Data processing method and system of intelligent electronic scale and storage medium

Abstract

The application relates to the technical field of data processing, and discloses a data processing method and system of an intelligent electronic scale and a storage medium. The method comprises the following steps: acquiring a current environmental parameter data set, extracting a temperature measurement value, determining a temperature interval, and calculating relative position information; calling humidity correction parameters, performing weighted processing to generate a boundary connection parameter set; inputting the boundary connection parameter set into a difference calculation unit to obtain a numerical difference and a slope difference of a connection point, and integrating to generate connection difference data, performing smoothing optimization processing, and generating an adjustment parameter set; constructing a transition correction function to calculate a continuous correction value across a full temperature range; acquiring an original weighing measurement signal, superimposing and operating the continuous correction value and the original weighing measurement signal, obtaining an adjusted reading, and after verification, starting the acquisition process of the current environmental parameter data set to form a closed loop processing. The application improves the weighing precision and stability of the intelligent electronic scale.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a data processing method, system and storage medium for an intelligent electronic scale. Background Technology

[0002] The weighing accuracy of smart electronic scales is easily affected by ambient temperature and humidity. The core weighing sensor is prone to measurement errors due to temperature drift and humidity interference, a problem that urgently needs to be addressed in the industry. Existing technologies often employ a segmented temperature compensation strategy, dividing the temperature into several independent intervals and configuring corresponding humidity correction parameters, achieving compensation through table lookup. However, this method suffers from significant parameter switching discontinuities at the boundaries of temperature intervals, easily leading to abrupt changes in weighing readings, especially in scenarios with sudden temperature changes, severely impacting measurement stability.

[0003] Meanwhile, traditional compensation methods lack rigorous verification mechanisms for environmental parameter acquisition, making them susceptible to noise interference. Furthermore, the absence of an effective real-time accuracy verification system after compensation makes it difficult to detect and correct compensation deviations in a timely manner. In addition, most solutions lack a complete closed-loop processing flow, failing to dynamically optimize compensation parameters based on the actual working environment. This results in insufficient adaptability and robustness under complex temperature and humidity conditions, making it difficult to meet the practical application requirements of high-precision weighing.

[0004] Therefore, how to achieve a continuous and smooth transition of humidity correction values ​​across the entire temperature range, and avoid reading jumps and boundary discontinuities caused by temperature range switching, has become a key issue in improving the measurement accuracy and stability of electronic scales in complex temperature and humidity environments. Summary of the Invention

[0005] To address the aforementioned technical problems, this application provides a data processing method, system, and storage medium for intelligent electronic scales, which improves the weighing accuracy and stability of intelligent electronic scales.

[0006] In a first aspect, this application provides a data processing method for an intelligent electronic scale, the method comprising:

[0007] The intelligent electronic scale acquires a dataset of current environmental parameters through a built-in sensor group, extracts temperature measurement values ​​based on the dataset, determines the temperature range to which the temperature measurement values ​​belong, and calculates the relative position information of the temperature measurement values ​​with respect to the boundaries of adjacent temperature ranges.

[0008] Based on the temperature range and the relative position information, the humidity correction parameters corresponding to the temperature range and adjacent temperature ranges are retrieved from the storage module of the smart electronic scale, and the humidity correction parameters are weighted to generate a boundary connection parameter set.

[0009] The boundary connection parameter set is input into the difference calculation unit to obtain the numerical difference and slope difference of the connection point, and then integrated to generate connection difference data. If any difference in the connection difference data exceeds the preset difference threshold, smoothing optimization processing is performed to generate an adjustment parameter set.

[0010] Based on the set of adjustment parameters, a transition correction function connecting adjacent temperature ranges is constructed, and a continuous correction value across the entire temperature range is calculated using the transition correction function.

[0011] The original weighing measurement signal of the smart electronic scale is acquired, and the continuous correction value is superimposed on the original weighing measurement signal to obtain the adjusted reading. The accuracy of the adjusted reading is verified. After the verification is successful, the next round of the acquisition process of the current environmental parameter dataset is started, forming a closed-loop process.

[0012] Secondly, this application provides a data processing system for an intelligent electronic scale, the system comprising:

[0013] The data acquisition unit is used by the intelligent electronic scale to acquire the current environmental parameter dataset through the built-in sensor group and transmit it to the main control unit;

[0014] The main control unit is used to extract temperature measurement values ​​based on the current environmental parameter dataset, determine the temperature range to which the temperature measurement values ​​belong, and calculate the relative position information of the temperature measurement values ​​relative to the boundaries of adjacent temperature ranges. Based on the temperature ranges and the relative position information, it retrieves humidity correction parameters corresponding to the temperature ranges and adjacent temperature ranges from the storage module of the smart electronic scale, and generates a boundary connection parameter set by weighting the humidity correction parameters. The boundary connection parameter set is input into the difference calculation unit, and the connection difference data fed back by the difference calculation unit is received. If any difference exceeds a preset difference threshold, smoothing optimization processing is performed to generate an adjustment parameter set. Based on the adjustment parameter set, a transition correction function connecting adjacent temperature ranges is constructed, and a continuous correction value across the entire temperature range is calculated through the transition correction function.

[0015] The difference calculation unit is used to process the boundary connection parameter set, obtain the numerical difference and slope difference of the connection point, integrate and generate the connection difference data and feed it back to the main control unit;

[0016] The weighing measurement unit is used to acquire the original weighing measurement signal and transmit it to the main control unit;

[0017] The main control unit is also used to superimpose the continuous correction value with the original weighing measurement signal to obtain the adjusted reading, verify the accuracy of the adjusted reading, and after the verification is passed, start the sensor group to obtain the next round of current environmental parameter dataset to form a closed-loop process.

[0018] A third aspect of this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the data processing method for an intelligent electronic scale described above.

[0019] Compared with the prior art, the beneficial effects of the present invention are at least as follows:

[0020] First, relying on signal conditioning and digital processing of the sensor array, combined with cyclic redundancy check and delay monitoring mechanisms, the real-time performance and accuracy of the environmental parameter dataset are ensured, laying a reliable data foundation for subsequent correction processes and effectively filtering out noise interference in the acquisition stage. Simultaneously, based on temperature range division and relative position information calculation, humidity correction parameters of the current range and adjacent ranges are linearly weighted and fused, breaking down the traditional barrier of independent temperature range parameters. The generated boundary connection parameter set achieves initial connection between parameters across ranges, avoiding the potential risks of abrupt switching. Then, using the dual-dimensional evaluation of numerical and slope differences from the difference calculation unit, combined with normalized weighted fusion to generate connection difference data, and iterative optimization through piecewise continuous function fitting, dual continuity of boundary parameters in both value and trend is achieved, completely eliminating reading jumps and drifts at temperature range boundaries. Next, a transition correction function is constructed using an adaptive interpolation method. After verification of the continuity of function values ​​and first derivatives, and sequence smoothness, continuous correction values ​​across the entire temperature range are generated, achieving a smooth transition of humidity correction across the entire domain, significantly improving weighing accuracy in complex temperature and humidity environments. Finally, a dual accuracy verification mechanism of benchmark error calibration and standard weight comparison is adopted, along with a backup correction parameter set to ensure the reliability of the adjusted readings. At the same time, data traceability and dynamic adaptation are achieved through non-volatile storage recording and closed-loop processing, which not only improves the robustness of the equipment, but also expands the scope of application in multiple scenarios. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a data processing method for an intelligent electronic scale according to an embodiment of this application;

