Weighing apparatus verification calibration data intelligent analysis method and system

Abstract

The invention relates to the technical field of weighing apparatus verification and calibration, and provides a weighing apparatus verification and calibration data intelligent analysis method and system, and the method comprises the steps: obtaining a plurality of error readings of a weighing apparatus in the verification and calibration process, the collection error values of the error readings, and the maintenance record of the weighing apparatus; correcting the error reading according to the acquisition error value and the maintenance record to obtain a corrected error reading; determining performance drift data of the weighing apparatus according to the plurality of correction error readings; and performing error analysis on the weighing apparatus according to the acquisition error value and the performance drift data. The analysis accuracy of error data of weighing apparatus verification and calibration can be improved.

Description

Technical Field

[0001] This application relates to the field of weighing instrument verification and calibration technology, and in particular to an intelligent analysis method and system for weighing instrument verification and calibration data. Background Technology

[0002] Current methods for analyzing weighing instrument calibration data often struggle to effectively uncover hidden patterns when processing massive amounts of historical data.

[0003] While some intelligent systems attempt to predict weighing instrument performance by correlating information such as equipment model, environmental parameters, and historical errors, they often struggle when faced with the complex and diverse sources of error in the data. These systems typically treat all observed errors as a single "error" without establishing mechanisms to differentiate between these sources. For example, they fail to effectively distinguish between performance degradation of the equipment itself, human recording bias, or benchmark changes caused by system maintenance. This approach leads to inaccurate predictions and may even mislead maintenance decisions, ultimately affecting the metrological reliability of the weighing instrument and the efficiency of calibration work. Summary of the Invention

[0004] This application provides an intelligent analysis method and system for weighing instrument verification and calibration data, which can improve the accuracy of error data analysis for weighing instrument verification and calibration.

[0005] To achieve the above objectives, this application adopts the following technical solution:

[0006] In a first aspect, this application discloses an intelligent analysis method for weighing instrument verification and calibration data, comprising the following steps: acquiring multiple error readings of the weighing instrument during the verification and calibration process, the acquisition error value of the error readings, and the maintenance record of the weighing instrument; correcting the error readings based on the acquisition error value and the maintenance record to obtain corrected error readings; determining the performance drift data of the weighing instrument based on the multiple corrected error readings; and performing error analysis on the weighing instrument based on the acquisition error value and the performance drift data.

[0007] This technical solution enables intelligent analysis of weighing instrument calibration data, effectively distinguishing errors from different sources, improving the accuracy and reliability of error analysis, and thus solving the problems of confused error sources and inaccurate analysis results in existing technologies.

[0008] Furthermore, when the error reading is manually collected by the data acquisition personnel, the acquisition error value of the error reading is obtained, including: acquiring the heart rate and blink rate of the data acquisition personnel during the error reading acquisition process; determining the fatigue index of the data acquisition personnel based on the heart rate and blink rate; acquiring a first preset correspondence; the first preset correspondence includes a one-to-one correspondence between multiple fatigue index ranges and multiple first error values; and using the first error value corresponding to the fatigue index range in the first preset correspondence as the acquisition error value.

[0009] This technical solution can quantify manual data collection errors through physiological indicators, effectively identify and quantify data deviations caused by operator fatigue, thereby improving the accuracy of corrections to the original error readings.

[0010] Furthermore, the fatigue index of the data collector is determined based on heart rate and blink frequency, including: normalizing the absolute value of the difference between the heart rate and the normal heart rate of the data collector under normal conditions to obtain a heart rate deviation index; normalizing the absolute value of the difference between the blink frequency and the blink frequency of the data collector under normal conditions to obtain a blink frequency deviation index; and using the average of the heart rate deviation index and the blink frequency deviation index as the fatigue index.

[0011] This technical solution enables the standardized processing of heart rate and blink rate data to more accurately calculate a comprehensive index reflecting the fatigue level of the data collectors, providing a more reliable basis for subsequent error correction.

[0012] In some preferred embodiments, when the error reading is acquired by the image recognition device through image recognition, obtaining the acquisition error value of the error reading includes: acquiring the ambient light intensity value of the environment where the weighing instrument is located and the edge sharpness of the image acquired by the image recognition device; determining the recognition accuracy of the image recognition based on the ambient light intensity value and the edge sharpness; acquiring a second preset correspondence; the second preset correspondence includes a one-to-one correspondence between multiple recognition accuracy ranges and multiple second error values; and using the second error value corresponding to the recognition accuracy range in the second preset correspondence as the acquisition error value.

[0013] This technical solution enables the assessment of image recognition accuracy in automated acquisition scenarios by considering ambient lighting and image quality. This allows for the quantification of errors introduced by automated acquisition and the mitigation of potential errors that may occur under specific conditions.

[0014] Preferably, determining the recognition accuracy of image recognition based on ambient light intensity and edge sharpness includes: normalizing the absolute value of the difference between the ambient light intensity and a preset ambient light intensity to obtain a light intensity deviation index; normalizing the absolute value of the difference between the edge sharpness and a preset edge sharpness to obtain an edge sharpness deviation index; and using the average of the light intensity deviation index and the edge sharpness deviation index as the recognition accuracy.

