Correction method and correction system for weighing sensor, medium and product

Abstract

The invention relates to the technical field of measurement correction, in particular to a correction method and system for a weighing sensor, a medium and a product, and the method comprises the steps: obtaining equipment parameters of a to-be-corrected weighing sensor, and obtaining a calibration experiment data set based on the equipment parameters; identifying change types corresponding to a plurality of error data sets in the calibration experiment data set, and determining a fitting error corresponding relation corresponding to the weighing sensor to be corrected based on the change types and the calibration experiment data set; obtaining a measured temperature corresponding to the current weighing moment and a weighing original signal of the weighing sensor to be corrected, and determining an error signal corresponding to the weighing sensor to be corrected at the current weighing moment based on a corresponding relation between the measured temperature and a fitting error; and determining a weighing actual signal corresponding to the weighing sensor to be corrected at the current weighing moment based on the weighing original signal and the error signal, and determining an actual weighing result based on the weighing actual signal. According to the invention, the accuracy of the weighing result can be improved.

Description

Technical Field

[0001] This application relates to the field of measurement calibration technology, and in particular to a calibration method, calibration system, medium, and product for weighing sensors. Background Technology

[0002] As a precision device that converts mechanical signals into electrical signals, the core function of a load cell mainly relies on the deformation of its internal elastic sensing element and the signal output of the conversion element. When a weight is applied, the elastic sensing element undergoes a small deformation, which is then converted into an electrical signal by components such as strain gauges. However, ambient temperature can directly interfere with these two core components through multiple dimensions, including physical, material, and electrical aspects, ultimately leading to measurement errors.

[0003] In conventional technology, multiple fixed temperature points are pre-set, and the output signal under no-load conditions is recorded and stored in the sensor's control chip. In actual use, if the ambient temperature is close to a certain fixed temperature point, the zero drift value of that point is directly called to correct the output signal. However, the ambient temperature in actual use is likely to be between the fixed temperature points. Therefore, the nearest signal correction method can be used. This nearest signal correction method can lead to a large error between the final weighing result and the actual situation. Summary of the Invention

[0004] To improve the accuracy of weighing results, this application provides a calibration method, calibration system, medium, and product for weighing sensors.

[0005] Firstly, this application provides a calibration method for a weighing sensor, employing the following technical solution: A calibration method for a weighing sensor, comprising: Obtain the equipment parameters of the weighing sensor to be calibrated, and obtain a calibration experiment dataset based on the equipment parameters. The calibration experiment dataset contains multiple sets of error data. Identify the change types corresponding to multiple error data groups in the calibration experiment dataset, and determine the fitting error correspondence of the weighing sensor to be calibrated based on the change types and the calibration experiment dataset; The measurement temperature and the original weighing signal of the weighing sensor to be calibrated at the current weighing time are obtained, and the error signal of the weighing sensor to be calibrated at the current weighing time is determined based on the correspondence between the measurement temperature and the fitting error. Based on the original weighing signal and the error signal, the actual weighing signal of the weighing sensor to be calibrated at the current weighing time is determined, and the actual weighing result is determined based on the actual weighing signal.

[0006] By adopting the above technical solution, after analyzing the change types corresponding to the calibration experimental dataset, the fitting error correspondence between the measured temperature and the error signal is selected in a targeted manner, instead of using a fixed fitting error correspondence. This facilitates the improvement of the fit between the misfitting error correspondence and the weighing sensor to be calibrated, thereby improving the accuracy of determining the error signal. Finally, by separating the error signal from the original weighing signal, the actual weighing signal retains only the effective weight signal, eliminating the weighing error caused by the measurement temperature in the environment, thus facilitating the improvement of the accuracy of the weighing results.

[0007] In one possible implementation, determining the fitting error correspondence of the weighing sensor to be calibrated based on the type of variation and the calibration experimental dataset includes: When the change type is linear, the calibration experimental dataset is fitted based on the first preset fitting formula to obtain the first fitting error correspondence. When the change type is nonlinear, the calibration experimental dataset is fitted based on the second preset fitting formula to obtain the second fitting error correspondence.

[0008] By adopting the above technical solution, a differentiated fitting strategy is used, which adapts the first preset fitting formula to linear changes and the second preset fitting formula to nonlinear changes, instead of using a single fixed formula to uniformly fit all error data sets. This facilitates the improvement of the fit between the fitting process and the calibration experimental dataset corresponding to the weighing sensor to be calibrated, thereby improving the accuracy when determining the error signal.

[0009] In one possible implementation, when the device parameters of the load cell to be calibrated include preset parameter features, the method further includes: Obtain multiple ambient temperature measurements and measurement locations corresponding to the current weighing time, and generate a temperature distribution map based on the ambient temperature measurements corresponding to each measurement location; The temperature distribution map is divided into multiple temperature ranges based on a preset temperature threshold. The temperature difference between the ambient temperature measurements of any two measurement locations within each temperature range is lower than the preset temperature threshold. Based on the device parameters of the weighing sensor to be calibrated, the sensitive elastic positions in the weighing sensor to be calibrated are determined, and each sensitive elastic position is added to a temperature distribution map containing multiple temperature ranges to obtain a comprehensive distribution map. Identify the target temperature range corresponding to each sensitive elastic position from the comprehensive distribution map, and determine the measurement temperature of the weighing sensor to be calibrated at the current weighing moment based on the average temperature value corresponding to the target temperature range of each sensitive elastic position and the temperature weight corresponding to each target temperature range.