[0023] Figure 2 This is a schematic diagram showing the comparison before and after smoothing optimization in an embodiment of this application;

[0024] Figure 3This is a schematic diagram comparing the accuracy verification results of the embodiments of this application;

[0025] Figure 4 This is a schematic diagram of the data processing system of an intelligent electronic scale according to an embodiment of this application. Detailed Implementation

[0026] This application provides a data processing method, system, and storage medium for an intelligent electronic scale. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data used can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0027] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of the data processing method for an intelligent electronic scale in this application includes:

[0028] Step S1: The smart electronic scale acquires the current environmental parameter dataset through its built-in sensor group, extracts the temperature measurement value based on the current environmental parameter dataset, determines the temperature range to which the temperature measurement value belongs, and calculates the relative position information of the temperature measurement value relative to the boundary of the adjacent temperature range.

[0029] The process of obtaining the current environment parameter dataset includes:

[0030] The sensor array acquires analog temperature and humidity signals in real time. These signals are then filtered and amplified sequentially to remove noise interference. An analog-to-digital converter transforms the conditioned analog signals into standard-format digital signals. The digital temperature and humidity signals are then integrated to generate a current environmental parameter dataset, which includes both temperature and humidity measurements. This dataset is stored in a temporary cache unit, and data verification and latency monitoring ensure its real-time performance and accuracy.

[0031] This includes determining the temperature range to which the temperature measurement value belongs and calculating the relative position information, including:

[0032] By dividing the temperature range into logically matched temperature ranges based on preset numerical ranges, the system calculates the absolute distance data between the temperature measurement value and the upper and lower boundaries of adjacent temperature ranges. The absolute distance data is then divided by the range width for normalization, generating relative position information that characterizes the distribution of temperature measurement values ​​within the range. Simultaneously, the system stores both the temperature range and the relative position information.

[0033] Specifically, the built-in sensor group of the intelligent electronic scale is the core hardware for sensing environmental parameters. It mainly includes a thermistor temperature sensor and a capacitive humidity sensor. Both types of sensors are integrated into the internal circuit board of the intelligent electronic scale and can respond in real time to changes in temperature and humidity in the weighing environment. In step S1, the sensor group starts real-time data acquisition. The thermistor temperature sensor, based on the thermodynamic effect of resistance changing with temperature, converts the physical change in ambient temperature into a continuous analog temperature signal. The capacitive humidity sensor, based on the characteristic that the dielectric constant of the medium changes with ambient humidity, converts the physical change in ambient humidity into a continuous analog humidity signal. At this time, the acquired temperature and humidity analog signals are analog quantities with small amplitudes and are easily affected by factors such as internal circuit noise of the intelligent electronic scale and external electromagnetic interference. Direct digitization would lead to data distortion; therefore, signal conditioning processing is required first.

[0034] Signal conditioning is a crucial step in analog signal preprocessing, its core function being to filter out noise interference and amplify the effective signal amplitude. Its execution follows a sequential logic of filtering and amplification. The filtering process employs a combined low-pass and median filtering algorithm. The core principle of the low-pass filtering algorithm is to allow low-frequency signals below a preset cutoff frequency to pass through while suppressing high-frequency noise signals above the cutoff frequency. Its input data consists of the original temperature and humidity analog signals collected by the sensor array, and its output data is the temperature and humidity analog signals after filtering out high-frequency noise. The median filtering algorithm is based on sorting statistics theory. Its core principle is to sort the values ​​in the neighborhood of the signal sampling point and select the median value as the output value for that sampling point. This effectively suppresses impulse noise and salt-and-pepper noise. The input data for this algorithm is the temperature and humidity analog signals after low-pass filtering, and its output data is the temperature and humidity analog signals further purified to eliminate impulse interference. After filtering, the amplifier circuit amplifies the conditioned analog signal. The amplifier circuit is built with a high-precision operational amplifier, and its core function is to amplify the weak temperature and humidity analog signal amplitude to a voltage range that the analog-to-digital converter can recognize. The input data for this process is the filtered temperature and humidity analog signal, and the output data is the temperature and humidity analog signal whose amplitude meets the requirements of analog-to-digital conversion.

[0035] After signal conditioning, the temperature and humidity analog signals need to be digitized by an analog-to-digital converter (ADC). The ADC is the core device for converting analog signals to digital signals. In this embodiment, a 12-bit or higher precision ADC is used. Its core working principle is to convert continuous analog signals into discrete binary digital signals through three steps: sampling, quantization, and encoding. Specifically, the sampling process discretizes the continuous temperature and humidity analog signals into a series of sampling points according to a preset sampling frequency; the quantization process maps the analog voltage value of each sampling point to a preset digital value level; and the encoding process converts the quantized digital value into standard binary code. The input data of the ADC is the amplified temperature and humidity analog signals, and the output data is a standard format temperature digital signal and humidity digital signal, which can be recognized and processed by the main control unit of the intelligent electronic scale.

[0036] After the digital conversion is completed, the main control unit of the intelligent electronic scale integrates the temperature and humidity digital signals to generate a current environmental parameter dataset containing both temperature and humidity measurements. The temperature measurement is the specific numerical value representing the ambient temperature after digital conversion, expressed in degrees Celsius (°C), and the humidity measurement is the specific numerical value representing the ambient relative humidity after digital conversion, expressed in percentage (%RH). To ensure the validity of the current environmental parameter dataset, the main control unit stores this dataset in a temporary cache unit. This temporary cache unit uses high-speed random access memory (RAM), whose core function is to achieve temporary data storage and high-speed retrieval, meeting the real-time requirements of data processing. Simultaneously, the main control unit initiates data verification and delay monitoring mechanisms. Data verification employs a Cyclic Redundancy Check (CRC) algorithm. The core principle of this algorithm is to generate a fixed-length checksum by performing polynomial operations on the current environmental parameter dataset. This checksum is then compared with a preset checksum to determine whether errors occurred during data transmission and storage. The input data is the current environmental parameter dataset, and the output data is the result of verification success or failure. The delay monitoring mechanism calculates the time interval from sensor acquisition to data storage completion and compares it with a preset maximum delay threshold. If the time interval exceeds the threshold, it is determined that there is a delay in data acquisition, the dataset is discarded, and re-acquisition is performed, thereby ensuring the real-time performance and accuracy of the current environmental parameter dataset.

