Batch calibration method and system for EMB force sensor

Abstract

The invention discloses a batch calibration method and system for EMB force sensors, and belongs to the technical field of sensor signal processing and automatic testing. The method comprises the following steps: acquiring original temperature measurement data in a plurality of preset temperature environments, and calculating a temperature calibration coefficient containing weight and coefficient value parameters through a polynomial fitting algorithm; obtaining original pressure measurement data under a plurality of preset pressure conditions, and carrying out parallel calculation on coefficients used for compensating gain drift, offset drift and nonlinear polynomials through a combined calibration algorithm to obtain pressure calibration coefficients; the applied force is calibrated by using the feedback value of the double standard component; and writing the calculated coefficient into a nonvolatile memory of the sensor. According to the method and the corresponding system, high-precision compensation of nonlinearity and temperature excursion of the sensor within 40-150 DEG C can be realized, multi-station automatic batch calibration and screening are supported, the calibration efficiency, precision and consistency are remarkably improved, and dependence on imported special equipment is reduced.

Description

Technical Field

[0001] This invention belongs to the field of sensor signal processing and automated testing technology, specifically relating to a batch calibration method and system for EMB force sensors. Background Technology

[0002] The accuracy of force sensors in electromechanical braking (EMB) systems is crucial for ensuring vehicle braking safety. These sensors need to operate accurately at extreme temperatures (-40°C to 150°C), and their measurement signals exhibit significant nonlinearity and temperature drift, requiring high-precision factory calibration for compensation. Currently, high-quality calibration processes heavily rely on imported specialized equipment and algorithms, which are costly and difficult to adapt flexibly to different sensor models. Especially during batch calibration, traditional methods are inefficient and struggle to guarantee consistent and rapid parameter calibration and compensation for each sensor across a wide temperature range. Therefore, there is an urgent need for an intelligent calibration method and system capable of automatically performing multi-point calibration of temperature and pressure, complex error compensation calculations, and batch data processing through efficient algorithms. This would improve calibration efficiency, accuracy, and consistency, while reducing reliance on foreign hardware and technologies. Summary of the Invention

[0003] [Technical Issues] The technical problem to be solved by this invention is: how to provide a method and system that can efficiently, accurately and consistently complete the batch calibration of EMB force sensors in a wide temperature range, thereby reducing the dependence on imported special technologies and equipment.

[0004] [Technical Solution] This invention provides a batch calibration method and system for EMB force sensors, aiming to solve the problems of high dependence on imported equipment, low batch calibration efficiency, and difficulty in ensuring consistent calibration accuracy over a wide temperature range in the existing EMB force sensor calibration process. See below for details: In a first aspect, the present invention provides a batch calibration method for EMB force sensors, comprising the following steps: Temperature calibration steps: Under multiple preset temperature environments, acquire the raw temperature measurement data of the force sensor to be calibrated, and based on the raw temperature measurement data, calculate a set of temperature calibration coefficients through a polynomial fitting algorithm. The temperature calibration coefficients include weight parameters and coefficient value parameters. Pressure calibration step: Under multiple preset temperature environments determined in the temperature calibration step, and under multiple preset pressure conditions, the original pressure measurement data of the force sensor to be calibrated is acquired, and based on the original pressure measurement data, a set of pressure calibration coefficients is calculated through a combined calibration algorithm; the combined calibration algorithm calculates in parallel the gain polynomial for compensating for gain drift, the offset polynomial for compensating for offset drift, and the linearization polynomial for correcting nonlinearity. Coefficient writing step: Write the calculated temperature calibration coefficient and pressure calibration coefficient into the non-volatile memory of the calibrated force sensor.

[0005] Optionally, in the pressure calibration step, the target force value applied to the calibrated force sensor is calibrated by force value feedback calibration, including: acquiring a first force feedback value from a first standard and a second force feedback value from a second standard; wherein the first standard is used to feed back the source value of the applied force; and calibrating the target force value based on the first force feedback value and the second force feedback value.

