Error analysis and precision control method of a topology mapping system based on zero-point mapping

Abstract

The application discloses a kind of error analysis and precision control method of topological structure mapping system based on zero point mapping.The method realizes systematic error management by error source identification, error transmission model, sensitivity analysis and precision budget, and realizes precision control by determining fidelity target, calculating error contribution, identifying key error source and allocating tolerance.The application is suitable for precision design, performance evaluation and tolerance allocation and other application scenarios of zero point mapping system.

Description

Technical Field

[0001] This invention relates to the field of system accuracy and error analysis technology, specifically to an error analysis and accuracy control method for a topology mapping system based on zero-point mapping. This invention can be applied to application scenarios requiring systematic error management, such as accuracy design, performance evaluation, and tolerance allocation for zero-point mapping systems. Background Technology

[0002] Zero-point mapping systems involve multiple stages, including zero-point positioning, zero-point locking, zero-point reconstruction, and structural transfer, each of which introduces errors. The cumulative effect of these errors can impact the overall system performance and even lead to transfer failure. However, current technologies lack systematic analysis and precision control methods for zero-point mapping system errors. Existing error analysis schemes have the following shortcomings: (i) Lack of error source identification. There is no systematic identification of the error sources in each stage of zero-point mapping. (ii) Lack of an error propagation model. It is unclear how errors in each stage affect the final transmission fidelity. (iii) Lack of precision budgeting methods. It is impossible to determine the precision requirements of each stage during the design phase. (iv) Lack of tolerance allocation method. There is no basis for allocating the overall accuracy requirements to each subsystem during system design. Therefore, existing technologies lack error analysis and accuracy control methods for zero-point mapping systems, and there is no solution that can systematically manage the accuracy of zero-point mapping systems. To address the above problems, this invention provides an error analysis and accuracy control method for a topology mapping system based on zero-point mapping. Summary of the Invention

[0003] This invention aims to solve the technical problem of the lack of error analysis and accuracy control methods in zero-point mapping systems, and provides an error analysis and accuracy control method for topology mapping systems based on zero-point mapping. By identifying error sources, establishing an error propagation model, performing accuracy budgeting, and allocating tolerances, the system accuracy can be systematically managed. (I) This invention provides an error analysis method for a topology mapping system based on zero-point mapping, comprising: Error source identification: Identify the error sources in each stage of the zero-point mapping system, including zero-point positioning error, zero-point locking error, zero-point reconstruction error, transmission process error, and environmental disturbance error. Error propagation model: Establishes the propagation relationship between errors in each stage and the final propagation fidelity. The error propagation model uses a first-order approximation or Monte Carlo method to calculate the final error. Sensitivity analysis: Analyze the sensitivity of errors in each stage to the final fidelity and identify key error sources. Accuracy budget: Based on the fidelity requirements, the allowable error range of each step is calculated. (II) The present invention also provides a method for precision control of a topology mapping system based on zero-point mapping, comprising the following steps: Step S1: Determine the overall fidelity target value based on the system fidelity requirements. Step S2: Calculate the contribution of errors in each stage to the overall fidelity using the error propagation model. Step S3: Identify the key error sources that contribute the most and control them first. Step S4: Allocate the overall fidelity target to the accuracy tolerance of each stage. Step S5: Design each subsystem according to the tolerance requirements to ensure that the error of each link is within the tolerance range. (III) The present invention has the following beneficial effects: (1) Systematic error source identification ensures the completeness of error analysis. (2) Error propagation model provides quantitative error prediction capability. (3) Sensitivity analysis identifies key error sources and improves the efficiency of precision control. (4) Precision budgeting method provides a basis for system design. Detailed Implementation

