Vehicle clamp positioning system based on AI

Abstract

The invention relates to the technical field of automatic control, in particular to an AI-based vehicle fixture positioning system, which comprises a topological structure adjusting module, which is used for monitoring contact pressure and position deviation on an assembly interface, collecting external load data between a fixture and a vehicle component, adjusting a structural layout, optimizing stress distribution of the fixture according to torque change, and adjusting the stress distribution of the fixture according to the torque change. And generating structure optimization configuration. According to the method, the quality problem caused by manual operation errors is effectively reduced by continuously monitoring the contact pressure and the position deviation in the assembly process and actively adjusting the structural layout and the stress distribution of the clamp, in addition, through the integrated data processing and machine learning technology, the change on a production line can be predicted and adapted, and the production efficiency is improved. High precision and efficiency of assembly operation are maintained according to the factors such as machine abrasion and environmental factors, the efficiency of the production process and the consistency of products are remarkably improved through the dynamic adjustment and prediction capacity, and stability and reliability in long-term operation are ensured.

Description

Technical Field

[0001] The present invention relates to the field of automatic control technology, and in particular to an AI-based vehicle fixture positioning system. Background Art

[0002] The field of automatic control technology mainly studies and applies technologies such as computers, sensors, and control algorithms to achieve automated operation of systems or equipment. This technical field is widely used in industrial production, transportation, aerospace, and other fields to improve the efficiency, accuracy, and reliability of the system. Automatic control systems automatically adjust the operating state of equipment by sensing and processing input signals to achieve the desired goals, reduce human intervention, and improve production efficiency and product quality.

[0003] Among them, the vehicle fixture positioning system refers to a device system used to accurately position and fix vehicle parts or complete vehicles during the manufacturing or assembly process. The system uses automatic control technology to sense the position of the fixture and the vehicle through sensors, and automatically adjusts the position and angle of the fixture in combination with control algorithms to ensure the accurate positioning of parts or complete vehicles during processing or assembly. Its uses include improving assembly efficiency, reducing human errors, and improving product consistency.

[0004] Although existing automatic control technologies can achieve a certain degree of automation, they have significant limitations in terms of precise control and real-time response. Traditional vehicle fixture positioning systems often lack real-time data analysis and predictive adjustment capabilities, making it difficult to adapt to immediate changes in the production process. This static control method makes the system inefficient when responding to rapidly changing production needs, and is prone to affecting the quality of the final product due to uneven stress distribution or accumulated assembly errors. The lack of effective environmental noise filtering and adaptive mechanisms also causes the system to perform poorly in the face of machine aging or environmental changes, further limiting its potential for application in high-standard production environments. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and propose an AI-based vehicle fixture positioning system.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: The vehicle fixture positioning system based on AI comprises: The topology adjustment module monitors the contact pressure and position deviation on the assembly interface, collects external load data between the fixture and the vehicle components, adjusts the structural layout, optimizes the stress distribution of the fixture according to the torque change, and generates the structural optimization configuration; The real-time data processing module receives the structural optimization configuration, uses the positioning sensor to monitor the contact status of the fixture and the vehicle component in real time, analyzes the data, filters the environmental noise, corrects the sensor deviation, refines the key monitoring indicators, and obtains real-time monitoring feedback; The model learning optimization module evaluates abnormal patterns in the data based on the real-time monitoring feedback, adjusts parameters according to changes in the operating data, performs fusion processing on multiple sets of real-time data, extracts optimization directions from them, and obtains model optimization parameters; The position prediction and adjustment module applies the model to optimize parameters, uses dynamic change analysis to predict the position change of the fixture in the differentiated production stage, adjusts the fixture position in real time with reference to machine wear and environmental factors, and establishes the position adjustment result.

[0007] As a further solution of the present invention, the step of obtaining the contact pressure and the position deviation is specifically as follows: Monitor the contact points between the fixture and the vehicle component at the assembly interface, collect data on pressure and contact area, using the formula: ; Calculate and generate contact pressure data points; in, represents the force applied to the contact surface, represents the contact area, represents the adjustment coefficient, represents contact pressure; The contact pressure data points are statistically processed using the formula: ; Calculate the weighted average of the contact pressure and introduce the formula: ; Calculate the weighted standard deviation of the pressure to generate the average contact pressure and position deviation; in, represents the i-th point in the contact pressure data point set, represents the total number of data points, represents the weight of the ith data point, represents the weighted average of the contact pressure, represents the weighted standard deviation of contact pressure; According to the average contact pressure and position deviation, the assembly interface is adjusted using the formula: ; Optimize the position of the fixture and generate contact pressure and position deviation;

[0008] in, represents the target pressure threshold, represents the adjustment amount, , They are the coefficients for adjusting the target pressure deviation and position deviation respectively.