[0010] By adopting the above technical solution, a temperature distribution map is generated by measuring the ambient temperature values ​​obtained from multiple measurement locations to divide the range. The sensitive elastic position is superimposed on each temperature range, instead of directly using the average temperature of a single measurement location or the overall average temperature as the overall measurement temperature of the load cell to be calibrated. The comprehensive distribution map makes it easier to intuitively display the spatial non-uniformity of ambient temperature at each measurement location in the load cell to be calibrated, avoiding the representative bias caused by using only a single point temperature to replace the overall temperature.

[0011] In one possible implementation, the method further includes: Obtain the elastic component parameters corresponding to each sensitive elastic position, and determine whether there is an associated elastic group among all sensitive elastic positions based on the elastic component parameters corresponding to each sensitive elastic position. The associated elastic group contains two associated sensitive elastic positions, and the correlation influence between the elastic component parameters corresponding to the two associated sensitive elastic positions is higher than the preset correlation influence threshold. If so, then based on the elastic element parameters of each associated sensitive elastic position within the associated elastic group, the influence type corresponding to each associated sensitive elastic position is determined, and the influence type includes active influence and passive influence; Based on the average temperature value corresponding to the target temperature range where the actively affected sensitive elastic position is located and the degree of correlation between it and the passively affected sensitive elastic position, the temperature correction value corresponding to the passively affected sensitive elastic position is determined. The actively affected sensitive elastic position is the associated sensitive elastic position with an active influence type, and the passively affected sensitive elastic position is the associated sensitive elastic position with a passive influence type. Based on the temperature correction value corresponding to the passively affected sensitive elastic position, the average temperature value corresponding to the target temperature range where the passively affected sensitive elastic position is located is optimized.

[0012] By adopting the above technical solution, since the sensitive elastic position in the load cell to be calibrated is the core component that bears the load deformation, the elastic parameters of the elastic component determine the mutual influence between the positions. By screening out strongly correlated elastic groups through the correlation influence degree threshold, it is easy to avoid temperature measurement deviation caused by omitting this coupling relationship.

[0013] In one possible implementation, the method further includes: The measured temperature change rate of each sensitive elastic position is obtained within a preset observation period, and the sensitive elastic positions with a measured temperature change rate higher than a preset change rate threshold are identified as the observed elastic positions. Identify the associated observation positions corresponding to the observed elastic position, and determine the observation range based on the observed elastic position and each associated observation position. The associated observation positions are associated sensitive elastic positions that have an associated influence relationship with the observed elastic position. Based on the observation area and the corresponding average rate of change of the observation range, the joint signal correction value corresponding to the observation range is determined. The average rate of change of the observation range is determined by the observation elastic position constituting the observation range and the measured temperature change rate corresponding to each associated observation position. The actual weighing signal is corrected based on the combined signal correction value.

[0014] By adopting the above technical solution, the dynamic temperature change characteristics of each sensitive elastic position are captured, which makes it easy to intuitively reflect the fluctuation trend of the ambient temperature faced by each sensitive elastic position over time. By determining the sensitive elastic position where the measured temperature change rate is higher than the preset change rate threshold as the observation elastic position, it is easy to avoid the problem of static correction lagging behind the actual error change due to ignoring the dynamic temperature change. After detecting the heat accumulation effect, the joint signal correction value is determined according to the heat accumulation situation, and the actual weighing signal is corrected for a second time, which makes it easier to further improve the accuracy of the weighing results.

[0015] In one possible implementation, when the observed area is less than a preset area threshold, the method further includes: The sensitive elastic range is determined based on the equipment parameters of the weighing sensor to be calibrated, and the sensitive elastic area corresponding to the sensitive elastic range is identified. Based on the ratio of the observed area to the sensitive elastic area, the area ratio corresponding to the observed range is determined, and a first weight is determined based on the area ratio. The second weight is determined based on the mean rate of change corresponding to the observed range; The joint signal correction value is optimized based on the first weight and the second weight.

[0016] By adopting the above technical solution, the area ratio is determined by the ratio of the observed area to the sensitive elastic area, which makes it easier to intuitively reflect the coverage of the observed range where heat accumulation may occur to the overall sensitive elastic range. Finally, the joint signal correction value is jointly optimized based on the coverage and the changes in heat accumulation within the observed range, which helps to improve the accuracy of the joint signal correction value, thereby improving the accuracy of the weighing results.

[0017] Secondly, this application provides a calibration system, which adopts the following technical solution: A calibration system comprising: At least one processor; Memory; At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: perform the above-described calibration method for the weighing sensor.

[0018] Thirdly, this application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium includes: a computer program stored thereon that can be loaded by a processor and executed by the above-described calibration method for a weighing sensor.

[0019] Fourthly, this application provides a computer program product, which adopts the following technical solution: A computer program product includes a computer program that, when executed by a processor, implements the above-described calibration method for a weighing sensor.

