Sensor error calibration method, device, equipment and storage medium

Abstract

The present application relates to the field of error calibration technology and discloses a sensor error calibration method, apparatus, device, and storage medium, including: collecting environmental status information of the sensor and calculating partition calibration parameters of each sensor based on the environmental status information; dividing each sensor into multiple dynamic calibration intervals based on the partition calibration parameters, wherein the dynamic calibration interval contains multiple error parameters, and the difference between the error parameters can dynamically adjust the interval range of adjacent dynamic calibration intervals; calculating the error compensation value of each dynamic calibration interval based on the error parameters; generating calibration information based on the error compensation value, and inputting the calibration information into each dynamic calibration interval to obtain a target calibration interval; and compensating the real-time weighing data of the sensor based on the target calibration interval. The method can respond to changes in the errors of multiple sensors in real time, ensuring that each sensor can be effectively calibrated under different working conditions, and further improving measurement accuracy.

Description

Technical Field

[0001] The present application relates to the technical field of error calibration, and in particular to a sensor error calibration method, apparatus, device, and storage medium. Background Art

[0002] In fields such as industrial automation, intelligent logistics, and medical equipment, scenarios where multiple sensors work together are becoming increasingly common. For example, large-scale weighing systems often deploy multiple pressure sensors in an array, spatially distributing them to cover different areas of the object being measured to improve measurement accuracy. However, the errors of each sensor in a multi-sensor system are not only affected by its own environment (such as temperature, humidity, and posture), but can also cause coupling errors due to differences in the spatial layout of sensors, signal interference, or different load transfer characteristics. Traditional single-sensor calibration methods only perform static parameter compensation for a single device and cannot address dynamic interaction errors between multiple sensors, resulting in the overall system measurement accuracy failing to meet high-precision requirements. Summary of the Invention

[0003] The main purpose of this application is to provide a sensor error calibration method, device, equipment and storage medium, aiming to solve the technical problem that the traditional single-sensor calibration method only performs static parameter compensation for a single device and cannot solve the dynamic interaction errors between multiple sensors.

[0004] To achieve the above objectives, the present application proposes a sensor error calibration method, which includes:

[0005] Collecting environmental status information of the sensors, and calculating partition calibration parameters of each of the sensors based on the environmental status information;

[0006] Dividing each of the sensors into a plurality of dynamic calibration intervals based on the partition calibration parameters, wherein the dynamic calibration intervals include a plurality of error parameters, and the differences between the error parameters can dynamically adjust the interval ranges of adjacent dynamic calibration intervals;

[0007] Calculating an error compensation value for each of the dynamic calibration intervals according to the error parameter;

[0008] generating calibration information based on the error compensation value, and inputting the calibration information into each of the dynamic calibration intervals to obtain a target calibration interval;

[0009] The real-time weighing data of the sensor is compensated according to the target calibration interval.

[0010] Optionally, the step of generating calibration information based on the error compensation value and inputting the calibration information into each of the dynamic calibration intervals to obtain a target calibration interval includes:

[0011] Obtaining a calibration position corresponding to the error compensation value, wherein the calibration position includes a key position and an edge position;

[0012] If the calibration position is a key position, correcting the error compensation value based on the first spatial coordinate of the error compensation value to generate first calibration information;

[0013] If the calibration position is an edge position, calculating a relative distance between the edge position and a key position closest to the edge position, and generating second calibration information based on the relative distance;

[0014] The first calibration information and / or the second calibration information are input into the corresponding dynamic calibration interval to obtain a target calibration interval.

[0015] Optionally, if the calibration position is a critical position, the step of correcting the error compensation value based on the spatial position coordinates of the error compensation value to generate first calibration information includes:

[0016] If the calibration position is a key position, obtaining the first spatial coordinates of the sensor and a coordinate set of adjacent sensors corresponding to the key position;

[0017] determining a compensation deviation of the error compensation value according to the first spatial coordinate, the coordinate set, and historical compensation values ​​of each of the sensors;

[0018] The error compensation value is corrected based on the compensation deviation to generate first calibration information.

[0019] Optionally, if the calibration position is an edge position, the step of calculating a relative distance between the edge position and a key position closest to the edge position, and generating second calibration information based on the relative distance includes:

[0020] If the calibration position is an edge position, obtaining the second spatial coordinates of the sensor at the edge position and the reference coordinates of the key position closest to the edge position;

[0021] Calculating the three-dimensional space distance and the azimuth angle between the second space coordinate and the reference coordinate;

[0022] A relative distance is obtained according to the three-dimensional space distance and the azimuth angle, and the error compensation value is corrected based on the relative distance to generate second calibration information.

[0023] Optionally, the step of compensating the real-time weighing data of the sensor according to the target calibration interval includes:

[0024] Acquiring real-time weighing data of the sensor, and identifying the duration and triggering frequency of the sensor in the dynamic calibration interval according to the real-time weighing data;

[0025] Calculating an error impact index of each of the target calibration intervals based on the duration and the trigger frequency;

[0026] If it is detected that the error impact index exceeds a preset error threshold, adjusting the interval range of the dynamic calibration interval corresponding to the error impact index to a preset tolerance range;

[0027] The target error compensation value is recalculated based on the preset tolerance range, and the real-time weighing data is compensated according to the target error compensation value.

[0028] Optionally, the environmental status information includes sensor spatial coordinates, environmental temperature, environmental humidity and attitude tilt angle;

[0029] The step of collecting environmental status information of the sensor and calculating the partition calibration parameters of the sensor based on the environmental status information includes:

[0030] Collect environmental status information from sensors;

[0031] establishing a measurement error distribution map based on the sensor spatial coordinates in the environmental state information;

[0032] Determining a measurement area according to the measurement error distribution map, and calculating a stability index of each measurement area according to the ambient temperature, ambient humidity, and attitude tilt angle;

[0033] Prioritizing the measurement areas based on the stability index, and determining the partition calibration parameters of the sensor in a preset calibration parameter database according to the ranking results.