[0006] Optionally, the method may also include a signal preprocessing step: receiving the original bridge signal from the calibrated force sensor and performing analog-to-digital conversion to obtain the original digital measurement value; converting the original digital measurement value from a signed integer format to an unsigned integer format, and further scaling it proportionally to a data value conforming to the SENT interface communication protocol.

[0007] Optionally, in the pressure calibration step, the gain polynomial and the offset polynomial each have three coefficients, and the linearization polynomial has four coefficients.

[0008] Optionally, the process also includes a batch calibration execution step: controlling multiple calibration stations to sequentially enter the calibration state; for each force sensor being calibrated at each station, sequentially executing the temperature calibration step and the pressure calibration step; automatically calculating the linearity of the sensor based on the calibration data and comparing it with a preset accuracy threshold to complete the automatic screening of qualified products; and establishing a calibration database to store the original temperature measurement data and the original pressure measurement data according to the station identifier; the automatic calculation of the sensor linearity based on the calibration data is based on the calibration data of the corresponding station in the calibration database.

[0009] Optionally, the method can operate in either an automatic calibration mode or a manual calibration mode; the automatic calibration mode is configured to automatically execute all calibration steps according to a preset program and generate a report in response to a single start command; the manual calibration mode allows manual input of parameters and control of the execution of individual steps.

[0010] Secondly, the present invention provides a batch calibration system for EMB force sensors, comprising: The data acquisition and communication unit, including an analog-to-digital conversion circuit and a signal conditioning chip, is used to connect with multiple calibrated force sensors to acquire raw temperature and pressure measurement data, and to amplify and perform preliminary digital compensation on the signals. The calibration calculation unit is communicatively connected to the data acquisition and communication unit and is used to execute the temperature calibration step and pressure calibration step in the batch calibration method described above. The process control and data processing unit is communicatively connected to the data acquisition and communication unit and the calibration calculation unit. It is used to execute the batch calibration execution step, signal preprocessing step and coefficient writing step in the batch calibration method described above, and supports both automatic and manual calibration modes.

[0011] Optionally, the process control and data processing unit further includes a data recording and exporting unit, which records complete calibration process data and supports on-demand export for traceability.

[0012] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the batch calibration method for EMB force sensors as described above.

[0013] [Beneficial Effects] 1. Achieved high-precision and consistent calibration over a wide temperature range: For the extreme temperature range of -40℃ to 150℃, this invention utilizes a combined calibration algorithm that incorporates temperature calibration coefficients with weighted and coefficient values, along with parallel computation of gain drift, offset drift, and nonlinear compensation polynomials. This enables high-precision digital modeling and comprehensive compensation of the sensor's nonlinearity and complex temperature drift effects. Combined with a force calibration step based on dual-standard component feedback, force transmission path errors are effectively eliminated, ensuring that each sensor achieves consistent and superior calibration accuracy in mass production environments.

[0014] 2. Significantly improved efficiency and automation of batch calibration: By integrating batch calibration execution steps with automatic screening functions, this invention enables the system to automatically control multiple calibration stations to sequentially complete the entire temperature and pressure calibration process, and automatically calculate performance indicators based on calibration data for qualified product screening. Supporting a one-click automatic calibration mode, it achieves full-process automation from calibration and testing to sorting, greatly improving production capacity and operational efficiency, and adapting to the needs of large-scale production.

[0015] 3. Enhanced system flexibility and reliability: This invention designs two complementary calibration modes, automatic and manual, which not only meet the stable operation requirements of rapid mass production but also provide flexible interactive means for R&D debugging, fault diagnosis, or calibration of special models. The complete signal preprocessing workflow (including analog-to-digital conversion, format conversion, and SENT protocol adaptation) and data recording and traceability functions provided by this invention ensure reliable compatibility with automotive-grade sensors and traceability of data throughout the entire process, thereby improving the overall reliability and quality management level of the system.

