Multi-dimensional self-adaptive positioning detection system and method for automobile tail door

Abstract

The invention relates to the technical field of automobile detection, and discloses an automobile tail gate multi-dimensional adaptive positioning detection system and method, and the system comprises a data collection module which is used for collecting multi-dimensional data on an automobile tail gate; the deformation analysis module is used for analyzing the three-dimensional deformation gradient of the sealing strip on the automobile tail door; the pose correction module is used for carrying out living body operation authentication on the automobile tail door based on the three-dimensional deformation gradient and the palm heat radiation image to obtain a living body authentication result, and analyzing the pose correction amount of the automobile tail door by utilizing the living body authentication result and the tail door coordinate deviation; the pressure identification module is used for performing timestamp alignment on the three-dimensional deformation gradient and the pose correction amount to obtain alignment dimension data, and identifying contact pressure distribution between the automobile tail door and the automobile by using the alignment dimension data; and the positioning detection module is used for determining a positioning detection result on the automobile tail door through the contact pressure distribution. According to the invention, multi-dimensional data can be fused, environment interference can be dynamically compensated, and time synchronization can be realized.

Description

Technical Field

[0001] This invention relates to the field of automotive testing technology, and in particular to a multi-dimensional adaptive positioning testing system and method for automotive tailgates. Background Technology

[0002] The multi-dimensional adaptive positioning detection system and method for automobile tailgates is an intelligent technology that achieves precise tailgate positioning by fusing multi-source data. The system includes modules for data acquisition, deformation analysis, pose correction, pressure recognition, and positioning detection. It can simultaneously acquire multi-dimensional data such as dual-frequency electromagnetic signals, sweep frequency excitation signals, palm thermal radiation images, and tailgate coordinate deviations. Based on this, it analyzes the three-dimensional deformation gradient of the sealing strip, performs live operation authentication, dynamically corrects the tailgate pose, and finally determines the positioning result through contact pressure distribution. Its core lies in the collaborative analysis and closed-loop control of multi-dimensional data to improve the accuracy and reliability of tailgate positioning.

[0003] In existing technologies, automotive tailgate detection systems mostly rely on single-type sensors, such as mechanical displacement or optical ranging, which have limited data dimensions and cannot fully reflect the deformation and contact status of the sealing strip. For example, traditional liveness detection relies solely on biometric recognition, such as fingerprints or faces, lacking dynamic verification of mechanical operation behavior and making it vulnerable to forgery attacks. At the same time, the interference of environmental temperature and humidity changes on sensor data is not effectively compensated, resulting in large fluctuations in detection results. In addition, the problem of asynchronous timestamps in data acquisition from different modules is common, affecting the accuracy of multi-source data fusion.

[0004] In summary, existing technologies suffer from several drawbacks, including insufficient detection dimensions due to the single data source, low security of liveness detection, poor environmental adaptability, and insufficient time synchronization accuracy. These issues directly restrict the reliability of tailgate positioning systems and their stability under complex operating conditions. There is an urgent need for a comprehensive solution that can integrate multi-dimensional data, dynamically compensate for environmental interference, and achieve high-precision time synchronization. Summary of the Invention

[0005] This invention provides a multi-dimensional adaptive positioning and detection system for automobile tailgates, the main purpose of which is to integrate multi-dimensional data, dynamically compensate for environmental interference, and achieve time synchronization.

[0006] To achieve the above objectives, the present invention provides a multi-dimensional adaptive positioning and detection system for automobile tailgates, comprising: a data acquisition module, a deformation analysis module, a pose correction module, a pressure recognition module, and a positioning and detection module; The data acquisition module is used to collect multi-dimensional data from the tailgate of a car, including dual-frequency electromagnetic signals, sweep frequency excitation signals, palm thermal radiation images, and tailgate coordinate deviations. The deformation analysis module is used to analyze the three-dimensional deformation gradient of the sealing strip on the car tailgate based on the dual-frequency electromagnetic signal and the sweep frequency excitation signal. The pose correction module is used to perform live operation authentication on the car tailgate based on the three-dimensional deformation gradient and the palm thermal radiation image, obtain the live authentication result, and analyze the pose correction amount of the car tailgate using the live authentication result and the tailgate coordinate deviation. The pressure recognition module is used to align the three-dimensional deformation gradient with the pose correction amount using timestamps to obtain alignment dimension data, and to use the alignment dimension data to identify the contact pressure distribution between the car tailgate and the car. The positioning detection module is used to determine the positioning detection result on the tailgate of the car based on the contact pressure distribution.

[0007] Optionally, the collection of multi-dimensional data from the car tailgate includes: A conductive fiber network is embedded in the sealing strip groove of the car tailgate; After alternating input of dual-frequency electromagnetic waves into the conductive fiber network, the current phase difference and signal attenuation value of the conductive fiber network with respect to the dual-frequency electromagnetic waves are collected. The current phase difference and the signal attenuation value are used as electromagnetic detection data; A vibration sensor is installed on the tailgate of the vehicle. After controlling the vibration sensor to vibrate the sealing strip at a linearly increasing frequency, the vibration amplitude of the sealing strip with respect to the linearly increasing frequency is recorded; The far-infrared image of the hand on the tailgate of the car is monitored by an infrared thermal imager deployed on the tailgate of the car. Convert the far-infrared image into an initial grayscale image; Environmental adaptive corrections are performed on the vibration amplitude and the initial grayscale image respectively to obtain the amplitude response value and the grayscale image; Convert the amplitude response values ​​over a continuous period of time into vibration detection data; The grayscale image is used as a thermal radiation map of the palm. After emitting a laser signal to the reflective markings on the tailgate of the vehicle using a triangulation device deployed on the tailgate, the tailgate coordinate deviation is calculated using the triangulation method.

[0008] Optionally, the step of performing environmental adaptive correction on the vibration amplitude and the initial grayscale image respectively to obtain the amplitude response value and the grayscale image includes: Monitor the ambient temperature and humidity of the vehicle's tailgate; Based on the ambient temperature, the initial grayscale image is subjected to environmental adaptive correction to obtain a grayscale image; Based on the ambient humidity, the vibration amplitude is subjected to environmental adaptive correction to obtain the amplitude response value.