[0034] Optionally, the error parameters include: a temperature compensation parameter, a humidity compensation parameter and a posture offset value;

[0035] The step of calculating the error compensation value of each dynamic calibration interval according to the error parameter includes:

[0036] Calculating a temperature and humidity error compensation parameter based on the temperature compensation parameter and the humidity compensation parameter of each dynamic calibration interval;

[0037] Calculating a zero point correction parameter based on the posture offset value of each dynamic calibration interval;

[0038] The temperature and humidity error compensation parameters and the zero point correction parameters are multi-dimensionally integrated to generate an error compensation value for each of the dynamic calibration intervals.

[0039] In addition, to achieve the above-mentioned purpose, the present application also proposes a sensor error calibration device, which includes:

[0040] An information acquisition module, configured to collect environmental status information of the sensors and calculate partition calibration parameters of the sensors based on the environmental status information;

[0041] an interval determination module, configured to divide each of the sensors into a plurality of dynamic calibration intervals based on the partition calibration parameters, wherein the dynamic calibration intervals include a plurality of error parameters, and the difference between the error parameters can dynamically adjust the interval range of adjacent dynamic calibration intervals;

[0042] an error calculation module, configured to calculate an error compensation value for each of the dynamic calibration intervals according to the error parameter;

[0043] an interval adjustment module, configured to generate calibration signal data based on the error compensation value, and input the calibration signal data into each of the dynamic calibration intervals to obtain a target calibration interval;

[0044] A data compensation module is used to compensate the real-time weighing data of the sensor according to the target calibration interval.

[0045] In addition, to achieve the above-mentioned purpose, the present application also proposes a sensor error calibration device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the computer program is configured to implement the steps of the sensor error calibration method described above.

[0046] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium, and when the computer program is executed by the processor, the steps of the sensor error calibration method described above are implemented.

[0047] The present application discloses a method for collecting environmental status information of sensors and calculating partition calibration parameters of each sensor based on the environmental status information; dividing each sensor into multiple dynamic calibration intervals based on the partition calibration parameters, wherein the dynamic calibration intervals contain multiple error parameters, and the difference between the error parameters can dynamically adjust the interval range of adjacent dynamic calibration intervals; calculating the error compensation value of each dynamic calibration interval based on the error parameters; generating calibration information based on the error compensation value, and inputting the calibration information into each dynamic calibration interval to obtain a target calibration interval; and compensating the real-time weighing data of the sensor based on the target calibration interval. The sensor is divided into multiple dynamic calibration intervals based on the partition calibration parameters, and the difference between the error parameters can dynamically adjust the range of adjacent intervals. The method can respond to changes in the errors of multiple sensors in real time, ensuring that each sensor can be effectively calibrated under different working conditions, and further improving measurement accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0049] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0050] Figure 1 This is a flow chart of the first embodiment of the sensor error calibration method of the present application;

[0051] Figure 2 Schematic diagram of the measurement area for this application;

[0052] Figure 3 This is a flow chart of a second embodiment of the sensor error calibration method of the present application;

[0053] Figure 4 Schematic diagram of the sensor calibration route for this application;

[0054] Figure 5 This is a flow chart of a second embodiment of the sensor error calibration method of the present application;

[0055] Figure 6 This is a schematic diagram of the structure of the anti-cheating supervision platform for this application;

[0056] Figure 7 This is a schematic diagram of the module structure of the sensor error calibration device according to an embodiment of the present application;

[0057] Figure 8 Schematic diagram of the device structure of the hardware operating environment involved in the sensor error calibration method in the embodiment of the present application.

[0058] The purpose, features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0059] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0060] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0061] The main solution of the embodiment of the present application is: collecting the environmental status information of the sensor, and calculating the partition calibration parameters of each sensor based on the environmental status information; dividing each sensor into multiple dynamic calibration intervals based on the partition calibration parameters, and the dynamic calibration interval contains multiple error parameters, and the difference between the error parameters can dynamically adjust the interval range of adjacent dynamic calibration intervals; calculating the error compensation value of each dynamic calibration interval based on the error parameters; generating calibration information based on the error compensation value, and inputting the calibration information into each dynamic calibration interval to obtain a target calibration interval; compensating the real-time weighing data of the sensor according to the target calibration interval.

[0062] Multi-sensor collaboration is becoming increasingly common in fields such as industrial automation, smart logistics, and medical devices. For example, large-scale weighing systems often deploy multiple pressure sensors in arrays, spatially distributed to cover different areas of the measured object to improve measurement accuracy. However, the errors of individual sensors in a multi-sensor system are not only affected by their own environment (such as temperature, humidity, and posture), but can also result in coupling errors due to differences in spatial layout, signal interference, or varying load transfer characteristics between sensors. Traditional single-sensor calibration methods only compensate for static parameters of a single device and fail to address dynamic interaction errors between multiple sensors, resulting in overall system accuracy failing to meet high-precision requirements. Measurement results from adjacent sensors can cause error transfer due to physical position differences. For example, the tilt of a sensor can alter its load sharing ratio with adjacent sensors. Different sensors may operate in different microenvironments (such as local temperature gradients and humidity differences), making traditional, unified calibration parameters inadequate for varying environmental interference. When multiple sensors operate synchronously, their error characteristics can exhibit nonlinear additive effects, making traditional independent calibration methods ineffective in eliminating such collaborative errors. In the existing technology, some solutions attempt to compensate for multi-sensor errors by adding reference sensors or offline calibration, but such methods require additional hardware costs and are difficult to adapt to real-time dynamic changes in scenarios.

[0063] Therefore, this application provides a joint calibration method based on multi-sensor environmental state information, which can dynamically analyze the spatial position relationship and environmental interference characteristics of each sensor and realize the collaborative optimization of multi-dimensional error parameters.

[0064] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, sensor calibration, and program execution functions, such as a data analysis and processing module, or an electronic device capable of implementing the above functions. The following uses the calibration execution module as an example to illustrate this embodiment and the following embodiments.

[0065] Based on this, the embodiment of the present application provides a sensor error calibration method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the sensor error calibration method of the present application.

[0066] In this embodiment, the sensor error calibration method includes:

[0067] Step S10: collecting environmental status information of the sensors, and calculating partition calibration parameters of each sensor based on the environmental status information.

