Calibration method and device of force sensor, electronic equipment, storage medium and product

Abstract

The application provides a force sensor calibration method and device, electronic equipment, storage medium and product; the method comprises the following steps: applying a single-dimensional load to each loading direction of the force sensor to obtain first original channel output data corresponding to each loading direction; determining a reference response vector corresponding to each loading direction based on the first original channel output data and corresponding load information; determining one or more first evaluation indexes based on the reference response vector; applying a multi-dimensional composite load to the force sensor to obtain second original channel output data; determining a multi-dimensional verification error index based on the reference response vector, the second original channel output data and corresponding multi-dimensional composite load information; determining a calibration quality score based on the first evaluation index and the multi-dimensional verification error index; and generating calibration reliability information representing the reliability of the single-dimensional reference calibration result based on the score interval where the calibration quality score is located. The scheme of the application values the good structure self-decoupling capability of the force sensor.

Description

Technical Field

[0001] This application relates to the field of sensor technology, and in particular to a calibration method, apparatus, electronic device, storage medium and product for a force sensor. Background Technology

[0002] In force sensor calibration, related technologies rely on multi-dimensional joint loading to obtain response data and complex full-matrix calibration to obtain decoupling matrices, regarding multi-dimensional joint loading as the sole standard for force sensor calibration. However, this method may use algorithms to mask root causes such as structural design defects, processing and assembly errors, and bridge layout defects, resulting in poor long-term product reliability and continuous exposure of quality issues with use. Furthermore, calibration based on multi-dimensional joint loading increases calibration costs and time. Summary of the Invention

[0003] In view of this, embodiments of this application provide a calibration method, apparatus, electronic device, storage medium, and product for a force sensor, aiming to provide a calibration method that takes into account the self-decoupling capability of the force sensor, thereby improving the field robustness of the sensor and reducing calibration costs and time.

[0004] The technical solution of this application embodiment is implemented as follows: In a first aspect, embodiments of this application provide a calibration method for a force sensor, the method comprising: A single-dimensional load is applied to each loading direction of the force sensor to obtain the first raw channel output data corresponding to each loading direction; Based on the first original channel output data and the corresponding load information, determine the reference response vector for each loading direction; One or more first evaluation indicators are determined based on the benchmark response vector; A multidimensional composite load is applied to the force sensor to obtain the output data of the second raw channel; The multidimensional verification error index is determined based on the benchmark response vector, the second original channel output data, and the corresponding multidimensional composite load information. The calibration quality score is determined based on the first evaluation index and the multidimensional verification error index. Based on the score range of the calibration quality score, calibration reliability information is generated to characterize the reliability of the single-dimensional benchmark calibration result.

[0005] In some embodiments, the first evaluation metric includes one or more of a self-decoupling error metric, a one-dimensional linear error metric, and a baseline response metric. The step of determining the calibration quality score based on the first evaluation metric and the multi-dimensional verification error metric includes: The calibration quality score is determined based on one or more of the following: self-decoupling error index, single-dimensional linear error index, and benchmark response index; the multi-dimensional verification error index; and the weight coefficients corresponding to each index. The method further includes: determining a calibration processing scheme based on the score range in which the calibration quality score is located.

[0006] In some embodiments, the first evaluation index includes a self-decoupling error index, and determining one or more first evaluation indices based on the benchmark response vector includes: Based on the reference response vector, determine the target direction response information and non-target direction response information corresponding to each loading direction; The self-decoupling error index is determined based on the target direction response information and the non-target direction response information.

[0007] In some embodiments, the first evaluation index includes a one-dimensional linear error index, and determining one or more first evaluation indices based on the benchmark response vector includes: The single-dimensional linear error index is determined based on the reference response vector corresponding to each loading direction, the first original channel output data, and the corresponding load information.

[0008] In some embodiments, the first evaluation index includes a benchmark response index, and determining one or more first evaluation indices based on the benchmark response vector includes: The benchmark response index is determined based on the degree of similarity between the benchmark response vectors corresponding to different loading directions.

[0009] In some embodiments, determining the multidimensional verification error index based on the reference response vector, the second original channel output data, and the corresponding multidimensional composite load information includes: Determine the one-dimensional decoupling parameters based on the aforementioned benchmark response vector; The predicted load information is determined based on the single-dimensional decoupling parameters and the output data of the second original channel. The multidimensional verification error index is determined based on the multidimensional composite load information and the predicted load information.

[0010] In some embodiments, the first evaluation index includes one or more of a self-decoupling error index, a one-dimensional linear error index, and a benchmark response index, and the method further includes: If it is determined that one or more of the self-decoupling error index, the single-dimensional linear error index, and the benchmark response index are not within the corresponding safety threshold range, and / or the multi-dimensional verification error index is not within the corresponding safety threshold range, then the calibration credibility information indicates that the single-dimensional benchmark calibration result is unreliable.

[0011] Secondly, embodiments of this application provide a calibration device for a force sensor, the device comprising: The acquisition module is used to apply a one-dimensional load to each loading direction of the force sensor to obtain the first raw channel output data corresponding to each loading direction; The reference response modeling module is used to determine the reference response vector corresponding to each loading direction based on the first original channel output data and the corresponding load information. The scoring module is used to determine one or more first evaluation indicators based on the benchmark response vector; The acquisition module is also used to apply a multidimensional composite load to the force sensor to obtain the second raw channel output data; The scoring module is also used to determine a multidimensional verification error index based on the benchmark response vector, the second original channel output data and the corresponding multidimensional composite load information; and to determine a calibration quality score based on the first evaluation index and the multidimensional verification error index. The decision module is used to generate calibration confidence information that characterizes the confidence of the single-dimensional benchmark calibration result based on the score range in which the calibration quality score is located.