[0009] Optionally, the step of analyzing the three-dimensional deformation gradient of the sealing strip on the car tailgate based on the electromagnetic detection data and the vibration detection data includes: Based on the electromagnetic detection data and the vibration detection data, the impedance change of the sealing strip on the tailgate of the vehicle is calculated using the following formula: ; in, This represents the amount of impedance change. This represents the change in electromagnetic impedance. This indicates the current phase difference in the electromagnetic detection data. This represents the signal attenuation value in the electromagnetic detection data. The imaginary component representing the change in electromagnetic impedance. Indicates the reference impedance. This indicates the vibration frequency corresponding to the vibration detection data. This represents the amplitude response value in the vibration detection data. This represents the change in vibration impedance; Based on the impedance change, the three-dimensional deformation gradient of the sealing strip on the tailgate of the automobile is calculated using the following formula: ; in, Represents the three-dimensional deformation gradient along the Z-axis. This represents the amount of impedance change. Indicates the length of the conductive fiber network. This indicates the elastic modulus of the sealing strip material. This indicates the Poisson's ratio of the sealing strip material.

[0010] Optionally, the step of performing liveness authentication on the car tailgate based on the three-dimensional deformation gradient and the palm thermal radiation map to obtain a liveness authentication result includes: The palm thermal radiation map is scaled up to a preset pixel image; The palm heat pattern features in the preset pixel image are extracted using the ResNet-18 model; Calculate the feature similarity between the palm heatprint features and the preset template features; Set the gradient range of the three-dimensional deformation gradient and the similarity threshold of the feature similarity respectively; When the three-dimensional deformation gradient conforms to the gradient interval and the feature similarity is higher than the similarity threshold, the successful liveness operation authentication is taken as the liveness authentication result. When the three-dimensional deformation gradient does not meet the gradient threshold and the feature similarity is not higher than the similarity threshold, the liveness authentication failure is taken as the liveness authentication result.

[0011] Optionally, the step of analyzing the pose correction amount of the vehicle tailgate using the liveness authentication result and the tailgate coordinate deviation includes: When the liveness authentication result is successful, the pose correction amount of the car tailgate is calculated based on the tailgate coordinate deviation.

[0012] Optionally, the step of aligning the three-dimensional deformation gradient with the pose correction amount using timestamps to obtain aligned dimension data includes: Query the first and second timestamps corresponding to the three-dimensional deformation gradient and the pose correction amount, respectively; Generate a reference timestamp between the first timestamp and the second timestamp; calculate the first time difference between the first timestamp and the reference timestamp, and the second time difference between the second timestamp and the reference timestamp, respectively; Based on the first time difference and the second time difference, data interpolation processing is performed on the three-dimensional deformation gradient and the pose correction amount to obtain interpolated gradient data and interpolated pose data. The interpolation gradient data and the interpolation correction data are concatenated to form aligned dimension data.

[0013] Optionally, identifying the contact pressure distribution between the car tailgate and the car using the alignment dimension data includes: Obtain the target data belonging to the same timestamp from the alignment dimension data; Obtain the target interpolation gradient and target correction amount from the target data; The corrected gradient value is obtained by performing gradient correction on the target interpolation gradient using the following formula: ; in, Indicates the corrected gradient value. This represents the pose correction amount on the X-axis. express Pose correction amount on the axis, express Pose correction amount on the axis, Represents the three-dimensional deformation gradient along the Z-axis. Represents a linear mapping function; Determine whether the standard pressure value corresponding to the correction gradient value can be queried in a preset gradient-pressure mapping table; When the standard pressure value corresponding to the correction gradient value can be queried in the preset gradient-pressure mapping table, the surface of the sealing strip on the car tailgate is divided into a grid to obtain the sealing strip grid; The contact pressure distribution of the sealing strip mesh is calculated using the standard pressure values ​​at the measuring points around the sealing strip mesh. When the standard pressure value corresponding to the correction gradient value cannot be found in the preset gradient-pressure mapping table, the standard pressure value corresponding to the correction gradient value is calculated according to the gradient-pressure mapping table. The contact pressure distribution between the tailgate and the vehicle is determined by the standard pressure value.

[0014] Optionally, determining the positioning detection result on the vehicle tailgate through the contact pressure distribution includes: Query the qualified pressure distributions in the contact pressure distribution that are higher than the preset pressure threshold; Calculate the compliance rate score of the compliance pressure distribution; Obtain the pose correction amount; Calculate the average value of the pose correction amount; The average of the pass rate score and the correction amount is used as the location detection result.

[0015] This invention also provides a multi-dimensional adaptive positioning detection method for automobile tailgates, characterized in that the method includes: Collect multi-dimensional data from the tailgate of a car, including dual-frequency electromagnetic signals, sweep frequency excitation signals, palm thermal radiation images, and tailgate coordinate deviations. The three-dimensional deformation gradient of the sealing strip on the car tailgate is analyzed based on the dual-frequency electromagnetic signal and the swept frequency excitation signal. Based on the three-dimensional deformation gradient and the palm thermal radiation image, live operation authentication is performed on the car tailgate to obtain the live authentication result. The pose correction amount of the car tailgate is analyzed by using the live authentication result and the tailgate coordinate deviation. The three-dimensional deformation gradient and the pose correction amount are time-stamped to obtain alignment dimension data, and the alignment dimension data is used to identify the contact pressure distribution between the car tailgate and the car. The positioning detection result on the car tailgate is determined by the contact pressure distribution.

[0016] Compared to the shortcomings mentioned in the background technology, the embodiments of the present invention employ alternating inputs of 1MHz and 10MHz dual-frequency electromagnetic waves into a conductive fiber network. By detecting the impedance change of the sealing strip through phase difference and attenuation value, it accurately reflects microscopic deformation. A vibration sensor excites the sealing strip with a linearly increasing frequency, records the amplitude response value, and captures the dynamic response of the sealing strip at different frequencies. An infrared thermal imager acquires far-infrared images, and biometric authentication is achieved by combining liveness detection features. Triangulation is used to calculate the three-dimensional coordinate deviation of the tailgate in real time. Multi-dimensional data covers electromagnetic, vibration, thermal radiation, and spatial pose information, providing comprehensive detection capabilities for sealing strip deformation gradient, operator identity, and tailgate pose deviation. This overcomes the limitations of insufficient data dimensions in traditional single-sensor systems. Furthermore, the embodiments of the present invention use formulas to correct thermal radiation images and dynamically compensate for temperature and humidity interference, avoiding misjudgments caused by environmental fluctuations in traditional systems. Furthermore, the embodiments of the present invention integrate the impedance change formula... By combining electromagnetic and vibration data, a three-dimensional deformation gradient is calculated. Using a physical model based on the electromagnetic and vibration data, the three-dimensional deformation gradient of the sealing strip is precisely quantified. Furthermore, this invention employs a dual authentication mechanism combining biometrics and dynamic mechanical operation characteristics to resist forgery attacks. Further, this invention uses a PID control formula to correct the tailgate pose in real time; closed-loop pose correction improves positioning accuracy, addressing the lack of dynamic verification in traditional systems. Furthermore, this invention aligns the three-dimensional deformation gradient with the pose correction amount using timestamps, resolving data fusion errors caused by asynchronous timestamps across multiple modules in traditional systems. Furthermore, this invention combines a gradient-pressure mapping table to achieve sub-millimeter-level accuracy in identifying contact pressure distribution, resolving the inaccuracy problem of multi-source data fusion in traditional systems. Finally, this invention improves system robustness under complex working conditions by comprehensively judging the positioning result based on the uniformity of contact pressure and the average value of the correction amount. Therefore, this invention can integrate multi-dimensional data, dynamically compensate for environmental interference, and achieve time synchronization. Attached Figure Description