[0068] It should be noted that environmental status information refers to information corresponding to external environmental factors that affect sensor measurement accuracy, including but not limited to ambient temperature, humidity, pressure, attitude tilt angle, and sensor spatial coordinates. Different environmental status information can have varying degrees of impact on sensor performance. For example, temperature changes can cause changes in the sensor's material properties, thereby affecting its measurement results. Zone calibration parameters are a set of parameters calculated based on the sensor's environmental status information for more accurate sensor calibration. They are used to divide the sensor's measurement range into different zones, each corresponding to different calibration rules to adapt to the sensor's error characteristics under different environmental conditions.

[0069] It can be understood that based on the environmental state information, the partition calibration parameters of each sensor can be calculated according to the characteristics and historical data of the sensor, a mathematical model between the environmental state information and the partition calibration parameters can be established, the preprocessed environmental state information is input into the calibration model, and the partition calibration parameters of each sensor are obtained through calculation of the model.

[0070] It should be understood that the partition calibration parameters can also be obtained in advance through extensive experiments and testing, collecting sensor error data under different environmental conditions and developing empirical tables based on this data. In practical applications, after collecting the sensor's environmental condition information, the corresponding partition calibration parameters can be directly searched in the empirical table. Of course, to ensure better robustness to environmental changes and improve calibration accuracy, fuzzy logic theory can also be used to handle the relationship between environmental condition information and partition calibration parameters. The environmental condition information is divided into different fuzzy sets, fuzzy rules are defined, and the partition calibration parameters are derived through fuzzy reasoning based on the collected environmental condition information.

[0071] Furthermore, in order to generate an error distribution map based on the sensor spatial coordinates, intuitively locate high error areas, and improve the accuracy of calibration parameter matching, the environmental state information includes the sensor spatial coordinates, ambient temperature, ambient humidity, and attitude tilt angle; the step S10 may include:

[0072] Collect environmental status information of the sensor; establish a measurement error distribution map based on the sensor spatial coordinates in the environmental status information; determine the measurement area according to the measurement error distribution map, and calculate the stability index of each measurement area according to the ambient temperature, ambient humidity and attitude inclination angle; prioritize each measurement area based on the stability index, and determine the partition calibration parameters of the sensor in a preset calibration parameter database according to the sorting result.

[0073] It's important to understand that the measurement error distribution map is a graph constructed based on the sensor's spatial coordinates, showing the distribution of sensor measurements at different spatial locations. A measurement region is a region with similar error characteristics, demarcated by the measurement error distribution map. Within the same measurement region, the sensor's error performance is relatively consistent. The stability index is a numerical measure of the error stability of each measurement region, taking into account factors such as ambient temperature, humidity, and attitude tilt angle. A higher stability index indicates that the sensor's measurement error within that region is less affected by environmental factors. The preset calibration parameter database stores calibration parameters corresponding to different stability indices and measurement regions. By querying this database, appropriate calibration parameters for each region can be determined for the sensor.

[0074] Specifically, various sensors are used to collect environmental status information. GPS or a positioning system is used to obtain the sensor spatial coordinates. A temperature sensor measures ambient temperature, a humidity sensor measures ambient humidity, and an accelerometer and gyroscope measure attitude and tilt angles. This collected information is transmitted to a data processing center for further processing. Using the sensor spatial coordinates as the coordinate axes and the measurement errors as numerical values, a measurement error distribution map is plotted using mapping software or algorithms. The measurement error distribution map is then observed and regions with similar error values ​​and a continuous distribution are classified as measurement regions. Algorithms such as cluster analysis can be used to automatically perform region division. For each measurement region, ambient temperature, humidity, and attitude and tilt angle data are collected. A mathematical model or weighted average model is developed to comprehensively consider the impact of these three factors on the error and calculate a stability index for that region. The measurement regions are then ranked from high to low based on their stability index. Regions with high stability indexes indicate relatively stable errors, relatively low calibration requirements, and low priority. Regions with low stability indexes indicate errors that are significantly affected by the environment, high calibration requirements, and high priority. Based on the ranking results, a pre-set calibration parameter database is searched for calibration parameters that match the stability index and regional characteristics of each measurement region. These calibration parameters are used as the partition calibration parameters of the sensor in the corresponding measurement area.

[0075] In step S20 , each of the sensors is divided into a plurality of dynamic calibration intervals based on the partition calibration parameters. The dynamic calibration intervals include a plurality of error parameters. The difference between the error parameters can dynamically adjust the interval range of adjacent dynamic calibration intervals.

[0076] It's important to note that the dynamic calibration interval divides the sensor's measurement range into multiple zones. These zones are not fixed but dynamically adjust based on changes in error parameters. Each zone can correspond to different error characteristics and calibration rules. Error parameters reflect sensor measurement error-related parameters, such as temperature compensation, humidity compensation, and zero offset. Changes in these parameters can affect the sensor's measurement accuracy and can be used to dynamically adjust the calibration interval.

[0077] It is understood that the dynamic calibration intervals can be pre-divided into fixed calibration intervals based on the sensor's historical data and experience. Each interval corresponds to a fixed calibration parameter. Alternatively, the sensor's measurement range can be divided into multiple calibration intervals, with interval adjustments only performed when an error parameter exceeds a preset threshold. When the error parameter is within the threshold, the interval range remains unchanged.

[0078] In one example, reference Figure 2 , Figure 2 This is a schematic diagram of the measurement area for this application. Figures 1-8 represent the eight sensors installed in the weighing system. By collecting environmental information about each sensor's location, such as temperature, humidity, its own posture, and vibrations caused by vehicle movement, zone calibration parameters for each sensor are selected from a preset calibration parameter database. Based on these zone calibration parameters, the measurement range of each sensor is divided into multiple dynamic calibration intervals. For sensor 1, this can be divided into a medium-weight measurement interval. During the actual weighing process, if the difference between an error parameter within sensor 1's low-weight measurement interval and the corresponding error parameter in an adjacent interval (such as the medium-weight measurement interval) exceeds a certain threshold—for example, if the weight change caused by a vehicle slowly entering the weighing area causes a change in sensor force, resulting in a change in error characteristics—the ranges of the low-weight and medium-weight measurement intervals will be dynamically adjusted, potentially narrowing the low-weight measurement interval and correspondingly expanding the medium-weight measurement interval to ensure accurate weighing measurements. Sensors 2 through 8 are similarly divided into dynamic calibration intervals based on their respective zone calibration parameters, and the interval ranges are dynamically adjusted based on the error parameter difference.

