive data of each planar grid on the grid surface based on the sampling data.

[0038] In one implementation, the electromagnetic field measuring device can perform multiple measurements on the vehicle under test under different vehicle operating conditions.

[0039] In one implementation, multiple vehicle operating conditions include vehicle stationary charging condition, vehicle low-speed driving condition, vehicle high-speed driving condition, vehicle rapid acceleration or deceleration condition, etc.

[0040] In one implementation, the comprehensive data is a feature vector composed of comprehensive field strengths on a preset frequency sequence.

[0041] In one implementation, when calculating the comprehensive data, the computer device first performs fusion processing on the measurement data of each test point under different vehicle operating conditions to obtain fused sampling data for each test point. Then, the computer device fuses the fused sampling data of multiple test points perpendicular to the chassis direction of the vehicle under test to obtain the fused corner point data corresponding to each corner point on the grid surface. Finally, the fused corner point data of the four corner points of each planar grid are fused to obtain the comprehensive data for each planar grid.

[0042] S104. Based on the baseline data and the comprehensive data of each planar grid on the grid surface, the evaluation score of each planar grid on the grid surface is calculated. A heat map of the grid surface is then generated based on the evaluation scores.

[0043] For example, the computer device performs calculations based on existing baseline data and comprehensive data of each planar grid on the grid surface, thereby deriving an evaluation score for each planar grid on the grid surface. Then, based on these evaluation scores, the computer device can generate a heat map of the grid surface.

[0044] In one implementation, the evaluation score is a quantitative indicator used to measure the electromagnetic field conditions of each planar grid. A computer can calculate this evaluation score using baseline data and aggregated data from each planar grid, employing methods such as the difference method or the ratio method.

[0045] In one implementation, the computer device can preset multiple colors and set a numerical range for the evaluation score for each color. The computer device can generate the heatmap by mapping the evaluation scores of each planar grid to the corresponding color.

[0046] In another implementation, the computer device can preset a color gradient from 0 to 255. The computer device can map the evaluation scores of each planar grid to the range of 0-255 to determine the different gradients of the color, thereby enabling the display of different depths of the color.

[0047] In this embodiment, benchmark data is calculated by sampling multiple commonly used devices under various operating conditions. Based on the test points of the vehicle under test, the vehicle under test is sampled under various vehicle operating conditions to calculate the comprehensive data of each planar grid on the grid surface of the vehicle under test. Then, based on the benchmark data and comprehensive data, the evaluation score of each planar grid is calculated, and a heat map of the grid surface is generated. This method achieves the effect of quantitative evaluation and intuitive display of the electromagnetic field situation inside the passenger compartment of the vehicle under test, and improves the effectiveness of the evaluation of electromagnetic field interference.

[0048] In one example, the process of calculating the reference data after the computer device acquires multiple sampling data uploaded by the electromagnetic field measurement device in step S101 above may include: S1011. Using an electromagnetic field measurement device, samples are taken from multiple commonly used devices under various operating conditions to obtain multiple first sampling data. The first sampling data represents a characteristic sequence composed of multiple first field strengths corresponding to multiple frequency points collected from a commonly used device under a certain operating condition.

[0049] For example, a computer device can acquire multiple first sampling data obtained by an electromagnetic field measuring device from multiple commonly used devices under various different operating conditions.

[0050] In one implementation, each first sampled data can be a single sample taken by a commonly used device in a certain operating state.

[0051] In one implementation, each first sampled data may include the first field strength directly acquired from each frequency point on a preset frequency sequence.

[0052] In one implementation, for each commonly used device in a certain working state, the computer device can acquire multiple first sampling data collected by the electromagnetic field measuring device multiple times.

[0053] S1012. Take the maximum value of the first field strength at each frequency point under different commonly used equipment and different operating conditions as the reference field strength of the frequency point. And combine the reference field strengths of each frequency point on the preset frequency sequence to form reference data.

[0054] For example, the computer device can process all the first sampled data, and for each frequency point, select the maximum value among all the first sampled data as the reference field strength for that frequency point. Then, the computer device can assemble the reference field strengths corresponding to each frequency point in the preset frequency sequence into a feature vector to obtain the final reference data.

[0055] In one implementation, the most pessimistic data is typically chosen when selecting the reference field strength. Based on this most pessimistic data, the computer device can more intuitively display worse-than-expected situations in the heat map, thus allowing users to more clearly understand the electromagnetic interference situation inside the vehicle's passenger compartment.

[0056] S1023. Calculate the mean data of the first sampled data at each frequency point under the same commonly used equipment and the same working state; use the preset weights of each commonly used equipment under each working state to calculate the weighted sum of the mean data of each commonly used equipment under each working state to obtain fused data; process the fused data using a preset up-float ratio to obtain reference data.

[0057] Steps S1023 and S1022 are parallel steps. The computer device can choose either step S1023 or step S1022 to calculate the reference data.

[0058] For example, the computer device can first calculate the mean of multiple initial sample data collected under the same commonly used equipment and the same operating conditions to obtain the mean data. This calculation can avoid the problem of inaccurate data caused by special circumstances in a single sampling.

[0059] Furthermore, the computer device can also set corresponding preset weights for each working state of each commonly used device. The computer device can use these preset weights to multiply the mean data of that working state of the commonly used device to obtain the corresponding weighted mean data. Then, the computer device can accumulate all the weighted mean data to obtain the final fused data.