[0017] Figure 1 This is a functional block diagram of a multi-dimensional adaptive positioning and detection system for a car tailgate provided in an embodiment of the present invention; Figure 2 A frequency-impedance mapping table for a multi-dimensional adaptive positioning detection method for automobile tailgate provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the pressure recognition process of a multi-dimensional adaptive positioning detection method for a car tailgate according to an embodiment of the present invention; Figure 4 This is a flowchart illustrating a multi-dimensional adaptive positioning and detection method for a car tailgate according to an embodiment of the present invention. The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0020] In practice, the server-side equipment deployed in the multi-dimensional adaptive positioning and detection system for car tailgates may consist of one or more devices. This system can be implemented as a business instance, a virtual machine, or hardware devices. For example, it can be implemented as a business instance deployed on one or more devices in a cloud node. Simply put, it can be understood as software deployed on a cloud node, providing multi-dimensional adaptive positioning and detection services for car tailgates to various user terminals. Alternatively, it can be implemented as a virtual machine deployed on one or more devices in a cloud node, with application software installed to manage various user terminals. Or, it can be implemented as a server composed of numerous identical or different types of hardware devices, with one or more devices configured to provide multi-dimensional adaptive positioning and detection services for car tailgates to various user terminals.

[0021] In terms of implementation, the multi-dimensional adaptive positioning and detection system for car tailgates and the user terminal are mutually compatible. That is, if the multi-dimensional adaptive positioning and detection system for car tailgates is implemented as an application installed on a cloud service platform, then the user terminal is implemented as a client that establishes a communication connection with the application; or if the multi-dimensional adaptive positioning and detection system for car tailgates is implemented as a website, then the user terminal is implemented as a webpage; or if the multi-dimensional adaptive positioning and detection system for car tailgates is implemented as a cloud service platform, then the user terminal is implemented as a mini-program in an instant messaging application.

[0022] Reference Figure 1 The diagram shown is a functional block diagram of a multi-dimensional adaptive positioning and detection system for a car tailgate provided in an embodiment of the present invention.

[0023] The multi-dimensional adaptive positioning and detection system 100 for a car tailgate described in this invention can be installed on a cloud server. In terms of implementation, it can be used as one or more service devices, or as an application installed on the cloud (e.g., a server or server cluster for multi-dimensional adaptive positioning and detection of a car tailgate), or it can be developed as a website. Depending on the functions implemented, the multi-dimensional adaptive positioning and detection system 100 for a car tailgate includes a data acquisition module 101, a deformation analysis module 102, a pose correction module 103, a pressure recognition module 104, and a positioning and detection module 105.

[0024] In this embodiment of the invention, in the tracking based on multi-dimensional adaptive positioning detection of a car tailgate, each of the above modules can be implemented independently and called upon other modules. This "calling" can be understood as one module connecting to multiple modules of another type and providing corresponding services to those connected modules. In the multi-dimensional adaptive positioning detection system for a car tailgate provided in this embodiment of the invention, the applicable scope of the multi-dimensional adaptive positioning detection architecture can be adjusted by adding modules and directly calling them without modifying the program code, achieving cluster-based horizontal expansion to quickly and flexibly expand the multi-dimensional adaptive positioning detection system for a car tailgate. In practical applications, the above modules can be set in the same device or different devices, or they can be set in a virtual device, such as a service instance in a cloud server.

[0025] The following describes the components and specific workflow of the multi-dimensional adaptive positioning and detection system for automobile tailgates, using specific embodiments as examples.

[0026] The data acquisition module 101 is used to collect multi-dimensional data on the tailgate of a car, including electromagnetic detection data, vibration detection data, palm thermal radiation map, and tailgate coordinate deviation.

[0027] This invention employs alternating inputs of 1MHz and 10MHz dual-frequency electromagnetic waves into a conductive fiber network. By detecting the impedance changes of the sealing strip through phase difference and attenuation value, it accurately reflects microscopic deformation. A vibration sensor excites the sealing strip with a linearly increasing frequency, records the amplitude response value, and captures the dynamic response of the sealing strip at different frequencies. An infrared thermal imager acquires far-infrared images, and biometric authentication is achieved by combining liveness detection features. Triangulation is used to calculate the three-dimensional coordinate deviation of the tailgate in real time. Multi-dimensional data covers electromagnetic, vibration, thermal radiation, and spatial pose information, providing comprehensive detection capabilities for sealing strip deformation gradient, operator identity, and tailgate pose deviation, overcoming the limitations of insufficient data dimensions in traditional single-sensor systems.

[0028] In one embodiment of the present invention, the acquisition of multi-dimensional data on the tailgate of a car includes: embedding a conductive fiber network in the sealing strip groove of the tailgate; after alternately inputting dual-frequency electromagnetic waves into the conductive fiber network, acquiring the current phase difference and signal attenuation value of the conductive fiber network with respect to the dual-frequency electromagnetic waves; using the current phase difference and the signal attenuation value as electromagnetic detection data; installing a vibration sensor on the tailgate of the car; controlling the vibration sensor to vibrate the sealing strip at a linearly increasing frequency, and recording the vibration amplitude of the sealing strip with respect to the linearly increasing frequency; and through a portion An infrared thermal imager mounted on the tailgate of the vehicle monitors the far-infrared image of a hand on the tailgate; the far-infrared image is converted into an initial grayscale image; environmental adaptive correction is performed on the vibration amplitude and the initial grayscale image respectively to obtain the amplitude response value and the grayscale image; the amplitude response value over a continuous time period is converted into vibration detection data; the grayscale image is used as a thermal radiation map of the hand; after emitting a laser signal to the reflective marking point on the tailgate of the vehicle through a triangulation ranging device deployed on the tailgate, the tailgate coordinate deviation is calculated using the triangulation ranging method.