[0012] Thirdly, embodiments of this application provide an electronic device, the electronic device including a processor and a memory for storing a computer program capable of running on the processor, wherein, The processor is configured to execute the steps of the method as described in the first aspect when running a computer program.

[0013] Fourthly, embodiments of this application provide a storage medium storing a computer program, which, when executed by a control device, implements the steps of the method described in the first aspect.

[0014] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a controlled device, implements the steps of the method described in the first aspect.

[0015] The technical solution provided in this application includes the following method: applying a single-dimensional load to each loading direction of a force sensor to obtain first raw channel output data corresponding to each loading direction; determining a reference response vector corresponding to each loading direction based on the first raw channel output data and the corresponding load information; determining one or more first evaluation indicators based on the reference response vector; applying a multi-dimensional composite load to the force sensor to obtain second raw channel output data; determining a multi-dimensional verification error indicator based on the reference response vector, the second raw channel output data, and the corresponding multi-dimensional composite load information; determining a calibration quality score based on the first evaluation indicators and the multi-dimensional verification error indicator; and generating calibration reliability information characterizing the reliability of the single-dimensional reference calibration result based on the score range of the calibration quality score.

[0016] This application's solution emphasizes the sensor's strong structural self-decoupling capability, using single-dimensional benchmark calibration as the core of the calibration process, and utilizing only a small amount of multi-dimensional loading for accuracy verification and quality assurance. Specifically, a benchmark response vector is established through single-dimensional standard loading in various directions. Analysis of the benchmark response vector yields a first evaluation index, used to evaluate the sensor's self-decoupling performance and the quality of the single-dimensional benchmark calibration. Then, a small amount of multi-dimensional composite loading is used to verify whether the single-dimensional benchmark calibration results still meet accuracy requirements under composite loading. The calibration quality score is determined by combining the first evaluation index and the multi-dimensional verification error index, and the reliability of the single-dimensional benchmark calibration results is determined based on the calibration quality score, thus determining whether the calibration results are suitable for shipment. In this way, the solution avoids using complex matrices to mask sensor structural defects, improves the sensor's field robustness, and reduces calibration costs and time. Attached Figure Description

[0017] Figure 1 This is a schematic flowchart of the force sensor calibration method provided in the embodiments of this application; Figure 2 This is a flowchart illustrating the calibration method for the force sensor provided in the application example of this application; Figure 3 This is a schematic diagram of the structure of the calibration device for the force sensor provided in the embodiments of this application; Figure 4 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0018] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0019] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of this application.

[0020] In force sensor calibration, different structures require different calibration routes. If the sensor has strong coupling at the structural level, complex full-matrix calibration or multi-dimensional joint loading is needed for remediation at the back end. If the sensor achieves clear physical separation of loads in each direction at the original strain or bridge output level through a centrally symmetrical structure, bridge circuit cancellation, and sensitive element arrangement, then a smaller amount of software correction can be used to improve accuracy.

[0021] This application considers that using multi-dimensional joint loading to obtain response data and relying on complex full-matrix calibration to obtain the decoupling matrix, and treating multi-dimensional joint loading as the sole standard for calibrating force sensors, may mask root causes such as structural design flaws, manufacturing and assembly errors, and bridge circuit layout defects through algorithms. This leads to poor long-term product reliability, with quality issues continuously emerging with use. Furthermore, calibration based on multi-dimensional joint loading increases calibration costs and time. For force sensors with good structural self-decoupling capabilities and linear response characteristics, single-dimensional loading can be used to establish reference responses in six independent directions. Any composite load can be decomposed into a linear combination of loads in six reference directions. Therefore, provided that the single-dimensional reference is accurate, the structure is linearly sound, and the bridge circuit response is stable, the composite force measurement capability can be obtained through the linear superposition of single-dimensional predicted loads. Multi-dimensional loading is more suitable as an accuracy verification and quality fallback, rather than as the primary means of remedying sensor structural defects.

[0022] In summary, the problems with the relevant technologies are as follows: 1. Treating multi-dimensional joint loading as the sole criterion for evaluating the quality of a six-dimensional force sensor ignores the sensor's structural self-decoupling capability. 2. In cases of high structural coupling, using complex matrix fitting as a remedy increases calibration equipment costs and time, and reduces field robustness. 3. Existing single-dimensional loading calibration is often understood only as a low-cost, simplified calibration, lacking a systematic definition of the relationship between single-dimensional benchmarks, linear superposition, and multi-dimensional verification. 4. Directly using multi-dimensional loading data to repair or train complex models may mask fundamental problems in structural design, bridge layout, or assembly status. 5. There is a lack of a clear calibration process that distinguishes between single-dimensional benchmark establishment and multi-dimensional verification acceptance to determine whether the structural self-decoupling design is truly effective.