[0037] After acquiring and verifying the current environmental parameter dataset, the process proceeds to determine the temperature range and calculate relative position information. First, the main control unit extracts the temperature measurement value from the current environmental parameter dataset and matches the temperature range corresponding to that measurement value based on a preset numerical range division logic. This preset numerical range division logic refers to the temperature range division rules pre-stored in the smart scale's storage module. These rules divide multiple continuous and non-overlapping temperature ranges according to the smart scale's applicable temperature range (e.g., -10℃ to 50℃), using either equal or non-equal spacing. For example, -10℃ to 0℃ is divided into the first temperature range, 0℃ to 10℃ into the second temperature range, and so on. Its core function is to provide the basis for the partitioned storage and retrieval of humidity correction parameters. The input data for this logic is the extracted temperature measurement value, and the output data is the specific temperature range identifier to which that temperature measurement value belongs.

[0038] Relative location information is a core parameter characterizing the specific distribution of temperature measurements within their respective temperature ranges and their correlation with the boundaries of adjacent temperature ranges. Its value ranges from 0 to 1 and is a crucial basis for the subsequent weighted fusion of humidity correction parameters. The specific calculation process is as follows: First, the absolute distance data between the temperature measurement value and the upper and lower boundaries of adjacent temperature ranges is calculated. The boundaries of adjacent temperature ranges include both the upper and lower boundaries of the temperature range. If the temperature measurement value is closer to the lower boundary of its temperature range, the adjacent temperature range is the lower temperature range; if it is closer to the upper boundary, the adjacent temperature range is the upper temperature range. The absolute distance data refers to the absolute difference between the temperature measurement value and the corresponding boundary temperature value, and its unit is the same as the temperature measurement value, in degrees Celsius (°C). Subsequently, the absolute distance data is divided by the interval width for normalization. The interval width refers to the difference between the upper and lower boundary temperatures of the temperature interval to which the temperature measurement belongs. The normalization process uses a linear normalization algorithm. The core principle of this algorithm is to map the original data to a numerical range of 0 to 1, eliminating the influence of data dimensions. Its input data are the absolute distance data and the interval width, and the output data is the normalized relative position information. For example, if the temperature interval to which the temperature measurement belongs is 0℃ to 10℃, the interval width is 10℃, and the temperature measurement is 2℃, its absolute distance from the lower boundary (0℃) is 2℃. Then the normalized relative position information is: 2 / 10 = 0.2, indicating that the temperature measurement is close to the lower boundary of its temperature interval and has a low correlation with the adjacent temperature interval below. If the temperature measurement is 9℃, its absolute distance from the upper boundary (10℃) is 1℃, then the normalized relative position information is 0.1, indicating that the temperature measurement is close to the upper boundary of its temperature interval and has a high correlation with the adjacent temperature interval above.

[0039] After the relative position information is calculated, the main control unit of the smart electronic scale synchronously stores the temperature range identifier to which the temperature measurement value belongs and the calculated relative position information in the temporary cache unit. This stored data will serve as the core input basis for the retrieval and weighted fusion of humidity correction parameters in the subsequent process of step S1, ensuring that the subsequent data processing steps can achieve reasonable connection of humidity correction parameters based on accurate temperature range positioning and position correlation analysis, laying the foundation for finally eliminating the jump in weighing readings.

[0040] Step S2: Based on the temperature range and relative position information, retrieve the humidity correction parameters corresponding to the temperature range and adjacent temperature ranges from the storage module of the smart electronic scale, and generate a boundary connection parameter set by weighting the humidity correction parameters.

[0041] The generation of the boundary connection parameter set includes:

[0042] The humidity correction parameters are summarized, and the correlation between the temperature measurement value and the boundary of the adjacent interval is calculated based on the relative position information. The humidity correction parameters of the adjacent temperature interval are extracted based on the correlation degree. The humidity correction parameters of the interval to which the temperature measurement value belongs are linearly weighted and fused with the humidity correction parameters of the adjacent intervals to generate a boundary connection parameter set that can reflect the influence of temperature change on humidity correction.

[0043] Specifically, the storage module of the smart electronic scale pre-stores a structured humidity correction parameter dataset. This dataset is classified and stored according to the aforementioned preset temperature range division logic. Specifically, each temperature range corresponds to a set of exclusive humidity correction parameters. The humidity correction parameters refer to a set of coefficients determined through a large number of calibration experiments in the early stage, used to compensate for the influence of humidity on the weighing measurement results under different temperature environments. Its core principle is based on the influence law of humidity on the sensor measurement accuracy under different temperature conditions, and a set of optimal compensation coefficients obtained through experimental fitting. The input is the environmental temperature and humidity data of a specific temperature range, and the output is the theoretical humidity influence compensation value after compensation correction. The purpose of this parameter set is to provide accurate humidity compensation basis for each temperature range, which is the foundation for ensuring weighing accuracy in complex environments. After the main control unit completes the identification of the temperature range to which the temperature measurement value belongs, it retrieves the humidity correction parameters for that temperature range from the corresponding storage address in the storage module based on the temperature range identifier. At the same time, based on the relative position information calculated in step S1, it determines the distribution trend of the current temperature measurement value within the range, that is, whether the temperature measurement value is closer to the lower boundary or the upper boundary of the range, and then determines the adjacent temperature ranges that need to be associated. If the relative position information indicates that the temperature measurement value is closer to the lower boundary, the humidity correction parameters of the lower adjacent temperature range are retrieved; if it is closer to the upper boundary, the humidity correction parameters of the upper adjacent temperature range are retrieved. This ensures that the parameters for subsequent weighted fusion cover the range features on both sides of the current temperature point.

[0044] After the humidity correction parameters are collected and retrieved, the core step is to calculate the correlation between the temperature measurement value and the boundaries of adjacent intervals, and then perform linear weighted fusion based on this correlation. The correlation degree is a numerical parameter used to quantify the strength of the correlation between the current temperature measurement value and the boundaries of adjacent temperature intervals. Its value range is directly related to the relative position information obtained in step S1, and is a core indicator reflecting the dependence of the current temperature point on the parameters of adjacent intervals during the transition between intervals. Specifically, based on the relative position information, if the temperature measurement value is close to the lower boundary of its interval, the correlation degree is positively correlated with the relative position information; if it is close to the upper boundary, it is positively correlated with 1 minus the relative position information. The input to this parameter is the relative position information from step S1, and the output is a correlation degree value between 0 and 1, providing a basis for subsequent weight allocation.