[0029] The sealing strip is a strip-shaped seal installed around the tailgate of a car, typically made of rubber or plastic. The conductive fiber network, embedded in the grooves of the sealing strip, is a conductive material used to conduct electromagnetic signals. For example, carbon fiber woven into a mesh changes resistance with the deformation of the sealing strip. The dual-frequency electromagnetic wave refers to two electromagnetic waves of different frequencies used to detect impedance changes in the sealing strip. For example, 1MHz and 10MHz electromagnetic waves are alternately input into the conductive fiber network, and the deformation of the sealing strip under the opening and closing action of the tailgate is analyzed through phase difference and attenuation value. The current phase difference refers to the phase shift difference of the dual-frequency electromagnetic waves after propagation in the conductive fiber; for example, the phase difference Δφ = 30° between 1MHz and 10MHz electromagnetic waves. The signal attenuation value refers to the value of the electromagnetic wave after propagation in the conductive fiber. Energy loss, such as signal attenuation from 10V to 8V, attenuation value A=2V, the vibration sensor refers to a device that emits vibration frequency and measures the vibration of the sealing strip, such as a vibration controller that emits vibration frequency and a piezoelectric sensor that measures the vibration of the sealing strip, the vibration controller refers to a tool that controls the excitation signal of the vibration table or vibration exciter, which can generate various complex vibration waveforms, the far-infrared image refers to the far-infrared image emitted by the palm on the tailgate of a car captured by an infrared thermal imager, for example, the infrared image shows the temperature distribution of the palm and is used for liveness authentication, the initial grayscale image refers to the grayscale image without environmental compensation, the vibration detection data refers to the amplitude response value that changes with time, such as the amplitude response value collected at an emission frequency of 1Hz from time 1 to time 3.

[0030] Optionally, the process of embedding a conductive fiber network in the sealing strip groove of the car tailgate refers to laying a dual-frequency electromagnetic response fabric in the tailgate sealing strip groove and using a vacuum adsorption process to achieve adaptive bonding of the curved surface to the sealing strip groove. Further, the process of installing a vibration sensor on the car tailgate includes, for example, installing a high-frequency vibration signal generator (2.4 / 5.8GHz) along the tailgate frame and arranging miniature signal detection probes at 5cm intervals; one is a linearly increasing frequency generator, and the other is a vibration amplitude detector, which together constitute the vibration sensor. Further, the process of calculating the tailgate coordinate deviation using triangulation after emitting a laser signal to the reflective markings on the car tailgate through a triangulation device deployed on the car tailgate includes, for example, attaching three very small reflective markings to the car tailgate, with a diameter of only 2... Like three tiny reflective "bullseyes," the laser emitter fires laser beams at these three marked points. The laser beams are reflected back after hitting the points. In practical applications, besides the laser emitter, there is a laser receiver and an angle measuring device (or a sensor that detects the position of the laser spot). The laser emitter, receiver, and angle measuring device constitute a triangulation ranging device. The laser emitter, receiver, and marked points form a triangle. Assuming the distance from the laser emitter to the marked point is D, and the baseline distance between the laser emitter and receiver is B (this distance is known), when the laser hits the marked point and reflects back, the distance D can be calculated using trigonometric functions by measuring the angle θ between the reflected laser and the baseline, or by measuring the change in the position of the laser spot on the receiver. For example, the tangent function tanθ = ... The distance D / B is calculated by measuring the angle θ. A reference coordinate system is established, typically the ideal position of the tailgate as a reference point. Three coordinate axes (X, Y, Z) are established. For example, the center of the tailgate can be used as the origin, with the X-axis pointing to the left, the Y-axis to the right, and the Z-axis perpendicular to the tailgate surface outwards. For each reflective marker, its spatial coordinates (X, Y, Z) are measured using triangulation. For example, the actual coordinates of the left marker are (X, Y, Z). Simultaneously, the ideal coordinates of this marker (assuming the tailgate is perfectly flat and accurately installed) are (X', Y', Z'). Comparing the actual and ideal coordinates yields the deviation in each direction. For example, the deviation of the left marker in the X direction is deviation(X) = X - X'. If the deviation is +0.3mm, it means the marker is offset by 0.3mm from its ideal position in the X direction. The deviations on the Y and Z axes are calculated similarly.

[0031] Furthermore, the embodiments of the present invention use a formula to correct the dynamic compensation of temperature and humidity interference in thermal radiation images, thereby avoiding misjudgments caused by environmental fluctuations in traditional systems.

[0032] In another embodiment of the present invention, the step of performing environmental adaptive correction on the vibration amplitude and the initial grayscale image to obtain the amplitude response value and the grayscale image includes: monitoring the ambient temperature and humidity of the vehicle tailgate; and performing environmental adaptive correction on the initial grayscale image according to the ambient temperature using the following formula to obtain the grayscale image: ; in, Represents the grayscale value in a grayscale image. This represents the grayscale value in the initial grayscale image. Indicates the temperature compensation coefficient. Indicates ambient temperature. Indicates reference temperature; Based on the ambient humidity, the vibration amplitude is adaptively corrected using the following formula to obtain the amplitude response value: ; in, Indicates the amplitude response value. This indicates the reference density value of the sealing strip material under dry conditions. This indicates the density value of the sealing strip material under the specified ambient humidity. This indicates the amplitude of the vibration.

[0033] in, The coefficients are obtained through experimental calibration, such as by fitting the data using multiple linear regression algorithms or least squares methods. , The density of the sealing strip material was measured under dry conditions, while This refers to the density of the sealing strip material under the stated environmental humidity, as recorded in historical data. For example, different environmental humidity levels were simulated over a historical period, and the density of the sealing strip material was measured. A humidity-density mapping table from Himeji Castle can be used to look up the density value of the sealing strip material under the stated environmental humidity. (Regarding the formula...) , Temperature changes can cause slight variations in the sensitivity of optical sensors, circuit noise, or the thermal expansion and contraction characteristics of materials, thus affecting the stability of grayscale values. Sealing strip materials (such as rubber) absorb water and expand when humidity increases, affecting their density. A decrease (or an increase in volume) leads to a decrease in vibration amplitude.

[0034] Furthermore, in this embodiment of the invention, electromagnetic and vibration data are integrated through the impedance change formula to calculate the three-dimensional deformation gradient. Combined with the physical model of electromagnetic and vibration data, the three-dimensional deformation gradient of the sealing strip is accurately quantified.