[0023] In various embodiments of this application, the strong structural self-decoupling capability of the force sensor is emphasized. Single-dimensional benchmark calibration is used as the core of the calibration process, with only a small amount of multi-dimensional loading employed for accuracy verification and quality assurance. Specifically, a benchmark response vector is established through single-dimensional standard loading in various directions. Analysis of the benchmark response vector yields a first evaluation index, used to evaluate the sensor's self-decoupling performance and the quality of the single-dimensional benchmark calibration. Then, a small amount of multi-dimensional composite load is used to verify whether the single-dimensional benchmark calibration result still meets the accuracy requirements under composite load. The calibration quality score is determined by combining the first evaluation index and the multi-dimensional verification error index. Based on the calibration quality score, the reliability of the single-dimensional benchmark calibration result is determined, thereby determining whether the calibration result is suitable for shipment. Thus, the solution of this application avoids using complex matrices to mask sensor structural defects, improves the sensor's field robustness, and reduces calibration costs and time.

[0024] The method provided in this application can be applied to force / torque sensors such as resistance strain gauge, MEMS, capacitive, piezoresistive, silicon-based, and fiber optic sensors. The following description uses a six-dimensional force sensor as an example. During calibration, the sensor can be loaded using weights, a standard force source, a torque loading arm, a six-dimensional loading stage, a robotic loading device, or an automatic calibration device.

[0025] This application provides a calibration method for a force sensor, such as... Figure 1 As shown, the method includes: Step 101: Apply a single-dimensional load to each loading direction of the force sensor to obtain the first raw channel output data corresponding to each loading direction.

[0026] Here, before formally calibrating the sensor, it is first confirmed that the sensor's elastic body, sensitive element, and bridge circuit arrangement can physically distinguish the six load directions. That is, the design goal of the sensor is to have good structural self-decoupling capability. Equation (1) represents the linear superposition of the six-dimensional loads.

[0027] (1) Formula (1) shows that any six-dimensional composite load can be obtained by linear combination of loads in six reference directions, which is the theoretical basis for the deduction of multi-dimensional forces from a single-dimensional reference response vector. Here, F is the six-dimensional load vector, including triaxial force and triaxial moment; Let be the unit vector of the j-th reference direction, corresponding to one of the six loading directions; Let Fx be the component of the six-dimensional composite load in the j-th reference direction. Here, the reference direction refers to any one of the loading directions among Fx, Fy, Fz, Mx, My, and Mz of the six-dimensional force sensor.

[0028] For example, after calibration begins, multiple single-dimensional standard loads are applied sequentially along the six directions of the six-dimensional force sensor. Each direction has multiple loading levels for the applied single-dimensional standard load, such as 10%, 20%, 30% of the range. Each time a load of a certain loading level is applied, the corresponding first raw channel output data is collected. Specifically, the raw channel output data can be the voltage output by the bridge circuit (in mV or V), the amplified voltage, or the ADC digital value. Formula (2) represents the single-dimensional standard load of the k-th level in the j-th direction, where only the target direction j has a non-zero load. This represents the single-dimensional standard load amplitude.

[0029] (2) Step 102: Determine the reference response vector corresponding to each loading direction based on the first original channel output data and the corresponding load information.

[0030] For example, the load in the j-th direction and the k-th level Below, the original output vector (first original channel output data) after deducting the zero point is denoted as... , This is the zero-point output vector, i.e., the original output vector under no external load conditions.

[0031] (3) Utilize multiple It can be fitted with the corresponding load values. , This represents the reference response vector corresponding to the j-th loading direction, i.e., the single-dimensional reference response vector.

[0032] Specifically, for a given loading direction, loading is performed based on multiple loading levels. Multiple load values ​​are then fitted with the corresponding original output vectors to form a straight line, thus... The least squares method is used to calculate the load direction corresponding to... For example, assuming there are 5 loading levels for a certain loading direction j, the goal is to minimize the sum of squared errors between the measured original output vector and the fitted original output vector, thereby obtaining the optimal fit. The reference response vector in that direction, and the method for calculating the sum of squared errors S is shown in formula (4).

[0033] (4) The reference response matrix, composed of reference response vectors in six directions, is shown in Equation (5).

[0034] (5) Each baseline response vector bj is obtained by fitting multiple single-dimensional loading levels with the corresponding original output vector.

[0035] Step 103: Determine one or more first evaluation indicators based on the benchmark response vector.

[0036] Here, at least one first evaluation index is determined based on the calculated baseline response vector. The first evaluation index may include one or more of the following: self-decoupling error index, one-dimensional linear error index, and baseline response index.

[0037] Step 104: Apply a multidimensional composite load to the force sensor to obtain the second raw channel output data.

[0038] Here, a small number of typical composite loads can be selected as verification points. At least one multidimensional composite load is applied to the six-dimensional force sensor, and the corresponding raw output vector (second raw channel output data) is collected.

[0039] Step 105: Determine the multidimensional verification error index based on the reference response vector, the second original channel output data, and the corresponding multidimensional composite load information; Specifically, the predicted load can be fitted based on the baseline response vector and the output data of the second original channel, and compared with the actual multidimensional composite load to obtain the value of the multidimensional verification error index.

[0040] Step 106: Determine the calibration quality score based on the first evaluation index and the multidimensional verification error index; Based on the score range of the calibration quality score, calibration reliability information is generated to characterize the reliability of the single-dimensional benchmark calibration result.