[0015] This technical solution enables a comprehensive evaluation of image recognition accuracy by standardizing light intensity and image sharpness, providing a more precise indicator for quantifying automated acquisition errors.

[0016] Furthermore, the error reading is corrected based on the collected error value and maintenance record to obtain the corrected error reading, including: determining the maintenance error value of the weighing instrument based on the maintenance record; using the difference between the error reading and the first value as the corrected error reading; the first value is the sum of the collected error value and the maintenance error value.

[0017] This technical solution can comprehensively consider both acquisition and maintenance errors, and make more comprehensive corrections to the original error readings, thereby obtaining corrected error readings that are closer to the true performance of the weighing instrument.

[0018] Based on this, the maintenance record includes the maintenance type. The maintenance error value of the weighing instrument is determined according to the maintenance record, including: obtaining a third preset correspondence; the third preset correspondence includes a one-to-one correspondence between multiple maintenance types and multiple third error values; and using the third error value corresponding to the maintenance type in the maintenance record in the third preset correspondence as the maintenance error value.

[0019] This technical solution allows for the quantification of the potential impact of different maintenance types on weighing instrument performance, thereby more accurately correcting systematic errors introduced by maintenance operations.

[0020] Furthermore, the performance drift data of the weighing instrument is determined based on multiple correction error readings, including: performing a moving average filter on the multiple correction error readings based on the acquisition time of each correction error reading to obtain multiple smoothed error readings; performing linear regression analysis on the multiple smoothed error readings to obtain a linear fitting line; and using the slope of the linear fitting line as the performance drift data.

[0021] This technical solution can effectively extract the true trend of the weighing instrument's performance over time through filtering and linear regression analysis, accurately quantify the performance drift of the weighing instrument, and avoid noise interference.

[0022] Furthermore, error analysis is performed on the weighing instrument based on the collected error value and performance drift data, including: determining whether the performance drift data is greater than the preset performance drift threshold; if so, determining that the weighing instrument has aging error; determining whether the collected error value is greater than the preset collected error threshold; if so, determining that the error reading of the weighing instrument is abnormal.

[0023] This technical solution can distinguish between two different types of errors—the aging of the weighing instrument itself and abnormal data acquisition—based on performance drift data and acquisition error values, providing a basis for decision-making in accurate maintenance and fault diagnosis.

[0024] Secondly, this application also discloses an intelligent analysis system for weighing instrument verification and calibration data, comprising: an acquisition device and a processing device; the acquisition device is used to acquire multiple error readings of the weighing instrument during the verification and calibration process, the acquisition error value of the error readings, and the maintenance record of the weighing instrument; the processing device is used to correct the error readings according to the acquisition error value and the maintenance record to obtain a corrected error reading; the processing device is used to determine the performance drift data of the weighing instrument according to the multiple corrected error readings; and the processing device is used to perform error analysis on the weighing instrument according to the acquisition error value and the performance drift data.

[0025] Beneficial effects

[0026] The intelligent analysis method for weighing instrument verification and calibration data disclosed in this application acquires multiple error readings, acquisition error values ​​of the error readings, and maintenance records of the weighing instrument during the verification and calibration process. Based on the acquisition error values ​​and maintenance records, the error readings are corrected to obtain corrected error readings. This effectively solves the problem in existing technologies where multiple error sources, such as manual recording deviations, misjudgments in automated acquisition, and system reference offsets introduced by maintenance operations, lead to complex and difficult-to-analyze historical error data. By correcting the error readings, this application can remove noise and non-real error components from the data, allowing subsequent analysis to focus more on the performance changes of the weighing instrument itself. Furthermore, this application determines the performance drift data of the weighing instrument based on multiple corrected error readings and performs error analysis on the weighing instrument by combining the acquisition error values ​​and performance drift data. This step-by-step and refined processing method enables this application to effectively distinguish between the aging error of the weighing instrument itself and data acquisition anomalies, avoiding the defect of existing intelligent analysis systems misjudging system reference offsets as sensor failures, thereby improving the accuracy and reliability of error prediction. This method enables accurate risk prediction of the weighing instrument's operating status, effectively guides the formulation of maintenance strategies, and significantly improves calibration efficiency and the long-term operating cost-effectiveness of the equipment. Attached Figure Description

[0027] Figure 1 A flowchart illustrating an intelligent analysis method for weighing instrument verification and calibration data provided in this application;

[0028] Figure 2 A flowchart illustrating an intelligent analysis method for weighing instrument verification and calibration data provided in this application;

[0029] Figure 3 A flowchart illustrating an intelligent analysis method for weighing instrument verification and calibration data provided in this application;

[0030] Figure 4 This is a schematic diagram of the architecture of an intelligent analysis system for weighing instrument verification and calibration data provided in this application. Detailed Implementation

[0031] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0032] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0033] Traditional methods for analyzing calibration data of weighing instruments often struggle to effectively uncover hidden patterns when processing massive amounts of historical data. While some intelligent systems attempt to predict instrument performance by correlating information such as equipment model, environmental parameters, and historical errors, they often fall short when faced with the complex and diverse sources of error within the data. These systems typically treat all observed errors as a single "error" without establishing mechanisms to differentiate between these sources. For example, they fail to effectively distinguish between performance degradation of the equipment itself, human recording bias, or benchmark changes caused by system maintenance. This approach leads to inaccurate predictions and may even mislead maintenance decisions, ultimately affecting the metrological reliability of weighing instruments and the efficiency of calibration work.