[0079] Step S30 : calculating an error compensation value for each of the dynamic calibration intervals according to the error parameter.

[0080] It's important to note that error compensation is a correction value calculated to eliminate or reduce errors in the sensor's measurement process. By applying the error compensation value to the sensor's measurement results, the measured value can be brought closer to the true value, thereby improving the sensor's measurement accuracy.

[0081] It should be understood that empirical formulas or lookup tables for error compensation can be established based on historical data. In practical applications, the corresponding error compensation value can be directly obtained from the empirical formula or lookup table based on the current error parameters. The relationship between the sensor's error parameters and error can also be modeled. By training the model with a large amount of historical data, the model can learn the variation patterns of the error. In practical applications, the current error parameters are input into the trained model, and the model outputs the corresponding error compensation value.

[0082] Furthermore, in order to solve the nonlinear error problem caused by multi-parameter coupling through the multi-dimensional integration of temperature and humidity compensation and zero point correction, the error parameters include: temperature compensation parameter, humidity compensation parameter and attitude offset value; step S30 includes:

[0083] The temperature and humidity error compensation parameters are calculated based on the temperature compensation parameters and humidity compensation parameters of each dynamic calibration interval; the zero point correction parameters are calculated based on the posture offset value of each dynamic calibration interval; the temperature and humidity error compensation parameters and the zero point correction parameters are multi-dimensionally integrated to generate the error compensation value of each dynamic calibration interval.

[0084] It should be noted that the temperature and humidity error compensation parameters are calculated by combining the temperature and humidity compensation parameters to compensate for measurement errors caused by changes in temperature and humidity. The zero correction parameters are calculated based on the attitude offset value and are used to correct zero offset errors caused by changes in sensor attitude. The attitude offset value indicates the degree to which the sensor's attitude in space (such as tilt and rotation) deviates from its ideal state.

[0085] It should be understood that within each dynamic calibration interval, the temperature compensation parameters and humidity compensation parameters of the interval can be weighted and summed to obtain the temperature and humidity error compensation parameters; the posture offset value for each dynamic calibration interval can be used to calculate the zero point correction parameter through the relationship between the posture offset value and the zero point offset.

[0086] It is understandable that through multi-dimensional fusion, the influence of multiple factors such as temperature, humidity, and posture on sensor errors is taken into account, and the errors are comprehensively corrected. Compared with considering only a single factor, the measurement results can be closer to the true value and the measurement accuracy can be significantly improved.

[0087] In one example, within a dynamic calibration interval, the temperature compensation parameter Tp of sensor 1 is 0.2, which means that for every 1°C change in temperature, the measured value deviates by 0.2 units. The humidity compensation parameter Hp is 0.1, which means that for every 1% change in humidity, the measured value deviates by 0.1 units. The temperature weight is determined by testing. , humidity weight , then the temperature and humidity error compensation parameters . The attitude offset value of sensor 1 , according to the predetermined relationship function between attitude offset and zero point correction f(Ao)=0.05×A o , calculate the zero point correction parameter E A =0.05×5=0.25. Adding the temperature and humidity error compensation parameters to the zero-point calibration parameters yields an error compensation value of E=0.16+0.25=0.41. If the current sensor measurement is 100, the error-compensated value is 99.59, making the measurement more accurate.

[0088] Step S40 : generating calibration information based on the error compensation value, and inputting the calibration information into each of the dynamic calibration intervals to obtain a target calibration interval.

[0089] It should be noted that calibration information includes relevant data such as error compensation values ​​and correction rules, which guide the sensor to calibrate and correct measured values ​​during the measurement process. Target calibration interval: After entering calibration information and performing calibration corrections, the final calibration interval for accurate measurement is determined, ensuring that the sensor's measurement accuracy within this interval meets the expected requirements.

[0090] As you can see, dynamic calibration intervals and targeted error compensation fully account for sensor error characteristics across different measurement ranges, enabling high-precision calibration and more accurate measurement results. Dynamically adjusting the calibration interval and compensation method based on the sensor's actual operating conditions also adapts to complex and changing measurement environments and sensor error variations.

[0091] Step S50: compensating the real-time weighing data of the sensor according to the target calibration interval.

[0092] It can be understood that compensation based on the target calibration interval can accurately correct the error characteristics within the interval, make full use of the calibration information determined in the previous calibration process, maximize measurement accuracy, and make the compensated weighing data closer to the true value.

[0093] Furthermore, in order to achieve adaptive optimization of the calibration strategy through dynamic monitoring and interval adjustment of the error impact index, compensating the real-time weighing data of the sensor according to the target calibration interval may include:

[0094] Real-time weighing data of the sensor is obtained, and the duration and trigger frequency of the sensor in the dynamic calibration interval are identified based on the real-time weighing data; the error influence index of each target calibration interval is calculated based on the duration and trigger frequency; if it is detected that the error influence index exceeds a preset error threshold, the interval range of the dynamic calibration interval corresponding to the error influence index is adjusted to a preset tolerance range; the target error compensation value is recalculated based on the preset tolerance range, and the real-time weighing data is compensated according to the target error compensation value.

[0095] In this embodiment, sensors are divided into multiple dynamic calibration intervals based on zone calibration parameters, and the difference between error parameters dynamically adjusts the ranges of adjacent intervals. This allows for real-time response to changes in multiple sensor errors, ensuring effective calibration of each sensor under varying operating conditions, further improving measurement accuracy.

[0096] Reference Figure 3 , Figure 3 This is a flow chart of a second embodiment of the sensor error calibration method of the present application. Based on the above-mentioned first embodiment, the second embodiment of the sensor error calibration method of the present application is proposed.

[0097] In the second embodiment, step S40 includes:

[0098] Step S401 : obtaining a calibration position corresponding to the error compensation value, wherein the calibration position includes a key position and an edge position.

[0099] It should be noted that the calibration position refers to the location within the sensor's measurement range that corresponds to a specific error compensation value. It reflects where the sensor generates measurement errors that require correction with that error compensation value. Key positions are locations within the sensor's measurement range that have a significant impact on measurement accuracy and represent representative error characteristics. The errors at these locations often play a critical role in the overall sensor's measurement accuracy. These are typically locations where the sensor is frequently used during normal operation or where error variations are most significant. Edge positions are locations at the boundaries of the sensor's measurement range. At these locations, the sensor's operating characteristics may differ from those in the center of the measurement range, and their error sources and characteristics may be unique, generally related to edge effects within the measurement range.