[0045] Linear weighted fusion is the core algorithm for connecting parameters across multiple temperature ranges in this embodiment. Its core principle is to assign different weight coefficients to humidity correction parameters in different temperature ranges, merging two or more discrete correction parameters into a continuous transition parameter set. This breaks down the independent barriers between the original temperature range parameters, achieving a smooth transition across ranges. The algorithm's input data includes the humidity correction parameters of the temperature measurement range, the humidity correction parameters of adjacent temperature ranges, and the calculated correlation degree. The correlation degree is used as the weight of the humidity correction parameters of the temperature measurement range, and the difference between 1 and the correlation degree is used as the weight of the humidity correction parameters of adjacent temperature ranges. The calculation is performed using a linear weighted formula. Specifically, the formula for calculating the boundary connection parameter set is: P = α × P1 + (1 - α) × P2, where P represents the boundary connection parameter set, α represents the correlation degree, P1 represents the humidity correction parameter of the temperature range, and P2 represents the humidity correction parameter of the adjacent temperature range. During the fusion process, the main control unit simultaneously performs abnormal parameter removal processing, that is, checks whether the parameter values ​​after weighted fusion are within the preset physical reasonable threshold range. If they exceed the threshold, they are determined to be abnormal parameters. They are then calibrated by replacing them with preset intermediate values ​​or retrieving the parameters to ensure the rationality and effectiveness of the boundary connection parameter set.

[0046] Through the aforementioned linear weighted fusion process, a boundary connection parameter set is ultimately generated that reflects the impact of temperature changes on humidity correction. This parameter set is no longer limited to the characteristics of a single temperature range but integrates the humidity compensation patterns of the current and adjacent ranges. Its core function is to provide basic data with cross-range continuity for subsequent difference calculations and smoothing optimizations. This fundamentally solves the problem of abrupt changes in humidity correction parameters caused by range switching in the original technology, laying a core foundation for achieving continuous and smooth correction across the entire temperature range. The generated boundary connection parameter set will be stored in a temporary cache unit as direct input data for the difference calculation unit in subsequent steps of S2, ensuring the coherence and logic of the entire data processing flow.

[0047] Step S3: Input the boundary connection parameter set into the difference calculation unit to obtain the numerical difference and slope difference of the connection point, and integrate them to generate connection difference data. If any difference in the connection difference data exceeds the preset difference threshold, perform smoothing optimization processing to generate an adjustment parameter set.

[0048] This includes integrating and generating data on connection differences, including:

[0049] After verifying the integrity of the boundary connection parameter set, the difference calculation unit extracts the boundary correction parameters and correction function sampling point sequences of adjacent temperature intervals from the boundary connection parameter set as the basic data for difference calculation. It performs point-by-point subtraction on the boundary correction parameters to obtain the original numerical differences at the connection points of adjacent temperature intervals, generates a numerical difference list, and marks high difference points exceeding a preset difference threshold. It uses the finite difference method to calculate the first derivative at the boundary of adjacent temperature intervals using the correction function sampling point sequence to obtain the slope of each interval boundary. The slope difference value is then used to generate the slope difference value of the connection point. Preset weighting coefficients for numerical differences and slope differences, normalize the mean and slope difference values ​​of the numerical difference list respectively, and then perform a weighted summation operation to integrate and generate connection difference data that comprehensively characterizes the degree of discontinuity at the connection points.

[0050] The generation of the adjustment parameter set includes:

[0051] If the numerical difference or slope difference at the connection point exceeds the corresponding preset difference threshold, the boundary connection parameter set is smoothed and optimized by using piecewise continuous function fitting. First, the length of the temperature transition interval is determined and the intermediate fusion point of the parameters on both sides of the boundary is calculated. Then, the parameter coefficients in the transition interval are adjusted iteratively until the numerical difference and slope difference converge to within the corresponding preset difference threshold. An optimized adjustment parameter set is generated and written to the storage module.

[0052] Specifically, the difference calculation unit is a functional module within the intelligent electronic scale responsible for performing data difference quantification calculations. Its core function is to perform mathematical operations on the input boundary connection parameter set, extracting discontinuous features at both the numerical and trend levels. After the boundary connection parameter set is input into the difference calculation unit, the unit first performs data integrity verification. This verification ensures that the parameter set has not been lost or erroneous during transmission by checking its length, data format, and feature identifiers. The input data for this verification process is the boundary connection parameter set generated in step S2, and the output data is either a confirmation signal indicating successful verification or an anomaly signal indicating failed verification. Only after successful verification will the difference calculation unit extract key data from the boundary connection parameter set, namely the boundary correction parameters and correction function sampling point sequence of adjacent temperature ranges, using these as the basis for subsequent difference calculations. The boundary correction parameter refers to the humidity correction coefficient directly corresponding to the temperature point at the boundary of the temperature range in the boundary connection parameter set, which is the core value determining the weighing correction result at the boundary. The correction function sampling point sequence refers to the set of discrete parameter points pre-collected to cover both sides of the temperature range boundary for constructing the humidity correction function, reflecting the trend characteristics of the humidity correction parameter with temperature changes.

[0053] Numerical difference is an indicator used to quantify the degree of direct deviation in the magnitude of humidity correction parameters at the boundaries of adjacent temperature ranges. Its calculation employs a point-by-point subtraction algorithm. The core principle of this algorithm is to subtract the boundary correction parameters corresponding to the same boundary temperature point from each adjacent temperature range, obtaining the original numerical difference for each corresponding point. The input data for this algorithm is the extracted boundary correction parameters of adjacent temperature ranges, and the output data is a list of numerical differences containing all original numerical differences. After generating the numerical difference list, the difference calculation unit compares each original numerical difference in the list with a preset difference threshold, marking high difference points that exceed the threshold. These high difference points are the direct numerical root cause of fluctuations in weighing readings.

[0054] The difference calculation unit further executes the slope difference calculation process. The slope difference is an indicator used to quantify the deviation of the humidity correction parameter from its trend (i.e., rate of change) at the boundary of adjacent temperature intervals. Its core calculation relies on the finite difference method. The finite difference method is a numerical calculation method that approximates the derivative of a function by using the difference between discrete data points. Its core principle is to use the ratio of the parameter value to the temperature value at adjacent sampling points in the correction function sampling point sequence to approximate the first derivative of the function at that point, thus reflecting the slope of the parameter change. The input data for this method is the correction function sampling point sequence, and the output data is the first derivative value of the correction function at each sampling point, i.e., the slope of the humidity correction parameter change. Based on the calculation results of the finite difference method, the difference calculation unit extracts the slope values ​​at the boundary of adjacent temperature intervals. It then subtracts the two slope values ​​using a slope difference calculation algorithm to obtain the slope difference value at the junction point. The input data for this algorithm is the slope value at the boundary of adjacent temperature intervals, and the output data is a single slope difference value. This value can accurately reflect the degree of abrupt change in the trend of the correction parameter change at the boundary.

[0055] Because numerical differences and slope differences have different physical dimensions, they cannot be directly fused. Therefore, the difference calculation unit is pre-configured with numerical difference weighting coefficients and slope difference weighting coefficients. These two weighting coefficients are determined based on a large number of calibration experiments and are used to balance the influence of numerical mutations and trend mutations on the final weighing correction effect. During the fusion process, the mean and slope difference values ​​of the numerical difference list are first normalized. The normalization process uses a linear normalization algorithm, the core principle of which is to map the original data to a numerical range of 0 to 1, eliminating the influence of different dimensions and numerical ranges. The input data of this algorithm are the mean and slope difference values ​​of the numerical difference list, as well as their respective preset maximum and minimum values. The output data are the normalized numerical difference feature values ​​and slope difference feature values. Subsequently, the difference calculation unit performs a weighted summation operation, multiplying the two normalized feature values ​​by their corresponding weight coefficients and then adding them together. The resulting comprehensive scalar value is the connection difference data. Therefore, the input is the normalized numerical difference feature value, the slope difference feature value, and the preset weight coefficients, and the output is the connection difference data that can comprehensively reflect the degree of discontinuity of the connection point. This data will serve as the core criterion for whether to perform smoothing optimization processing in the future.