[0041] Here, a comprehensive calibration quality score Q can be calculated based on the above indicators. The calibration reliability information can be generated based on the score range of Q, which characterizes the reliability of the single-dimensional benchmark calibration results. Specifically, the calibration reliability information may include: passed, requires linear correction, requires retesting, requires structural / bridge circuit inspection, and complex model remediation is not recommended.

[0042] Understandably, the solution in this application emphasizes the strong structural self-decoupling capability of the force sensor, using single-dimensional benchmark calibration as the core of the calibration process, and only utilizing a small amount of multi-dimensional loading for accuracy verification and quality assurance. Specifically, a benchmark response vector is established through single-dimensional standard loading in various directions. Analysis of the benchmark response vector yields a first evaluation index, used to evaluate the sensor's self-decoupling performance and the quality of the single-dimensional benchmark calibration. Then, a small amount of multi-dimensional composite load is used to verify whether the single-dimensional benchmark calibration result still meets the accuracy requirements under composite loads. The calibration quality score is determined by combining the first evaluation index and the multi-dimensional verification error index, and the reliability of the single-dimensional benchmark calibration result is determined based on the calibration quality score, thus determining whether the calibration result is suitable for shipment. In this way, the solution in this application avoids using complex matrices to mask sensor structural defects, improves the sensor's field robustness, and reduces calibration costs and time.

[0043] In some embodiments, the first evaluation index includes a self-decoupling error index, and determining one or more first evaluation indices based on the benchmark response vector includes: Based on the reference response vector, determine the target direction response information and non-target direction response information corresponding to each loading direction; The self-decoupling error index is determined based on the target direction response information and the non-target direction response information.

[0044] For example, self-decoupling error index The calculation formula is shown in formula (6). This index represents the ratio of the non-target direction response to the target direction response and is used to determine whether the non-target direction response is controlled under unidimensional loading.

[0045] (6) in, The target direction response vector (target direction response information) is the reference response vector corresponding to the j-th loading direction. η is the response vector of the i-th non-target direction in the reference response vector corresponding to the j-th loading direction (non-target direction response information); η is a very small positive number to prevent the denominator from being zero.

[0046] Understandable The smaller the value, the smaller the response in the non-target direction under unidimensional loading, and the better the self-decoupling performance of the sensor.

[0047] In practical applications, the largest self-decoupling error index among the six directions can be used. It serves as the self-decoupling error index used in the final calibration quality score calculation.

[0048] In some embodiments, the first evaluation index includes a one-dimensional linear error index, and determining one or more first evaluation indices based on the benchmark response vector includes: The single-dimensional linear error index is determined based on the reference response vector corresponding to each loading direction, the first original channel output data, and the corresponding load information.

[0049] For example, formula (7) is a one-dimensional linear error index. The calculation formula is used to determine whether a single-dimensional benchmark can be used for linear superposition.

[0050] (7) Here, the single-dimensional linear error index The smaller the value, the better the linearity in the j-direction, and the more reliable the principle of linear superposition.

[0051] In practical applications, the largest single-dimensional linear error index among the six directions can be used. It serves as a single-dimensional linear error index for the final calibration quality score calculation.

[0052] In some embodiments, the first evaluation index includes a benchmark response index, and determining one or more first evaluation indices based on the benchmark response vector includes: The benchmark response index is determined based on the degree of similarity between the benchmark response vectors corresponding to different loading directions.

[0053] For example, the cosine correlation coefficient between different reference response vectors is used as an indicator of similarity. Formula (8) is the cosine correlation coefficient of the reference response vectors corresponding to different directions. The larger the value, the more similar the two reference response vectors are. Let be the reference response vector in the j-direction. for The reference response vector in the direction.

[0054] (8) Take the maximum value among all cosine correlation coefficients. As the benchmark response index, formula (9) is the benchmark response index. The calculation formula.

[0055] (9) in, The smaller the value, the easier it is to distinguish the output modes in the six directions; The larger the value (closer to 1), the more similar the output patterns in the two directions are, making them easy to confuse.

[0056] In some embodiments, determining the multidimensional verification error index based on the reference response vector, the second original channel output data, and the corresponding multidimensional composite load information includes: Determine the one-dimensional decoupling parameters based on the aforementioned benchmark response vector; The predicted load information is determined based on the single-dimensional decoupling parameters and the output data of the second original channel. The multidimensional verification error index is determined based on the multidimensional composite load information and the predicted load information.

[0057] Here, the inverse matrix of the reference response matrix, composed of reference response vectors in six directions, can be obtained to yield the reference decoupling matrix. Thus, each parameter of the reference decoupling matrix is ​​a one-dimensional decoupling parameter. Multiplying each row of the one-dimensional decoupling parameter in the reference decoupling matrix by the original channel output data (second original channel output data) corresponding to the multi-dimensional loading yields the force or torque in a loading direction. The forces or torques in each direction constitute the predicted load information. Alternatively, multiplying the reference decoupling matrix by the second original channel output data yields the predicted load information.

[0058] Formula (10) is used to calculate the multidimensional verification error index. The formula is used to evaluate the measurement error of the composite load verification point.

[0059] (10) in, The standard composite load actually applied to the m-th multidimensional verification point (multidimensional composite load information); This represents the composite load (predicted load information) calculated based on the single-dimensional decoupling parameters; the superscript v indicates verification data; η is a very small positive number to prevent the denominator from being zero; Let be the relative error of the m-th multidimensional verification point.