[0060] Optionally, the sum of the multiple preset weights can be 1.

[0061] Alternatively, when the sum of the multiple preset weights is not equal to 1, in order to ensure that the final fused data is within the normal numerical range, the computer device can also divide the fused data by the sum of all preset weights, thereby correcting the fused data.

[0062] Optionally, the preset weight can be determined based on the usage probability of the frequently used device and the usage probability of the frequently used device in this working state. This fusion method can more fully leverage the user's usage frequency of the frequently used device and the working state, achieving calculations that are closer to the user's actual situation and improving the user authenticity of the benchmark data.

[0063] Finally, to ensure the pessimism of the baseline data, the computer device can also store an upsizing ratio. The computer device can use this upsizing ratio to multiply the fused data to obtain the final baseline data. Optionally, this upsizing ratio is a value greater than 1. For example, the upsizing ratio can be 1.1, 1.2, etc.

[0064] In this example, multiple first-sample data points are obtained by sampling various operating states of several commonly used devices using electromagnetic field measurement equipment. Based on these first-sample data points, a reference field strength at each frequency point is determined, thus forming reference data. This method provides an accurate reference for subsequent electromagnetic field assessments. This reference is relevant to users' daily lives, allowing for more intuitive comparisons and improving the user experience.

[0065] In one example, step S102 above, the process of drawing a grid surface composed of planar grids in the interior space of the passenger compartment of the vehicle under test, includes: S1021. Scan the interior space of the passenger compartment of the vehicle under test to obtain the boundary information of the interior space of the passenger compartment.

[0066] For example, the computer device can first activate the spatial scanning function to perform a comprehensive scan of the interior space of the passenger compartment of the vehicle under test, thereby obtaining the boundary information of the interior space of the passenger compartment.

[0067] In one implementation, boundary information refers to the outline of the interior space of the passenger compartment of the vehicle under test. This boundary is typically composed of features such as windows and doors. Computer equipment can then assemble boundary information based on this outline.

[0068] In one implementation, the area below the lower edge of the window typically has numerous fixed components. The area above the lower edge of the window experiences a shrinking interior space due to the sloping surfaces of the A, B, and C pillars. Therefore, the computer equipment can obtain a plane parallel to the chassis of the vehicle under test at the height of the lower edge of the window as a cross-section. This cross-section can be the largest cross-section within the passenger compartment of the vehicle under test. Furthermore, this cross-section can have fewer fixed components within the vehicle, improving scanning efficiency.

[0069] In one implementation, the sectional surface of the height of the lower edge of the window can be composed of glass such as the window, windshield, and rear window.

[0070] For example, the boundary contour of the passenger compartment interior space of the vehicle under test can be as follows: Figure 3 As shown.

[0071] S1022. Based on boundary information, determine the maximum inscribed rectangle of the crew compartment interior space. Also determine the four corner points of the maximum inscribed rectangle.

[0072] For example, after acquiring the boundary information, the computer device uses a specific geometric calculation algorithm to determine the maximum inscribed rectangle of the passenger compartment's interior space. Then, the computer device can obtain the four corner points of this maximum inscribed rectangle.

[0073] In one implementation, the maximum inscribed rectangle refers to the rectangle with the largest unfoldable area within the given interior space boundary of the crew cabin.

[0074] In another implementation, the maximum inscribed rectangle refers to a rectangle drawn within the given interior space boundary of the crew compartment. Furthermore, the area between the boundary of this rectangle and the outline is minimized.

[0075] The setting of the maximum inscribed rectangle allows for a faster determination of the grid surface range within the passenger compartment of the vehicle under test, thereby improving the efficiency of planar grid drawing.

[0076] S1023. Based on the preset side length, starting from the four corner points, draw a planar network in the largest inscribed rectangle, and form a grid surface based on the planar grid.

[0077] For example, the computer device draws a planar grid inside the largest inscribed rectangle, starting from the four corner points of the rectangle based on a preset side length. These planar grids can form a grid surface within the rectangle.

[0078] In one implementation, the preset side length is a pre-set length value based on the accuracy requirements of the magnetic field estimation. For example, the preset side length can be 10 cm. This preset side length can determine the size of the planar grid.

[0079] In one implementation, when drawing a planar grid, the computer device can use the preset side length as the row and column spacing, and start from the four corner points to draw lines parallel to the two sides of the matrix. These lines can form a regular planar grid.

[0080] In one implementation, the computer device can also adjust the preset side length according to the length and width of the maximum inscribed rectangle, so that the planar grids divided by the maximum inscribed rectangle have the same length and width.

[0081] Optionally, the computer device can calculate the ratio of the length of the maximum inscribed rectangle to the preset side length to obtain the number of horizontal grids. The computer device can also calculate the ratio of the length of the maximum inscribed rectangle to the number of horizontal grids to obtain the actual length. This actual length can be a value greater than or equal to the preset side length. Similarly, the computer device can calculate the ratio of the width of the maximum inscribed rectangle to the preset side length to obtain the number of vertical grids. The computer device can also calculate the ratio of the width of the maximum inscribed rectangle to the number of vertical grids to obtain the actual width. This actual width can also be a value greater than or equal to the preset side length.