[0034] In this regard, such as Figure 1 As shown, this application proposes an intelligent analysis method for weighing instrument verification and calibration data, including:

[0035] S101. Obtain multiple error readings of the weighing instrument during the verification and calibration process, the acquisition error value of the error readings, and the maintenance records of the weighing instrument.

[0036] S102. Correct the error reading based on the collected error value and maintenance record to obtain the corrected error reading.

[0037] S103. Determine the performance drift data of the weighing instrument based on multiple correction error readings.

[0038] S104. Perform error analysis on the weighing instrument based on the collected error values ​​and performance drift data.

[0039] This application obtains multi-source error information and corrects the original error readings, thereby more accurately reflecting the true performance of the weighing instrument and further analyzing its performance drift and potential anomalies, effectively improving the accuracy and reliability of weighing instrument verification and calibration data analysis.

[0040] To better understand the intelligent analysis method for weighing instrument verification and calibration data proposed in this application, the key terms and implementation environment involved will be explained in detail below.

[0041] "Weighing instruments" refers to equipment used to measure the mass of objects, such as electronic scales, platform scales, and truck scales.

[0042] "Error reading" refers to the deviation between the actual measured value and the standard value of a weighing instrument during the verification and calibration process. These readings are the basic data for evaluating the performance of the weighing instrument.

[0043] "Acquisition error value" refers to the error introduced during the acquisition of error readings, which may come from human operation errors or identification errors of automated equipment.

[0044] "Maintenance records" refer to the various maintenance activities performed on a weighing instrument during its life cycle, such as firmware upgrades and component replacements. These activities may affect the performance of the weighing instrument.

[0045] "Corrected error reading" refers to more accurate error data obtained by adjusting the original error reading after taking into account the acquisition error value and maintenance records.

[0046] "Performance drift data" refers to the trend data of the performance of a weighing instrument over time, which usually reflects the aging or wear of the internal components of the weighing instrument.

[0047] "Error analysis" refers to the process of assessing the type, source, and severity of errors in a weighing instrument based on corrected error readings, collected error values, and performance drift data.

[0048] This method can be implemented in various environments, such as weighing instrument calibration laboratories, production workshops, or on-site calibration points. Data can be acquired through various methods, including manual entry, automatic sensor acquisition, and image recognition, and then processed and analyzed by a computer system.

[0049] The intelligent analysis method for weighing instrument verification and calibration data proposed in this application focuses on the comprehensive consideration and correction of multi-source errors, thereby achieving more accurate performance evaluation of weighing instruments. The main features of this method will be elaborated in detail below.

[0050] First, the method includes acquiring multiple error readings of the weighing instrument during the verification and calibration process, the acquisition error value of the error readings, and the maintenance records of the weighing instrument.

[0051] Obtaining multiple error readings of a weighing instrument during the verification and calibration process can be achieved in several ways. For example, calibration personnel can manually record the error values ​​on the weighing instrument's display screen and input them into a data acquisition system. Alternatively, they can directly read the error data stored internally by connecting to the weighing instrument's digital interface. Furthermore, image recognition technology can be used to capture images of the weighing instrument's display screen using a camera and automatically identify the error values.

[0052] One of the key innovations of this application is obtaining the acquisition error value of the error readings. When the error readings are manually collected by personnel, the acquisition error value can be obtained by assessing the personnel's state. For example, it can be assessed by observing subjective factors such as the personnel's level of focus and operational proficiency, and a corresponding error value can be assigned based on the assessment results. When the error readings are collected by an image recognition device through image recognition, the acquisition error value can be obtained by assessing the accuracy of image recognition. For example, it can be determined by analyzing objective factors such as image clarity and lighting conditions, and assessing the accuracy of image recognition based on these factors.

[0053] The maintenance records of a weighing instrument can be obtained by consulting its historical maintenance files. For example, maintenance dates, maintenance details, and other information can be found in a paper logbook and entered into the system. Alternatively, if the weighing instrument has an electronic maintenance log, the maintenance records can be directly exported from the log.

[0054] Secondly, the method includes correcting the error reading based on the collected error value and maintenance records to obtain a corrected error reading.