[0035] In one embodiment of the present invention, analyzing the three-dimensional deformation gradient of the sealing strip on the tailgate of the vehicle based on the electromagnetic detection data and the vibration detection data includes: calculating the impedance change of the sealing strip on the tailgate of the vehicle based on the electromagnetic detection data and the vibration detection data using the following formula: ; in, This represents the amount of impedance change. This represents the change in electromagnetic impedance. This indicates the current phase difference in the electromagnetic detection data. This represents the signal attenuation value in the electromagnetic detection data. The imaginary component representing the change in electromagnetic impedance. Indicates the reference impedance. This indicates the vibration frequency corresponding to the vibration detection data. This represents the amplitude response value in the vibration detection data. This represents the change in vibration impedance; Based on the impedance change, the three-dimensional deformation gradient of the sealing strip on the tailgate of the automobile is calculated using the following formula: ; in, Represents the three-dimensional deformation gradient along the Z-axis. This represents the amount of impedance change. Indicates the length of the conductive fiber network. This indicates the elastic modulus of the sealing strip material. This indicates the Poisson's ratio of the sealing strip material.

[0036] The three-dimensional deformation gradient refers to the deformation rate and rate of change of the sealing strip in three-dimensional space, as described in the formula. Vibration causes deformation of the conductive fiber network (within the sealing strip), leading to a change in resistance (piezoresistive effect). The traditional strain formula is ε = σ / E (the relationship between stress σ and elastic modulus E), but here... Equivalent to generalized stress, introduced The term originates from the plane strain assumption.

[0037] See Figure 2 The image shows a frequency-impedance mapping table for a multi-dimensional adaptive positioning detection method for a car tailgate according to an embodiment of the present invention. In the frequency-impedance mapping table, the frequency is defined in the above formula. The impedance is the reference impedance in the above formula.

[0038] The pose correction module 103 is used to perform live operation authentication on the car tailgate based on the three-dimensional deformation gradient and the palm thermal radiation map, obtain the live authentication result, and analyze the pose correction amount of the car tailgate using the live authentication result and the tailgate coordinate deviation.

[0039] Furthermore, embodiments of the present invention employ a dual authentication mechanism combining biometrics and dynamic mechanical operation characteristics to resist forgery attacks.

[0040] In one embodiment of the present invention, the step of performing liveness authentication on the tailgate of a car based on the three-dimensional deformation gradient and the palm thermal radiation map to obtain a liveness authentication result includes: scaling the palm thermal radiation map to a preset pixel image; extracting palm thermal texture features from the preset pixel image using a ResNet-18 model; calculating the feature similarity between the palm thermal texture features and preset template features; setting the gradient range of the three-dimensional deformation gradient and the similarity threshold of the feature similarity; when the three-dimensional deformation gradient conforms to the gradient range and the feature similarity is higher than the similarity threshold, the liveness authentication is considered successful as the liveness authentication result; when the three-dimensional deformation gradient does not conform to the gradient threshold and the feature similarity is not higher than the similarity threshold, the liveness authentication is considered unsuccessful as the liveness authentication result.

[0041] The preset pixel image refers to the image obtained by scaling the palm thermal radiation map according to a set pixel size, which facilitates subsequent feature extraction and processing. For example, a 256×256 grayscale image can be input. The ResNet-18 model refers to an 18-layer residual neural network used to extract image features. The palm thermal pattern feature refers to the feature vector of the palm thermal radiation map, for example, a 128-dimensional feature vector. The feature similarity refers to the degree of similarity between the palm thermal pattern feature and the preset template feature. The preset template feature refers to the palm feature of the car owner corresponding to the tailgate. The feature similarity can be calculated by the cosine similarity method. The gradient interval refers to the range of values ​​of the three-dimensional deformation gradient. The set gradient interval is used to determine whether the deformation of the sealing strip is within the normal operating range. The similarity threshold is the minimum required value of feature similarity, used to determine whether the palm thermal pattern feature and the preset template are sufficiently similar, thereby determining whether the identity authentication is successful.

[0042] Furthermore, in this embodiment of the invention, the tailgate pose is corrected in real time through PID control formula, and the closed-loop pose correction improves the positioning accuracy, solving the problem of lack of dynamic verification in traditional systems.

[0043] In one embodiment of the present invention, the step of analyzing the pose correction amount of the vehicle tailgate using the liveness authentication result and the tailgate coordinate deviation includes: when the liveness authentication result is successful, calculating the pose correction amount of the vehicle tailgate according to the tailgate coordinate deviation using the following formula: ; ; ; in, This represents the pose correction amount on the X-axis. express Pose correction amount on the axis, express Pose correction amount on the axis, This indicates the actual coordinates of the tailgate. This represents the desired coordinates of the tailgate. Indicates the tailgate coordinate deviation. This represents the rate of change of the tailgate's coordinate position. This represents the proportional gain coefficient on the X-axis. Represents the differential gain coefficient on the X-axis. This indicates the actual coordinates of the tailgate. This indicates the desired coordinates of the tailgate. Indicates the tailgate coordinate deviation. This represents the rate of change of the tailgate's coordinate position. express The proportional gain coefficient on the axis, express Differential gain coefficient on the axis, This indicates the actual coordinates of the tailgate. This represents the desired coordinates of the tailgate. Indicates the tailgate coordinate deviation. This represents the rate of change of the tailgate's coordinate position. express The proportional gain coefficient on the axis, express Differential gain coefficient on the axis.

[0044] The position correction amount refers to the displacement required to adjust the position and posture of the tailgate.

[0045] The pressure recognition module 104 is used to align the three-dimensional deformation gradient with the pose correction amount using timestamps to obtain alignment dimension data, and to use the alignment dimension data to identify the contact pressure distribution between the car tailgate and the car.

[0046] Furthermore, this embodiment of the invention solves the data fusion error caused by the asynchronous timestamps of multiple modules in traditional systems by aligning the three-dimensional deformation gradient with the pose correction amount using timestamps.

[0047] In one embodiment of the present invention, the step of aligning the three-dimensional deformation gradient and the pose correction amount with timestamps to obtain alignment dimension data includes: querying the first timestamp and the second timestamp corresponding to the three-dimensional deformation gradient and the pose correction amount respectively; generating a reference timestamp between the first timestamp and the second timestamp; calculating the first time difference between the first timestamp and the reference timestamp and the second time difference between the second timestamp and the reference timestamp respectively; performing data interpolation processing on the three-dimensional deformation gradient and the pose correction amount according to the first time difference and the second time difference respectively to obtain interpolated gradient data and interpolated pose data; and concatenating the interpolated gradient data and the interpolated correction data into alignment dimension data.