[0082] S1024. Based on boundary information, determine the center position of the interior space of the passenger compartment. Based on the preset side length, draw planar rectangles starting from the center position to form a grid surface.

[0083] The execution of step S1024 is parallel to the execution of steps S1022 and S1023 described above. The computer device may choose to execute step S1024, or execute steps S1022 and S1023.

[0084] For example, the computer device can draw a minimum circumscribed rectangle based on this boundary information. The computer device can determine the center position of the minimum circumscribed rectangle as the center position of the interior space of the crew compartment.

[0085] Furthermore, the computer equipment can draw lateral and longitudinal reference lines starting from the center position, based on the driving direction of the vehicle under test and perpendicular to the driving direction. The computer equipment can then extend a planar rectangle outwards from the boundary contour of the vehicle under test based on these lateral and longitudinal reference lines.

[0086] Based on this method, the computer equipment does not need to consider the boundary consistency of the grid surface, but only the integrity of each planar grid. This method can more completely divide the interior space of the crew cabin, facilitating more comprehensive analysis by technicians.

[0087] Optionally, the computer device can delete a planar grid if any corner points in the planar grid extend beyond the boundary contour. This ensures that all four corner points of each planar grid are within the boundary contour.

[0088] In this example, the boundary information is obtained by scanning the passenger compartment of the vehicle under test, thereby determining the maximum inscribed rectangle and its corner points. Based on the corner points, a planar grid is drawn within the range of the maximum inscribed rectangle to form a grid surface, thus achieving the effect of accurately dividing the standard area for subsequent electromagnetic field testing.

[0089] In one example, in step S102 above, after completing the drawing of the mesh surface, the computer device can further divide multiple three-dimensional meshes in a direction perpendicular to the chassis of the vehicle under test, based on a preset side length. This process may include: S1025. Based on the corner points of the grid surface, multiple corner points are determined upwards or downwards in the passenger compartment interior space of the vehicle under test, based on a preset side length, in a direction perpendicular to the chassis of the vehicle under test. These multiple corner points form multiple three-dimensional grids.

[0090] For example, the computer device can draw auxiliary lines perpendicular to the direction of the vehicle chassis under test based on each corner point of the grid surface. On each auxiliary line, the computer device can determine multiple points upwards or downwards from the corner point of the grid surface, according to the preset side length. These points are the corner points of the 3D grid.

[0091] In one implementation, during the drawing of the corner point, the computer equipment may disregard the fixed components inside the vehicle under test.

[0092] In one implementation, when the height of the grid point is the height of the lower edge of the window, the downward distance can be the distance from the lower edge of the window to the floor inside the vehicle. Optionally, in the downward direction, the boundary of the interior space can be considered to coincide with the boundary defined by the lower edge of the window.

[0093] In one implementation, when the height of the grid point is the height of the lower edge of the window, the upward distance can be determined based on the space formed by the A, B, and C pillars of the vehicle. Optionally, if a corner point of the 3D grid exceeds the interior space of the vehicle, the 3D grid is discarded.

[0094] Optionally, the three-dimensional shape formed by the grid generated in this way typically does not form a complete cuboid, but rather creates a certain slope along pillars A, B, and C. This method allows for the setting of more test points within the passenger compartment of the vehicle under test, thereby improving the comprehensiveness and accuracy of the test.

[0095] In this example, a three-dimensional mesh is generated by expanding upwards and downwards based on the mesh surface, thereby achieving the effect of accurately dividing the standard region for subsequent electromagnetic field testing.

[0096] In one example, step S103 above, where the computer device calculates the comprehensive data of each planar grid on the grid surface based on the sampling data obtained by sampling each test point under various vehicle operating conditions, includes: S1031. Using electromagnetic field measurement equipment, sample test points under various vehicle operating conditions of the vehicle under test to obtain vehicle sampling data. The vehicle sampling data is a feature vector composed of sampled field strengths on a preset frequency sequence.

[0097] For example, the electromagnetic field measuring device measures sampling data at various measurement points in the vehicle under test when the vehicle is under different overall vehicle operating conditions. A computer can then acquire the sampling data uploaded by the electromagnetic field measuring device.

[0098] In one implementation, each sampled data may include the sampled field strength corresponding to each frequency point in a preset frequency sequence obtained when the electromagnetic field measuring device samples a certain test point under a vehicle operating condition.

[0099] In one implementation, based on each vehicle operating condition, the computer device can acquire multiple sampling data points obtained from multiple measurements performed by the electromagnetic field measuring device at each test point. The computer device can calculate the average of these multiple sampling data points, which serves as the sampling data point corresponding to that vehicle operating condition. This calculation of the average can avoid discrepancies caused by data instability and improve data validity.

[0100] S1032. Based on the preset operating condition weights, the vehicle sampling data under various vehicle operating conditions corresponding to each test point are weighted and summed to obtain the first test point data corresponding to each pilot test.

[0101] For example, the computer device may store preset operating condition weights. The computer device can perform weighted summation on the sampled data of each test point under different vehicle operating conditions to obtain the first test point data corresponding to each test point.

[0102] In one implementation, the operating condition weights are determined based on the usage frequency of each operating condition. Different vehicle operating conditions can correspond to different operating condition weights.