[0055] After obtaining the original error reading, the acquired error value, and the maintenance record, the original error reading needs to be corrected. For example, the acquired error value can be simply subtracted from the original error reading to eliminate the deviation introduced during the acquisition process. Meanwhile, the maintenance record may also affect the performance of the weighing instrument; for example, after a maintenance, the instrument's reference may have shifted slightly. Therefore, the original error reading can be further adjusted based on the maintenance type and time recorded in the maintenance record. For instance, if the maintenance record shows that the weighing instrument underwent a firmware upgrade, the error reading after the upgrade can be corrected accordingly based on the degree of impact of the firmware upgrade on the instrument's performance.

[0056] Furthermore, the method includes determining the performance drift data of the weighing instrument based on multiple correction error readings.

[0057] After obtaining the corrected error readings, it is necessary to analyze the performance drift trend of the weighing instrument. For example, the corrected error readings can be arranged in chronological order and plotted as a time series. By observing this time series, a preliminary judgment can be made as to whether the performance of the weighing instrument has drifted. To quantify the performance drift more accurately, statistical analysis can be performed on the corrected error readings. For example, the mean, standard deviation, and other statistics of the corrected error readings can be calculated, and the trends of these statistics over time can be observed. In addition, regression analysis and other methods can be used to fit the relationship between the corrected error readings and time, thereby obtaining the performance drift data of the weighing instrument.

[0058] Finally, the method includes error analysis of the weighing instrument based on the acquired error value and performance drift data.

[0059] After determining the acquisition error value and performance drift data, a comprehensive error analysis of the weighing instrument can be performed. For example, performance drift data can be used to determine if the weighing instrument is showing signs of aging. If the performance drift data shows a continuously increasing trend, it may indicate that the internal components of the weighing instrument are aging. Simultaneously, the acquisition error value can be used to determine if there are any abnormalities in the error reading acquisition process. If the acquisition error value is consistently high, it may indicate problems with the operator's operation or low recognition accuracy of the image recognition device. By comprehensively analyzing the acquisition error value and performance drift data, the type and source of error in the weighing instrument can be diagnosed more accurately, providing a basis for subsequent maintenance and verification.

[0060] The intelligent analysis method for weighing instrument verification and calibration data proposed in this application provides a comprehensive data foundation for subsequent error correction and analysis by acquiring multiple error readings, acquisition error values ​​of these error readings, and maintenance records of the weighing instrument during the verification and calibration process. Specifically, after acquiring this multi-source data, the original error readings are first corrected based on the acquisition error values ​​and maintenance records to obtain corrected error readings. This step is crucial, as it effectively isolates errors introduced by factors other than the weighing instrument itself, such as manual operation, automated acquisition environment, or system maintenance, allowing subsequent analysis to focus more on the performance of the weighing instrument itself. For example, if the acquisition error value is high, it indicates that there may be a large deviation in the original reading; correction can eliminate the interference of this deviation on the true performance evaluation. Similarly, firmware upgrades or component replacements in the maintenance records may cause a step change in the weighing instrument's reference value; correction can eliminate the impact of such reference value changes on the judgment of performance drift trends.

[0061] Subsequently, the performance drift data of the weighing instrument is determined based on multiple corrected error readings. The corrected error readings more accurately reflect the performance status of the weighing instrument at different points in time, thus allowing for a more precise analysis of its long-term performance trends. For example, by performing time series or regression analysis on the corrected error readings, the slow, continuous performance degradation trend caused by aging, wear, and other factors can be identified—that is, performance drift data. This drift data is a key indicator for assessing the health status of the weighing instrument and predicting its remaining lifespan.

[0062] Finally, error analysis is performed on the weighing instrument based on the acquired error value and performance drift data. This step combines the corrected performance drift trend with the error level in the original acquisition process for a comprehensive judgment. For example, if the performance drift data shows a clear aging trend in the weighing instrument, while the acquisition error value is low, it can be determined that the weighing instrument itself has aging errors. Conversely, if the performance drift data is relatively stable, but the acquisition error value remains consistently high, it may indicate an anomaly in the error reading acquisition process, such as improper manual operation or a defect in the automated identification system. Through this comprehensive analysis, this method can effectively distinguish between multiple error sources, such as the weighing instrument's own performance degradation, abnormal data acquisition, and benchmark changes caused by maintenance, avoiding the drawbacks of traditional methods that confuse all errors. This provides a reliable basis for accurate maintenance and risk warning of the weighing instrument.

[0063] The intelligent analysis method for weighing instrument verification and calibration data proposed in this application has significant advantages and innovations compared to existing technologies. Traditional methods for analyzing weighing instrument verification and calibration data often struggle to effectively uncover hidden patterns when processing massive amounts of historical data, and fail to effectively distinguish between performance degradation of the equipment itself, human recording bias, or benchmark changes caused by system maintenance. This approach leads to inaccurate predictions and may even mislead maintenance decisions.