[0009] As a further solution of the present invention, the step of obtaining the structural optimization configuration is specifically: Analyze the contact pressure and position deviation, conduct a preliminary structural analysis, and use the formula: ; Calculate the stress distribution in the contact area between the fixture and the vehicle component to generate a preliminary stress distribution; in, represents the elastic modulus of the material, represents Poisson's ratio, represents the preliminary stress distribution; To analyze the preliminary stress distribution, the formula is used: ; Calculate the generated stress root mean square; in, Represents the stress distribution values, represents the total number of stress values, represents the root mean square of stress distribution; Based on the RMS stress, adjust the design using the formula: ; Generate structural optimization configuration; in, , represent the maximum and minimum values ​​in the stress distribution, respectively. represents the design adjustment factor, Represents structural optimization configuration.

[0010] As a further solution of the present invention, the step of obtaining the real-time monitoring feedback is specifically as follows: Deploy positioning sensors to monitor the contact status of the fixture and vehicle components, using the formula: ; Calculate the preliminary contact pressure and generate the original contact pressure data; in, represents the sensor signal, Represents the adjustment coefficient, which is used to balance the signal gain. represents the calculated original contact pressure; The raw contact pressure data is filtered for environmental noise using the formula: ; Get the filtered pressure data; in, represents the environmental noise, Represents the noise filtering coefficient, which is used to adjust the attenuation of noise impact. represents the contact pressure after filtration; Based on the filtered pressure data, the sensor deviation is corrected and the key monitoring indicators are extracted using the formula: ; Generate real-time monitoring feedback; in, represents the correction factor, represents the theoretical pressure threshold, represents the pressure-dependent weighting factor used to adjust the feedback sensitivity, Represents monitoring indicators.

[0011] As a further solution of the present invention, the step of obtaining the model optimization parameters is specifically as follows: Receive the real-time monitoring feedback and evaluate the data for abnormal patterns using the formula: ; Calculate the standard deviation of the data and calculate the variance based on the standard deviation and the mean using the formula: ; generating anomaly pattern evaluation values; in, Represents a single monitoring feedback, Represents the average value of monitoring feedback, represents the sample size, represents the standard deviation, represents the abnormal mode evaluation value, It is a small amount that avoids the denominator being zero; According to the abnormal mode evaluation value, the operating parameters are adjusted using the formula: ; Calculate and obtain adjusted operating parameters; in, represents the adjustment factor, To adjust the parameters, represents the adjusted operating parameters; The multiple adjusted operating parameters are fused and the optimization direction is extracted using the formula: ; Generate model optimization parameters; in, Representative The weight of the group data, Represents the number of data sets, Represents the optimization direction after fusion, that is, the model optimization parameters.

[0012] As a further solution of the present invention, the step of obtaining the position adjustment result is specifically: The model is used to optimize the parameters and predict the position change of the fixture in the differentiated production stage through the dynamic change analysis method, using the formula: ; generating predicted position changes; in, represents the dynamic change coefficient, represents the environmental resistance coefficient; Based on the predicted position change, the fixture position is adjusted in real time with reference to machine wear and environmental factors, using the formula: ; Get the adjusted fixture position; in, represents the machine wear coefficient, represents the environmental impact factor, Represents the adjusted fixture position; Based on the adjusted fixture position, the formula is used: ; Ensure that the result is non-negative and generate the position adjustment result .