[0048] For example, the process of querying the first and second timestamps corresponding to the three-dimensional deformation gradient and the pose correction amount respectively includes querying the timestamp of the three-dimensional deformation gradient data, such as 15:00:00.000010, and querying the timestamp of the pose correction amount data, such as 15:00:00.000005. Further, the process of generating a reference timestamp between the first and second timestamps includes determining a reference timestamp, such as 15:00:00.000000, as a reference time for alignment. The process of calculating the first time difference between the first timestamp and the reference timestamp, and the second time difference between the second timestamp and the reference timestamp respectively, includes, for example, the three-dimensional deformation gradient time difference: 15:00:00.000010 - 15:00:00.000000 = +0.000010 seconds, and the pose correction amount time difference: 15:00:00.000005 - 15:00:00.000000 = +0.000005 seconds, further, based on the first time difference and the second time difference, data interpolation processing is performed on the three-dimensional deformation gradient and the pose correction amount respectively. For example, based on the time difference (+0.000010 seconds), linear interpolation or spline interpolation methods are used to calculate the interpolated gradient data of the three-dimensional deformation gradient at the reference timestamp (15:00:00.000000) and the interpolated pose data of the pose correction amount at the reference timestamp (15:00:00.000000) using the same interpolation method based on the time difference (+0.000005 seconds). The interpolated gradient data and the interpolated correction data are then concatenated into aligned dimension data. For example, the interpolated three-dimensional deformation gradient data and the pose correction amount data are combined together to form aligned dimension data.

[0049] Furthermore, this embodiment of the invention achieves sub-millimeter-level accuracy identification of contact pressure distribution by combining a gradient-pressure mapping table, thus solving the problem of inaccurate multi-source data fusion in traditional systems.

[0050] In one embodiment of the present invention, identifying the contact pressure distribution between the car tailgate and the car using the alignment dimension data includes: obtaining target data belonging to the same time stamp in the alignment dimension data; obtaining the target interpolation gradient and target correction amount in the target data; and performing gradient correction on the target interpolation gradient using the following formula to obtain the corrected gradient value: ; in, Indicates the corrected gradient value. This represents the pose correction amount on the X-axis. express Pose correction amount on the axis, express Pose correction amount on the axis, Represents the three-dimensional deformation gradient along the Z-axis. Represents a linear mapping function; Determine whether the standard pressure value corresponding to the corrected gradient value can be found in a preset gradient-pressure mapping table; if the standard pressure value corresponding to the corrected gradient value can be found in the preset gradient-pressure mapping table, divide the surface of the sealing strip on the tailgate into a grid to obtain a sealing strip grid; calculate the contact pressure distribution of the sealing strip grid using the standard pressure values ​​at the measuring points around the sealing strip grid; if the standard pressure value corresponding to the corrected gradient value cannot be found in the preset gradient-pressure mapping table, calculate the standard pressure value corresponding to the corrected gradient value using the following formula based on the gradient-pressure mapping table: ; in, Indicates the standard pressure value. express The corresponding pressure value, Indicates the corrected gradient value. This represents the nearest lower bound of the corrected gradient value in the gradient-pressure map. This represents the nearest upper bound of the corrected gradient values ​​in the gradient-pressure map. express The corresponding pressure value; The contact pressure distribution between the tailgate and the vehicle is determined by the standard pressure value.

[0051] in, This refers to the mapping relationship between the experimentally calibrated pose correction and the deformation gradient, which can be determined through methods such as regression models and least squares methods. In the gradient-pressure mapping table, the first column represents the gradient value, and the second column represents the pressure value corresponding to each gradient value. For example, the first row of data might have a gradient of 0.01 and a pressure value of 4, while the second row might have a gradient of 0.03 and a pressure value of 12. When the standard pressure value corresponding to the corrected gradient value can be found in the preset gradient-pressure mapping table, the corrected gradient value, for example, 0.01, will be found in the first column of data. When the standard pressure value corresponding to the corrected gradient value cannot be found in the preset gradient-pressure mapping table, only the corrected gradient value, for example, 0.02, which falls between 0.01 and 0.03, will be found. 4 + (0.02-0.01) / (0.03-0.01) × (12-4), where the contact pressure distribution refers to the pressure distribution on the sealing strip, used to indicate the uniformity and sealing of the contact between the car tailgate and the car.

[0052] Optionally, the principle of determining the contact pressure distribution between the car tailgate and the car using the standard pressure value is similar to the principle of dividing the surface of the sealing strip on the car tailgate into a grid to obtain a sealing strip grid; and calculating the contact pressure distribution of the sealing strip grid using the standard pressure values ​​at the surrounding measuring points. The sealing strip grid is equivalent to a rectangle, and the surrounding measuring points refer to the four vertices of the rectangle. The average of the four standard pressure values ​​at the four vertices is used as the contact pressure distribution of this rectangle. Thus, the contact pressure distribution of each sealing strip grid can be calculated. It should be noted that the sealing strip grid here is similar in meaning to the aforementioned conductive fiber network. The sealing strip grid here is formed by the grid distribution created on the sealing strip by the tool for detecting the standard pressure value.

[0053] See Figure 3 The diagram shown is a flowchart illustrating the pressure recognition process of a multi-dimensional adaptive positioning detection method for a car tailgate according to an embodiment of the present invention.

[0054] The positioning detection module 105 is used to determine the positioning detection result on the car tailgate by the contact pressure distribution.

[0055] The embodiments of the present invention improve the robustness of the system under complex working conditions by comprehensively judging the positioning result based on the uniformity of contact pressure and the average value of correction.

[0056] In one embodiment of the present invention, determining the positioning detection result on the car tailgate through the contact pressure distribution includes: querying the qualified pressure distribution in the contact pressure distribution that is higher than a preset pressure threshold; calculating the qualified percentage score of the qualified pressure distribution; obtaining the pose correction amount; calculating the average correction amount of the pose correction amount; and using the qualified percentage score and the average correction amount as the positioning detection result.

[0057] The preset pressure threshold refers to a pre-defined pressure value standard used to distinguish whether the contact pressure meets the expected requirements. When testing the tailgate positioning of a car, it serves as a benchmark value for judging whether the contact pressure distribution is qualified. By comparing the actual measured contact pressure distribution with the preset pressure threshold, it can be determined whether the contact between the tailgate and the car meets the design requirements. The compliance percentage score refers to the proportion of the compliant pressure distribution to the total contact pressure distribution. The mean correction amount includes the mean values ​​on the X, Y, and Z axes. The mean correction amount refers to the average value of the pose correction amount, representing the average degree of magnitude of the pose correction amount. This average value is used to evaluate the overall situation of tailgate position and posture correction. The positioning detection result refers to the result of combining the pass rate score with the average correction amount. This result is used to comprehensively evaluate the positioning accuracy and contact quality of the tailgate. The tighter the contact, the closer the tailgate is to the car and the more accurate the positioning. The smaller the average correction amount, the smaller the deviation between the actual posture and the expected posture of the tailgate and the more accurate the positioning. A moderate average correction amount indicates that the tailgate positioning is within a reasonable range and can meet the usage requirements after appropriate correction. An excessively large average correction amount indicates that the tailgate positioning deviation is large and may require further adjustment or inspection of the positioning system.