[0064] The core innovation of this application lies in introducing "acquisition error values" and "maintenance records" to correct the original error readings, thereby obtaining "corrected error readings." This correction process is generally lacking in existing technologies. For example, existing systems typically use the original error readings directly for analysis, ignoring human or environmental errors that may be introduced during data acquisition, and failing to consider the potential impact of maintenance activities on the weighing instrument's reference. By stripping away and correcting these errors caused by factors other than the weighing instrument itself, this application can more accurately reflect the true performance of the weighing instrument, avoiding misjudgments caused by data noise and reference offsets.

[0065] Furthermore, this application determines the performance drift data of the weighing instrument based on the corrected error readings, and performs error analysis on the weighing instrument by combining the acquired error value and the performance drift data. This multi-dimensional and hierarchical analysis method enables this application to effectively distinguish between aging errors and abnormal error reading acquisition. For example, when performance drift data shows that the weighing instrument is aging, if the acquired error value is low, it can be accurately determined that the problem is due to the aging of the weighing instrument itself; while if the acquired error value is high, it may indicate a problem in the data acquisition process. This refined error analysis capability is difficult to achieve with existing technologies. Existing systems often treat all observed errors as the same "error," making it impossible to accurately identify the source of the error, thus affecting the accuracy of maintenance decisions.

[0066] In summary, this application effectively solves the problems of inaccurate data analysis and unclear distinction of error sources in the prior art by introducing the acquisition of multi-source error information, correction of error readings, and multi-dimensional error analysis. It significantly improves the accuracy and reliability of weighing instrument verification and calibration data analysis, and provides strong technical support for the precise maintenance and risk warning of weighing instruments.

[0067] This application further proposes a method for obtaining the acquisition error value of the error reading when the error reading is manually collected by the acquisition personnel, so as to improve the accuracy of the acquisition error value.

[0068] Specifically, such as Figure 2 As shown, when the error readings are manually collected by the data acquisition personnel, the acquisition error value of the error readings includes:

[0069] S201. During the acquisition of error readings, the heart rate and blink rate of the data acquisition personnel.

[0070] S202. Determine the fatigue index of the data collector based on heart rate and blink rate.

[0071] S203. Obtain the first preset correspondence relationship, and take the first error value corresponding to the fatigue index range in the first preset correspondence relationship as the acquisition error value.

[0072] The first preset correspondence includes a one-to-one correspondence between multiple fatigue index ranges and multiple first error values.

[0073] Acquiring the heart rate and blink rate of the data collector refers to acquiring physiological data of the data collector in real-time or near real-time during error reading collection, using wearable sensors, non-contact monitoring devices, or other physiological signal acquisition devices. Heart rate reflects the physiological load and mental stress level of the data collector, while blink rate is closely related to visual fatigue and concentration. These physiological indicators are considered effective parameters for assessing human fatigue. Furthermore, determining the fatigue index of the data collector based on heart rate and blink rate aims to transform complex physiological data into a quantifiable and easily processed indicator of fatigue level. This fatigue index can comprehensively reflect the mental and physical state of the data collector during the data collection process, thus providing a basis for assessing potential errors introduced by their operation.

[0074] In practical applications, a first preset correspondence is obtained. This correspondence can be understood as an empirical mapping table or model established through a large amount of experimental data. Its purpose is to associate different degrees of fatigue with specific error values. This correspondence ensures that the fatigue index can be effectively converted into an actual acquisition error value. Therefore, the first error value corresponding to the fatigue index range in the first preset correspondence is taken as the acquisition error value. This means that once the fatigue index of the acquisition personnel is determined, the system can find and determine a specific error value from the correspondence according to preset rules, as the acquisition error value introduced in this manual acquisition operation.

[0075] The proposed method quantifies the fatigue level of data acquisition personnel by monitoring their heart rate and blink rate, and correlates these values ​​with preset error values. This allows for an objective assessment and acquisition of the acquisition error introduced by manual operation. This method considers individual differences and fluctuations in condition that may occur during manual data acquisition, making the determination of acquisition error values ​​more scientific and precise. It is precisely because of the consideration of the physiological state of the data acquisition personnel that the acquisition error values ​​can more accurately reflect the actual situation, providing a more reliable basis for subsequent error correction and performance analysis.

[0076] By employing the aforementioned technical solution, the acquisition error value of the error reading can be obtained more accurately when the error reading is manually collected by the data acquisition personnel. This avoids the problem of inaccurate error value assessment caused by subjective factors such as personnel fatigue, thereby improving the reliability and accuracy of subsequent error correction and weighing instrument performance analysis. This makes the intelligent analysis method for weighing instrument verification and calibration data more practical and robust.

[0077] Specifically, the following methods can be used to determine the fatigue index of data collectors based on heart rate and blink rate.

[0078] like Figure 3As shown, based on the intelligent analysis method for weighing instrument verification and calibration data described above, the fatigue index of the data collector is determined according to heart rate and blink frequency, including:

[0079] S301. Normalize the absolute value of the difference between the heart rate and the normal heart rate of the data collector under normal conditions to obtain the heart rate deviation index.

[0080] S302. Normalize the absolute value of the difference between the blink frequency and the blink frequency of the data collector under normal conditions to obtain the blink frequency deviation index.