[0013] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by continuously monitoring the contact pressure and position deviation during the assembly process, the structural layout and the stress distribution of the fixture are actively adjusted, effectively reducing quality problems caused by manual operation errors. In addition, through integrated data processing and machine learning technology, changes on the production line, such as machine wear and environmental factors, can be predicted and adapted to maintain high precision and efficiency of assembly operations. This dynamic adjustment and prediction capability significantly improves the efficiency of the production process and the consistency of the product, ensuring stability and reliability in long-term operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a system flow chart of the present invention; Figure 2 A flow chart of the steps for obtaining contact pressure and position deviation of the present invention; Figure 3 A flowchart of the steps for obtaining the structure optimization configuration of the present invention; Figure 4 A flowchart of the steps for obtaining real-time monitoring feedback of the present invention; Figure 5 A flow chart of the steps for obtaining the optimization parameters of the model of the present invention; Figure 6This is a flow chart of the steps for obtaining the position adjustment results of the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0016] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0017] Embodiment 1 See also Figure 1 , the AI-based vehicle fixture positioning system includes: The topology adjustment module monitors the contact pressure and position deviation on the assembly interface, collects external load data between the fixture and the vehicle components, adjusts the structural layout, optimizes the stress distribution of the fixture according to the torque change, and generates the structural optimization configuration; The real-time data processing module receives the structural optimization configuration, uses the positioning sensor to monitor the contact status of the fixture and vehicle components in real time, analyzes the data, filters the environmental noise, corrects the sensor deviation, refines the key monitoring indicators, and obtains real-time monitoring feedback; The model learning optimization module evaluates abnormal patterns in the data based on real-time monitoring feedback, adjusts parameters according to changes in operating data, performs fusion processing on multiple sets of real-time data, extracts optimization directions from them, and obtains model optimization parameters; The position prediction and adjustment module applies model optimization parameters, uses dynamic change analysis to predict the position changes of the fixture during the differentiated production stage, adjusts the fixture position in real time with reference to machine wear and environmental factors, and establishes the position adjustment results.

[0018] Structural optimization configuration includes stress distribution optimization, contact pressure adjustment, and position deviation correction. Real-time monitoring feedback includes key monitoring indicators, sensor calibration, and environmental noise filtering. Model optimization parameters specifically refer to abnormal mode evaluation and parameter adjustment. Position adjustment results include fixture position changes, machine wear and environmental factor matching.

[0019] See also Figure 2, the specific steps for obtaining contact pressure and position deviation are: Monitor the contact points between the fixture and the vehicle component at the assembly interface, collect data on pressure and contact area, using the formula: ; Calculate and generate contact pressure data points; in, represents the force applied to the contact surface, represents the contact area, represents the adjustment coefficient, represents contact pressure; The contact pressure data points are statistically processed using the formula: ; Calculate the weighted average of the contact pressure and introduce the formula: ; Calculate the weighted standard deviation of the pressure to generate the average contact pressure and position deviation; in, represents the i-th point in the contact pressure data point set, represents the total number of data points, represents the weight of the ith data point, represents the weighted average of the contact pressure, represents the weighted standard deviation of contact pressure; According to the average contact pressure and position deviation, the assembly interface is adjusted using the formula: ; Optimize the position of the fixture and generate contact pressure and position deviation; in, represents the target pressure threshold, represents the adjustment amount, , They are the coefficients for adjusting the target pressure deviation and position deviation respectively.

[0020] Set data: Force applied to the contact surface Newton; Contact area square meters (set to a small contact area); Adjustment coefficient (Refer to the slight error in actual measurement); formula: ; Calculation process: Newton, adjustment factor Used to adjust for small errors in the actual measured force.

[0021] Pascal, which represents the contact pressure.

[0022] Analysis: The calculated 21,000 Pascals is the pressure applied at the point of contact.

[0023] Set data: Total number of data points ; Contact pressure at each data point They are Pascal; The weight of each data point All are set to 1; formula: ; ; Calculation process: Pascal; Pascal; Variance part calculation: ; Pascal; Analysis: Calculated average contact pressure Pascal.

[0024] Standard deviation of contact pressure Pascal, which shows the degree of variation between data points.

[0025] Set data: Target pressure threshold Pascal; Adjustment factor and ; formula: ; Calculation process: ; ; ; Analysis: Adjustment amount Pascals represent the maximum adjustment required to achieve the target pressure or reduce position deviation.

[0026] See also Figure 3 , the specific steps for obtaining the structural optimization configuration are: Analyze the contact pressure and position deviation, conduct preliminary structural analysis, and use the formula: ; Calculate the stress distribution in the contact area between the fixture and the vehicle component to generate a preliminary stress distribution; in, represents the elastic modulus of the material, represents Poisson's ratio, represents the preliminary stress distribution; Analyze the preliminary stress distribution using the formula: ; Calculate the generated stress root mean square; in, Represents the stress distribution values, represents the total number of stress values, represents the root mean square of stress distribution; Adjust the design based on the RMS stress using the formula: ; Generate structural optimization configuration; in, , represent the maximum and minimum values ​​in the stress distribution, respectively. represents the design adjustment factor, Represents structural optimization configuration.

[0027] formula: ; Parameter interpretation and derivation: : The elastic modulus of the material. For steel, It can be taken as about 200GPa (or 200000MPa).