[0058] Compared to the shortcomings mentioned in the background technology, the embodiments of the present invention employ alternating inputs of 1MHz and 10MHz dual-frequency electromagnetic waves into a conductive fiber network. By detecting the impedance change of the sealing strip through phase difference and attenuation value, it accurately reflects microscopic deformation. A vibration sensor excites the sealing strip with a linearly increasing frequency, records the amplitude response value, and captures the dynamic response of the sealing strip at different frequencies. An infrared thermal imager acquires far-infrared images, and biometric authentication is achieved by combining liveness detection features. Triangulation is used to calculate the three-dimensional coordinate deviation of the tailgate in real time. Multi-dimensional data covers electromagnetic, vibration, thermal radiation, and spatial pose information, providing comprehensive detection capabilities for sealing strip deformation gradient, operator identity, and tailgate pose deviation. This overcomes the limitations of insufficient data dimensions in traditional single-sensor systems. Furthermore, the embodiments of the present invention use formulas to correct thermal radiation images and dynamically compensate for temperature and humidity interference, avoiding misjudgments caused by environmental fluctuations in traditional systems. Furthermore, the embodiments of the present invention integrate the impedance change formula... By combining electromagnetic and vibration data, a three-dimensional deformation gradient is calculated. Using a physical model based on the electromagnetic and vibration data, the three-dimensional deformation gradient of the sealing strip is precisely quantified. Furthermore, this invention employs a dual authentication mechanism combining biometrics and dynamic mechanical operation characteristics to resist forgery attacks. Further, this invention uses a PID control formula to correct the tailgate pose in real time; closed-loop pose correction improves positioning accuracy, addressing the lack of dynamic verification in traditional systems. Furthermore, this invention aligns the three-dimensional deformation gradient with the pose correction amount using timestamps, resolving data fusion errors caused by asynchronous timestamps across multiple modules in traditional systems. Furthermore, this invention combines a gradient-pressure mapping table to achieve sub-millimeter-level accuracy in identifying contact pressure distribution, resolving the inaccuracy problem of multi-source data fusion in traditional systems. Finally, this invention improves system robustness under complex working conditions by comprehensively judging the positioning result based on the uniformity of contact pressure and the average value of the correction amount. Therefore, this invention can integrate multi-dimensional data, dynamically compensate for environmental interference, and achieve time synchronization.

[0059] like Figure 4 The diagram shown is a flowchart illustrating a multi-dimensional adaptive positioning and detection method for a car tailgate according to an embodiment of the present invention. In this embodiment, the multi-dimensional adaptive positioning and detection method for a car tailgate includes: Collect multi-dimensional data from the tailgate of a car, including dual-frequency electromagnetic signals, sweep frequency excitation signals, palm thermal radiation images, and tailgate coordinate deviations. The three-dimensional deformation gradient of the sealing strip on the car tailgate is analyzed based on the dual-frequency electromagnetic signal and the swept frequency excitation signal. Based on the three-dimensional deformation gradient and the palm thermal radiation image, live operation authentication is performed on the car tailgate to obtain the live authentication result. The pose correction amount of the car tailgate is analyzed by using the live authentication result and the tailgate coordinate deviation. The three-dimensional deformation gradient and the pose correction amount are time-stamped to obtain alignment dimension data, and the alignment dimension data is used to identify the contact pressure distribution between the car tailgate and the car. The positioning detection result on the car tailgate is determined by the contact pressure distribution.

[0060] In the several embodiments provided by this invention, it should be understood that the provided systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0061] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0062] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A multi-dimensional adaptive positioning and detection system for automobile tailgates, characterized in that, The system for multi-dimensional adaptive positioning and detection of the car tailgate includes: a data acquisition module, a deformation analysis module, a pose correction module, a pressure recognition module, and a positioning detection module. The data acquisition module is used to collect multi-dimensional data from the tailgate of a car, including dual-frequency electromagnetic signals, sweep frequency excitation signals, palm thermal radiation images, and tailgate coordinate deviations. The deformation analysis module is used to analyze the three-dimensional deformation gradient of the sealing strip on the car tailgate based on the dual-frequency electromagnetic signal and the sweep frequency excitation signal. The pose correction module is used to perform live operation authentication on the car tailgate based on the three-dimensional deformation gradient and the palm thermal radiation image, obtain the live authentication result, and analyze the pose correction amount of the car tailgate using the live authentication result and the tailgate coordinate deviation. The pressure recognition module is used to align the three-dimensional deformation gradient with the pose correction amount using timestamps to obtain alignment dimension data, and to use the alignment dimension data to identify the contact pressure distribution between the car tailgate and the car. The positioning detection module is used to determine the positioning detection result on the tailgate of the car based on the contact pressure distribution.

2. The multi-dimensional adaptive positioning and detection system for automobile tailgate as described in claim 1, characterized in that, The multi-dimensional data collected from the car's tailgate includes: A conductive fiber network is embedded in the sealing strip groove of the car tailgate; After alternating input of dual-frequency electromagnetic waves into the conductive fiber network, the current phase difference and signal attenuation value of the conductive fiber network with respect to the dual-frequency electromagnetic waves are collected. The current phase difference and the signal attenuation value are used as electromagnetic detection data; A vibration sensor is installed on the tailgate of the vehicle. After controlling the vibration sensor to vibrate the sealing strip at a linearly increasing frequency, the vibration amplitude of the sealing strip with respect to the linearly increasing frequency is recorded; The far-infrared image of the hand on the tailgate of the car is monitored by an infrared thermal imager deployed on the tailgate of the car. Convert the far-infrared image into an initial grayscale image; Environmental adaptive corrections are performed on the vibration amplitude and the initial grayscale image respectively to obtain the amplitude response value and the grayscale image; Convert the amplitude response values ​​over a continuous period of time into vibration detection data; The grayscale image is used as a thermal radiation map of the palm. After emitting a laser signal to the reflective markers on the tailgate of the vehicle using a triangulation device deployed on the tailgate, the tailgate coordinate deviation is calculated using the triangulation method.