[0103] In another implementation, the preset operating condition weights are values ​​pre-set based on the degree of influence of different vehicle operating conditions on the electromagnetic field. For example, if the electromagnetic field generated by the vehicle during acceleration has a significant impact on the test results, then the weight value corresponding to the acceleration condition will be relatively high.

[0104] In one implementation, the sum of the weights of the multiple operating conditions can be 1.

[0105] In another implementation, the computer device can calculate the weighted sum and then divide that weighted sum by the sum of all operating condition weights to obtain the data for the first test point. This method ensures that the data for the first test point is within the correct data range.

[0106] S1033. Obtain the first test point data of multiple test points corresponding to a planar grid in the grid surface, which are perpendicular to the chassis of the vehicle under test, and calculate the comprehensive data of the planar grid based on the first test point data.

[0107] For example, the computer device can first determine a planar grid within a mesh surface. Then, the computer device can determine first test point data for multiple test points corresponding to that planar grid in a direction perpendicular to the chassis of the vehicle under test. Finally, the computer device can obtain comprehensive data for the planar grid based on these first test point data using a specific calculation method.

[0108] In one implementation, the computer device can calculate the mean of all the data from the first test point to obtain the comprehensive data of the planar grid.

[0109] In another implementation, the computer device can obtain the maximum value among all the data from the first test points as the comprehensive data of the planar grid. Optionally, during the selection of this maximum value, the computer device can select the maximum field strength corresponding to each frequency point on a preset frequency sequence to form the final comprehensive data.

[0110] In this example, by sampling test points under various vehicle operating conditions to obtain sampling data, and by using the method of weighted summation of operating conditions and comprehensive calculation of planar grid data, the electromagnetic field conditions corresponding to different planar grids in the passenger compartment interior space of the vehicle under test are accurately evaluated.

[0111] In one example, in step S1033 above, the process by which the computer device calculates the comprehensive data of the planar mesh for the first test point data of multiple test points corresponding to a planar mesh in the mesh surface in a direction perpendicular to the chassis of the vehicle under test includes: S10331. Obtain test point data from multiple test points perpendicular to the chassis direction of the vehicle under test, and use the maximum value of each frequency point in the frequency sequence among the multiple test point data to form the second test point data.

[0112] For example, the computer device can first determine the planar mesh that needs to be calculated. Then, the computer device can determine the four corner points corresponding to the planar mesh. The computer device can then acquire first test point data for multiple test points corresponding to these four corner points in a direction perpendicular to the chassis of the vehicle under test.

[0113] Next, for each frequency point in the frequency sequence, the computer device finds the maximum value corresponding to that frequency point among multiple first test point data. Finally, the computer device combines the maximum values ​​corresponding to each frequency point in order in the frequency sequence to form the second test point data.

[0114] In one implementation, when generating the 3D mesh, the computer device can determine points in a direction perpendicular to the chassis of the vehicle under test based on these corner points. Therefore, the computer device can determine a series of test points in a direction perpendicular to the chassis of the vehicle under test based on the corner points of the planar mesh.

[0115] In one implementation, the computer device can determine the maximum value of each frequency point in the frequency sequence by traversing each frequency point. Furthermore, after determining the current frequency point, the computer device can select the maximum value from multiple test point data points perpendicular to the chassis direction of the vehicle under test. This maximum value is the maximum field strength.

[0116] In one implementation, the selection of this maximum value is also based on a pessimistic strategy, choosing the most pessimistic scenario that the vehicle under test might encounter. Based on this value, the electromagnetic field of the vehicle under test can be evaluated under the most pessimistic condition. Furthermore, based on this evaluation result, designers can achieve more effective optimization. And, based on this evaluation result, users can better understand the vehicle's performance under the most pessimistic scenario.

[0117] S10332. Obtain the second test point data of the four test points corresponding to the planar grid, and use the average of the four second test point data as the comprehensive data of the planar grid.

[0118] For example, after the computer device calculates the second test point data corresponding to the four corner points of each planar grid, the computer device can calculate the average of these four second test point data as the comprehensive data of the planar grid.

[0119] In one implementation, the calculation of this mean can avoid the inaccuracy of test results that may occur based on single-point measurements. Using this mean as the comprehensive data for the planar mesh allows the comprehensive data to better describe the planar mesh, thereby improving the validity of the evaluation results.

[0120] In this example, by acquiring multiple test point data in the vertical direction and extracting the maximum value of each frequency point in the frequency sequence to form the second test point data, and then using the average value of the second test point data corresponding to four test points in the planar grid as the means of comprehensive data, the comprehensive data of the planar grid is more effectively integrated, avoiding the influence of isolated values ​​and outliers, thereby improving the effectiveness of the comprehensive data.

[0121] In one example, the specific process of calculating the evaluation score of each planar mesh on the grid surface based on the reference data and the comprehensive data of each planar mesh on the grid surface in step S104 above includes: S1041. Calculate the ratio of the composite data of the planar grid on the grid surface to the reference data to obtain the first ratio vector. The first ratio vector is an eigenvector composed of the ratio of the composite field strength to the reference field strength on a preset frequency sequence.

[0122] For example, the computer device can first calculate the ratio of the composite data to the reference data for each planar grid, and then obtain a first ratio vector.

[0123] In one implementation, a computer device can calculate the ratio between the comprehensive field strength corresponding to each frequency point in the comprehensive data and the reference field strength corresponding to that frequency point in the reference data. The ratios corresponding to all frequency points within the preset frequency range constitute the first ratio vector.