[0028] : Poisson's ratio of the material. For most metals, The value of is between 0.25 and 0.35. Set it to 0.3.

[0029] : Contact pressure adjustment amount, set the measured adjustment amount to 150MPa.

[0030] Calculation process: set up : ; Result analysis: Calculated stress distribution Approx. , which indicates the stress level of the structure in the contact area under the current material and pressure adjustment state.

[0031] formula: ; Parameter parsing and derivation: : Stress distribution value, set the stress measurement value for the 5 difference points to ]MPa.

[0032] : The number of measurement points is 5.

[0033] Calculation process: ; Result analysis: The obtained stress root mean square It is 33317.28MPa, which indicates that on average the stress state of the structure is relatively even without extreme deviation.

[0034] formula: ; Parameter interpretation and derivation: and : Set the maximum and minimum stress values ​​to 35000MPa and 32000MPa respectively.

[0035] : Design adjustment factor, set to 0.05, to adjust the sensitivity of the optimization.

[0036] Calculation process: ; Result analysis: Structural optimization configuration The value of is about 0.050, which means that under the current stress conditions and adjustment factors, the impact of the optimization configuration is relatively small, and it is necessary to adjust the factors or refer to other optimization parameters.

[0037] See also Figure 4 ,The specific steps for obtaining real-time monitoring feedback are: Deploy positioning sensors to monitor the contact status of the fixture and vehicle components, using the formula: ; Calculate the preliminary contact pressure and generate the original contact pressure data; in, represents the sensor signal, Represents the adjustment coefficient, which is used to balance the signal gain. represents the calculated original contact pressure; The raw contact pressure data is filtered for environmental noise using the formula: ; Get the filtered pressure data; in, represents the environmental noise, Represents the noise filtering coefficient, which is used to adjust the attenuation of noise impact. represents the contact pressure after filtration; Based on the filtered pressure data, the sensor deviation is corrected and the key monitoring indicators are extracted using the formula: ; Generate real-time monitoring feedback; in, represents the correction factor, represents the theoretical pressure threshold, represents the pressure-dependent weighting factor used to adjust the feedback sensitivity, Represents monitoring indicators.

[0038] formula: ; in:

[0039] : Sensor signal, pressure reading obtained from actual measurement (unit: Newton, N).

[0040] : Structural optimization configuration, a factor obtained through previous engineering calculations, dimensionless.

[0041] : Adjustment coefficient used to balance signal gain, determined based on historical data analysis.

[0042] Calculation example: set up: Sensor signal ; Structural optimization configuration ; Adjustment coefficient ; Calculation process: ; Analysis:

[0043] What you get It is about 135.71N, which represents the adjusted contact pressure for further analysis.

[0044] formula: ; in: : Original contact pressure.

[0045] : Environmental noise, the noise level obtained from field measurement data (unit: Newton, N).

[0046] : Noise filtering coefficient, obtained through system performance testing.

[0047] Calculation example: set up: Environmental noise ; Noise filtering coefficient ; Calculation process: ; Analysis: Pressure after filtration It is approximately 136.86N, which represents the net contact pressure adjusted for ambient noise.

[0048] formula: ; in: : Pressure after filtration.

[0049] : Correction factor to ensure accurate sensor data, determined according to the calibration process.

[0050] : Theoretical pressure threshold, set based on design specifications.

[0051] : Weight factor, adjusts the impact of sensor data on feedback.

[0052] Calculation example: set up: Correction factor ; Theoretical pressure threshold ; Weight Factor ; Calculation process: ; Analysis: Get real-time monitoring feedback It is approximately 0.9823, which is a dimensionless ratio that represents the ratio of actual pressure to theoretical pressure and is used to evaluate whether the performance of the structure is within the expected range.

[0053] See also Figure 5 , the steps for obtaining the model optimization parameters are as follows: Receive real-time monitoring feedback and evaluate the data for abnormal patterns using the formula: ; Calculate the standard deviation of the data and calculate the variance based on the standard deviation and the mean using the formula: ; generating anomaly pattern evaluation values; in, Represents a single monitoring feedback, Represents the average value of monitoring feedback, represents the sample size, represents the standard deviation, represents the abnormal mode evaluation value, It is a small amount that avoids the denominator being zero; According to the abnormal mode evaluation value, adjust the operating parameters using the formula: ; Calculate and obtain adjusted operating parameters; in, represents the adjustment factor, To adjust the parameters, represents the adjusted operating parameters; The multiple adjusted operating parameters are fused and the optimization direction is extracted using the formula: ; Generate model optimization parameters; in, Representative The weight of the group data, Represents the number of data sets, Represents the optimization direction after fusion, that is, the model optimization parameters.