[0060] Understandable The smaller the value, the better the superposition effect of the load predicted by the single-dimensional reference response vector under the verification of multi-dimensional composite load, and the single-dimensional reference calibration result still meets the accuracy requirements under composite load.

[0061] In practical applications, the largest relative error can be used among all multidimensional verification points. As a multidimensional verification error index.

[0062] In some embodiments, the first evaluation metric includes one or more of a self-decoupling error metric, a one-dimensional linear error metric, and a baseline response metric. The step of determining the calibration quality score based on the first evaluation metric and the multi-dimensional verification error metric includes: The calibration quality score is determined based on one or more of the following: self-decoupling error index, single-dimensional linear error index, and benchmark response index; the multi-dimensional verification error index; and the weight coefficients corresponding to each index. The method further includes: determining a calibration processing scheme based on the score range in which the calibration quality score is located.

[0063] For example, formula (11) is the formula for calculating the calibration quality score Q.

[0064] (11) Where w1 is the weighting coefficient corresponding to the self-decoupling error index, w2 is the weighting coefficient corresponding to the single-dimensional linear error index, w3 is the weighting coefficient corresponding to the baseline response index, and w4 is the weighting coefficient corresponding to the multi-dimensional verification error index. It can be understood that the sum of the weighting coefficients of all indices involved in calculating the calibration quality score Q is 1.

[0065] For example, if Q < T1, the calibration confidence information is determined to be passed, indicating that the single-dimensional benchmark is clear, the structure is well self-decoupled, and the multi-dimensional verification error meets the requirements. The calibration processing scheme is to use the single-dimensional benchmark calibration results (including the benchmark response vector and the corresponding single-dimensional decoupling parameters) and generate a calibration report.

[0066] If T1≤Q<T2, then the calibration reliability information is determined as follows: linear correction is required, indicating that the single-dimensional benchmark is basically valid, but there is a slight proportional or zero-point deviation. The calibration treatment plan is to re-verify whether the values ​​of each indicator are within the safe range after performing a small amount of linear correction, and whether the calibration quality score Q is <T1.

[0067] If T2≤Q<T3, then the calibration reliability information is determined as follows: retesting is required, indicating that the single-dimensional linearity or verification error in some directions is close to the warning value. The calibration treatment plan is to retest the relevant directions or verification points.

[0068] If T3≤Q<T4, then the calibration reliability information is determined as follows: structural / bridge circuit check is required, indicating that the structural self-decoupling index or the independence of the reference response does not meet the requirements. The calibration solution is to check the bridge circuit cancellation, patch position, structural processing and assembly status, and then retest.

[0069] If Q ≥ T4, then the calibration reliability information is determined as follows: complex remediation is not recommended, indicating that multidimensional verification has failed and the baseline quality is poor. The calibration handling solution is to return to the structural and process optimization stage, without using complex matrices or models to mask the sensor's inherent problems. It is understood that the specific values ​​of T1-T4 can be determined according to the actual situation, and this application embodiment does not limit this.

[0070] In some embodiments, the first evaluation index includes one or more of a self-decoupling error index, a one-dimensional linear error index, and a benchmark response index, and the method further includes: If it is determined that one or more of the self-decoupling error index, the single-dimensional linear error index, and the benchmark response index are not within the corresponding safety threshold range, and / or the multi-dimensional verification error index is not within the corresponding safety threshold range, then the calibration credibility information indicates that the single-dimensional benchmark calibration result is unreliable.

[0071] Based on the above examples, each of the above indicators has a corresponding safety threshold range. If the self-decoupling error indicator... If the error is too large, exceeding the self-decoupling error threshold, the single-dimensional benchmark calibration is determined to be unreliable, and a calibration report cannot be directly generated for shipment. If the single-dimensional linear error index... If the value is too large, exceeding the single-dimensional linear error threshold, then the single-dimensional benchmark calibration is deemed unsuccessful; if the benchmark response index... If the value is too large, exceeding the baseline response threshold, then the single-dimensional baseline calibration is deemed unsuccessful; if the multi-dimensional verification error index... If the value is too large, exceeding the multidimensional verification error threshold, then the single-dimensional benchmark calibration is deemed unsuccessful. It is understood that the specific values ​​of the aforementioned thresholds can be determined based on actual circumstances, and this application does not impose such limitations.

[0072] The technical solution provided in this application includes the following method: applying a single-dimensional load to each loading direction of a force sensor to obtain first raw channel output data corresponding to each loading direction; determining a reference response vector corresponding to each loading direction based on the first raw channel output data and the corresponding load information; determining one or more first evaluation indicators based on the reference response vector; applying a multi-dimensional composite load to the force sensor to obtain second raw channel output data; determining a multi-dimensional verification error indicator based on the reference response vector, the second raw channel output data, and the corresponding multi-dimensional composite load information; determining a calibration quality score based on the first evaluation indicators and the multi-dimensional verification error indicator; and generating calibration reliability information characterizing the reliability of the single-dimensional reference calibration result based on the score range of the calibration quality score.