[0020] In summary, this application includes at least one of the following beneficial technical effects: After analyzing the types of changes corresponding to the calibration experimental dataset, a targeted fitting error correspondence between the measured temperature and the error signal is selected, instead of using a fixed fitting error correspondence. This facilitates the improvement of the fit between the misfitting error correspondence and the weighing sensor to be calibrated, thereby improving the accuracy of determining the error signal. Finally, by separating the error signal from the original weighing signal, the actual weighing signal retains only the effective weight signal, eliminating the weighing error caused by the measurement temperature in the environment, thus facilitating the improvement of the accuracy of the weighing results.

[0021] A temperature distribution map is generated by measuring the ambient temperature values ​​at multiple measurement locations to divide the range. Sensitive elastic positions are superimposed on each temperature range instead of directly using the average temperature of a single measurement location or the overall average temperature as the overall measurement temperature of the load cell to be calibrated. The comprehensive distribution map makes it easier to intuitively show the spatial non-uniformity of ambient temperature at each measurement location of the load cell to be calibrated, avoiding the representative bias caused by using only a single point temperature to replace the overall temperature. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating a calibration method for a weighing sensor according to an embodiment of this application. Figure 2 This is a schematic diagram of a process for determining the measured temperature in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a correction system according to an embodiment of this application. Detailed Implementation

[0023] The following is in conjunction with the appendix Figures 1 to 3This application will be described in further detail.

[0024] After reading this specification, those skilled in the art may make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

[0025] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] It should be noted that, in the optional embodiments of this application, the data related to object information, when applied to specific products or technologies, requires the permission or consent of the object. Furthermore, the collection, use, and processing of this data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. In other words, if the embodiments of this application involve data related to an object, it must be obtained with the object's authorization and consent, the authorization and consent of relevant departments, and in accordance with the relevant laws, regulations, and standards of the country and region. If the embodiments involve personal information, the acquisition of all personal information requires the individual's consent. If sensitive information is involved, the separate consent of the information subject is required. The embodiments also need to be implemented with the object's authorization and consent.

[0027] Specifically, this application provides a calibration method for a weighing sensor, executed by a calibration system. This calibration system can be a server or a terminal device. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet, laptop, desktop computer, etc., but is not limited to these. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, and this application does not impose any limitations on this.

[0028] refer to Figure 1 , Figure 1 This is a flowchart illustrating a calibration method for a weighing sensor according to an embodiment of this application. The method includes steps S110-S140, wherein: Step S110: Obtain the equipment parameters of the weighing sensor to be calibrated, and obtain the calibration experiment dataset based on the equipment parameters. The calibration experiment dataset contains multiple sets of error data.

[0029] Specifically, the load cell to be calibrated is one that requires weighing calibration, and whose weighing accuracy is highly susceptible to environmental temperature fluctuations. The device parameters of the load cell to be calibrated include, but are not limited to, structural parameters, material parameters, performance parameters, and usage limitations. These are not specifically limited in this embodiment. The device parameters can be uploaded to the calibration system in advance by relevant personnel; the specific method of acquisition is not specifically limited in this embodiment. The calibration experimental dataset corresponds to the load cell to be calibrated and includes multiple error data sets (T...). 标 , ΔF 标 ), where T 标 This can be considered as the calibration experimental temperature, ΔF 标 This can be considered as a calibration experiment error signal. The calibration experiment dataset can be obtained based on the equipment parameters of the weighing sensor to be calibrated, through controlled variables and experimental measurements. For example, T needs to be determined based on the usage limitation parameters of the weighing sensor to be calibrated. 标 The range of values, etc., and the number of error data sets in the calibration experimental dataset are not specifically limited in this embodiment of the application, and can be set by relevant technical personnel according to actual calibration needs.

[0030] Step S120: Identify the change types corresponding to multiple error data groups in the calibration experimental dataset, and determine the corresponding fitting error relationship of the weighing sensor to be calibrated based on the change types and the calibration experimental dataset.

[0031] Specifically, by analyzing multiple error data sets contained in the calibration experimental dataset, it is easier to determine ΔF. 标 Follow T 标 To determine whether the change is linear, one can first follow the T... 标 Sort multiple sets of error data in ascending order to obtain ordered error data sets, and then categorize them according to the horizontal axis T. 标 The vertical axis is ΔF 标 Plot the ordered error data sets as a scatter plot, and observe the changing trends of multiple error data sets based on the scatter plot. If multiple ΔF 标 With T 标 The changes in ΔF are approximately distributed around a straight line, i.e., ΔF 标 Follow T 标 If the increase or decrease is uniform, without obvious bending or abrupt changes, then the type of change corresponding to the calibration experimental dataset can be determined to be linear; if multiple ΔF 标 With T 标 The change in ΔF is distributed as a curve, i.e., ΔF 标 Follow T 标If the rate of change is uneven, or exhibits inflection points, fluctuations, or other nonlinear characteristics, the change type corresponding to the calibration experimental dataset can be determined to be nonlinear. Different change types correspond to different fitting methods. To improve the fit between the fitting process and the calibration experimental dataset corresponding to the weighing sensor to be calibrated, this application embodiment, when determining the corresponding relationship of the fitting error for the weighing sensor to be calibrated based on the change type and the calibration experimental dataset, may specifically include: When the change type is linear, the calibration experimental dataset is fitted based on the first preset fitting formula to obtain the first fitting error correspondence; when the change type is nonlinear, the calibration experimental dataset is fitted based on the second preset fitting formula to obtain the second fitting error correspondence.