[0016] 4. A low-cost, domestically produced, high-performance calibration solution has been developed: The core advantage of this invention stems from the innovation in algorithms, software logic, and system architecture, which can be implemented using general-purpose domestic computing hardware and data acquisition units. This method and system reduce reliance on specific imported dedicated calibration equipment and core algorithms, providing controllable and low-cost calibration technology support for the research and development and large-scale production of domestic EMB force sensors, and has significant industrial value. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 The temperature calibration flowchart provided for this invention.

[0019] Figure 2 The pressure calibration flowchart provided for this invention. Detailed Implementation

[0020] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] It should be noted that the core innovation of this invention lies in the calibration method and system. The method can be implemented based on a general-purpose hardware platform. For example, the signal conditioning chip and analog-to-digital converter circuit involved in the embodiments are optional components for realizing basic functions such as signal amplification and conversion, and they can be commercially available general-purpose products in the art.

[0022] Example 1 This embodiment provides a batch calibration method for EMB force sensors. This method achieves batch calibration of multiple sensors by performing a series of calibration steps, particularly temperature and pressure calibration steps incorporating a unique algorithm. High-precision and high-efficiency calibration within an extreme temperature range of 40℃ to 150℃. The core advantage of this method lies in its significant improvement in calibration accuracy and efficiency through software algorithms and a systematic process, while reducing dependence on specific hardware devices.

[0023] 1. Signal preprocessing steps It receives the raw analog bridge signal output from the calibrated force sensor. The signal is amplified by a signal conditioning chip, with an analog pre-amplification rate of up to 200 times to accommodate different sensor types such as full-bridge or half-bridge sensors.

[0024] Subsequently, an analog-to-digital converter (ADC) circuit is used to convert the amplified analog signal into a digital signal. This ADC employs fully differential switched-capacitor technology, making it largely insensitive to short-term and long-term clock frequency instability, thus improving its anti-interference capability. This design enhances the reliability of signal processing, enabling it to withstand the harsh operating conditions of automotive-grade applications.

[0025] The converted original digital measurement value is a 16-bit signed integer, ranging from... 32768 to 32767. To adapt to the SENT interface communication protocol commonly used in the automotive industry, this data needs to be formatted: First, the original signed integer is converted to an unsigned integer in the range of 0 to 32767; then, since the maximum value of the SENT interface is 4095, the above unsigned integer is divided by 4 to obtain a data value conforming to the SENT protocol. Where 0... The range 16383 corresponds to normal pressure measurement, and 16384... The 32767 range is used for overpressure measurement. A complete signal preprocessing and protocol adaptation process ensures that sensor data can be seamlessly integrated into the vehicle's communication network, guaranteeing system-level reliability and compatibility.

[0026] 2. Temperature Calibration Procedure like Figure 1 As shown, under multiple preset temperature environments, this embodiment is set to... The raw temperature measurement data of the calibrated force sensor is acquired from 40℃ to 150℃. Each temperature point needs to be maintained for a sufficient time to allow the internal temperature of the sensor to conform to the environment. In this embodiment, it is set to be maintained for at least 3 hours to improve the accuracy of the output temperature.

[0027] Each temperature point needs to be maintained for a sufficient time to allow the internal temperature of the sensor to stabilize with the environment. The criterion for "sufficient time" can be a fixed preset duration (such as at least 3 hours in this embodiment), or it can be determined by real-time monitoring of the temperature-sensing diode integrated within the calibrated sensor chip, and stability is defined as the temperature reading changing less than a set threshold for a continuous period. Ensuring temperature stability improves the accuracy of subsequent calibration data.

[0028] The specific temperature values ​​and sequence of the "multiple preset temperature environments" here can be set according to the sensor's operating temperature range, customer needs, and calibration accuracy requirements. For example, for a sensor with an operating temperature range of -40℃ to 150℃, multiple characteristic temperature points (such as -40℃, -20℃, 25℃, 80℃, and 150℃) can be selected, including the low-temperature boundary, the room-temperature point, and the high-temperature boundary. A recommended implementation sequence is as follows: first, perform calibration at the room-temperature point (25℃) to quickly detect the connection status and basic product functions; then, perform calibration at the low-temperature point (-40℃); and finally, perform calibration at the high-temperature point (150℃). The number and interval of temperature points can be selected by balancing calibration efficiency and accuracy requirements.