[0056] The main control unit of the intelligent electronic scale compares the numerical difference feature value and slope difference feature value in the connection difference data with the corresponding preset difference threshold. If either feature value exceeds the corresponding preset difference threshold, it is determined that the discontinuity of the current boundary connection parameter set exceeds the allowable range, and smoothing optimization processing needs to be initiated. If neither feature value exceeds the threshold, the boundary connection parameter set is directly used as the adjustment parameter set, and no optimization is required.

[0057] The smoothing optimization process employs piecewise continuous function fitting. Piecewise continuous function fitting is a numerical fitting method that divides a complex function into multiple intervals, constructs an independent fitting function within each interval, and ensures continuity between intervals. Its core principle is to construct piecewise functions that preserve the original characteristics of humidity correction parameters within each temperature interval while forcing a continuous transition at interval boundaries. The input data for this method includes a boundary connection parameter set, a preset temperature transition interval length, and a difference convergence threshold. The output data is an optimized set of adjustment parameters. In the specific execution process, the temperature transition interval length is first determined. This length refers to the temperature range extending outwards from the temperature interval boundary. This length is determined based on the temperature measurement resolution of the smart scale and the rate of temperature and humidity change in the actual usage scenario. Its purpose is to provide a reasonable transition range for parameter fusion. Subsequently, based on the length of the temperature transition interval and the boundary connection parameter set, the intermediate fusion point of the parameters on both sides of the boundary is calculated. The intermediate fusion point refers to the transition parameter point within the temperature transition interval that can take into account the characteristics of the temperature interval correction parameters on both sides. The calculation principle is based on the boundary correction parameters and slope characteristics, and the parameter distribution within the transition interval is initially determined by linear interpolation.

[0058] The iterative adjustment employs a gradient descent-type iterative algorithm. Its core principle is to optimize by ensuring that both the numerical and slope differences converge to within a preset difference threshold. The coefficients of the piecewise continuous function within the transition interval are continuously adjusted. After each adjustment, the numerical and slope differences at the boundary of the transition interval are recalculated and compared with the corresponding preset thresholds. If the convergence condition is not met, the coefficients are adjusted again until both differences are below the corresponding preset difference thresholds. This iterative process ensures that the generated set of adjustment parameters satisfies both numerical continuity and slope continuity at the boundaries of adjacent temperature intervals, completely eliminating parameter abrupt changes and sudden shifts.

[0059] Once the iterative adjustment is complete and both the numerical difference and the slope difference have reached the convergence requirement, the difference calculation unit determines the optimized parameter set as the adjustment parameter set. Subsequently, the main control unit of the smart electronic scale writes the adjustment parameter set into the storage module, overwriting the original corresponding boundary connection parameter set. This provides a continuous parameter basis for the subsequent construction of the transition correction function and also realizes the dynamic update of the correction parameters, ensuring that the smart electronic scale can maintain the smooth continuity of humidity correction during long-term use.

[0060] For example, Figure 2 This diagram illustrates the difference between before and after smoothing optimization, visually demonstrating the technical effectiveness of the smoothing optimization process by comparing the humidity correction parameter curves before and after optimization. Figure 2In the diagram, the dashed curve (dark gray) represents the correction parameters before optimization. There is a significant numerical jump at the temperature range boundary (25°C). This jump originates from the independent storage and direct switching of parameters in the traditional segmented compensation strategy, which is the direct cause of the weighing reading jump. The solid curve (black) represents the correction parameters after the smoothing optimization process described in step S3 of this embodiment. It achieves a dual continuous transition of numerical value and slope at the same temperature range boundary, completely eliminating parameter jumps.

[0061] Step S4: Construct a transition correction function connecting adjacent temperature ranges based on the adjustment parameter set, and calculate the continuous correction value across the entire temperature range through the transition correction function.

[0062] The calculated continuous correction values ​​across the entire temperature range include:

[0063] Optimized boundary connection parameters are extracted from the set of adjustment parameters. An interpolation method is selected according to the parameter type of the boundary connection parameters to construct a transition correction function. After verifying that the function value and first derivative of the transition correction function are continuous at the boundary of adjacent temperature intervals, the transition correction function is applied point by point to each temperature point in the entire temperature range to calculate the continuous correction value, and the continuous correction value sequence is summarized. The smoothness of the continuous correction value sequence is verified so that the difference between adjacent points and the rate of change of the derivative are both less than the corresponding preset threshold.

[0064] Specifically, the main control unit extracts optimized boundary connection parameters from the adjustment parameter set in the storage module. These parameters are core parameters that meet the dual requirements of numerical and slope continuity after smoothing optimization in step S3. They cover the correction coefficients at the boundaries of each temperature range and the key fusion point parameters within the transition range, serving as the basic data for constructing the transition correction function. Since different types of boundary connection parameters correspond to different humidity influence variation patterns, in order to ensure fitting accuracy, the main control unit automatically selects an appropriate interpolation method based on the parameter type of the boundary connection parameters. Interpolation is a numerical analysis method that calculates unknown data points from known discrete data points. Its core principle is to construct a continuous function based on the distribution characteristics of known points to approximate the data variation trend. In this embodiment, the interpolation methods that can be selected include linear interpolation, polynomial interpolation, or spline interpolation. The selection of different interpolation methods is determined based on the distribution complexity of the boundary connection parameters. If the parameter distribution shows a linear variation trend, linear interpolation is selected; if the parameter distribution has high-order nonlinear characteristics, cubic spline interpolation is selected to ensure that the constructed function can accurately fit the actual humidity correction pattern.

[0065] Based on the selected interpolation method and the extracted boundary connection parameters, the main control unit executes the construction process of the transition correction function. The transition correction function is a mathematical function that connects adjacent temperature ranges and describes the continuous change of humidity correction values ​​with temperature. Its domain covers the entire operating temperature range of the intelligent electronic scale, and its value range is the corresponding humidity correction value range. The core function of this function is to transform the discrete optimization parameters obtained in step S3 into a continuous correction model, providing real-time and continuous correction basis for any temperature point. During the construction process, the interpolation algorithm uses the optimized boundary connection parameters as known control points, calculates the coefficients of the interpolation basis function through mathematical fitting, and finally generates a complete transition correction function. The input data of this algorithm are the extracted boundary connection parameters and the selected interpolation method type, and the output is an analytical expression of the transition correction function that can cover the entire temperature range.