[0032] Specifically, by selecting corresponding fitting formulas for different types of changes, it is easier to improve the accuracy of the fitting formula in matching the true changing patterns of the error, and avoid insufficient fitting accuracy caused by formula mismatch. Different types of changes correspond to different preset fitting formulas.

[0033] The first preset fitting formula is ΔF=k×T+b, where k and b are based on the least squares method from multiple error data sets (T). 标 , ΔF 标 The first fitting error correspondence is obtained by fitting k and b to the first preset fitting formula. The slope k can be understood as the change in error signal corresponding to a unit temperature change, directly reflecting the linear rate of change of the error signal with the measured temperature. For example, k = 0.005mV / ℃ means that for every 1℃ increase in temperature, the error signal ΔF will increase by 0.005mV. The intercept b can be understood as the basic error signal at temperature T = 0℃, that is, the fixed error of the weighing sensor itself when the measured temperature is the reference point. For example, b = 0.02mV means that at 0℃, even without additional temperature interference, the error signal is still 0.02mV. Once k and b are determined through fitting, the first preset fitting formula ΔF = k × T + b is transformed from a preset form to a deterministic relationship that can directly calculate the error ΔF corresponding to any temperature T, that is, the first fitting error correspondence.

[0034] The second preset fitting formula is ΔF = a0 + a1T + a2T² + ... + a n Tⁿ, where n is a positive integer representing the order of the polynomial. The specific order needs to be determined based on the nonlinear characteristics of the calibration experimental dataset, where a0, a1, a2, ..., a n Based on the nonlinear least squares method or Gauss-Newton method, from multiple error data sets (T 标 , ΔF 标The polynomial coefficients are obtained by fitting n and the polynomial coefficients into a second preset fitting formula, and the second fitting error correspondence can be obtained. When n and the polynomial coefficients a0, a1, a2, ..., a... n After being determined through fitting, the second preset fitting formula is ΔF = a0 + a1T + a2T² + … + a n Tⁿ is transformed from a preset form to be assigned a value into a deterministic relationship that can directly calculate the error ΔF corresponding to any temperature T, i.e., the second fitting error correspondence.

[0035] By employing a differentiated fitting strategy—adapting a first preset fitting formula to linear changes and a second preset fitting formula to nonlinear changes—instead of uniformly fitting all error data sets with a single fixed formula, it is easier to improve the fit between the fitting process and the calibration experimental dataset corresponding to the weighing sensor to be calibrated, thereby improving the accuracy when determining the error signal.

[0036] Step S130: Obtain the measured temperature and the original weighing signal of the weighing sensor to be calibrated at the current weighing time, and determine the error signal of the weighing sensor to be calibrated at the current weighing time based on the correspondence between the measured temperature and the fitting error.

[0037] Specifically, after determining the fitting error correspondence of the load cell to be calibrated, the measured temperature and the original weighing signal at the current weighing moment can be collected. This can be achieved by using a temperature sensor installed near the load cell or at a critical circuit location to collect the measured temperature at the current weighing moment, and simultaneously collecting the original weighing signal. The temperature sensor can be a PT100, NTC thermistor, etc., and the specific temperature sensor is not specifically limited in this embodiment. The original weighing signal refers to the original output signal of the load cell after it has been subjected to a load, without any error correction operation; its essence is the direct conversion result of load-mechanical deformation-electrical signal. By directly importing the acquired measured temperature into the determined fitting error correspondence, the error signal of the load cell at the current weighing moment can be obtained. For example, if the fitting error correspondence is ΔF = k × T + b, and the measured temperature acquired at the current weighing moment is assigned a value to T in the fitting error correspondence, the error signal ΔF of the calibrated load cell at the current weighing moment can be obtained.

[0038] Step S140: Based on the original weighing signal and the error signal, determine the actual weighing signal of the weighing sensor to be calibrated at the current weighing time, and determine the actual weighing result based on the actual weighing signal.

[0039] Specifically, for ease of description, the original weighing signal can be defined as F_raw, the actual weighing signal as F_compensated, and the error signal ΔF determined. Then, the original weighing signal F_raw and the error signal ΔF can be imported into the calculation formula for the actual weighing signal to obtain the corrected actual weighing signal. The calculation formula for the actual weighing signal is: F_compensated = F_raw - ΔF. The actual weighing signal F_compensated is an electrical signal, which can be converted into a physical weight value through a preset conversion relationship. This preset conversion relationship is the correspondence between the electrical signal and the physical weight value; its specific details are not specifically limited in this embodiment.

[0040] In this embodiment of the application, after analyzing the change type corresponding to the calibration experimental dataset, a targeted fitting error correspondence between the measured temperature and the error signal is selected instead of a fixed fitting error correspondence. This facilitates the improvement of the fit between the misfitting error correspondence and the weighing sensor to be calibrated, thereby improving the accuracy when determining the error signal. Finally, by separating the error signal from the original weighing signal, the actual weighing signal retains only the effective weight signal, eliminating the weighing error caused by the measurement temperature in the environment, thereby facilitating the improvement of the accuracy of the weighing results.