[0029] Based on the acquired raw temperature measurement data, a set of temperature calibration coefficients is calculated using a polynomial fitting algorithm. Polynomial fitting refers to modeling the relationship between the preprocessed sensor temperature digital output and the ambient temperature. The fitting process employs the least squares method, using the Levenberg-Marquardt (LM) algorithm for iterative optimization, simultaneously solving for the weight parameter and the coefficient value parameter. The system operates within the sensor's operating temperature range (…). Sample data from multiple temperature points within the range of 40℃ to 150℃ were collected to construct a compensation model that incorporates temperature drift and nonlinearity. The model was then fitted with the goal of minimizing the weighted sum of squared errors until the error between the model output and the measured data met the preset accuracy requirements (e.g., ±0.5%FS). After fitting, the obtained weights and coefficient values ​​together constitute the temperature calibration coefficients.

[0030] Specifically, this calibration coefficient is jointly defined by the weight parameter and the value parameter, and their calculation relationship can be expressed as follows: Where Ci represents the i-th calibration coefficient, Value is the original data value after amplification and analog-to-digital conversion, and Weight is the weighting parameter for binary scaling (linear transformation) of this value. By independently configuring the weight and value for each calibration coefficient, this calibration model can more flexibly and accurately describe the nonlinearity of the sensor's temperature characteristics, laying a high-precision foundation for subsequent wide-temperature-range compensation.

[0031] In terms of model selection, this embodiment is flexible. Depending on the trade-off between accuracy and efficiency, models of different complexities can be selected for fitting, such as: (1) first-order polynomial (linear model): at least 2 temperature data points are required; (2) second-order polynomial: at least 3 temperature data points are required; (3) a linear approximation model based on more data points (such as up to 16) can also be used. The system collects a corresponding number of temperature point sample data within the sensor's working temperature range, constructs a compensation model and fits it until the error between the model output and the measured data meets the preset accuracy requirements (such as ±0.5%FS). After fitting, the obtained weights and coefficient values ​​together constitute the temperature calibration coefficients. This flexible fitting strategy enhances the adaptability of the method to different sensor characteristics and calibration accuracy requirements. If the calculation is successful, a series of coefficient pairs are obtained, such as w_tsi_0, c_tsi_0; w_tsi_1, c_tsi_1, etc.

[0032] In this embodiment, the sensor temperature count value With Celsius value The conversion between them is done using the following formula:

[0033] In the formula: The digital value obtained after the temperature signal is converted from analog to digital; The current ambient temperature, such as -20℃; The upper limit clamping value for digital output specified by the SENT (Single-sided nibble transmission) protocol is 4088 in this embodiment; The lower limit clamping value for digital output specified by the SENT protocol is 1 in this embodiment; The upper limit of the temperature that the sensor can sample is typically 437.85℃; The lower limit of the temperature that the sensor can sample, typically -73.025℃.

[0034] Based on the above parameters, when the ambient temperature is -20℃, IC_TEMP[counts] = 425 can be calculated.

[0035] Furthermore, to adapt to specific communication or processing needs, the count value can be converted into a percentage, and the conversion formula is as follows:

[0036] in: : The percentage corresponding to the temperature count value.

[0037] Substitution =425, which can be calculated. .

[0038] 3. Pressure Calibration Procedure like Figure 2 As shown, pressure calibration is performed at the environmental point where temperature calibration is completed. Raw pressure measurement data of the calibrated force sensor are acquired under multiple preset pressure conditions. After a pressure change, the pressure must remain stable before sampling; in this embodiment, this is set to remain stable for at least 1 minute to improve the accuracy of the output force.