[0066] After the transition correction function is constructed, it is not used directly for calculation. Instead, a continuity verification is performed first to ensure it fully meets the core requirement of eliminating jumps in this embodiment. The continuity verification mainly targets the boundaries of adjacent temperature intervals, verifying the continuity of function values ​​and the continuity of the first derivative. Function value continuity means that the calculation results of the transition correction function for two adjacent temperature intervals are equal at the boundary temperature point, ensuring that the correction value does not have a numerical jump at the boundary. First derivative continuity means that the slope of change of the transition correction function for two adjacent temperature intervals is equal at the boundary temperature point, ensuring that the trend of change of the correction value does not have abrupt changes at the boundary. This verification process is implemented by substituting the boundary temperature point into the transition correction function and its derivative for calculation. The input data is the constructed transition correction function and the boundary temperature values ​​of each temperature interval. The output data is a verification pass indicator or a verification failure indicator. The transition correction function can only be used when both continuity requirements are met. If the verification fails, the interpolation fitting process is re-executed until the continuity requirement is met.

[0067] In the point-by-point calculation phase of the continuous correction value, the main control unit of the smart scale traverses all temperature points within the entire operating temperature range according to a preset temperature resolution. Temperature resolution refers to the smallest unit the smart scale can identify temperature changes, directly determining the precision of the continuous correction value sequence. For each temperature point, the main control unit substitutes its temperature value into the transition correction function, obtaining the corresponding humidity correction value through function calculation. This process is called point-by-point application of the transition correction function. Its principle is to use the mapping relationship of continuous functions to match a unique and continuous correction value for each discrete temperature sampling point. As the calculation progresses, the correction values ​​corresponding to all temperature points are calculated and summarized sequentially, ultimately forming a continuous correction value sequence covering the entire temperature range. This sequence is a set of correction values ​​arranged in ascending temperature order, fully reflecting the continuous trend of humidity correction values ​​changing with temperature. Its input data includes the transition correction function and the temperature point sequence across the entire temperature range; the output data is the continuous correction value sequence corresponding one-to-one with the temperature point sequence.

[0068] To further ensure the smoothness of the continuous correction value sequence and avoid local fluctuations caused by minor errors in interpolation calculations, the main control unit performs smoothness verification on the generated continuous correction value sequence. Smoothness verification mainly includes two dimensions: adjacent point difference verification and derivative rate of change verification. Adjacent point difference verification involves calculating the absolute difference between two adjacent correction values ​​in the continuous correction value sequence and comparing it with a preset difference threshold to ensure that the variation range of adjacent correction values ​​is within the allowable range. Derivative rate of change verification involves calculating the first derivative of the continuous correction value sequence using the finite difference method, then calculating the rate of change between adjacent derivatives and comparing it with a preset rate of change threshold to ensure that the rate of change of the correction values ​​is stable. Both thresholds are preset based on the weighing accuracy requirements of the intelligent electronic scale. The input data for smoothness verification are the continuous correction value sequence, the preset difference threshold, and the preset rate of change threshold. The output data is a verification pass flag or a verification failure flag. Only when both the adjacent point difference and the derivative rate of change are less than the corresponding preset thresholds is the continuous correction value sequence deemed to meet the smoothness requirements. If the verification fails, the main control unit will readjust the fitting accuracy of the interpolation method or re-execute the smoothing optimization process in step S3 until the generated continuous correction value sequence simultaneously meets the dual requirements of continuity and smoothness.

[0069] Through the complete process described above, the resulting continuous correction value sequence achieves a smooth transition without abrupt changes or sudden shifts across the entire temperature range, completely resolving the problem of fluctuating weighing readings caused by temperature range switching in traditional methods. This continuous correction value sequence is stored in the high-speed cache of the smart scale, serving as the core basis for correcting the original weighing measurement signal in subsequent steps, thus laying a crucial foundation for achieving high-precision weighing in complex temperature and humidity environments.

[0070] Step S5: Obtain the original weighing measurement signal of the smart electronic scale, superimpose the continuous correction value with the original weighing measurement signal to obtain the adjusted reading, and verify the accuracy of the adjusted reading. After the verification is successful, start the next round of the current environmental parameter dataset acquisition process to form a closed loop.

[0071] The accuracy verification of the adjusted readings includes:

[0072] The system retrieves pre-stored reference error data from the smart electronic scale's storage module. Based on the current ambient temperature measurement, it obtains the reference error for the corresponding temperature point through direct matching or linear interpolation. The theoretical calibration value is obtained by subtracting the reference error from the initial adjusted reading. The deviation between the theoretical calibration value and the actual value of the standard weight is calculated. If the absolute value of the deviation is less than the preset allowable error threshold, the verification is considered successful. If the deviation exceeds the allowable error threshold, an anomaly flag is triggered and a backup correction parameter set is activated. After verification, the current temperature and humidity, the adjusted reading, and the deviation are recorded in non-volatile memory. The verified adjusted reading is then transmitted to the output module for display.

[0073] Specifically, the main control unit obtains the original weighing measurement signal from the weighing measurement module of the intelligent electronic scale. The original weighing measurement signal refers to the electrical signal converted from the physical change of the object's weight by the weighing sensor. After signal conditioning and analog-to-digital conversion, it becomes a digital signal that directly reflects the initial weighing result before temperature and humidity correction. After obtaining the original weighing measurement signal, the main control unit, based on the current ambient temperature measurement value collected in step S1, indexes the corresponding continuous correction value from the continuous correction value sequence generated in step S4 from the cache. This continuous correction value is a precise compensation amount for the influence of humidity on the weighing result under the current temperature and humidity environment. Subsequently, the main control unit performs a superposition operation in the arithmetic logic unit. The superposition operation is the algebraic summation of the original weighing measurement signal and the continuous correction value. Its core principle is to offset the interference of humidity on the weighing accuracy through compensation. The input data of this operation is the original weighing measurement signal and the corresponding continuous correction value, and the output data is the preliminary adjusted reading. This reading has completed the continuous smooth correction for temperature and humidity, theoretically eliminating the reading jump problem caused by temperature range switching.

[0074] After obtaining the initial adjusted reading, the main control unit will execute a rigorous accuracy verification process to ensure the actual accuracy of the reading. This process is the last line of defense for ensuring the weighing accuracy of the smart electronic scale. The first step of the accuracy verification is to retrieve the reference error data. The reference error data refers to the dataset that characterizes the inherent systematic error of the smart electronic scale, obtained during the factory calibration stage by conducting a large number of repeatable experiments with standard weights at different temperature points. This dataset is stored in the non-volatile storage module of the smart electronic scale according to temperature points, and its core function is to provide an absolute reference standard for reading calibration under different temperature environments. The main control unit obtains the reference error of the corresponding temperature point based on the current ambient temperature measurement value using either direct matching or linear interpolation. Direct matching means that when the current temperature measurement value is exactly the same as the preset temperature point in the reference error data, the reference error corresponding to that temperature point is directly retrieved. The input data for this method is the current temperature measurement value and the reference error dataset, and the output data is the matched reference error. Linear interpolation, on the other hand, means that when the current temperature measurement value is between two preset temperature points, the reference error of the current temperature point is calculated by linear fitting based on the reference errors of these two preset temperature points. The core principle of the linear interpolation algorithm is to assume that the systematic error between two known points changes linearly, and to calculate the error value of the unknown point by constructing a linear equation. Its input data is the current temperature measurement value, two adjacent preset temperature points and their corresponding reference errors, and the output data is the reference error of the current temperature point obtained by interpolation.