[0073] This application's solution emphasizes the sensor's strong structural self-decoupling capability, using single-dimensional benchmark calibration as the core of the calibration process, and utilizing only a small amount of multi-dimensional loading for accuracy verification and quality assurance. Specifically, a benchmark response vector is established through single-dimensional standard loading in various directions. Analysis of the benchmark response vector yields a first evaluation index, used to evaluate the sensor's self-decoupling performance and the quality of the single-dimensional benchmark calibration. Then, a small amount of multi-dimensional composite loading is used to verify whether the single-dimensional benchmark calibration results still meet accuracy requirements under composite loading. The calibration quality score is determined by combining the first evaluation index and the multi-dimensional verification error index, and the reliability of the single-dimensional benchmark calibration results is determined based on the calibration quality score, thus determining whether the calibration results are suitable for shipment. In this way, the solution avoids using complex matrices to mask sensor structural defects, improves the sensor's field robustness, and reduces calibration costs and time.

[0074] The following section provides a more detailed description of this application with reference to application examples.

[0075] Figure 2 This is a schematic diagram of the calibration process for the force sensor provided in the application example of this application, including: Step 201: Confirm structural self-decoupling; then proceed to step 202. Specifically, it is first confirmed that the sensor’s elastic body, sensitive element and bridge circuit arrangement can physically distinguish the six load directions, that is, the design goal of the sensor should be to have good structural self-decoupling capability.

[0076] Step 202, load the single-dimensional standard; then proceed to step 203; Specifically, single-dimensional standard loads are applied sequentially along six directions, and the original channel outputs corresponding to multiple loading levels are collected in each direction.

[0077] Step 203: Establish a one-dimensional baseline; then proceed to step 204. Here, six baseline response vectors and corresponding single-dimensional baseline decoupling parameters are obtained from the single-dimensional loaded data.

[0078] Step 204: Evaluate the baseline quality; then proceed to step 205; Here, we determine whether the structural self-decoupling degree (self-decoupling error index), one-dimensional linearity (one-dimensional linearity error index), and baseline response mode independence (baseline response index) meet the requirements.

[0079] Step 205: Set up multi-dimensional verification points; then proceed to step 206. Here, we compare the actual composite load with the linear superposition prediction results of the single-dimensional benchmark.

[0080] Step 206: Perform multidimensional verification; then proceed to step 207. Here, we compare the results of the linear superposition of the measured response to the composite load and the predicted load from the single-dimensional reference response vector.

[0081] Step 207: Output the conclusion.

[0082] Here, if the verification passes, the single-dimensional benchmark calibration is confirmed to be valid; if the verification fails, issues such as structure, bridge circuit, linearity, assembly, or single-dimensional benchmark are traced back.

[0083] In this application example, a calibration device for a force sensor is also provided. This includes: The single-dimensional loading control module is used to control single-dimensional standard loading in six directions.

[0084] The response acquisition module is used to acquire the raw channel output and the six-dimensional output (actual load values).

[0085] The baseline response modeling module is used to establish a single-dimensional baseline response vector in six directions.

[0086] The structural self-decoupling evaluation module is used to calculate the response ratio and directional discrimination of non-target directions.

[0087] The multidimensional verification module is used to perform a small number of composite load verification points.

[0088] The linear superposition verification module is used to compare multidimensional measured results with single-dimensional benchmark prediction results.

[0089] The anomaly backtracking module is used to locate structural, bridge, assembly, linearity, or single-dimensional benchmark problems.

[0090] The report generation module is used to generate single-dimensional benchmark calibration and multi-dimensional verification reports.

[0091] Based on the above analysis, the purpose of this application example is to provide a method and system for single-dimensional benchmark calibration and multi-dimensional verification of a structurally self-decoupled six-dimensional force sensor. This method utilizes the physical self-decoupling capability formed by the cancellation of the sensor structure and bridge circuit. A benchmark response vector and single-dimensional decoupling parameters are established through single-dimensional standard loading in six directions. Then, a small number of multi-dimensional composite loads are used to verify the linear superposition relationship and composite force accuracy. If the multi-dimensional verification fails, the system prioritizes indicating structural, bridge circuit, linearity, assembly, or single-dimensional benchmark anomalies, rather than simply adding complex fitting models to mask the problem.

[0092] In this application, if the sensor structure possesses good self-decoupling capability, the six reference directions should form clear, stable, and distinguishable response modes at the original strain or bridge output level. Therefore, the role of single-dimensional loading is to accurately determine the six reference response vectors, while the role of multi-dimensional loading is to verify whether the superposition of these reference response vectors can cover the composite working conditions. This application does not exclude multi-dimensional loading, but limits its application: multi-dimensional loading is mainly used for verification, acceptance, and quality assurance, and is not used to construct complex remedial matrices.

[0093] In summary, the beneficial effects of this application are at least as follows: 1. Reduce reliance on expensive and complex combined loading equipment and improve calibration efficiency.

[0094] 2. Highlight the value of structural self-decoupling and bridge-circuit cancellation, and avoid using complex models to cover up structural defects.

[0095] 3. Use a small number of multi-dimensional verification points as a quality safety net, balancing efficiency and final accuracy.

[0096] 4. When verification fails, it can guide the user back to structural, bridge, assembly, or single-dimensional benchmark issues.

[0097] In order to implement the method of the embodiments of this application, the embodiments of this application also provide a calibration device for a force sensor, which corresponds to the calibration method of the force sensor described above. The steps in the calibration method embodiments of the force sensor are also fully applicable to the calibration device embodiments of the force sensor.