[0041] Furthermore, to avoid the representativeness deviation caused by using only a single point temperature to replace the overall temperature, when the equipment parameters of the load cell to be calibrated include preset parameter features, the method provided in this application embodiment further includes steps S210-S240, such as... Figure 2 As shown, where: Step S210: Obtain multiple ambient temperature measurements and measurement locations corresponding to the current weighing time, and generate a temperature distribution map based on the ambient temperature measurements corresponding to each measurement location.

[0042] Specifically, a preset feature recognition algorithm can be used to identify whether the equipment parameters of the load cell to be calibrated contain preset parameter features. The specific preset feature recognition algorithm is not specifically limited in this application embodiment. The preset parameter features can be a rated load higher than a preset load threshold, a body size higher than a preset size threshold, etc. The preset load threshold and preset size threshold are not specifically limited in this application embodiment. When the equipment parameters of the load cell to be calibrated contain preset parameter features, it indicates that the load cell to be calibrated is a large load cell. There may be a problem of temperature measurement deviation due to inaccurate measurement position. For example, large load cells are large in size and easily affected by local environment, such as direct sunlight and local heat sources. Temperature measurement points need to be designed around the area with risk of temperature difference and close to the core sensitive components to avoid missing key positions and causing temperature field distortion.

[0043] When the load cell to be calibrated is determined to be a large load cell, the corresponding measurement temperature can be determined by collecting ambient temperature measurements from multiple measurement locations and creating a temperature distribution map, instead of using a single-point acquisition method. The number of measurement locations is not limited in this embodiment, as long as the measurement area formed by connecting all measurement locations can cover the load cell to be calibrated. A preset data visualization tool can be used to import multiple ambient temperature measurements collected at the current weighing time and their corresponding measurement locations into a preset distribution map to obtain the temperature distribution map. This preset data visualization tool can be Matlab's heatmap function, Python's Seaborn library, Excel's conditional formatting, etc. The preset temperature distribution map is a planar coordinate system with the measurement location on the horizontal axis and the ambient temperature measurement value on the vertical axis. The method of generating the temperature distribution map based on the ambient temperature measurements corresponding to multiple measurement locations is not specifically limited in this embodiment.

[0044] Step S220: Divide the temperature distribution map into multiple temperature ranges based on a preset temperature threshold. The temperature difference between any two measurement locations within each temperature range is lower than the preset temperature threshold.

[0045] Specifically, the temperature distribution map can be divided based on a preset temperature threshold. Each temperature range contains ambient temperature measurements corresponding to at least two measurement locations, and the temperature difference between at least two ambient temperature measurements within the same temperature range is lower than the preset temperature threshold. In this embodiment, the specific preset temperature threshold is not specifically limited.

[0046] Step S230: Based on the equipment parameters of the load cell to be calibrated, determine the sensitive elastic position in the load cell to be calibrated, and add each sensitive elastic position to the temperature distribution map containing multiple temperature ranges to obtain a comprehensive distribution map.

[0047] Specifically, the sensitive elastic location is the core area in a large weighing sensor that directly bears the load and is most sensitive to temperature changes. Its location requires extracting key information such as structure, materials, and strain gauge layout from the equipment parameters. Based on the determined coordinates of the sensitive elastic location, it is accurately superimposed onto a temperature distribution map. The key is to ensure that the coordinate system of the sensitive elastic location coordinates is consistent with the coordinate system of the temperature distribution map. After superposition, the temperature range of each sensitive elastic location can be clearly identified. To visually view the temperature range within which each sensitive elastic position falls, the temperature ranges can be integrated into the temperature distribution map through boundary drawing and information annotation. The specific implementation process is as follows: First, determine the average temperature value corresponding to each temperature range based on at least two ambient temperature measurements within each temperature range. Then, determine the drawing boundary color corresponding to each temperature range based on a preset drawing color mapping relationship. Finally, draw the boundary of the temperature distribution map based on at least two measurement positions within each temperature range and the drawing boundary color, so as to integrate each temperature range into the temperature distribution map. Adding each sensitive elastic position to the temperature distribution map of the melted multiple temperature ranges yields a comprehensive distribution map. The preset drawing color mapping relationship is the correspondence between the average temperature value and the drawing boundary color; the specific details are not specifically limited in this embodiment.

[0048] Step S240: Identify the target temperature range corresponding to each sensitive elastic position from the comprehensive distribution map. Based on the average temperature value corresponding to the target temperature range of each sensitive elastic position and the temperature weight corresponding to each target temperature range, determine the measurement temperature of the weighing sensor to be calibrated at the current weighing moment.

[0049] Specifically, different average temperature values ​​correspond to different temperature weights; that is, different temperature ranges correspond to different temperature weights. The temperature weights for different average temperature values ​​can be determined based on a preset weight mapping relationship. This preset weight mapping relationship is the correspondence between average temperature values ​​and temperature weights; the higher the average temperature value, the higher the corresponding temperature weight. The specific content of the preset weight mapping relationship is not specifically limited in this embodiment. After determining the target temperature range for each sensitive elastic position, the temperature weight for the corresponding target temperature range is obtained. Finally, the measurement temperature of the weighing sensor to be calibrated at the current weighing moment is determined through a weighted calculation.