[0039] Based on the original pressure measurement data, a set of pressure calibration coefficients is calculated using a combined calibration algorithm. This combined calibration algorithm is the core of this method for improving accuracy, as it can process multiple error sources of the sensor in parallel. The combined calibration algorithm calculates three polynomials in parallel: a gain polynomial for compensating for gain drift, a shift polynomial for compensating for offset drift, and a linearization polynomial for correcting nonlinearity. The gain and shift polynomials each have three coefficients, and the linearization polynomial has four coefficients. Each coefficient has two parameters: a value and a weight. Extensive experimental verification shows that the model structure using three-coefficient gain / shift polynomials and four-coefficient linearization polynomials can optimally balance computational complexity and storage overhead while ensuring compensation accuracy, making it the preferred embodiment of this invention. This parallel polynomial calculation architecture can perform one-time, high-precision digital compensation for the nonlinearity, gain drift, and offset drift of the sensor over a wide temperature range, thereby significantly improving the accuracy and consistency of the calibrated sensor.

[0040] The combined calibration algorithm establishes a unified mathematical model containing all terms to be compensated and simultaneously solves for all parameters (including the coefficients of each polynomial and the weights) in a single optimization process. The algorithm's input includes the raw sensor output signals acquired under different preset temperature and pressure conditions, along with their corresponding ambient temperature data.

[0041] This unified mathematical model couples gain drift, offset drift, and nonlinear effects together for comprehensive compensation. In this embodiment, the complete mathematical model for real-time sensor output correction can be expressed as:

[0042] in, The actual force value after compensation; This is the sensor's original output voltage; The sensor's operating temperature; , , , , , These are the polynomial coefficients obtained through a combined calibration algorithm. These coefficients are the result of calculations performed internally on the chip, representing coefficient pairs consisting of specific Value and Weight parameters, which are written into the sensor after calibration.

[0043] The gain polynomial is mainly used to compensate for sensor sensitivity drift caused by temperature T changes (i.e., gain drift across the entire temperature range); the offset polynomial is mainly used to compensate for sensor zero-point drift caused by temperature T changes (i.e., offset drift across the entire temperature range); and the linearization polynomial is mainly used to compensate for the inherent nonlinearity of the sensor's original output U, as well as the coupling error between temperature and nonlinearity. Through a one-time optimization of the unified model, all coefficients of the three sets of polynomials—gain, offset, and linearization—are finally output simultaneously.

[0044] The algorithm attempts to find the curve that best approximates the behavior of the main sensor of the bridge circuit. If the calculation is successful, the three sets of coefficients are updated: Offset polynomial coefficients: fb_wo2, fb_wol, fb_wo0, fb_co2, fb_col, fb_co0; Gain polynomial coefficients: fb_wg2, fb_wgl, fb_wg0, fb_cg2, fb_cgl, fb_cg0; Linearized polynomial coefficients: fb_wl3, fb_wl2, fb_wl1, fb_wlo, fb_cl3, fb_cl2, fb_cl1, fb_clo.

[0045] Here, "successful calculation" means that the error between the curve obtained by fitting the least squares method and the actual sensor data is less than the preset tolerance threshold, indicating that the set of coefficients can effectively characterize and compensate for the error characteristics of the sensor under the current conditions.

[0046] The coefficients of the polynomial (such as fb_wo2, fb_co2, etc.) are themselves digital values ​​after amplification and analog-to-digital conversion (e.g., values ​​ranging from 1 to 32767). These are mathematical parameters obtained by mathematically modeling the original signals output by the sensor under different preset temperatures and pressures (signals that already carry the sensor's temperature drift, nonlinearity, and other physical characteristics). These coefficients together constitute a digital compensation model, which establishes a mapping relationship between the sensor's original digital output and the ambient temperature and applied pressure, used for real-time digital correction in subsequent sensor operation.

[0047] The specific method for "updating the three sets of coefficients" is as follows: All coefficients of the successfully calculated and verified gain, offset, and linearization polynomials are written to the non-volatile memory inside the signal conditioning chip of the calibrated force sensor via the communication interface. If the old calibration coefficients are already stored in this memory, the new coefficients will overwrite them. The chip will then use these coefficients in subsequent operations to perform high-precision digital compensation on the sensor's real-time output.