3. The multi-dimensional adaptive positioning and detection system for automobile tailgate as described in claim 2, characterized in that, The step of performing environmental adaptive correction on the vibration amplitude and the initial grayscale image respectively to obtain the amplitude response value and the grayscale image includes: Monitor the ambient temperature and humidity of the vehicle's tailgate; Based on the ambient temperature, the initial grayscale image is subjected to environmental adaptive correction to obtain a grayscale image; Based on the ambient humidity, the vibration amplitude is subjected to environmental adaptive correction to obtain the amplitude response value.

4. The multi-dimensional adaptive positioning and detection system for automobile tailgate as described in claim 1, characterized in that, The step of analyzing the three-dimensional deformation gradient of the sealing strip on the car tailgate based on the electromagnetic detection data and the vibration detection data includes: Based on the electromagnetic detection data and the vibration detection data, the impedance change of the sealing strip on the car tailgate is calculated using the following formula: ； in, This represents the amount of impedance change. This represents the change in electromagnetic impedance. This indicates the current phase difference in the electromagnetic detection data. This represents the signal attenuation value in the electromagnetic detection data. The imaginary component representing the change in electromagnetic impedance. Indicates the reference impedance. This indicates the vibration frequency corresponding to the vibration detection data. This represents the amplitude response value in the vibration detection data. This represents the change in vibration impedance; Based on the impedance change, the three-dimensional deformation gradient of the sealing strip on the tailgate of the automobile is calculated using the following formula: ； in, Represents the three-dimensional deformation gradient along the Z-axis. This represents the amount of impedance change. Indicates the length of the conductive fiber network. This indicates the elastic modulus of the sealing strip material. This indicates the Poisson's ratio of the sealing strip material.

5. The multi-dimensional adaptive positioning and detection system for automobile tailgate as described in claim 1, characterized in that, The process of performing liveness authentication on the car tailgate based on the three-dimensional deformation gradient and the palm thermal radiation map to obtain the liveness authentication result includes: The palm thermal radiation map is scaled up to a preset pixel image; The palm heat pattern features in the preset pixel image are extracted using the ResNet-18 model; Calculate the feature similarity between the palm heatprint features and the preset template features; Set the gradient range of the three-dimensional deformation gradient and the similarity threshold of the feature similarity respectively; When the three-dimensional deformation gradient conforms to the gradient interval and the feature similarity is higher than the similarity threshold, the successful liveness operation authentication is taken as the liveness authentication result. When the three-dimensional deformation gradient does not meet the gradient threshold and the feature similarity is not higher than the similarity threshold, the liveness authentication failure is taken as the liveness authentication result.

6. The multi-dimensional adaptive positioning and detection system for automobile tailgate as described in claim 1, characterized in that, The step of analyzing the pose correction amount of the vehicle tailgate using the liveness authentication result and the tailgate coordinate deviation includes: When the liveness authentication result is successful, the pose correction amount of the car tailgate is calculated based on the tailgate coordinate deviation.

7. The multi-dimensional adaptive positioning and detection system for automobile tailgate as described in claim 1, characterized in that, The step of aligning the three-dimensional deformation gradient with the pose correction amount using timestamps to obtain aligned dimension data includes: Query the first and second timestamps corresponding to the three-dimensional deformation gradient and the pose correction amount, respectively; Generate a reference timestamp between the first timestamp and the second timestamp; calculate the first time difference between the first timestamp and the reference timestamp, and the second time difference between the second timestamp and the reference timestamp, respectively; Based on the first time difference and the second time difference, data interpolation processing is performed on the three-dimensional deformation gradient and the pose correction amount to obtain interpolated gradient data and interpolated pose data. The interpolation gradient data and the interpolation correction data are concatenated to form aligned dimension data.

8. The multi-dimensional adaptive positioning and detection system for automobile tailgate as described in claim 1, characterized in that, The step of identifying the contact pressure distribution between the car tailgate and the car using the alignment dimension data includes: Obtain the target data belonging to the same timestamp from the alignment dimension data; Obtain the target interpolation gradient and target correction amount from the target data; The corrected gradient value is obtained by performing gradient correction on the target interpolation gradient using the following formula: ； in, Indicates the correction gradient value. This represents the pose correction amount on the X-axis. express Pose correction amount on the axis, express Pose correction amount on the axis, Represents the three-dimensional deformation gradient along the Z-axis. Represents a linear mapping function; Determine whether the standard pressure value corresponding to the correction gradient value can be queried in a preset gradient-pressure mapping table; When the standard pressure value corresponding to the correction gradient value can be queried in the preset gradient-pressure mapping table, the surface of the sealing strip on the car tailgate is divided into a grid to obtain the sealing strip grid. The contact pressure distribution of the sealing strip mesh is calculated using the standard pressure values ​​at the measuring points around the sealing strip mesh. When the standard pressure value corresponding to the correction gradient value cannot be found in the preset gradient-pressure mapping table, the standard pressure value corresponding to the correction gradient value is calculated according to the gradient-pressure mapping table. The contact pressure distribution between the tailgate and the vehicle is determined by the standard pressure value.

9. The multi-dimensional adaptive positioning and detection system for automobile tailgate as described in claim 1, characterized in that, The determination of the positioning detection result on the vehicle tailgate through the contact pressure distribution includes: Query the qualified pressure distributions in the contact pressure distribution that are higher than the preset pressure threshold; Calculate the compliance rate score of the compliance pressure distribution; Obtain the pose correction amount; Calculate the average value of the pose correction amount; The average of the pass rate score and the correction amount is used as the location detection result.

10. A multi-dimensional adaptive positioning and detection method for automobile tailgates, characterized in that, The method includes: Collect multi-dimensional data from the tailgate of a car, including dual-frequency electromagnetic signals, sweep frequency excitation signals, palm thermal radiation images, and tailgate coordinate deviations. The three-dimensional deformation gradient of the sealing strip on the car tailgate is analyzed based on the dual-frequency electromagnetic signal and the swept frequency excitation signal. Based on the three-dimensional deformation gradient and the palm thermal radiation image, live operation authentication is performed on the car tailgate to obtain the live authentication result. The pose correction amount of the car tailgate is analyzed by using the live authentication result and the tailgate coordinate deviation. The three-dimensional deformation gradient and the pose correction amount are time-stamped to obtain alignment dimension data, and the alignment dimension data is used to identify the contact pressure distribution between the car tailgate and the car. The positioning detection result on the car tailgate is determined by the contact pressure distribution.

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