[0100] It is understood that determining the calibration position requires establishing a mapping relationship between sensor measurements and positions based on the sensor's design specifications, installation layout, and actual measurement experiments. For linear sensors, the measurement value can be linearly mapped to the physical position based on its measurement range and scale. For sensor arrays with complex shapes or layouts, a coordinate system and spatial mapping algorithms may be required to determine the position corresponding to the measurement value.

[0101] It should be understood that determining key locations requires extensive experimental testing and data analysis, or by analyzing sensor measurement data and error characteristics at different locations based on sensor usage experience and industry standards. Edge position identification can be performed based on the boundaries of the sensor's measurement range, identifying locations within the boundary area as edge locations.

[0102] Step S402 : If the calibration position is a key position, the error compensation value is corrected based on the first spatial coordinate of the error compensation value to generate first calibration information.

[0103] It should be noted that the first spatial coordinates are a set of coordinate values ​​that determine the spatial position of the error compensation value. They are used to determine the specific spatial location of the error compensation value at the key location, allowing correction of the error compensation value based on this location information. The first calibration information is the information obtained by correcting the error compensation value and the first spatial coordinates.

[0104] Furthermore, in order to combine the adjacent sensor coordinate sets with historical compensation values ​​to eliminate the isolated errors of single-point calibration, the calibration robustness of the core area is improved by correcting the spatial coordinate compensation deviation of the key position. The step S402 may include:

[0105] If the calibration position is a critical position, obtain the first spatial coordinates of the sensor corresponding to the critical position and the coordinate set of adjacent sensors; determine the compensation deviation of the error compensation value based on the first spatial coordinates, the coordinate set and the historical compensation values ​​of each of the sensors; correct the error compensation value based on the compensation deviation to generate first calibration information.

[0106] It should be noted that historical compensation values ​​are records of error compensation values ​​applied by the sensor during past measurements for different environments and measurement conditions. The coordinate set of adjacent sensors refers to the spatial coordinates of other sensors adjacent to the target sensor at a critical location. Compensation deviation measures the difference between the current error compensation value and the ideal compensation value.

[0107] Specifically, after the calibration location is determined to be a critical location, the sensor's own positioning system or a preset coordinate system based on the installation layout is used to obtain the first spatial coordinates of the sensor corresponding to the critical location. Simultaneously, the coordinates of adjacent sensors are collected through sensor network communication or pre-set neighbor relationships to determine the spatial distribution of surrounding sensors. Historical compensation values ​​for each sensor under different periods and operating conditions are then extracted from the sensor's historical data storage unit. The first spatial coordinates, the coordinates of adjacent sensors, and the historical compensation values ​​of each sensor are used as input parameters and fed into a mathematical model based on statistical methods and the physical properties of the sensors. The model calculates the difference between the error compensation value at the current critical location and the ideal compensation value, which accounts for the influence of surrounding sensors and historical compensation data, to determine a compensation deviation. Based on the calculated compensation deviation, the original error compensation value is corrected. If the compensation deviation is positive, the error compensation value needs to be increased; if it is negative, the error compensation value needs to be reduced. The corrected error compensation value is integrated with relevant calibration parameters (such as the calibration location, the underlying model algorithm, and the sensor information involved in the calculation) to generate first calibration information.

[0108] In one example, the first spatial coordinates of sensor 1 in the platform coordinate system are obtained as (3, 5, 2) in meters. The coordinates of adjacent sensors 2, 3, and 4 are determined through the sensor network to be {(2, 5, 2), (3, 6, 2), (4, 5, 2)}. The historical compensation values ​​of sensor A and its adjacent sensors B, C, and D are extracted from the historical data repository. Under similar cargo weight and distribution conditions, the historical compensation values ​​of sensor 1 fluctuated between 0.5 and 1.2, and the adjacent sensors also have corresponding compensation records. Using this data as input, a pre-built model calculates the current compensation deviation of sensor 1 to be 0.2. If the original compensation value is 0.8, the corrected compensation value is 1.0.

[0109] Step S403 : If the calibration position is an edge position, a relative distance between the edge position and a key position closest to the edge position is calculated, and second calibration information is generated based on the relative distance.

[0110] Furthermore, in order to enhance the anti-interference capability of the boundary area by correcting the relative distance between the edge position and the key position, the step S403 may include:

[0111] If the calibration position is an edge position, obtain the second spatial coordinates of the sensor at the edge position and the reference coordinates of the key position closest to the edge position; calculate the three-dimensional spatial distance and azimuth angle between the second spatial coordinates and the reference coordinates; obtain the relative distance based on the three-dimensional spatial distance and the azimuth angle, and correct the error compensation value based on the relative distance to generate second calibration information.

[0112] It should be noted that the second spatial coordinates are used to precisely identify the spatial coordinates of the edge sensor's position information, while the reference coordinates are the spatial coordinates of the key position closest to the edge. The three-dimensional distance is calculated using the spatial coordinates and represents the straight-line distance between the edge sensor (second spatial coordinates) and the closest key position (reference coordinates) in three-dimensional space. The azimuth angle describes the angular relationship of the edge sensor in space relative to the closest key position. The second calibration information includes the result of correcting the original error compensation value based on the relative distance, as well as a collection of information such as relevant calibration parameters and instructions.

[0113] In one example, the second spatial coordinates of the edge position and the reference coordinates of the adjacent key position are extracted from the sensor array or spatial positioning system. The absolute distance between the two points is calculated using the Euclidean distance formula:

[0114]

[0115] in, , , are the spatial coordinates of each sensor respectively. The angle between the line connecting the two coordinates and the reference plane (such as the horizontal plane) is calculated using the vector dot product formula:

[0116]

[0117] in, is the vector from the edge to the key position, is the normal vector of the reference plane. According to the physical meaning of distance and angle, the environmental interference coefficient k is designed. k can be dynamically adjusted according to the actual working conditions. The relative distance technical formula is:

[0118]

[0119] The original error compensation value is calculated based on the relative distance. Perform dynamic scaling or filtering. If the relative distance exceeds a threshold, the compensation parameter is exponentially attenuated:

[0120]

[0121] Where λ is the attenuation coefficient. The compensation weight of the attitude offset value can also be adjusted based on the angle, for example, increasing the zero-point correction strength at larger tilt angles. The corrected compensation value is encapsulated as an executable instruction, and the second calibration information is loaded in real time through the configuration interface of the sensor signal link.