[0004] Example 1: Error analysis and accuracy budgeting of a zero-point mapping system. This embodiment performs error analysis and accuracy estimation on a zero-point mapping system. S1. Error Source Identification. Key error sources are identified as: zero-point positioning accuracy error (σ_pos=0.5%), zero-point locking drift (σ_lock=0.3% / h), zero-point reconstruction error (σ_rec=0.4%), and environmental disturbance (σ_env=0.2%). S2. Error propagation model. Establish the error propagation relationship: Total fidelity error σ_total = sqrt(σ_pos² + σ_lock² + σ_rec² + σ_env²) = sqrt(0.25 + 0.09 + 0.16 + 0.04) = 0.735%. S3. Sensitivity Analysis. Calculate the contribution of each error source to the overall error: positioning error contributes 34%, locking error contributes 12%, reconstruction error contributes 22%, and environmental disturbance contributes 5% (normalized after square root). S4. Accuracy Budget. If the overall fidelity of the target is ≥99% (error ≤1%), the tolerances for each step can be appropriately relaxed. Implementation results: Accuracy budgeting was completed, and the system design was based on solid evidence. Example 2: System performance prediction and verification based on error model. This embodiment demonstrates how to predict system performance using an error propagation model and verifies it through experiments. S1. Theoretical Prediction. Based on the design tolerances of each stage, the overall fidelity F_pred is calculated and predicted using an error propagation model. S2. Experimental Measurement. Execute the complete zero-point mapping and transfer process in the actual system and measure the overall fidelity F_meas. S3. Bias Analysis. Compare F_pred and F_meas, and calculate the prediction bias ΔF = |F_pred - F_meas|. If ΔF is less than a preset threshold (e.g., 0.01), the model is valid. S4. Model Correction. If ΔF exceeds the threshold, the parameters in the error propagation model are corrected based on the measured data, and the prediction model is updated. Implementation results: The prediction bias of the error propagation model is less than 0.5%, and the model can be used for subsequent system design and performance optimization. Example 3: Sensitivity analysis and control strategy optimization of key error sources. This example demonstrates how to identify key error sources and optimize control strategies through sensitivity analysis. S1. Sensitivity Ranking. Perform sensitivity analysis on the error sources in each stage and calculate the sensitivity coefficient S_i = ∂F / ∂σ_i of each error source to the overall fidelity. S2. Identification of key error sources. After sorting, the three error sources with the highest sensitivity were identified: zero-point positioning accuracy (S=0.85), zero-point reconstruction error (S=0.72), and environmental disturbance (S=0.58). S3. Control Strategy Allocation. Prioritize allocating precision control resources to key error sources: improve zero-point positioning accuracy (increase scan density), reduce zero-point reconstruction error (improve reconstruction algorithm accuracy), and enhance environmental isolation (add shielding layer). S4. Optimization and Verification. After optimizing the control strategy, remeasure the overall fidelity to confirm the improvement effect. Implementation results: After the control strategy was optimized, the overall fidelity improved from 0.985 to 0.997, and the contribution of key error sources was reduced by more than 50%. Example 4: Dynamic precision monitoring and adaptive tolerance adjustment. This embodiment demonstrates how the system dynamically monitors changes in accuracy and adaptively adjusts the tolerance during operation. S1. Online monitoring. The system monitors error indicators (zero drift rate, reconstruction deviation, environmental parameters) in real time during operation. S2. Error Trend Prediction. Based on monitoring data, predict the error change trend to determine whether the tolerance will be exceeded in the future. S3. Adaptive Adjustment. When the prediction error is about to exceed the tolerance limit, the system automatically tightens the tolerance threshold or triggers the calibration process. S4. Tolerance Restoration. After the error returns to the normal range, the system restores the preset tolerance. Implementation results: Dynamic precision monitoring reduced the fidelity fluctuation of the system under environmental disturbances from ±0.02 to ±0.005, and adaptive tolerance adjustment effectively prevented transmission failure caused by exceeding the precision limit. Example 5: Error allocation and optimization of multi-subsystem cascaded systems. This embodiment demonstrates an error allocation method when multiple subsystems are cascaded. S1. System Decomposition. The zero-point mapping system is decomposed into several subsystems: zero-point positioning subsystem, zero-point locking subsystem, zero-point reconstruction subsystem, structure transfer subsystem, and fidelity verification subsystem. S2. Subsystem Error Modeling. An error model is established for each subsystem, and the design tolerance and cost function for each subsystem are determined. S3. Cascaded Error Allocation. A cascaded error allocation algorithm is used to minimize the total cost while satisfying the overall fidelity constraint. The algorithm iteratively allocates tolerances among subsystems, prioritizing the allocation of more lenient tolerances to cost-sensitive subsystems. S4. Verification of Allocation Results. Design each subsystem according to the allocated tolerances, and measure the overall fidelity after integration. Implementation results: Cascaded error distribution balances the tolerance distribution of each subsystem, reduces the total cost by 25%, and meets the overall fidelity target (≥0.99). Attached Figure Description