[0054] formula: ; Calculation process: : A single data point for real-time monitoring feedback.

[0055] : The average value of all monitoring feedback.

[0056] : The total number of data points.

[0057] Set up the data: There are five monitoring feedback data settings: ; average value Calculated as: ; Calculate the square of the deviation of each point from the mean and sum them: ; Standard Deviation for: ; Result analysis: Calculated standard deviation Represents the average deviation size of this set of data and is used for further data analysis.

[0058] formula: ; Calculation process: : Adjustment coefficient, set to 1.5.

[0059] : Abnormal mode evaluation value, for simplicity, set The variance is 3.08.

[0060] : The standard deviation calculated from the above results .

[0061] : Adjustment parameter, set to 0.5.

[0062] Operation: ; Result analysis: The adjusted operating parameters are obtained , used for optimization and adjustment in subsequent steps.

[0063] formula: ; Calculation process: There are only three sets of data, and the weights are .

[0064] Adjusted parameters for each group The settings are the same, both .

[0065] Operation: ; Result analysis: Optimization direction after fusion for , represents the comprehensive operational indicators obtained by integrating differentiated data sources, providing a basis for further decision-making.

[0066] See also Figure 6 , the steps for obtaining the position adjustment result are as follows: Using the model optimization parameters, the dynamic change analysis method is used to predict the position change of the fixture in the differentiated production stage, using the formula: ; generating predicted position changes; in, represents the dynamic change coefficient, represents the environmental resistance coefficient; Based on the predicted position change, the fixture position is adjusted in real time with reference to machine wear and environmental factors, using the formula:

[0067] Get the adjusted fixture position; in, represents the machine wear coefficient, represents the environmental impact factor, Represents the adjusted fixture position; According to the adjusted fixture position, use the formula:

[0068] Ensure that the result is non-negative and generate the position adjustment result .

[0069] formula: ; :Model optimization parameters, set by historical data analysis, such as the average optimization value from the previous production cycle, set value .

[0070] :Dynamic change coefficient, obtained through real-time monitoring system sensor data, such as the real-time running speed or load of the equipment, setting the real-time monitoring value to .

[0071] : Environmental resistance coefficient can be obtained through environmental monitoring equipment, including temperature and humidity factors that affect the operation of the machine. .

[0072] Calculation process: ; illustrate: It represents the predicted position change in millimeters or displacement units, depending on the configuration of the fixture system.

[0073] formula: ; : Machine wear coefficient, estimated from equipment maintenance records and wear data monitored by sensors, set to .

[0074] : Environmental impact coefficient, and Similarly, obtain it through environmental monitoring equipment and reuse the previous .

[0075] Calculation process: ; illustrate: The value of is 0.35, which indicates the adjusted fixture position, reflecting the position adjustment after applying the optimized parameters in the actual production environment.

[0076] formula: ;

[0077] Calculation process: ;

[0078] illustrate:

[0079] Ensure that the position adjustment result is non-negative, Indicates the fixture position adjustment value, which is used to set in the control system.

[0080] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. AI-based vehicle fixture positioning system, characterized by: The system comprises: The topology adjustment module monitors the contact pressure and position deviation on the assembly interface, collects external load data between the fixture and the vehicle components, adjusts the structural layout, optimizes the stress distribution of the fixture according to the torque change, and generates the structural optimization configuration; The real-time data processing module receives the structural optimization configuration, uses the positioning sensor to monitor the contact status of the fixture and the vehicle component in real time, analyzes the data, filters the environmental noise, corrects the sensor deviation, refines the key monitoring indicators, and obtains real-time monitoring feedback; The model learning optimization module evaluates abnormal patterns in the data based on the real-time monitoring feedback, adjusts parameters according to changes in the operating data, performs fusion processing on multiple sets of real-time data, extracts optimization directions from them, and obtains model optimization parameters; The position prediction and adjustment module applies the model to optimize parameters, uses dynamic change analysis to predict the position change of the fixture in the differentiated production stage, adjusts the fixture position in real time with reference to machine wear and environmental factors, and establishes the position adjustment result.