[0124] S1042. Superimpose the frequency weight vector onto the first ratio vector to obtain the second ratio vector. The frequency weight vector includes the influence coefficients of each frequency on the human body in the preset frequency sequence.

[0125] For example, a frequency weight vector is pre-stored in the computer device. This frequency weight vector contains weight information corresponding to each frequency point. The computer device superimposes the frequency weight vector onto a first ratio vector to obtain a second ratio vector.

[0126] In one implementation, the weight information in the frequency weight vector consists of the weight coefficient for each frequency point. The computer device can obtain the optimized second ratio vector by multiplying the values ​​in the frequency weight vector corresponding to the same frequency point with the values ​​in the first ratio vector.

[0127] In another implementation, the weight information in the frequency weight vector is the incremental value for each frequency point. The computer device can obtain the optimized second ratio vector by adding the values ​​in the frequency weight vector corresponding to the same frequency point to the values ​​in the first ratio vector.

[0128] S1043. Obtain the maximum value in the second ratio vector as the evaluation score of the planar grid.

[0129] For example, the computer device obtains the maximum value in the second ratio vector and uses it as the evaluation score of the planar grid.

[0130] Alternatively, the computer device can also obtain the mean of the ratios in the second ratio vector as the evaluation score of the planar grid.

[0131] In this example, a first ratio vector is obtained by comparing the comprehensive data of the planar grid with the reference data. A second ratio vector is obtained by superimposing a frequency weight vector on the first ratio vector. The maximum value in the second ratio vector is then used as a means of evaluating the score, thereby achieving the effect of accurately quantifying the electromagnetic field strength of each planar grid region.

[0132] In one example, the aforementioned electromagnetic field measuring device may be one or more of the following: an ELT-400 electromagnetic radiation measuring instrument, a data acquisition instrument, and a medium- and short-wave electromagnetic field probe.

[0133] Figure 4 A flowchart illustrating a method for assessing electromagnetic interference in a crew cabin, provided as an embodiment of this application. Figures 1 to 3 Based on the illustrated embodiment, when the commonly used device is a mobile phone and the electromagnetic field measuring device is a medium- or short-wave electromagnetic field probe, such as Figure 4 As shown, the process includes the following steps: S401. Obtain baseline data.

[0134] For example, a user can use a medium- or short-wave electromagnetic field probe to perform at least one data acquisition on at least one mobile phone to obtain a first measurement value. Based on the acquired first measurement value, a computer device can obtain final reference data.

[0135] In one implementation, when multiple data collections are performed on a mobile phone, these multiple collections can be executed in different operating states of the phone. For example, these operating states could be making a call, the phone in standby mode, the phone playing music, the phone transmitting data via Bluetooth, or the phone charging.

[0136] In one implementation, during a single sampling process of a mobile phone, a computer device can acquire the field strength of the phone at different frequencies based on an electromagnetic field probe. For example, as... Figure 4 As shown, this electromagnetic field probe can measure field strength at frequencies from 10Hz to 1,000,000Hz. For example, this frequency range can be 10Hz-400kHz or 400kHz-30MHz.

[0137] In one implementation, the electromagnetic field of the mobile phone can be collected at a sampling location. This sampling location can be a position close to the mobile phone.

[0138] In one implementation, the computer device can collect first measurement values ​​based on mobile phones from multiple brands under various working states, and generate a feature matrix. This feature matrix can be a two-dimensional matrix. Each value in this feature matrix... , can correspond to the first The brand of mobile phones, in the first The first measured value under a certain working state. Optionally, the first measured value is specifically the field strength versus frequency curve obtained by the electromagnetic field probe within a preset frequency range.

[0139] In another implementation, the computer device can collect first measurement values ​​from multiple brands of mobile phones under various working conditions, and generate a feature matrix. This feature matrix can be a three-dimensional matrix. Each value in this feature matrix... , can correspond to the first The brand of mobile phones, in the first In the current working state, the first Field strength at each frequency point.

[0140] In one implementation, the computer device can obtain the average value of mobile phones of various brands under various working states at various frequency points to obtain the reference field strength corresponding to each frequency point.

[0141] In another implementation, the computer device can obtain the maximum value of each brand of mobile phone in each working state at each frequency point, and obtain the reference field strength corresponding to each frequency point.

[0142] S402. Mark the interior of the passenger compartment of the vehicle under test with a grid.

[0143] For example, the computer device can first acquire the interior space of the passenger compartment of the vehicle under test. Then, the computer device can divide the interior space of the passenger compartment of the vehicle under test into a grid to obtain grid markings.

[0144] In one example, the process may specifically include the following steps: S4021. Determine the grid boundary based on the passenger compartment boundary as a reference point.

[0145] In one implementation, the computer device can determine the grid boundary by extending inward by a first preset length, using the passenger compartment boundary as a reference point. For example, the first preset length can be 10 cm.

[0146] In another implementation, the computer device can determine the largest rectangle as the grid boundary based on the crew cabin boundary.

[0147] S4022. The computer device can select four vertices of the boundary and use these four vertices as test points. Then, the computer device can use these four vertices as initial points and divide the rectangular area into grid-like test points at intervals of a second preset length to obtain a grid surface composed of planar grids.

[0148] In one implementation, the mesh surface can be as follows: Figure 3 As shown.