[0005] Figure 1 This is a schematic diagram of the error source identification and error propagation model of the present invention. Figure 2 This is a flowchart of the error propagation model calculation. Figure 3 Flowchart for precision budgeting and tolerance allocation. Terminology Explanation

[0006] Error source identification: The process of systematically identifying the sources of error in each stage of zero-point mapping. Error propagation model: Establishing a quantitative propagation relationship between errors in each stage and the final fidelity. Sensitivity analysis: An analytical method for analyzing the degree of impact of errors in each stage on the final fidelity. Accuracy budgeting: The process of working backward from the fidelity requirements to deduce the allowable error range for each step.

Claims

1. An error analysis method for a topology mapping system based on zero-point mapping, characterized in that, include: Error source identification: Identify zero-point positioning error, zero-point locking error, zero-point reconstruction error, transmission process error, and environmental disturbance error; Error propagation model: Establishing the propagation relationship between errors in each stage and the final fidelity; Sensitivity analysis: Analyzing the sensitivity of errors at each stage to the final fidelity; Accuracy budget: Based on the fidelity requirements, the allowable error range of each step is calculated.

2. The error analysis method according to claim 1, characterized in that: The error propagation model uses a first-order approximation or Monte Carlo method to calculate the final error, and the combined error satisfies σ_total = sqrt(σ_pos² + σ_lock² + σ_rec² + σ_xfer² + σ_env²).

3. The error analysis method according to claim 1, characterized in that: The sensitivity analysis calculates the sensitivity coefficient S_i = ∂F / ∂σ_i of each error source to the overall fidelity, and identifies the key error sources that have the greatest impact on the overall fidelity by sorting them.

4. The error analysis method according to claim 1, characterized in that: The accuracy budget is calculated by back-deriving the allowable error tolerance σ_i_alloc for each stage based on the overall fidelity target F_target. The tolerance for each stage satisfies sqrt(Σσ_i_alloc²) ≤ 1 - F_target.

5. A precision control method for a topology mapping system based on zero-point mapping, characterized in that, Includes the following steps: Determine the overall fidelity target value based on the system fidelity requirements; The contribution of errors in each stage to the overall fidelity is calculated using an error propagation model. Identify the key error sources that contribute the most to the error; The overall fidelity target is allocated as accuracy tolerances for each stage; Design each subsystem according to the tolerance requirements to ensure that the error in each link is within the tolerance range.

6. The precision control method according to claim 5, characterized in that: After identifying the key error sources that contribute the most, precision control resources are preferentially allocated to the key error sources. When resources are limited, a more lenient tolerance is applied to error sources with lower sensitivity coefficients.

7. The precision control method according to claim 5, characterized in that: The accuracy tolerance allocation adopts a cascaded error allocation algorithm to minimize the total cost while meeting the comprehensive fidelity constraint. The algorithm iteratively allocates tolerances among subsystems, giving priority to allocating lenient tolerances to cost-sensitive subsystems.

8. The precision control method according to claim 5, characterized in that: It also includes dynamic accuracy monitoring and adaptive tolerance adjustment steps: The system monitors error indicators at each stage in real time during operation. Predict the trend of error changes based on monitoring data; Automatically tighten the tolerance threshold or trigger a calibration process when the prediction error is about to exceed the tolerance. Once the error returns to the normal range, the preset tolerance is restored.

9. The precision control method according to claim 5, characterized in that: It also includes model validation and correction steps: Based on the design tolerance of each stage, the overall fidelity F_pred is calculated and predicted using the error propagation model. The overall fidelity F_meas was measured in a real system. Calculate the prediction bias ΔF = |F_pred - F_meas|; When ΔF exceeds the preset threshold, the error propagation model parameters are corrected based on the measured data.

10. A precision control chip, characterized in that: The chip includes the error calculation unit, tolerance allocation unit, and monitoring and adjustment unit of the precision control method according to any one of claims 5-9, and the chip is integrated into the zero-point mapping system control module.

Images