[0122] Step S404: input the first calibration information and / or the second calibration information into the corresponding dynamic calibration interval to obtain a target calibration interval.

[0123] In one example, reference Figure 4 , Figure 4 This is a schematic diagram of the sensor calibration route for this application. The diagram shows a sensor network system consisting of eight sensors (labeled 1-8), interconnected and ultimately connected to a meter. During system operation, the calculated error compensation value must be used to determine its corresponding calibration location, which is categorized as a critical location or an edge location. In this sensor network, some sensors may be located at critical locations, significantly impacting measurement accuracy; sensors at the edge of the network or at the limit of the measurement range may be edge locations. For example, in a weighing scenario, a sensor bearing the primary weight pressure point may be a critical location, while a sensor located at the edge of the weighing platform may be an edge location. If the calibration location corresponding to a certain error compensation value is a critical location, such as sensor 3 at a critical location, the error compensation value is corrected based on the first spatial coordinate of the error compensation value (i.e., the spatial coordinates of sensor 3). If the calibration location is an edge location, such as sensor 1 at an edge location, the relative distance between it and the closest critical location (assuming sensor 2 is the closest critical location to sensor 1) must be calculated. The three-dimensional spatial distance and azimuth angle are calculated using the spatial coordinates of the two sensors to obtain the relative distance, which is then used to generate the second calibration information. Next, enter the first calibration information for the dynamic calibration interval corresponding to sensor 3, and the second calibration information for the dynamic calibration interval corresponding to sensor 1. This process yields the target calibration intervals within which the sensor's measurement accuracy is optimized, enabling more accurate measurements and data transmission to the instrument display.

[0124] In this embodiment, a temperature and humidity error compensation parameter is calculated based on the temperature and humidity compensation parameters for each dynamic calibration interval; a zero-point correction parameter is calculated based on the attitude offset value for each dynamic calibration interval; and these temperature and humidity error compensation parameters and the zero-point correction parameter are multi-dimensionally fused to generate an error compensation value for each dynamic calibration interval. This multi-dimensional fusion of temperature and humidity compensation and zero-point correction solves the problem of nonlinear errors caused by multi-parameter coupling.

[0125] Reference Figure 5 , Figure 5This is a flow chart of a second embodiment of the sensor error calibration method of the present application. Based on the above-mentioned first embodiment, the second embodiment of the sensor error calibration method of the present application is proposed.

[0126] In the second embodiment, step S50 includes:

[0127] Step S501 : acquiring real-time weighing data of the sensor, and identifying the duration and triggering frequency of the sensor in the dynamic calibration interval according to the real-time weighing data.

[0128] It should be noted that real-time weighing data is the weight of the object being weighed, collected in real time by the sensor during its current operation. It reflects the weight of the object acting on the sensor at that moment. Duration is the length of time that the real-time weighing data collected by the sensor remains within a dynamic calibration interval, reflecting the duration of the measurement state within that interval. Trigger frequency is the number of times the real-time weighing data collected by the sensor enters a dynamic calibration interval within a certain time period, reflecting the frequency with which measurement data appears within that interval.

[0129] Specifically, the sensor's data acquisition module continuously acquires the weight data of the object under test at a set sampling frequency. The sensor determines which dynamic calibration interval the real-time weighing data falls into based on the pre-defined dynamic calibration intervals. When the real-time weighing data enters a dynamic calibration interval, a timing function is initiated. If the real-time weighing data subsequently remains within that interval, the timing continues; if the data leaves that interval, the timing stops, and the duration of this time is recorded as the duration within that interval. During the set statistical period, each time the real-time weighing data enters a dynamic calibration interval, a trigger frequency counter increments by 1.

[0130] Step S502 : calculating the error impact index of each target calibration interval based on the duration and the trigger frequency.

[0131] It should be noted that the Error Impact Index (EI) is a value calculated by combining the duration and trigger frequency. It measures the overall impact of errors on measurement results within the target calibration interval. A larger value indicates a greater impact of errors on measurement results within that interval.

[0132] In one example, a mathematical model for calculating the error impact index needs to be determined. A weighted summation method can be used, namely the error impact index:

[0133]

[0134] Among them, D represents the duration, F represents the trigger frequency, and They are the weight coefficients corresponding to duration and trigger frequency. The determination of weight coefficients needs to be combined with the characteristics of the sensor and the requirements of the actual application scenario. For some scenarios with high requirements for measurement stability, the weight of duration It can be set relatively large; for scenarios where measurements change frequently, the weight of the trigger frequency Maybe more important.

[0135] Step S503 : If it is detected that the error impact index exceeds a preset error threshold, the interval range of the dynamic calibration interval corresponding to the error impact index is adjusted to a preset tolerance range.

[0136] It should be noted that the interval range is the range of the dynamic calibration interval in the measurement dimension, and the preset tolerance range is the reasonable range of allowable error that is pre-set. If the error impact index of the dynamic calibration interval exceeds the standard, the interval range is adjusted to within the preset tolerance range to ensure acceptable measurement accuracy.

[0137] It is understood that when the error impact index is determined to exceed the preset error threshold, the range of the dynamic calibration interval corresponding to the error impact index is adjusted. Specifically, this adjustment is performed to bring it within the preset tolerance range. For example, if the original dynamic calibration interval range is 3-8 kg and the preset tolerance range is 4-7 kg, the interval will be adjusted to 4-7 kg. This adjustment process involves modifying the upper and lower limits of the interval and can be achieved through a preset algorithm or manually set rules.

[0138] It should be understood that when it is detected that the error impact index exceeds the preset error threshold, the range of the dynamic calibration interval may be adjusted according to a fixed ratio instead of the preset tolerance range.

[0139] Step S504: recalculate the target error compensation value based on the preset tolerance range, and compensate the real-time weighing data according to the target error compensation value.