[0048] During the pressure calibration process, a first force feedback value from a first standard component (standard force sensor) and a second force feedback value from a second standard component (standard force sensor) are acquired. The first standard component is located at the output end of the force source (such as a servo press) or outside the temperature chamber to directly monitor and feedback the force applied by the force source. The second standard component is located inside the temperature chamber and close to the installation position of the force sensor being calibrated to monitor the actual force value transmitted to the force sensor being calibrated.

[0049] Before formal calibration, the first and second standard parts must be mutually calibrated: the calibration system simultaneously collects and compares the force feedback values ​​of both, and performs comparative analysis using software. If the deviation between the two feedback values ​​exceeds a preset threshold, it indicates an abnormality in the force transmission path or an abnormality in the condition of the standard part itself, requiring debugging or recalibration to eliminate the potential impact of the three elements of force—direction, point of application, etc.—on measurement consistency. This mutual calibration operation ensures the benchmark uniformity of the entire measurement chain.

[0050] During the calibration process, the target force value applied to the calibrated force sensor is calibrated and controlled in a closed loop based on the first and second force feedback values. Specifically, the system uses the feedback value of the first standard component as the main control signal to adjust the output of the servo press in real time, ensuring that the force source value it provides meets the preset target. At the same time, the system continuously monitors the feedback value of the second standard component, compares and verifies it in real time with the feedback value of the first standard component and the target force value, confirming that the force value transmitted to the calibrated sensor is accurate. Through the closed-loop control formed by the first and second standard components and the force source, errors that may be introduced from the transmission path (such as fixtures and mechanisms inside the temperature chamber) from the force source to the sensor are effectively monitored and compensated.

[0051] Based on the force feedback and closed-loop calibration steps of the above dual standard components, the absolute accuracy and traceability of the force applied to the calibrated sensor are ensured, which is the key guarantee for achieving high consistency in batch calibration.

[0052] 4. Coefficient writing steps The calculated temperature calibration coefficient and pressure calibration coefficient are written into the non-volatile memory of the calibrated force sensor.

[0053] 5. Batch calibration execution steps Multiple calibration stations are controlled to sequentially enter the calibration state. For the force sensor being calibrated at each station, temperature calibration and pressure calibration steps are executed sequentially. During batch calibration, a calibration database is established locally to efficiently and systematically manage and process data from multiple stations. During data acquisition, the system sequentially collects the raw signals (including temperature and pressure signals) output by the sensors at each station according to a preset program (e.g., from station #1 to station #N), and stores the amplified and analog-to-digital converted raw measurement data, along with its corresponding station identification information, in the database in real time. For example, in the temperature calibration stage, the system sequentially collects and stores the raw temperature data of all stations at a specific temperature point (e.g., 25℃); in the pressure calibration stage, it sequentially collects and stores the raw pressure data of all stations at different pressure points.

[0054] The database establishes independent data records for each sensor at each workstation, ensuring data isolation and traceability. Once data from all preset temperature and pressure points has been collected, the system can retrieve the complete dataset from the database for any specific workstation for independent calibration calculations, or perform unified calculations on batch data from all workstations. This database-based centralized management and on-demand processing architecture enables synchronous, orderly acquisition and independent, flexible processing of multi-workstation data, providing core data support for efficient and accurate batch automated calibration.

[0055] By programmatically controlling the automated cycle of multiple workstations, efficient batch calibration is achieved, significantly increasing calibration capacity. Based on all data collected during the calibration process, the linearity and other performance indicators of each sensor are automatically calculated and compared with preset accuracy thresholds, thereby completing the automatic screening of qualified products. Specifically, the linearity calculation is based on data from a preset series of linearity test points. In this embodiment, linearity is evaluated using the following model: Where VOUT is the sensor output voltage, VCC is the supply voltage, and F is the input force. The calculated linearity error is output as a percentage of full scale (%FS). The preset accuracy threshold (e.g., ±0.5%FS) is the optimal balance point determined experimentally and is suitable for the sensor across the entire temperature range (…). Performance evaluation is performed at temperatures ranging from 40℃ to 150℃ and across the entire range. The integrated automatic screening function enables the unified calibration, testing, and sorting process, eliminating human error and improving overall efficiency and reliability.