[0149] In one implementation, the center console, floor, seats, and other equipment are not considered during the division of the test points.

[0150] In another implementation, if fixed equipment such as the center console, floor, or seats is encountered during the division of the test point, the test point will not be set at that location.

[0151] In one implementation, the second preset length can be 10cm. Then the grid matrix includes a 10cm × 10cm grid.

[0152] In another implementation, the length and width of the grid can have different values. Optionally, the length can be a second preset length, and the width can be a third preset length. For example, the second preset length can be 10cm, and the third preset length can be 15cm. Then the grid matrix includes a 10cm × 15cm grid.

[0153] S4023. Based on the completion of test point marking on the grid surface, the computer equipment can mark grid points upward or downward in the direction perpendicular to the chassis of the vehicle under test, with a second preset length interval, thereby dividing the interior space into three-dimensional space points.

[0154] In one implementation, the computer device can use a fourth preset length to divide the space vertically. For example, the fourth preset length can be 20cm. For example, the three-dimensional grid can be 10cm×10cm×20cm. Alternatively, the three-dimensional grid can also be 10cm×15cm×20cm.

[0155] In one implementation, the three-dimensional mesh can form a cuboid. The height of the cuboid can be composed of various heights of the rectangular region in step S3022, which can be reached by the cross-section of the vehicle interior.

[0156] In another implementation, the cuboid formed by the three-dimensional grid is the largest cuboid inside the passenger compartment of the vehicle under test.

[0157] In another example, the computer device can determine the location of the center point of the passenger compartment inside the vehicle under test after determining the space of the passenger compartment. Then, the computer device can expand the test points from the center point in the horizontal, vertical and three-dimensional directions according to a second preset length to obtain a three-dimensional network inside the passenger compartment.

[0158] In one implementation, the test point is discarded when it exceeds the outer boundary of the vehicle under test.

[0159] In one implementation, when the test point is located on a fixed device such as the center console, floor, or seat, the test point is discarded.

[0160] S403. Under different vehicle operating conditions, use an electromagnetic field probe to collect data at the test points indicated by the grid in the passenger compartment to obtain the second measurement value.

[0161] In one implementation, for the test points in the above grid, the computer equipment can collect data under different vehicle operating conditions using an ELT-400 electromagnetic radiation measuring instrument, a data acquisition instrument, and a medium- and short-wave electromagnetic field probe, respectively, to obtain multiple second measurement values ​​under various vehicle operating conditions. These second measurement values ​​are compared with... Figure 2 Similarly, the curve is composed of frequency and field strength.

[0162] In one implementation, the multiple vehicle operating conditions may include six scenarios: vehicle stationary, vehicle at a constant speed of 80 km / h, vehicle accelerating rapidly from 0-100 km / h, vehicle decelerating rapidly from 0-100 km / h, vehicle charging at maximum DC current, and vehicle charging via AC. Optionally, in this case, each test point will obtain six sets of curves composed of frequency and field strength.

[0163] In one implementation, for each frequency point, the computer device can compare the field strength under different vehicle operating conditions and take the maximum value as the final field strength corresponding to that frequency.

[0164] In one implementation, for each frequency, the computer equipment can compare the field strength at various test points in the vertical direction of the crew cabin. The computer equipment can then use the highest field strength as the... Figure 3 The final field strength at this frequency corresponding to the test point on the two-dimensional plane is shown.

[0165] S404. Process the data corresponding to each test point in the grid.

[0166] For example, the computer device can acquire data corresponding to the test points at the four corners of each grid, and calculate the mean of these four data points as the sampled data for that grid. The computer device can calculate the ratio of this sampled data to standard data, obtaining a ratio vector. The maximum value in this ratio vector is then obtained as the composite value for that grid.

[0167] S405. Draw a heat map based on the combined values ​​of each grid.

[0168] In one implementation, the computer device can have multiple preset color levels. The computer device can determine the color level corresponding to a grid based on the comprehensive value of each grid, and then use the color of that color level to fill the grid to obtain the final heat map.

[0169] For example, a composite value in the range of 0-1 corresponds to red, 1-2 corresponds to blue, and 2-3 corresponds to green.

[0170] In another implementation, the computer device can preset a color. The computer device can map the composite value of each grid cell to a range of 0-255. Then, based on the mapped value, the computer device can display the color at different depths within the grid cell, thus obtaining a heatmap.

[0171] For example, the heat map can be like Figure 5 As shown.

[0172] In this embodiment, a comprehensive and accurate assessment of the electromagnetic field distribution and intensity of the vehicle's passenger compartment is achieved by using data collected from multiple brands of mobile phones in multiple states to determine the baseline value, marking the three-dimensional grid of the vehicle's passenger compartment and collecting data under multiple operating conditions, and then processing the data to draw a heat map.