[0098] like Figure 3 As shown, the calibration device for the force sensor includes: an acquisition module 301, a reference response modeling module 302, a scoring module 303, and a decision module 304; The acquisition module 301 is used to apply a single-dimensional load to each loading direction of the force sensor to obtain the first raw channel output data corresponding to each loading direction; The reference response modeling module 302 is used to determine the reference response vector corresponding to each loading direction based on the first original channel output data and the corresponding load information. Scoring module 303 is used to determine one or more first evaluation indicators based on the benchmark response vector; The acquisition module 301 is also used to apply a multidimensional composite load to the force sensor to obtain the second raw channel output data; The scoring module 303 is also used to determine a multidimensional verification error index based on the benchmark response vector, the second original channel output data and the corresponding multidimensional composite load information; and to determine a calibration quality score based on the first evaluation index and the multidimensional verification error index. The decision module 304 is used to generate calibration confidence information that characterizes the confidence of the single-dimensional benchmark calibration result based on the score range in which the calibration quality score is located.

[0099] In some embodiments, the first evaluation index includes one or more of a self-decoupling error index, a one-dimensional linear error index, and a benchmark response index, and the scoring module 303 is specifically used for: The calibration quality score is determined based on one or more of the following: self-decoupling error index, single-dimensional linear error index, and benchmark response index; the multi-dimensional verification error index; and the weight coefficients corresponding to each index. The method further includes: determining a calibration processing scheme based on the score range in which the calibration quality score is located.

[0100] In some embodiments, the first evaluation index includes a self-decoupling error index, and the scoring module 303 is specifically used for: Based on the reference response vector, determine the target direction response information and non-target direction response information corresponding to each loading direction; The self-decoupling error index is determined based on the target direction response information and the non-target direction response information.

[0101] In some embodiments, the first evaluation index includes a one-dimensional linear error index, and the scoring module 303 is specifically used for: The single-dimensional linear error index is determined based on the reference response vector corresponding to each loading direction, the first original channel output data, and the corresponding load information.

[0102] In some embodiments, the first evaluation index includes a benchmark response index, and the scoring module 303 is specifically used for: The benchmark response index is determined based on the degree of similarity between the benchmark response vectors corresponding to different loading directions.

[0103] In some embodiments, the scoring module 303 is specifically used for: Determine the one-dimensional decoupling parameters based on the aforementioned benchmark response vector; The predicted load information is determined based on the single-dimensional decoupling parameters and the output data of the second original channel. The multidimensional verification error index is determined based on the multidimensional composite load information and the predicted load information.

[0104] In some embodiments, the first evaluation index includes one or more of a self-decoupling error index, a one-dimensional linear error index, and a benchmark response index, and the decision module 304 is further configured to: If it is determined that one or more of the self-decoupling error index, the single-dimensional linear error index, and the benchmark response index are not within the corresponding safety threshold range, and / or the multi-dimensional verification error index is not within the corresponding safety threshold range, then the calibration credibility information indicates that the single-dimensional benchmark calibration result is unreliable.

[0105] It should be noted that the calibration device for the force sensor provided in the above embodiments is only illustrated by the division of the above-described program modules. In practical applications, the above processing can be assigned to different program modules as needed, that is, the internal structure of the device can be divided into different program modules to complete all or part of the processing described above. Furthermore, the calibration device for the force sensor provided in the above embodiments and the calibration method embodiments for the force sensor belong to the same concept; the specific implementation process is detailed in the method embodiments and will not be repeated here.

[0106] Based on the hardware implementation of the above program modules, and in order to implement the method of the embodiments of this application, the embodiments of this application also provide an electronic device. Figure 4 The diagram shows only an exemplary structure of the electronic device, not the entire structure; implementation is possible as needed. Figure 4 The structure shown may be part or all of the structure.

[0107] like Figure 4 As shown, the electronic device 400 provided in this application embodiment includes at least one processor 401, a memory 402, and a user interface 403. The various components in the electronic device 400 are coupled together via a bus system 404. It can be understood that the bus system 404 is used to implement communication between these components. In addition to a data bus, the bus system 404 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in… Figure 4 The general designated all buses as Bus System 404.

[0108] The user interface 403 may include a monitor, keyboard, mouse, trackball, click wheel, buttons, touchpad, or touch screen.

[0109] The memory 402 in this embodiment is used to store various types of data to support the operation of the electronic device 400. Examples of such data include any computer program used to operate on the electronic device 400.

[0110] The force sensor calibration method disclosed in this application embodiment can be applied to, or implemented by, processor 401. Processor 401 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the force sensor calibration method can be completed by the integrated logic circuitry in the hardware of processor 401 or by instructions in software form. The processor 401 can be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 401 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this application can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software modules can be located in a storage medium, specifically memory 402. Processor 401 reads information from memory 402 and, in conjunction with its hardware, completes the steps of the force sensor calibration method provided in the embodiments of this application.

[0111] In an exemplary embodiment, the electronic device 400 may be implemented by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers (MCUs), microprocessors, or other electronic components to perform the aforementioned method.

[0112] It is understood that memory 402 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), ferromagnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM); magnetic surface memory can be disk storage or magnetic tape storage. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), Sync Link Dynamic Random Access Memory (SLDRAM), and Direct Rambus Random Access Memory (DRRAM).The memories described in the embodiments of this application are intended to include, but are not limited to, these and any other suitable types of memories.

[0113] In an exemplary embodiment, this application also provides a storage medium, namely a computer storage medium, specifically a computer-readable storage medium, such as a memory 402 that stores a computer program. The computer program can be executed by the processor 401 of the electronic device 400 to complete the steps described in the method of this application embodiment. The computer-readable storage medium can be a ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface memory, optical disc, or CD-ROM, etc.