[0081] S303. The average of the heart rate deviation index and the blink rate deviation index is used as the fatigue index.

[0082] Specifically, the heart rate deviation index is a quantitative indicator obtained by normalizing the absolute value of the difference between the data collector's heart rate and their normal heart rate under normal conditions. Its purpose is to reflect the degree to which the heart rate deviates from the normal level. The normal heart rate can be understood as the average heart rate of the data collector in a non-working or relaxed state, which can be obtained through long-term monitoring or measurement under specific baseline conditions. The purpose of normalization is to eliminate the influence of individual differences in heart rate, making the heart rate deviation index comparable. Methods such as maximum-minimum normalization or Z-score normalization can be used.

[0083] The blink rate deviation index is a quantitative indicator obtained by normalizing the absolute value of the difference between the blink rate of the data collector and their normal blink rate under normal conditions. Its purpose is to reflect the degree to which the blink rate deviates from the normal level. The normal blink rate can be understood as the average blink rate of the data collector under non-fatigue conditions, which can also be obtained through long-term monitoring or measurement under specific baseline conditions. The principle of normalization is similar to that of the heart rate deviation index, aiming to standardize the deviation in blink rate.

[0084] In practical applications, the fatigue index can be understood as an indicator that comprehensively reflects the physiological fatigue state of data collectors. By using the average of the heart rate deviation index and the blink rate deviation index as the fatigue index, the degree of fatigue of data collectors can be assessed more comprehensively and objectively. For example, when both heart rate and blink rate deviate from normal values, the fatigue index will increase accordingly, indicating that the data collector may be in a state of fatigue.

[0085] This application's method quantifies heart rate deviation and blink rate deviation indices by monitoring the heart rate and blink rate of data collection personnel and comparing them to baseline values ​​under normal conditions. Heart rate and blink rate are important indicators reflecting human physiological state, and abnormal changes in these indices are often closely related to fatigue levels. By normalizing these deviations, the influence of individual differences and measurement units can be eliminated, allowing physiological data from different data collection personnel to be compared on a uniform scale. Finally, averaging these two normalized deviation indices comprehensively reflects the overall fatigue level of the data collection personnel, thus providing an objective and quantitative basis for subsequently determining the data collection error value.

[0086] The above technical solution enables an objective and quantitative assessment of the fatigue level of personnel based on their physiological characteristics (heart rate and blink rate). This assessment method avoids errors caused by subjective judgment, making the determination of the fatigue index more scientific and accurate. Consequently, it allows for more precise acquisition of data collection error values, thereby improving the accuracy of correcting original error readings and ultimately enhancing the overall reliability of intelligent analysis of weighing instrument calibration data.

[0087] This application further proposes a method for obtaining the acquisition error value of the error reading when the error reading is acquired by an image recognition device through image recognition, including:

[0088] The system obtains the ambient light intensity value of the environment where the weighing instrument is located and the edge sharpness of the image acquired by the image recognition device; determines the recognition accuracy of the image recognition based on the ambient light intensity value and the edge sharpness; obtains a second preset correspondence; the second preset correspondence includes a one-to-one correspondence between multiple recognition accuracy ranges and multiple second error values; and uses the second error value corresponding to the recognition accuracy range in the second preset correspondence as the acquisition error value.

[0089] Specifically, ambient light intensity refers to the light intensity of the surrounding environment when the weighing instrument is being calibrated. This value can be measured in real time using a light sensor, such as a lux meter or a light sensor integrated into an image recognition device. Its purpose is to quantify external environmental factors affecting image quality. Edge sharpness can be understood as the clarity or contrast of object edges in an image acquired by the image recognition device. This value can be calculated using image processing algorithms. For example, edge detection algorithms such as the Sobel operator, Laplacian operator, or Canny operator can be used to process the image and evaluate the gradient or intensity changes of the edges. Its purpose is to reflect the quality of the image itself, thus affecting the accuracy of recognition. Image recognition accuracy refers to the accuracy with which the image recognition device recognizes the weighing instrument readings under specific lighting and image quality conditions. This accuracy value is calculated by combining ambient light intensity and edge sharpness, and is used to quantify the reliability of the image recognition process.

[0090] In practical applications, the second preset correspondence is specifically a pre-established mapping table stored in a database or configuration file. Its purpose is to directly look up and obtain the corresponding acquisition error value based on the calculated recognition accuracy. This correspondence can be established and adjusted based on a large amount of experimental data, historical data, or expert experience to ensure its accuracy and applicability. For example, when the recognition accuracy is within a certain range, it corresponds to a specific second error value, which is used as the error estimate for the current image recognition acquisition.

[0091] This application's solution, by introducing two key parameters—ambient light intensity and edge sharpness—enables a more comprehensive and objective evaluation of the potential errors in image recognition device acquisition error readings. It is precisely because the accuracy of image recognition is easily affected by external environmental factors (such as lighting) and the image's own quality (such as sharpness) that it becomes possible to directly quantify acquisition error values ​​based on these factors. By combining ambient light intensity and edge sharpness to determine the recognition accuracy of image recognition, this application establishes an evaluation index that more closely reflects actual recognition performance.