[0056] The method in this embodiment supports both automatic and manual calibration modes, and the dual-mode design enhances the flexibility of the system.

[0057] In automatic calibration mode, the system has anomaly handling logic: if a communication interruption or other fault occurs at a certain workstation, the system will skip that workstation and mark it as abnormal, while recording the alarm type in the database; if data anomalies or coefficient calculation failures occur during calibration, the system will save the collected raw data and record alarm information in the database for subsequent analysis. In automatic calibration mode, the operator can start the process with one click to complete the entire process according to the preset procedure and automatically generate a report, greatly simplifying the operation and improving production efficiency.

[0058] In manual calibration mode, operators can intervene in specific parameters including but not limited to: signal gain factor, setting of calibration temperature and pressure points, stabilization time of each temperature and pressure point, and control the repetition of test steps for individual temperature or pressure points. Manual calibration mode allows for manual input of calibration parameters and control of individual steps, providing flexible debugging methods for sensor development, fault diagnosis, or calibration of special models.

[0059] Example 2 This embodiment provides a batch calibration system for implementing the method described in Embodiment 1. The system includes: 1. Data Acquisition and Communication Unit: This unit establishes communication connections with multiple calibrated force sensors to acquire raw temperature and pressure measurement data. It includes an analog-to-digital converter (ADC) circuit to convert the analog bridge signals output by the sensors into digital signals. The unit also contains a signal conditioning chip for signal amplification and integrates a microcontroller (a 16-bit RISC microcontroller) for digital compensation. Its analog pre-amplification ratio can reach up to 200x. Digital compensation, performed by the microcontroller during or before the ADC conversion, aims to optimize the stability of the voltage output and reduce the sensitivity of the sensor signal to chip manufacturing processes and environmental temperature changes, thereby achieving preliminary correction of offset, gain, temperature drift, and nonlinearity. The high-performance ADC and signal conditioning capabilities of this unit are the physical basis for ensuring the quality of the raw data and achieving high-precision calibration. Its design can be implemented based on general-purpose or domestically produced chips, helping to reduce the overall system cost and technological dependence.

[0060] 2. The calibration calculation unit performs the temperature calibration, pressure calibration, and force feedback calibration steps. Specifically, this unit runs algorithms to: calculate the temperature calibration coefficient based on polynomial fitting; calculate the gain, offset, and linearized polynomial coefficients in parallel using a combined calibration algorithm to obtain the pressure calibration coefficient; and calculate the target force calibration value based on the feedback values ​​of the first and second standard components. This unit is the "intelligent core" of the system; its integrated advanced algorithms directly improve calibration accuracy and consistency, and are entirely software-defined, facilitating upgrades and adaptation to different sensor models.

[0061] 3. The process control and data processing unit executes batch calibration steps, signal preprocessing steps, and coefficient writing steps, supporting both automatic and manual calibration modes. This unit is responsible for scheduling the task sequence of multiple calibration stations, controlling the signal preprocessing process, managing the writing of calibration coefficients to the sensor's non-volatile memory, and providing a human-machine interface. Furthermore, this unit includes a data recording and export unit for recording complete calibration process data and supporting on-demand export for traceability. As the central control unit, this unit ensures the orderly, efficient, and reliable operation of the entire calibration process. Its data recording and traceability functions provide comprehensive data support for product quality management, enhancing the controllability and transparency of the entire calibration process.

[0062] This embodiment of the system, through the synergy of the aforementioned units, achieves wide-temperature range control of the EMB force sensor (…). High-precision batch calibration (40℃ to 150℃). The advantage of the entire system lies in the fact that its superior performance mainly stems from the innovation of algorithms and system architecture, rather than the dependence on specific precision mechanical structures, thus providing a clear path for building a domestically produced, low-cost, and high-performance calibration solution.