[0050] In this embodiment of the application, a temperature distribution map is generated by measuring the ambient temperature values ​​obtained from multiple measurement locations to divide the range, and the sensitive elastic position is superimposed on each temperature range, instead of directly using the average temperature of a single measurement location or the overall average temperature as the overall measurement temperature of the load cell to be calibrated. The comprehensive distribution map makes it easier to intuitively display the spatial non-uniformity of ambient temperature faced by each measurement location in the load cell to be calibrated, and avoids the representative deviation caused by using only a single point temperature to replace the overall temperature.

[0051] Furthermore, since the sensitive elastic position within the load cell to be calibrated is the core component bearing the load deformation, and its elastic parameters determine the mutual influence between positions, the method provided in this application embodiment further includes: Obtain the elastic component parameters corresponding to each sensitive elastic location. Based on the elastic component parameters corresponding to each sensitive elastic location, determine whether all sensitive elastic locations contain associated elastic groups. If an associated elastic group contains two associated sensitive elastic locations, and the correlation influence between the elastic component parameters corresponding to the two associated sensitive elastic locations is higher than a preset correlation influence threshold, then based on the elastic component parameters of each associated sensitive elastic location within the associated elastic group, determine the influence type corresponding to each associated sensitive elastic location. The influence type includes active influence and passive influence. Based on the average temperature value corresponding to the target temperature range of the actively affected sensitive elastic location and the correlation influence with the passively affected sensitive elastic location, determine the temperature correction value corresponding to the passively affected sensitive elastic location. The actively affected sensitive elastic location is the associated sensitive elastic location with an active influence type, and the passively affected sensitive elastic location is the associated sensitive elastic location with a passive influence type. Based on the temperature correction value corresponding to the passively affected sensitive elastic location, optimize the average temperature value corresponding to the target temperature range of the passively affected sensitive elastic location.

[0052] Specifically, the elastic element parameters are core data reflecting the mechanical properties and interrelationships of the corresponding sensitive elastic positions. These parameters can be obtained from the equipment parameters of the load cell to be calibrated. The elastic element parameters include, but are not limited to, elastic element material, elastic element size, elastic element connection method, coefficient of thermal expansion, stress-strain coefficient, and positional relationship parameters. Specific details are not limited in this embodiment. Parameter matching analysis of the elastic element parameters at each sensitive elastic position can determine whether all sensitive elastic positions contain associated elastic groups. An associated elastic group contains two associated sensitive elastic positions, and the correlation influence between the elastic element parameters corresponding to the two associated sensitive elastic positions is higher than a preset correlation influence threshold. The higher the parameter matching degree obtained from parameter matching of the elastic element parameters at the sensitive elastic position, the higher the corresponding correlation influence. The specific preset correlation influence threshold is not limited in this embodiment.

[0053] Based on the elastic component parameters of each associated sensitive elastic location within the associated elastic group, the connection method of the elastic components corresponding to two associated sensitive elastic locations can be determined. In the associated elastic group, the location with higher stiffness and higher load-bearing ratio of the elastic component with active influence will significantly affect the elastic component corresponding to another associated sensitive elastic location within the associated elastic group due to the deformation caused by temperature changes. Conversely, the location with lower stiffness and secondary load-bearing ratio of the elastic component with passive influence within the associated elastic group is mainly affected by the elastic component with active influence. Therefore, when determining the average temperature value corresponding to the target temperature range of a passively affected sensitive elastic position, in addition to considering the target temperature range of the passively affected sensitive elastic position, it is also necessary to consider the influence of elastic elements actively affecting the sensitive elastic position within the same associated elastic group. For example, if the associated elastic group includes elastic element 'a' corresponding to an actively affected sensitive elastic position and elastic element 'b' corresponding to a passively affected sensitive elastic position, and their average temperature values ​​are determined to be 38℃ and 36℃ respectively based on their respective target temperature ranges, then the average temperature value corresponding to elastic element 'b' needs to be corrected based on the degree of association between 'a' and 'b'. The higher the degree of association, the higher the corresponding temperature correction value. The temperature correction value corresponding to the degree of association can be determined based on a preset correction mapping relationship, which is the correspondence between the degree of association and the temperature correction value. For example, after determining the temperature correction value of elastic element 'b' to be 2℃ based on the preset correction mapping relationship, the original average temperature value needs to be corrected based on the temperature correction value, i.e., 36 + 2 = 38℃.

[0054] In the embodiments of this application, since the sensitive elastic position in the load cell to be calibrated is the core component of the load cell to bear the load deformation, the elastic parameters of the elastic component determine the mutual influence between the positions. By filtering out strongly correlated elastic groups through the correlation influence degree threshold, it is easy to avoid temperature measurement deviation caused by omitting such coupling relationship.

[0055] Furthermore, to further improve the accuracy of the weighing results, the method provided in this application embodiment also includes: The system acquires the measured temperature change rate of each sensitive elastic position within a preset observation period and identifies the sensitive elastic positions with a measured temperature change rate higher than a preset threshold as the observed elastic positions. It then identifies associated observation positions corresponding to the observed elastic positions and determines the observation range based on the observed elastic positions and each associated observation position. Associated observation positions are the associated sensitive elastic positions that have a correlation with the observed elastic positions. Based on the observation area and the corresponding average change rate of the observation range, a joint signal correction value is determined for the observation range. The average change rate of the observation range is determined by the measured temperature change rates of the observed elastic positions constituting the observation range and each associated observation position. Finally, the system corrects the actual signal based on the joint signal correction value.