[0173] Figure 6 A structural diagram of an electromagnetic interference assessment device for a crew cabin provided in this application embodiment is shown below. Figure 6 As shown, the crew cabin electromagnetic interference assessment device 600 includes: The reference module 601 is used to sample multiple commonly used devices under various operating conditions using an electromagnetic field measurement device, and calculate reference data based on the sampled data; wherein, the reference data is a feature vector composed of reference field strengths on a preset frequency sequence; The sampling module 602 is used to determine a grid surface composed of multiple planar grids parallel to the chassis direction of the vehicle under test in the passenger compartment interior space of the vehicle under test based on a preset side length; and to divide multiple three-dimensional grids in the direction perpendicular to the chassis direction of the vehicle under test based on the grid surface and the preset side length; to determine the grid points of the three-dimensional grids as test points; to use an electromagnetic field measurement device to sample the test points under various vehicle operating conditions of the vehicle under test, and to calculate the comprehensive data of each planar grid on the grid surface based on the sampled data; the comprehensive data is a feature vector composed of the comprehensive field strength on a preset frequency sequence. Evaluation module 603 is used to calculate the evaluation score of each planar grid on the grid surface based on the baseline data and the comprehensive data of each planar grid on the grid surface; and to generate a heat map of the grid surface based on the evaluation score.

[0174] In one example, sampling module 602 is used for: Electromagnetic field measurement equipment is used to sample test points under various vehicle operating conditions of the vehicle under test to obtain vehicle sampling data; the vehicle sampling data is a feature vector composed of the sampling field strength on a preset frequency sequence. Based on the preset operating condition weights, the vehicle sampling data under various vehicle operating conditions corresponding to each test point are weighted and summed to obtain the first test point data corresponding to each pilot test. Obtain the first test point data of multiple test points corresponding to a planar grid in the grid plane, which are perpendicular to the chassis of the vehicle under test, and calculate the comprehensive data of the planar grid based on the first test point data.

[0175] In one example, sampling module 602 is used for: Acquire test point data from multiple test points perpendicular to the chassis direction of the vehicle under test, and use the maximum value of each frequency point in the frequency sequence among the multiple test point data to form the second test point data; Obtain the second test point data of the four test points corresponding to the planar grid, and use the average of the four second test point data as the comprehensive data of the planar grid.

[0176] In one example, the weight of each operating condition is determined based on its usage frequency.

[0177] In one example, evaluation module 603 is used for: The ratio of the composite data of the planar grid on the grid surface to the reference data is calculated to obtain the first ratio vector; the first ratio vector is an eigenvector composed of the ratio of the composite field strength to the reference field strength on the preset frequency sequence. The frequency weight vector is superimposed on the first ratio vector to obtain the second ratio vector; the frequency weight vector includes the influence coefficient of each frequency on the human body in the preset frequency sequence. Obtain the maximum value in the second ratio vector as the evaluation score for the planar grid.

[0178] In one example, sampling module 602 is used for: Scan the interior space of the passenger compartment of the vehicle under test to obtain the boundary information of the interior space of the passenger compartment; Based on boundary information, determine the maximum inscribed rectangle of the crew cabin interior space; and determine the four corner points of the maximum inscribed rectangle; Based on the preset side length, starting from the four corner points, a planar network is drawn in the largest inscribed rectangle, and a grid surface is formed based on the planar grid.

[0179] In one example, the reference module 601 is used for: Electromagnetic field measurement equipment is used to sample multiple commonly used devices under various operating conditions to obtain multiple first sampling data; the first sampling data represents a feature sequence composed of multiple first field strengths corresponding to multiple frequency points collected by a commonly used device under a certain operating condition; The maximum value of each frequency point among multiple first field strengths is taken as the reference field strength of the frequency point; and the reference field strengths of each frequency point on the preset frequency sequence are combined to form reference data.

[0180] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0181] In this embodiment, the crew cabin electromagnetic interference assessment device is presented in the form of a functional unit. Here, a unit refers to an application-specific integrated circuit (ASIC), a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0182] Figure 7 A structural diagram of a computer device provided in an embodiment of this application, such as... Figure 7 As shown, the computer device 700 includes one or more processors 701, memory 702, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interface). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 7 Take the 701 processor as an example.

[0183] Processor 701 may be a central processing unit, a network processor, or a combination thereof. Processor 701 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GPA), or any combination thereof.

[0184] The memory 702 stores instructions executable by at least one processor 701 to cause the at least one processor 701 to perform the method shown in the above embodiments.

[0185] The memory 702 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 702 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 702 may optionally include memory remotely located relative to the processor 701, and these remote memories can be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0186] The memory 702 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 702 may also include a combination of the above types of memory.

[0187] The computer device also includes a communication interface 703 for communicating with other devices or communication networks.

[0188] This application also provides a computer-readable storage medium. The methods described in this application can be implemented in hardware or firmware, or implemented as recordable on a storage medium, or implemented as computer code downloaded over a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and subsequently stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the methods shown in the above embodiments are implemented.

[0189] This application provides a computer program product including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the method of any embodiment of this application.

[0190] Although embodiments of this application have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of this application, and all such modifications and variations fall within the scope defined by the appended claims.

[0191] Although embodiments of this application have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of this application, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for assessing electromagnetic interference in a crew cabin, characterized in that, The method includes: Electromagnetic field measurement equipment is used to sample multiple commonly used devices under various operating conditions, and reference data is calculated based on the sampled data; wherein, the reference data is a feature vector composed of reference field strengths on a preset frequency sequence; Based on a preset side length, a grid surface composed of multiple planar grids parallel to the chassis direction of the vehicle under test is determined in the passenger compartment interior space of the vehicle under test; and based on the grid surface and the preset side length, multiple three-dimensional grids are divided in the direction perpendicular to the chassis of the vehicle under test; and the grid points of the three-dimensional grids are determined as test points. Using an electromagnetic field measurement device, the test points are sampled under various vehicle operating conditions of the vehicle under test, and the comprehensive data of each planar grid on the grid surface is calculated based on the sampled data; the comprehensive data is a feature vector composed of the comprehensive field strength on a preset frequency sequence. Based on the baseline data and the comprehensive data of each planar grid on the grid surface, the evaluation score of each planar grid on the grid surface is calculated; and based on the evaluation score, a heat map of the grid surface is generated.