2. The AI-based vehicle fixture positioning system according to claim 1, characterized in that: The steps for obtaining the contact pressure and position deviation are specifically as follows: Monitor the contact points between the fixture and the vehicle component at the assembly interface, collect data on pressure and contact area, using the formula: ； Calculate and generate contact pressure data points; in, represents the force applied to the contact surface, represents the contact area, represents the adjustment coefficient, represents contact pressure; The contact pressure data points are statistically processed using the formula: ； Calculate the weighted average of the contact pressure and introduce the formula: ； Calculate the weighted standard deviation of the pressure to generate the average contact pressure and position deviation; in, represents the i-th point in the contact pressure data point set, represents the total number of data points, represents the weight of the ith data point, represents the weighted average of the contact pressure, represents the weighted standard deviation of contact pressure; According to the average contact pressure and position deviation, the assembly interface is adjusted using the formula: ； Optimize the position of the fixture and generate contact pressure and position deviation; in represents the target pressure threshold, represents the adjustment amount, , They are the coefficients for adjusting the target pressure deviation and position deviation respectively.

3. The AI-based vehicle fixture positioning system according to claim 2, characterized in that: The steps for obtaining the structural optimization configuration are specifically as follows: Analyze the contact pressure and position deviation, conduct a preliminary structural analysis, and use the formula: ； Calculate the stress distribution in the contact area between the fixture and the vehicle component to generate a preliminary stress distribution; in, represents the elastic modulus of the material, represents Poisson's ratio, represents the preliminary stress distribution; To analyze the preliminary stress distribution, the formula is used: ； Calculate the generated stress root mean square; in, Represents the stress distribution values, represents the total number of stress values, represents the root mean square of stress distribution; Based on the RMS stress, adjust the design using the formula: ； Generate structural optimization configuration; in, , represent the maximum and minimum values ​​in the stress distribution, respectively. represents the design adjustment factor, Represents structural optimization configuration.

4. The AI-based vehicle fixture positioning system according to claim 3, characterized in that: The steps for obtaining the real-time monitoring feedback are specifically as follows: Deploy positioning sensors to monitor the contact status of the fixture and vehicle components, using the formula: ； Calculate the preliminary contact pressure and generate the original contact pressure data; in, represents the sensor signal, Represents the adjustment coefficient, which is used to balance the signal gain. represents the calculated original contact pressure; The raw contact pressure data is filtered for environmental noise using the formula: ； Get the filtered pressure data; in, represents the environmental noise, Represents the noise filtering coefficient, which is used to adjust the attenuation of noise impact. represents the contact pressure after filtration; Based on the filtered pressure data, the sensor deviation is corrected and the key monitoring indicators are extracted using the formula: ； Generate real-time monitoring feedback; in, represents the correction factor, represents the theoretical pressure threshold, represents the pressure-dependent weighting factor used to adjust the feedback sensitivity, Represents monitoring indicators.

5. The AI-based vehicle fixture positioning system according to claim 4, characterized in that: The steps for obtaining the model optimization parameters are specifically as follows: Receive the real-time monitoring feedback and evaluate the data for abnormal patterns using the formula: ； Calculate the standard deviation of the data and calculate the variance based on the standard deviation and the mean using the formula: ； generating anomaly pattern evaluation values; in, Represents a single monitoring feedback, Represents the average value of monitoring feedback, represents the sample size, represents the standard deviation, represents the abnormal mode evaluation value, It is a small amount that avoids the denominator being zero; According to the abnormal mode evaluation value, the operating parameters are adjusted using the formula: ； Calculate and obtain adjusted operating parameters; in, represents the adjustment factor, To adjust the parameters, represents the adjusted operating parameters; The multiple adjusted operating parameters are fused and the optimization direction is extracted using the formula: ； Generate model optimization parameters; in, Representative The weight of the group data, Represents the number of data sets, Represents the optimization direction after fusion, that is, the model optimization parameters.

6. The AI-based vehicle fixture positioning system according to claim 5, characterized in that: The steps for obtaining the position adjustment result are specifically as follows: The model is used to optimize the parameters and predict the position change of the fixture in the differentiated production stage through the dynamic change analysis method, using the formula: ； generating predicted position changes; in, represents the dynamic change coefficient, represents the environmental resistance coefficient; Based on the predicted position change, the fixture position is adjusted in real time with reference to machine wear and environmental factors, using the formula: ； Get the adjusted fixture position; in, represents the machine wear coefficient, represents the environmental impact factor, Represents the adjusted fixture position; Based on the adjusted fixture position, the formula is used: ； Ensure that the result is non-negative and generate the position adjustment result .

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