[0056] Specifically, the preset observation time period is a period of time prior to the current weighing moment. The specific duration is not specifically limited in this embodiment. Based on the historical measured temperature of each sensitive elastic position at each observation moment within the preset observation time period, the rate of change of the measured temperature at each sensitive elastic position within the preset observation time period can be easily determined. The specific implementation process is not detailed here. Based on a preset rate of change threshold, sensitive elastic positions with a measured temperature change rate higher than the preset rate of change threshold can be identified as observation elastic positions. The specific preset rate of change threshold is not specifically limited in this embodiment. The area where the observation elastic position is located is an area where heat accumulation may occur.

[0057] After determining the observation elastic position, the associated observation position can be identified by acquiring the associated elastic group containing the observation elastic position. The associated observation position is another associated sensitive elastic position in the same associated elastic group as the observation elastic position. The observation range can be determined by connecting each observation elastic position and each associated observation position, and the observation area corresponding to the observation range can be determined by calculating the coordinate values. After averaging the measured temperature change rates corresponding to the observation elastic position and each associated observation position, the average change rate corresponding to the observation range can be obtained. Finally, the joint signal correction value corresponding to the observation range is determined based on the preset signal correction mapping relationship. A second correction is then performed on the actual signal based on the joint signal correction value. The preset signal correction mapping relationship is the correspondence between the parameter combination of the observation area and the average change rate and the joint signal correction value. The specific details are not specifically limited in this embodiment. By capturing the dynamic temperature change characteristics of each sensitive elastic position, it is easy to intuitively reflect the fluctuation trend of the ambient temperature faced by each sensitive elastic position over time. By determining the sensitive elastic position where the measured temperature change rate is higher than the preset change rate threshold as the observation elastic position, it is easy to avoid the problem of static correction lagging behind the actual error change caused by ignoring dynamic temperature changes.

[0058] Furthermore, when the observed area is smaller than a preset area threshold, the method provided in this application embodiment further includes: The sensitive elastic range is determined based on the equipment parameters of the weighing sensor to be calibrated, and the sensitive elastic area corresponding to the sensitive elastic range is identified. The area ratio corresponding to the observation range is determined according to the ratio between the observed area and the sensitive elastic area, and the first weight is determined according to the area ratio. The second weight is determined according to the average rate of change corresponding to the observation range. The joint signal correction value is optimized based on the first weight and the second weight.

[0059] Specifically, the preset area threshold can be determined by relevant staff based on historical experimental data and then uploaded to the calibration system. When the observed area is less than the preset area threshold, it indicates that the coverage of the observed range to the sensitive elastic range is low. The sensitive elastic range is a two-dimensional geometric range composed of all sensitive elastic positions. The sensitive elastic area corresponding to the sensitive elastic range can be calculated based on the coordinates of all sensitive elastic positions.

[0060] The observed area of ​​the observation range is compared with the sensitive elastic area of ​​the sensitive elastic range to determine the area ratio between the two. Then, based on a first preset weight mapping relationship, a first weight corresponding to the area ratio is determined. The first preset weight mapping relationship is the correspondence between the area ratio and the first weight; the higher the area ratio, the higher the first weight. A second weight corresponding to the mean rate of change is determined according to a second preset weight mapping relationship. The second preset weight mapping relationship is the correspondence between the mean rate of change and the second weight; the higher the mean rate of change, the higher the second weight. The sum of the first weight and the second weight is calculated to obtain the total weight. Based on a preset optimized mapping relationship, the final correction value corresponding to the total weight is determined. Based on the final correction value, the joint signal correction value is optimized. The preset optimized mapping relationship is the correspondence between the total weight and the final correction value. Specific details are not limited in this embodiment.

[0061] In this embodiment of the application, the area ratio is determined by the ratio of the observed area to the sensitive elastic area, which makes it easier to intuitively reflect the coverage of the observed range where heat accumulation may occur to the overall sensitive elastic range. Finally, the joint signal correction value is jointly optimized based on the coverage and the changes in heat accumulation within the observed range, which helps to improve the accuracy of the joint signal correction value, thereby improving the accuracy of the weighing results.

[0062] This application provides a calibration system, such as... Figure 3 As shown, Figure 3 The calibration system 300 shown includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the calibration system 300 may also include a transceiver 304. It should be noted that in practical applications, the transceiver 304 is not limited to one, and the structure of this calibration system 300 does not constitute a limitation on the embodiments of this application.

[0063] Processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0064] Bus 302 may include a pathway for transmitting information between the aforementioned components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 302 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The symbol is represented by only one line, but this does not mean that there is only one bus or one type of bus.

[0065] The memory 303 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0066] The memory 303 is used to store application code that executes the solution of this application, and its execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the foregoing method embodiments.

[0067] The calibration system includes, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Tablet PCs), PMPs (Portable Multimedia Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. It can also be used for servers, etc. Figure 3 The calibration system shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0068] This application provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments.

[0069] This application provides a computer program product including a computer program that, when executed by a processor, implements the methods described in any of the above embodiments.