2. The method according to claim 1, characterized in that, Using electromagnetic field measurement equipment, the test points are sampled under various vehicle operating conditions of the vehicle under test, and the comprehensive data of each planar grid on the grid surface is calculated based on the sampled data, including: Using an electromagnetic field measurement device, the test points are sampled under various vehicle operating conditions of the vehicle under test to obtain vehicle sampling data; the vehicle sampling data is a feature vector composed of the sampling field strength on a preset frequency sequence. Based on preset operating condition weights, the vehicle sampling data under various vehicle operating conditions corresponding to each test point are weighted and summed to obtain the first test point data corresponding to each pilot test. The data of the first test point, which corresponds to a plane grid in the grid surface and is perpendicular to the chassis direction of the vehicle under test, is obtained, and the comprehensive data of the plane grid is calculated based on the first test point data.

3. The method according to claim 2, characterized in that, Acquire the first test point data of multiple test points corresponding to a planar grid in the grid surface, perpendicular to the chassis direction of the vehicle under test, and calculate the comprehensive data of the planar grid based on the first test point data, including: Acquire test point data of multiple test points perpendicular to the chassis direction of the vehicle under test, and form a second test point data by taking the maximum value of each frequency point in the frequency sequence among the multiple test point data; Obtain the second test point data of four test points corresponding to the planar grid, and use the average of the four second test point data as the comprehensive data of the planar grid.

4. The method according to claim 2, characterized in that, The weights of the operating conditions are determined based on the frequency of use of each operating condition.

5. The method according to any one of claims 1-4, characterized in that, Based on the baseline data and the comprehensive data of each planar grid on the grid surface, the evaluation score of each planar grid on the grid surface is calculated, including: Calculate the ratio of the composite data of the planar grid on the grid surface to the reference data to obtain a first ratio vector; the first ratio vector is a feature vector composed of the ratio of the composite field strength to the reference field strength on a preset frequency sequence; The frequency weight vector is superimposed on the first ratio vector to obtain the second ratio vector; the frequency weight vector includes the influence coefficient of each frequency on the human body in the preset frequency sequence. The maximum value in the second ratio vector is obtained as the evaluation score of the planar grid.

6. The method according to any one of claims 1-4, characterized in that, Based on a preset side length, a grid surface composed of multiple planar grids parallel to the chassis direction of the vehicle under test is determined in the interior space of the passenger compartment, including: Scan the interior space of the passenger compartment of the vehicle under test to obtain the boundary information of the interior space of the passenger compartment; Based on the boundary information, the maximum inscribed rectangle of the passenger compartment interior space is determined; and the four corner points of the maximum inscribed rectangle are determined. Based on the preset side length, starting from the four corner points, a planar network is drawn in the largest inscribed rectangle, and the grid surface is formed based on the planar grid.

7. The method according to any one of claims 1-4, characterized in that, Electromagnetic field measurement equipment was used to sample multiple commonly used devices under various operating conditions, and baseline data was calculated based on the sampled data, including: Electromagnetic field measurement equipment is used to sample multiple commonly used devices under various operating conditions to obtain multiple first sampling data; the first sampling data represents a feature sequence composed of multiple first field strengths corresponding to multiple frequency points collected by a commonly used device under a certain operating condition; The maximum value of each frequency point among multiple first field strengths is taken as the reference field strength of the frequency point; and the reference field strengths of each frequency point on the preset frequency sequence are used to form the reference data.

8. A passenger cabin electromagnetic interference assessment device, characterized in that, The device includes: The reference module is used to sample multiple commonly used devices under various operating conditions using electromagnetic field measurement equipment, and calculate reference data based on the sampled data; wherein, the reference data is a feature vector composed of reference field strengths on a preset frequency sequence; The sampling module is used to determine a grid surface composed of multiple planar grids parallel to the chassis direction of the vehicle under test in the passenger compartment interior space of the vehicle under test, based on a preset side length; and to divide multiple three-dimensional grids in the direction perpendicular to the chassis direction of the vehicle under test based on the grid surface and the preset side length; to determine the grid points of the three-dimensional grids as test points; to sample the test points under various vehicle operating conditions using an electromagnetic field measurement device; and to calculate the comprehensive data of each planar grid on the grid surface based on the sampled data; the comprehensive data is a feature vector composed of comprehensive field strength on a preset frequency sequence. The evaluation module is used to calculate the evaluation score of each planar grid on the grid surface based on the benchmark data and the comprehensive data of each planar grid on the grid surface; and to generate a heat map of the grid surface based on the evaluation score.

9. A computer device, characterized in that, include: A memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, the processor executing the computer instructions to perform the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the method of any one of claims 1 to 7.

11. A computer program product, characterized in that, Includes computer instructions for causing a computer to perform the method of any one of claims 1 to 7.

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