[0075] After obtaining the reference error at the corresponding temperature point, the main control unit calculates the theoretical calibration value, which is achieved by subtracting the reference error from the initial adjusted reading. This is done by subtracting the inherent system error of the intelligent electronic scale to obtain a theoretical value closer to the true weight of the object. The input data for this calculation is the initial adjusted reading and the reference error, and the output data is the theoretical calibration value. Next, the main control unit calculates the deviation between the theoretical calibration value and the actual value of the standard weight. The actual value of the standard weight refers to the absolute weight value marked on the standard weight used for factory calibration; it is the only absolute standard for measuring weighing accuracy. The deviation is calculated using subtraction, that is, by subtracting the actual value of the standard weight from the theoretical calibration value. The input data for this is the theoretical calibration value and the actual value of the standard weight, and the output data is the deviation value characterizing the degree of deviation from the reading.

[0076] Subsequently, the main control unit compares the absolute value of the deviation with a preset allowable error threshold. The allowable error threshold is the maximum allowable error range preset according to the accuracy level of the intelligent electronic scale, and it is the core basis for determining whether the reading is qualified. If the absolute value of the deviation is less than the preset allowable error threshold, the accuracy verification is deemed successful, and the preliminary adjusted reading is the final valid adjusted reading. If the deviation exceeds the allowable error threshold, the verification is deemed unsuccessful, and the main control unit will immediately trigger an anomaly flag and automatically activate the backup correction parameter set. The backup correction parameter set consists of pre-stored, rigorously verified general correction parameters. Its function is to ensure that the intelligent electronic scale can still output a reading that meets the basic accuracy requirements when an anomaly occurs in the main correction process, thus avoiding equipment downtime.

[0077] After accuracy verification is completed, regardless of the verification result, the main control unit will synchronously record the current ambient temperature and humidity data, the final adjusted reading, and the calculated deviation value to non-volatile memory. Non-volatile memory refers to a storage device that can retain data even after power failure. Its core function is to retain weighing data and calibration records, providing data support for subsequent equipment maintenance, parameter optimization, and quality traceability. For the adjusted reading that has passed verification, the main control unit will transmit it to the output module of the intelligent electronic scale. The output module includes a display screen and a signal output interface. Its function is to convert the digital reading into visual information that the user can recognize or a transmittable electrical signal to complete the final reading display.

[0078] Simultaneously with outputting the adjusted reading, the main control unit immediately sends a start command to the sensor group and data acquisition module, initiating the next round of acquiring the current environmental parameter dataset. This completes the closed-loop processing of the entire data processing method. The core principle of closed-loop processing is based on the dynamic changing characteristics of the intelligent electronic scale's working environment. Through a continuous cycle of "environmental acquisition - parameter correction - weighing adjustment - closed-loop verification," it ensures that every weighing operation adapts to the real-time temperature and humidity environment, fundamentally solving the problem of unstable readings in traditional electronic scales under dynamically changing temperature and humidity conditions, and achieving high-precision weighing across the entire temperature range and all working periods.

[0079] For example, Figure 3 This diagram illustrates the comparison of accuracy verification results. By comparing the distribution of uncorrected and corrected weighing errors at different temperature points, it visually demonstrates the technical effectiveness of the accuracy verification process. Figure 3 In the figure, the dashed curve (dark gray, square mark) represents the uncorrected original error, which has obvious fluctuations and drifts at different temperature points, and some data points exceed the preset allowable error threshold range; the solid curve (black, circular mark) represents the error after being corrected by the method described in steps S1 to S5 of this embodiment, which is significantly reduced and stably approaches zero.

[0080] The data processing method of an intelligent electronic scale according to an embodiment of this application has been described above. The data processing system of an intelligent electronic scale according to an embodiment of this application is described below. Please refer to [link / reference]. Figure 4 One embodiment of the data processing system for an intelligent electronic scale in this application includes:

[0081] The data acquisition unit is used by the smart electronic scale to acquire the current environmental parameter dataset through the built-in sensor group and transmit it to the main control unit.

[0082] The main control unit extracts temperature measurements from the current environmental parameter dataset, determines the temperature range to which the temperature measurements belong, and calculates the relative position information of the temperature measurements relative to the boundaries of adjacent temperature ranges. Based on the temperature range and relative position information, it retrieves humidity correction parameters corresponding to the temperature range and adjacent temperature ranges from the storage module of the smart electronic scale, and generates a boundary connection parameter set by weighting the humidity correction parameters. The boundary connection parameter set is input into the difference calculation unit, and the connection difference data fed back by the difference calculation unit is received. If any difference exceeds a preset difference threshold, smoothing optimization processing is performed to generate an adjustment parameter set. Based on the adjustment parameter set, a transition correction function connecting adjacent temperature ranges is constructed, and a continuous correction value across the entire temperature range is calculated through the transition correction function.

[0083] The difference calculation unit is used to process the boundary connection parameter set, obtain the numerical difference and slope difference of the connection point, integrate and generate connection difference data and feed it back to the main control unit.

[0084] The weighing measurement unit is used to acquire the original weighing measurement signal and transmit it to the main control unit.

[0085] The main control unit is also used to superimpose the continuous correction value with the original weighing measurement signal to obtain the adjusted reading, verify the accuracy of the adjusted reading, and start the sensor group to obtain the next round of current environmental parameter dataset after the verification is passed, thus forming a closed-loop process.

[0086] This application also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when the instructions are executed on a computer, cause the computer to perform the steps of the data processing method for the intelligent electronic scale.

[0087] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0088] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0089] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A data processing method for an intelligent electronic scale, characterized in that, The method includes: The intelligent electronic scale acquires a dataset of current environmental parameters through a built-in sensor group, extracts temperature measurement values ​​based on the dataset, determines the temperature range to which the temperature measurement values ​​belong, and calculates the relative position information of the temperature measurement values ​​with respect to the boundaries of adjacent temperature ranges. Based on the temperature range and the relative position information, the humidity correction parameters corresponding to the temperature range and adjacent temperature ranges are retrieved from the storage module of the smart electronic scale, and the humidity correction parameters are weighted to generate a boundary connection parameter set. The boundary connection parameter set is input into the difference calculation unit to obtain the numerical difference and slope difference of the connection point, and then integrated to generate connection difference data. If any difference in the connection difference data exceeds the preset difference threshold, smoothing optimization processing is performed to generate an adjustment parameter set. Based on the set of adjustment parameters, a transition correction function connecting adjacent temperature ranges is constructed, and a continuous correction value across the entire temperature range is calculated using the transition correction function. The original weighing measurement signal of the smart electronic scale is acquired, and the continuous correction value is superimposed on the original weighing measurement signal to obtain the adjusted reading. The accuracy of the adjusted reading is verified. After the verification is successful, the next round of the acquisition process of the current environmental parameter dataset is started, forming a closed-loop process.