[0092] Based on this, by utilizing a pre-defined second correspondence, the recognition accuracy is mapped to a specific second error value, thereby achieving refined and intelligent acquisition of image recognition acquisition error values. This method avoids the problem of insufficient or inaccurate estimation of image recognition errors that may exist in traditional methods, providing a more reliable basis for subsequent error reading correction.

[0093] Through the above technical solution, this application effectively addresses the problem that traditional methods fail to fully consider the impact of environment and image quality on the acquisition error value when collecting image recognition error readings. By comprehensively analyzing ambient light intensity and edge sharpness to determine the recognition accuracy of the image, and based on this, the acquisition error value is obtained, making the correction of the weighing instrument error reading more accurate. This method significantly improves the accuracy and reliability of acquisition error value assessment, thus providing a more solid data foundation for the intelligent analysis of weighing instrument verification and calibration data, ultimately improving the accuracy of weighing instrument performance evaluation and the efficiency of verification and calibration.

[0094] In some preferred embodiments, a specific example is given below. Assume that during a weighing instrument calibration process, the ambient light intensity of the environment in which the weighing instrument is located is measured to be 500 lux by a light sensor. After the image acquired by the image recognition device is processed by an edge detection algorithm, its edge sharpness is calculated to be 0.85. The system first calculates the current image recognition accuracy to be 92% based on a preset algorithm, combining the 500 lux ambient light intensity and the 0.85 edge sharpness. Subsequently, the system queries a pre-established second preset correspondence. This correspondence may be defined as follows: when the recognition accuracy is between 90% and 95%, the corresponding second error value is 0.05 units. Therefore, this application uses 0.05 units as the acquisition error value for this error reading. This acquisition error value is then used to correct the original error reading to eliminate or reduce errors introduced by environmental and image quality factors during the image recognition process, thereby obtaining a more accurate corrected error reading.

[0095] This application further proposes a method for determining the recognition accuracy of image recognition based on the aforementioned ambient light intensity value and edge sharpness, including:

[0096] The absolute value of the difference between the ambient light intensity value and the preset ambient light intensity value is normalized to obtain the light intensity deviation index; the absolute value of the difference between the edge sharpness and the preset edge sharpness is normalized to obtain the edge sharpness deviation index; the average value of the light intensity deviation index and the edge sharpness deviation index is used as the recognition accuracy.

[0097] Specifically, the ambient light intensity value refers to the actual light intensity of the environment in which the weighing instrument is located, which can be measured in real time by a light sensor. The preset ambient light intensity value refers to the light intensity value under ideal or standard working conditions that allows the image recognition device to achieve the best recognition effect. This value can be preset according to the characteristics of the image recognition device and the application scenario. The absolute value of the difference between the ambient light intensity value and the preset ambient light intensity value is normalized to eliminate the influence of different units and dimensions of light intensity, converting it into a dimensionless deviation index, namely the light intensity deviation index. This index can intuitively reflect the degree to which the current lighting conditions deviate from ideal conditions. For example, normalization can be achieved by dividing the absolute difference by the preset ambient light intensity value or the maximum value within a certain range, ensuring that the index is between 0 and 1.

[0098] Edge sharpness refers to the clarity of object edges in an image, reflecting image quality. Higher edge sharpness results in a clearer image and generally higher recognition accuracy. The preset edge sharpness refers to the optimal edge sharpness achievable by the image recognition device under ideal image acquisition conditions; this value can also be set according to the performance and application requirements of the image recognition device. Normalizing the absolute value of the difference between the current edge sharpness and the preset edge sharpness transforms the edge sharpness deviation into a standardized index, the edge sharpness deviation index, to measure the degree to which image quality deviates from the ideal state. For example, normalization can be achieved by dividing the absolute difference by the preset edge sharpness or its maximum value within a certain range.

[0099] In practical applications, the average of the illumination intensity deviation index and the edge sharpness deviation index is used as the recognition accuracy. This aims to comprehensively consider the impact of ambient lighting conditions and image quality on image recognition accuracy. By averaging these two factors, the weight of each can be balanced in the recognition accuracy evaluation, allowing the final recognition accuracy to more comprehensively and objectively reflect the performance of the image recognition device under current conditions.

[0100] This application's solution quantifies the deviations of ambient light intensity and image edge sharpness from the ideal state, respectively, and normalizes them into deviation indices. This unifies two influencing factors with different physical meanings and dimensions onto a comparable scale. Subsequently, by calculating the average of these two deviation indices, the overall recognition performance of the image recognition device under the current environmental and image quality conditions can be comprehensively evaluated. This method avoids the one-sidedness of single-factor evaluation and the weight imbalance problem that may result from directly using raw values, making the determination of recognition accuracy more scientific and reasonable.