[0063] Example 3 This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the batch calibration method as described in Embodiment 1. Through this storage medium, the core algorithm and process of the calibration method of this invention are encapsulated in software. This storage medium allows the calibration method of this invention to be deployed in software form on various computing devices, which greatly enhances the method's versatility and portability, enabling high-performance calibration capabilities to be quickly deployed on different industrial computers or embedded platforms, further reducing the technical application threshold and implementation cost.

[0064] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A batch calibration method for EMB force sensors, characterized in that, Includes the following steps: Temperature calibration steps: Under multiple preset temperature environments, acquire the raw temperature measurement data of the force sensor to be calibrated, and based on the raw temperature measurement data, calculate a set of temperature calibration coefficients through a polynomial fitting algorithm. The temperature calibration coefficients include weight parameters and coefficient value parameters. Pressure calibration step: Under multiple preset temperature environments determined in the temperature calibration step, and under multiple preset pressure conditions, the original pressure measurement data of the force sensor to be calibrated is acquired, and based on the original pressure measurement data, a set of pressure calibration coefficients is calculated through a combined calibration algorithm; the combined calibration algorithm calculates in parallel the gain polynomial for compensating for gain drift, the offset polynomial for compensating for offset drift, and the linearization polynomial for correcting nonlinearity. Coefficient writing step: Write the calculated temperature calibration coefficient and pressure calibration coefficient into the non-volatile memory of the calibrated force sensor.

2. The batch calibration method according to claim 1, characterized in that, In the pressure calibration step, the target force value applied to the calibrated force sensor is calibrated by force value feedback calibration, including: acquiring a first force feedback value from a first standard and a second force feedback value from a second standard; wherein the first standard is used to feed back the source value of the applied force; and calibrating the target force value based on the first force feedback value and the second force feedback value.

3. The batch calibration method according to claim 1, characterized in that, It also includes a signal preprocessing step: receiving the original bridge signal from the calibrated force sensor and performing analog-to-digital conversion to obtain the original digital measurement value; converting the original digital measurement value from signed integer format to unsigned integer format, and further scaling it proportionally to a data value conforming to the SENT interface communication protocol.

4. The batch calibration method according to claim 1, characterized in that, In the pressure calibration step, the gain polynomial and the offset polynomial each have three coefficients, and the linearization polynomial has four coefficients.

5. The batch calibration method according to claim 1, characterized in that, It also includes batch calibration execution steps: controlling multiple calibration stations to enter the calibration state sequentially; for the force sensor being calibrated at each station, executing the temperature calibration step and pressure calibration step sequentially; automatically calculating the linearity of the sensor based on the calibration data and comparing it with a preset accuracy threshold to complete the automatic screening of qualified products; and establishing a calibration database to store the original temperature measurement data and original pressure measurement data according to the station identifier; the automatic calculation of the sensor linearity based on the calibration data is based on the calibration data of the corresponding station in the calibration database.

6. The batch calibration method according to claim 1, characterized in that, The method can operate in either automatic calibration mode or manual calibration mode; the automatic calibration mode is configured to automatically execute all calibration steps according to a preset program and generate a report in response to a single start command; the manual calibration mode allows manual input of parameters and control of the execution of individual steps.

7. A batch calibration system for EMB force sensors, characterized in that, include: The data acquisition and communication unit, including an analog-to-digital conversion circuit and a signal conditioning chip, is used to connect with multiple calibrated force sensors to acquire raw temperature and pressure measurement data, and to amplify and perform preliminary digital compensation on the signals. The calibration calculation unit is communicatively connected to the data acquisition and communication unit and is used to execute the temperature calibration step and the pressure calibration step in the batch calibration method as described in any one of claims 1 to 6. The process control and data processing unit is communicatively connected to the data acquisition and communication unit and the calibration calculation unit, and is used to execute the batch calibration execution step, signal preprocessing step and coefficient writing step in the batch calibration method as described in any one of claims 1 to 6, and supports both automatic and manual calibration modes.

8. The batch calibration system according to claim 7, characterized in that, The process control and data processing unit also includes a data recording and export unit, which records complete calibration process data and supports on-demand export for traceability.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the batch calibration method as described in any one of claims 1 to 6.

Images