[0070] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0071] The above description is only a partial embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A calibration method for a weighing sensor, characterized in that, include: Obtain the equipment parameters of the weighing sensor to be calibrated, and obtain a calibration experiment dataset based on the equipment parameters. The calibration experiment dataset contains multiple sets of error data. Identify the change types corresponding to multiple error data groups in the calibration experiment dataset, and determine the fitting error correspondence of the weighing sensor to be calibrated based on the change types and the calibration experiment dataset; The measurement temperature and the original weighing signal of the weighing sensor to be calibrated at the current weighing time are obtained, and the error signal of the weighing sensor to be calibrated at the current weighing time is determined based on the correspondence between the measurement temperature and the fitting error. Based on the original weighing signal and the error signal, the actual weighing signal of the weighing sensor to be calibrated at the current weighing time is determined, and the actual weighing result is determined based on the actual weighing signal.

2. The calibration method for a weighing sensor according to claim 1, characterized in that, The step of determining the fitting error correspondence of the weighing sensor to be calibrated based on the change type and the calibration experimental dataset includes: When the change type is linear, the calibration experimental dataset is fitted based on the first preset fitting formula to obtain the first fitting error correspondence. When the change type is nonlinear, the calibration experimental dataset is fitted based on the second preset fitting formula to obtain the second fitting error correspondence.

3. The calibration method for a weighing sensor according to claim 1, characterized in that, When the device parameters of the load cell to be calibrated include preset parameter features, it also includes: Obtain multiple ambient temperature measurements and measurement locations corresponding to the current weighing time, and generate a temperature distribution map based on the ambient temperature measurements corresponding to each measurement location; The temperature distribution map is divided into multiple temperature ranges based on a preset temperature threshold. The temperature difference between the ambient temperature measurements of any two measurement locations within each temperature range is lower than the preset temperature threshold. Based on the device parameters of the weighing sensor to be calibrated, the sensitive elastic positions in the weighing sensor to be calibrated are determined, and each sensitive elastic position is added to a temperature distribution map containing multiple temperature ranges to obtain a comprehensive distribution map. Identify the target temperature range corresponding to each sensitive elastic position from the comprehensive distribution map, and determine the measurement temperature of the weighing sensor to be calibrated at the current weighing moment based on the average temperature value corresponding to the target temperature range of each sensitive elastic position and the temperature weight corresponding to each target temperature range.

4. The calibration method for a weighing sensor according to claim 3, characterized in that, Also includes: Obtain the elastic component parameters corresponding to each sensitive elastic position, and determine whether there is an associated elastic group among all sensitive elastic positions based on the elastic component parameters corresponding to each sensitive elastic position. The associated elastic group contains two associated sensitive elastic positions, and the correlation influence between the elastic component parameters corresponding to the two associated sensitive elastic positions is higher than the preset correlation influence threshold. If so, then based on the elastic element parameters of each associated sensitive elastic position within the associated elastic group, the influence type corresponding to each associated sensitive elastic position is determined, and the influence type includes active influence and passive influence; Based on the average temperature value corresponding to the target temperature range where the actively affected sensitive elastic position is located and the degree of correlation between it and the passively affected sensitive elastic position, the temperature correction value corresponding to the passively affected sensitive elastic position is determined. The actively affected sensitive elastic position is the associated sensitive elastic position with an active influence type, and the passively affected sensitive elastic position is the associated sensitive elastic position with a passive influence type. Based on the temperature correction value corresponding to the passively affected sensitive elastic position, the average temperature value corresponding to the target temperature range where the passively affected sensitive elastic position is located is optimized.

5. A calibration method for a weighing sensor according to claim 4, characterized in that, Also includes: The measured temperature change rate of each sensitive elastic position is obtained within a preset observation period, and the sensitive elastic positions with a measured temperature change rate higher than a preset change rate threshold are identified as the observed elastic positions. Identify the associated observation positions corresponding to the observed elastic position, and determine the observation range based on the observed elastic position and each associated observation position. The associated observation positions are associated sensitive elastic positions that have an associated influence relationship with the observed elastic position. Based on the observation area and the corresponding average rate of change of the observation range, the joint signal correction value corresponding to the observation range is determined. The average rate of change of the observation range is determined by the observation elastic position constituting the observation range and the measured temperature change rate corresponding to each associated observation position. The actual weighing signal is corrected based on the combined signal correction value.

6. A calibration method for a weighing sensor according to claim 5, characterized in that, When the observed area is less than a preset area threshold, the following is also included: The sensitive elastic range is determined based on the equipment parameters of the weighing sensor to be calibrated, and the sensitive elastic area corresponding to the sensitive elastic range is identified. Based on the ratio of the observed area to the sensitive elastic area, the area ratio corresponding to the observed range is determined, and a first weight is determined based on the area ratio. The second weight is determined based on the mean rate of change corresponding to the observed range; The joint signal correction value is optimized based on the first weight and the second weight.

7. A calibration system, characterized in that, The calibration system includes: At least one processor; Memory; At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: perform a calibration method for a weighing sensor according to any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, include: The computer program is stored and can be loaded by a processor and executed as described in any one of claims 1-6 for the calibration of a weighing sensor.

9. A computer program product, characterized in that, It includes a computer program, which, when executed by a processor, implements the steps of a calibration method for a weighing sensor according to any one of claims 1-6.

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