[0140] It should be noted that the target error compensation value is a recalculated value used to correct the sensor measurement error after taking into account the preset tolerance range. It is a compensation amount determined to make the sensor's measurement results as close to the true value as possible.

[0141] Specifically, if it is detected that the error influence index exceeds the preset error threshold, a reasonable tolerance range is predetermined based on factors such as the sensor type, measurement accuracy requirements, and operating environment, through experimental testing, theoretical analysis, or reference to industry standards. Real-time weighing data and related error parameters are collected within the dynamic calibration interval. Based on the preset tolerance range, the error compensation value is recalculated in combination with the collected data and error parameters to obtain a target error compensation value. The recalculated target error compensation value is applied to the real-time weighing data to obtain a corrected measurement result. Recalculating the target error compensation value based on the preset tolerance range can fully consider the actual error situation of the sensor and the accuracy requirements of the application scenario, making the error compensation more accurate, effectively improving the accuracy of the measurement results, and meeting the needs of high-precision measurement. In addition, according to changes in factors such as the sensor working environment and the measurement object, by adjusting the preset tolerance range and recalculating the target error compensation value, it can flexibly adapt to different situations to ensure that the sensor can provide more accurate measurement results under various conditions.

[0142] In one example, reference Figure 6 , Figure 6 This is a structural diagram of the anti-cheating supervision platform for this application. In the weighing system architecture, the weighing sensor (group) converts the weight of the object into an electrical signal. When subjected to pressure, its internal resistance changes, thereby outputting an electrical signal related to the weight. The signal is transmitted to the signal data processing circuit through the signal acquisition and measurement circuit, where it is filtered, analog-to-digital converted, and other processing is performed. The processed data is stored in the data memory. The timing and logic controllers coordinate the work of each part, and the compensation module compensates for the error of the data according to the relevant algorithm. The data is transmitted to the instrument display through the input / output line, and can also be sent to the anti-cheating platform via the wireless communication transmission line to monitor cheating behavior and realize the accurate collection, processing, display and supervision of weighing data.

[0143] In this embodiment, real-time weighing data from the sensor is acquired, and the duration and trigger frequency of the sensor in the dynamic calibration interval are identified based on the real-time weighing data. An error impact index is calculated for each target calibration interval based on the duration and trigger frequency. If the error impact index exceeds a preset error threshold, the interval range of the dynamic calibration interval corresponding to the error impact index is adjusted to a preset tolerance range. A target error compensation value is recalculated based on the preset tolerance range, and the real-time weighing data is compensated based on the target error compensation value. By dynamically monitoring the error impact index and adjusting the interval range, adaptive optimization of the calibration strategy is achieved.

[0144] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the sensor error calibration method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0145] This application also provides a sensor error calibration device, please refer to Figure 7 , the sensor error calibration device includes:

[0146] An information acquisition module 10 is used to collect environmental status information of the sensors and calculate the partition calibration parameters of each sensor based on the environmental status information;

[0147] An interval determination module 20 is configured to divide each sensor into a plurality of dynamic calibration intervals based on the partition calibration parameters, wherein the dynamic calibration intervals include a plurality of error parameters, and the difference between the error parameters can dynamically adjust the interval range of adjacent dynamic calibration intervals;

[0148] an error calculation module 30, configured to calculate an error compensation value for each of the dynamic calibration intervals according to the error parameter;

[0149] an interval adjustment module 40 for generating calibration signal data based on the error compensation value, and inputting the calibration signal data into each of the dynamic calibration intervals to obtain a target calibration interval;

[0150] The data compensation module 50 is used to compensate the real-time weighing data of the sensor according to the target calibration interval.

[0151] The sensor error calibration device provided in this application utilizes the sensor error calibration method described in the aforementioned embodiments. This device can address the technical issue of traditional single-sensor calibration methods, which only perform static parameter compensation for a single device and fail to address dynamic interaction errors between multiple sensors. Compared to the prior art, the sensor error calibration device provided in this application achieves the same beneficial effects as the sensor error calibration method described in the aforementioned embodiments. Other technical features of the sensor error calibration device are the same as those disclosed in the aforementioned embodiments and are not further elaborated here.

[0152] The present application provides a sensor error calibration device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the sensor error calibration method in the above-mentioned embodiment one.

[0153] Reference below Figure 8, which shows a schematic diagram of the structure of a sensor error calibration device suitable for implementing embodiments of the present application. The sensor error calibration device in the embodiments of the present application can include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 8 The sensor error calibration device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0154] like Figure 8 As shown, the sensor error calibration device may include a processing device 1001 (e.g., a central processing unit, graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory 1002 or programs loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the sensor error calibration device. The processing device 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: an input device 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; a storage device 1003 including, for example, a magnetic tape or hard disk; and a communication device 1009. The communication device 1009 can allow the sensor error calibration device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a sensor error calibration device with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented or have alternatively.

[0155] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a read-only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are performed.

[0156] The sensor error calibration device provided in this application, utilizing the sensor error calibration method described in the aforementioned embodiment, addresses the technical issue of traditional single-sensor calibration methods, which only perform static parameter compensation for a single device and fail to address dynamic interaction errors between multiple sensors. Compared to the prior art, the sensor error calibration device provided in this application achieves the same beneficial effects as the sensor error calibration method described in the aforementioned embodiment. Other technical features of this sensor error calibration device are the same as those disclosed in the aforementioned embodiment and are not further elaborated here.

[0157] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0158] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0159] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, a computer program) stored thereon, wherein the computer-readable program instructions are used to execute the sensor error calibration method in the above embodiment.

[0160] The computer-readable storage medium provided herein may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including, but not limited to, wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0161] The computer-readable storage medium may be included in the sensor error calibration device; or may exist independently without being assembled into the sensor error calibration device.

[0162] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the sensor error calibration device, the sensor error calibration device executes the sensor error calibration method described above.

[0163] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0164] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of code, and the module, program segment or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, as well as the combination of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0165] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0166] The computer-readable storage medium provided herein stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned sensor error calibration method. This computer-readable storage medium addresses the technical issue that traditional single-sensor calibration methods only perform static parameter compensation for a single device and fail to address dynamic interaction errors between multiple sensors. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided herein are similar to those of the sensor error calibration method provided in the aforementioned embodiments and are not further elaborated here.