[0114] In an exemplary embodiment, this application also provides a computer program product, including a computer program that can be executed by a processor 401 of an electronic device 400 to perform the steps described in the method of this application embodiment.

[0115] It should be noted that terms such as "first" and "second" are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. In this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0116] It should be understood that the phrase "some embodiments" throughout the specification means that a particular feature, structure, or characteristic related to an embodiment is included in at least one embodiment of this application. Therefore, "some embodiments" appearing throughout the specification does not necessarily refer to the same embodiment.

[0117] In the description of the embodiments of this application, it should be noted that, unless otherwise explicitly specified and limited, the terms "connected" or "linked" should be interpreted broadly. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, an electrical connection, or a connection that allows communication between the components; it can be a direct connection or an indirect connection through an intermediate medium; it can be the internal communication between two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0118] Furthermore, the technical solutions described in the embodiments of this application can be combined arbitrarily without conflict.

[0119] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A calibration method for a force sensor, characterized in that, The method includes: A single-dimensional load is applied to each loading direction of the force sensor to obtain the first raw channel output data corresponding to each loading direction; Based on the first original channel output data and the corresponding load information, determine the reference response vector for each loading direction; A first evaluation index is determined based on the benchmark response vector; the first evaluation index includes one or more of the following: self-decoupling error index, single-dimensional linear error index, and benchmark response index. A multidimensional composite load is applied to the force sensor to obtain the output data of the second raw channel; The multidimensional verification error index is determined based on the benchmark response vector, the second original channel output data, and the corresponding multidimensional composite load information. The calibration quality score is determined based on the first evaluation index and the multidimensional verification error index. Based on the score range of the calibration quality score, calibration reliability information is generated to characterize the reliability of the single-dimensional benchmark calibration result.

2. The method according to claim 1, characterized in that, The step of determining the calibration quality score based on the first evaluation index and the multidimensional verification error index includes: The calibration quality score is determined based on one or more of the following: self-decoupling error index, single-dimensional linear error index, and benchmark response index; the multi-dimensional verification error index; and the weight coefficients corresponding to each index. The method further includes: determining a calibration processing scheme based on the score range in which the calibration quality score is located.

3. The method according to claim 1, characterized in that, The first evaluation index includes a self-decoupling error index, and determining the first evaluation index based on the benchmark response vector includes: Based on the reference response vector, determine the target direction response information and non-target direction response information corresponding to each loading direction; The self-decoupling error index is determined based on the target direction response information and the non-target direction response information.

4. The method according to claim 1, characterized in that, The first evaluation index includes a one-dimensional linear error index, and the step of determining the first evaluation index based on the benchmark response vector includes: The single-dimensional linear error index is determined based on the reference response vector corresponding to each loading direction, the first original channel output data, and the corresponding load information.

5. The method according to claim 1, characterized in that, The first evaluation index includes a benchmark response index, and determining the first evaluation index based on the benchmark response vector includes: The benchmark response index is determined based on the degree of similarity between the benchmark response vectors corresponding to different loading directions.

6. The method according to claim 1, characterized in that, The determination of the multidimensional verification error index based on the benchmark response vector, the second original channel output data, and the corresponding multidimensional composite load information includes: Determine the one-dimensional decoupling parameters based on the aforementioned benchmark response vector; The predicted load information is determined based on the single-dimensional decoupling parameters and the output data of the second original channel. The multidimensional verification error index is determined based on the multidimensional composite load information and the predicted load information.

7. The method according to claim 1, characterized in that, The method further includes: If it is determined that one or more of the self-decoupling error index, the single-dimensional linear error index, and the benchmark response index are not within the corresponding safety threshold range, and / or the multi-dimensional verification error index is not within the corresponding safety threshold range, then the calibration credibility information indicates that the single-dimensional benchmark calibration result is unreliable.

8. A calibration device for a force sensor, characterized in that, The device includes: The acquisition module is used to apply a one-dimensional load to each loading direction of the force sensor to obtain the first raw channel output data corresponding to each loading direction; The reference response modeling module is used to determine the reference response vector corresponding to each loading direction based on the first original channel output data and the corresponding load information. The scoring module is used to determine a first evaluation index based on the benchmark response vector; the first evaluation index includes one or more of the following: a self-decoupling error index, a one-dimensional linear error index, and a benchmark response index. The acquisition module is also used to apply a multidimensional composite load to the force sensor to obtain the second raw channel output data; The scoring module is also used to determine a multidimensional verification error index based on the benchmark response vector, the second original channel output data and the corresponding multidimensional composite load information; and to determine a calibration quality score based on the first evaluation index and the multidimensional verification error index. The decision module is used to generate calibration confidence information that characterizes the confidence of the single-dimensional benchmark calibration result based on the score range in which the calibration quality score is located.

9. An electronic device, characterized in that, The electronic device includes a processor and a memory for storing a computer program capable of running on the processor, wherein the processor, when running the computer program, performs the steps of the method according to any one of claims 1 to 7.

10. A storage medium storing a computer program, characterized in that, When the computer program is executed by the control device, it implements the steps of the method according to any one of claims 1 to 7.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the controlled device, it implements the steps of the method according to any one of claims 1 to 7.

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