2. The data processing method for the intelligent electronic scale according to claim 1, characterized in that, Obtain the current environment parameter dataset, including: The sensor group acquires temperature and humidity simulation signals in real time, and performs signal conditioning processing such as filtering and amplification on the temperature and humidity simulation signals in sequence to filter out noise interference. The conditioned analog signal is converted into a standard format digital signal by an analog-to-digital converter, and the temperature digital signal and humidity digital signal are integrated to generate the current environmental parameter dataset, which includes temperature measurement value and humidity measurement value. The current environmental parameter dataset is stored in a temporary cache unit, and the real-time performance and accuracy of the current environmental parameter dataset are ensured through data verification and latency monitoring.

3. The data processing method for the intelligent electronic scale according to claim 1, characterized in that, Determine the temperature range to which the temperature measurement belongs and calculate the relative position information, including: The temperature range corresponding to the temperature measurement value is matched by a preset numerical range division logic. The absolute distance data between the temperature measurement value and the upper and lower boundaries of the adjacent temperature range is calculated. The absolute distance data is divided by the range width for normalization processing to generate relative position information that characterizes the distribution of the temperature measurement value within the range. At the same time, the temperature range and the relative position information are stored.

4. The data processing method for the intelligent electronic scale according to claim 1, characterized in that, Generate a boundary connection parameter set, including: The humidity correction parameters are summarized, and the correlation between the temperature measurement value and the boundary of the adjacent interval is calculated based on the relative position information. The humidity correction parameters of the adjacent temperature interval are extracted based on the correlation degree. The humidity correction parameters of the interval to which the temperature measurement value belongs are linearly weighted and fused with the humidity correction parameters of the adjacent interval using the correlation degree as the weight, so as to generate the boundary connection parameter set that can reflect the influence of temperature change on humidity correction.

5. The data processing method for the intelligent electronic scale according to claim 1, characterized in that, Integrate and generate transitional difference data, including: After performing integrity verification on the boundary connection parameter set, the difference calculation unit extracts the boundary correction parameters and correction function sampling point sequence of adjacent temperature ranges in the boundary connection parameter set as the basic data for difference calculation. Perform point-by-point subtraction on the boundary correction parameters to obtain the original numerical difference at the junction of adjacent temperature ranges, generate a list of numerical differences and mark high difference points that exceed the preset difference threshold; The first derivative at the boundary of adjacent temperature intervals is calculated using the finite difference method on the sampling point sequence of the correction function to obtain the slope of each interval boundary. The slope difference value of the connection point is generated by the slope difference calculation. The system presets weighting coefficients for numerical differences and slope differences. After normalizing the mean and slope difference values ​​in the numerical difference list, it performs a weighted summation operation to generate connection difference data that can comprehensively characterize the degree of discontinuity at the connection points.

6. The data processing method for the intelligent electronic scale according to claim 1, characterized in that, Generate a set of adjustment parameters, including: If the numerical difference or slope difference at the connection point exceeds the corresponding preset difference threshold, the boundary connection parameter set is smoothed and optimized by using piecewise continuous function fitting. First, the length of the temperature transition interval is determined and the intermediate fusion point of the parameters on both sides of the boundary is calculated. Then, the parameter coefficients in the transition interval are adjusted iteratively until the numerical difference and slope difference converge to within the corresponding preset difference threshold. The optimized adjustment parameter set is then generated and written to the storage module.

7. The data processing method for the intelligent electronic scale according to claim 1, characterized in that, The continuous correction values ​​across the entire temperature range are calculated, including: Optimized boundary connection parameters are extracted from the set of adjustment parameters. An interpolation method is selected according to the parameter type of the boundary connection parameters to construct a transition correction function. After verifying that the function value and first derivative of the transition correction function are continuous at the boundary of adjacent temperature intervals, the transition correction function is applied point by point to each temperature point in the entire temperature range to calculate the continuous correction value, and the continuous correction value sequence is summarized. The smoothness of the continuous correction value sequence is verified so that the difference between adjacent points and the rate of change of the derivative are both less than the corresponding preset threshold.

8. The data processing method for the intelligent electronic scale according to claim 1, characterized in that, The accuracy of the adjusted readings is verified, including: The system retrieves pre-stored reference error data from the smart electronic scale's storage module. Based on the current ambient temperature measurement, it obtains the reference error at the corresponding temperature point through direct matching or linear interpolation. The theoretical calibration value is obtained by subtracting the reference error from the initial adjusted reading. The deviation between the theoretical calibration value and the actual value of the standard weight is calculated. If the absolute value of the deviation is less than the preset allowable error threshold, the verification is deemed successful. If the deviation exceeds the allowable error threshold, an anomaly flag is triggered and the backup correction parameter set is activated. After verification, the current temperature and humidity, the adjusted reading, and the deviation are recorded in non-volatile memory, and the verified adjusted reading is then transmitted to the output module for display.

9. A data processing system for an intelligent electronic scale, used to implement the data processing method for an intelligent electronic scale as described in any one of claims 1-8, characterized in that, The system includes: The data acquisition unit is used by the intelligent electronic scale to acquire the current environmental parameter dataset through the built-in sensor group and transmit it to the main control unit; The main control unit is used to extract temperature measurement values ​​based on the current environmental parameter dataset, determine the temperature range to which the temperature measurement values ​​belong, and calculate the relative position information of the temperature measurement values ​​relative to the boundaries of adjacent temperature ranges. Based on the temperature ranges and the relative position information, it retrieves humidity correction parameters corresponding to the temperature ranges and adjacent temperature ranges from the storage module of the smart electronic scale, and generates a boundary connection parameter set by weighting the humidity correction parameters. The boundary connection parameter set is input into the difference calculation unit, and the connection difference data fed back by the difference calculation unit is received. If any difference exceeds a preset difference threshold, smoothing optimization processing is performed to generate an adjustment parameter set. Based on the adjustment parameter set, a transition correction function connecting adjacent temperature ranges is constructed, and a continuous correction value across the entire temperature range is calculated through the transition correction function. The difference calculation unit is used to process the boundary connection parameter set, obtain the numerical difference and slope difference of the connection point, integrate and generate the connection difference data and feed it back to the main control unit; The weighing measurement unit is used to acquire the original weighing measurement signal and transmit it to the main control unit; The main control unit is also used to superimpose the continuous correction value with the original weighing measurement signal to obtain the adjusted reading, verify the accuracy of the adjusted reading, and after the verification is passed, start the sensor group to obtain the next round of current environmental parameter dataset to form a closed-loop process.

10. A computer-readable storage medium storing instructions thereon, characterized in that, When the instruction is executed by the processor, it implements a data processing method for an intelligent electronic scale as described in any one of claims 1-8.

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