[0167] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A sensor error calibration method, characterized in that: The sensor error calibration method comprises: Collecting environmental status information of the sensors, and calculating partition calibration parameters of each of the sensors based on the environmental status information; Dividing each of the sensors into a plurality of dynamic calibration intervals based on the partition calibration parameters, wherein the dynamic calibration intervals include a plurality of error parameters, and the differences between the error parameters can dynamically adjust the interval ranges of adjacent dynamic calibration intervals; Calculating an error compensation value for each of the dynamic calibration intervals according to the error parameter; generating calibration information based on the error compensation value, and inputting the calibration information into each of the dynamic calibration intervals to obtain a target calibration interval; compensating the real-time weighing data of the sensor according to the target calibration interval; The error parameters include temperature compensation parameters, humidity compensation parameters and attitude offset values; The step of calculating the error compensation value of each dynamic calibration interval according to the error parameter includes: Calculating a temperature and humidity error compensation parameter based on the temperature compensation parameter and the humidity compensation parameter of each dynamic calibration interval; Calculating a zero point correction parameter based on the posture offset value of each dynamic calibration interval; Perform multi-dimensional fusion of the temperature and humidity error compensation parameters and the zero point correction parameters to generate an error compensation value for each dynamic calibration interval; The step of generating calibration information based on the error compensation value and inputting the calibration information into each of the dynamic calibration intervals to obtain a target calibration interval includes: Obtaining a calibration position corresponding to the error compensation value, wherein the calibration position includes a key position and an edge position; If the calibration position is a key position, correcting the error compensation value based on the first spatial coordinate of the error compensation value to generate first calibration information; If the calibration position is an edge position, calculating a relative distance between the edge position and a key position closest to the edge position, and generating second calibration information based on the relative distance; The first calibration information and / or the second calibration information are input into the corresponding dynamic calibration interval to obtain a target calibration interval.

2. The sensor error calibration method according to claim 1, wherein: If the calibration position is a critical position, the step of correcting the error compensation value based on the spatial position coordinates of the error compensation value to generate first calibration information includes: If the calibration position is a key position, obtaining the first spatial coordinates of the sensor and a coordinate set of adjacent sensors corresponding to the key position; determining a compensation deviation of the error compensation value according to the first spatial coordinate, the coordinate set, and historical compensation values ​​of each of the sensors; The error compensation value is corrected based on the compensation deviation to generate first calibration information.

3. The sensor error calibration method according to claim 1, wherein: If the calibration position is an edge position, the step of calculating the relative distance between the edge position and the key position closest to the edge position, and generating second calibration information based on the relative distance includes: If the calibration position is an edge position, obtaining the second spatial coordinates of the sensor at the edge position and the reference coordinates of the key position closest to the edge position; Calculating the three-dimensional space distance and the azimuth angle between the second space coordinate and the reference coordinate; A relative distance is obtained according to the three-dimensional space distance and the azimuth angle, and the error compensation value is corrected based on the relative distance to generate second calibration information.

4. The sensor error calibration method according to claim 1, wherein: The step of compensating the real-time weighing data of the sensor according to the target calibration interval includes: Acquiring real-time weighing data of the sensor, and identifying the duration and triggering frequency of the sensor in the dynamic calibration interval according to the real-time weighing data; Calculating an error impact index of each of the target calibration intervals based on the duration and the trigger frequency; If it is detected that the error impact index exceeds a preset error threshold, adjusting the interval range of the dynamic calibration interval corresponding to the error impact index to a preset tolerance range; The target error compensation value is recalculated based on the preset tolerance range, and the real-time weighing data is compensated according to the target error compensation value.

5. The sensor error calibration method according to any one of claims 1 to 4, characterized in that: The environmental status information includes the sensor space coordinates, environmental temperature, environmental humidity and attitude tilt angle; The step of collecting environmental status information of the sensor and calculating the partition calibration parameters of the sensor based on the environmental status information includes: Collect environmental status information from sensors; establishing a measurement error distribution map based on the sensor spatial coordinates in the environmental state information; Determining a measurement area according to the measurement error distribution map, and calculating a stability index of each measurement area according to the ambient temperature, ambient humidity, and attitude tilt angle; Prioritizing the measurement areas based on the stability index, and determining the partition calibration parameters of the sensor in a preset calibration parameter database according to the ranking results.

6. A sensor error calibration device, characterized in that: The device comprises: An information acquisition module, configured to collect environmental status information of the sensors and calculate partition calibration parameters of the sensors based on the environmental status information; an interval determination module, configured to divide each of the sensors into a plurality of dynamic calibration intervals based on the partition calibration parameters, wherein the dynamic calibration intervals include a plurality of error parameters, and the difference between the error parameters can dynamically adjust the interval range of adjacent dynamic calibration intervals; an error calculation module, configured to calculate an error compensation value for each of the dynamic calibration intervals according to the error parameter; an interval adjustment module, configured to generate calibration signal data based on the error compensation value, and input the calibration signal data into each of the dynamic calibration intervals to obtain a target calibration interval; a data compensation module, configured to compensate the real-time weighing data of the sensor according to the target calibration interval; The error calculation module is further configured to calculate a temperature and humidity error compensation parameter based on the temperature compensation parameter and the humidity compensation parameter of each dynamic calibration interval; calculate a zero point correction parameter based on the posture offset value of each dynamic calibration interval; and perform multi-dimensional fusion of the temperature and humidity error compensation parameter and the zero point correction parameter to generate an error compensation value for each dynamic calibration interval; The interval adjustment module is also used to obtain the calibration position corresponding to the error compensation value, and the calibration position includes a key position and an edge position; if the calibration position is a key position, the error compensation value is corrected based on the first spatial coordinate of the error compensation value to generate first calibration information; if the calibration position is an edge position, the relative distance between the edge position and the key position closest to the edge position is calculated, and second calibration information is generated based on the relative distance; the first calibration information and / or the second calibration information are input into the corresponding dynamic calibration interval to obtain a target calibration interval.

7. A sensor error calibration device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the sensor error calibration method according to any one of claims 1 to 5.

8. A storage medium, characterized in that: The storage medium is a computer-readable storage medium. A computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the sensor error calibration method according to any one of claims 1 to 5 are implemented.

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