[0101] The above technical solution provides a quantitative and comprehensive method for evaluating image recognition accuracy. This method fully considers the impact of ambient lighting and image quality—two key factors—on image recognition accuracy. Through standardized deviation indices and average value calculations, the determination of recognition accuracy becomes more objective and accurate. This allows for more reliable acquisition of error values ​​from error readings, thereby improving the accuracy and reliability of intelligent analysis of weighing instrument calibration data and providing more precise data support for the performance evaluation and maintenance of weighing instruments.

[0102] In some embodiments of this application, the above-mentioned correction of the error reading based on the collected error value and maintenance records to obtain the corrected error reading specifically includes the following steps:

[0103] The maintenance error value of the weighing instrument generated by the maintenance record is determined based on the maintenance record; the difference between the error reading and the first value is used as the correction error reading; the first value is the sum of the acquisition error value and the maintenance error value.

[0104] Specifically, when calibrating a weighing instrument, the first step is to determine the potential errors introduced by maintenance operations based on the instrument's maintenance records. Maintenance records can contain various information, such as the type of maintenance, the time of maintenance, and the parts replaced. This information can be used to assess the potential systematic or random errors in the weighing instrument after maintenance, thus obtaining a quantified maintenance error value. For example, certain types of maintenance operations may temporarily alter some performance parameters of the weighing instrument, causing a certain deviation in its readings.

[0105] Furthermore, after obtaining the weighing instrument's error reading, the acquisition error value, and the aforementioned determined maintenance error value, a corrected error reading is obtained by subtracting a first value from the original error reading. This first value is defined as the sum of the acquisition error value and the maintenance error value. This means that the original error reading includes both the error introduced by the acquisition process and the error introduced by the weighing instrument's maintenance operations. By subtracting the sum of these two errors from the original error reading, a more accurate corrected error reading that better reflects the actual performance of the weighing instrument can be obtained.

[0106] This application's solution quantifies and subtracts the acquisition error and maintenance error values ​​contained in the original error readings, thereby achieving effective correction of the error readings. Specifically, the acquisition error value reflects potential deviations during data acquisition, such as fatigue during manual acquisition or environmental influences during image recognition; while the maintenance error value reflects potential performance changes in the weighing instrument after maintenance. By separating these two known error sources from the original error readings, subsequent performance drift data determination and error analysis can focus more on the inherent performance changes of the weighing instrument itself, rather than the effects of external interference or maintenance operations. This ensures the accuracy and reliability of subsequent analyses.

[0107] The above technical solutions effectively eliminate or reduce the impact of errors introduced during data acquisition and weighing instrument maintenance on the original error readings. This makes the obtained corrected error readings more accurately reflect the actual performance of the weighing instrument during the verification and calibration process, thus providing more accurate and reliable basic data for subsequent performance drift data determination and error analysis. Consequently, the accuracy and effectiveness of intelligent analysis of weighing instrument verification and calibration data are significantly improved, avoiding misjudgments or analytical biases caused by external factors.

[0108] In some embodiments described above in this application, a method for determining the maintenance error value of a weighing instrument based on maintenance records is proposed. However, in its implementation, if the determination of the maintenance error value lacks standardized and quantitative basis, the assessment of the error value may be subjective or inaccurate, thereby affecting the accuracy of the final corrected error reading. If the above problems are not addressed, the analysis results of the calibration data of the weighing instrument may not fully reflect the true state of the instrument, thus affecting the accurate judgment of the performance drift and error source of the instrument. To address this, this application further proposes a more accurate and systematic method for determining the maintenance error value of a weighing instrument by introducing a preset correspondence between maintenance type and error value to achieve a quantitative assessment of the maintenance error value.

[0109] In this regard, this application further proposes the method for determining the maintenance error value of a weighing instrument based on maintenance records, wherein the maintenance records include the maintenance type, and determining the maintenance error value of the weighing instrument based on the maintenance records includes:

[0110] Obtain the third preset correspondence; the third preset correspondence includes a one-to-one correspondence between multiple maintenance types and multiple third error values; use the third error value corresponding to the maintenance type in the maintenance record in the third preset correspondence as the maintenance error value.

[0111] Specifically, maintenance records refer to detailed records of operations such as servicing, repairing, and replacing parts on weighing instruments. These records typically include the maintenance date, the personnel performing the maintenance, and the key maintenance type. Maintenance type can be understood as a classification of specific maintenance operations performed on the weighing instrument, such as "routine cleaning," "parts replacement," "sensor calibration," "mechanical adjustment," and "software upgrade." Different maintenance types may have varying degrees of impact on the performance of the weighing instrument, and therefore, their corresponding maintenance error values ​​will also differ. The third preset correspondence refers to a pre-established set of data used to associate different maintenance types with their corresponding third error values. This correspondence can be stored in a database, configuration file, or lookup table. The third error value is a quantified error value determined for each maintenance type based on historical data, expert experience, or experimental test results. For example, for the maintenance type "parts replacement," if a core measuring component is replaced, the introduced error value may be relatively large; while for "routine cleaning," the introduced error value may be small or even zero. The "one-to-one correspondence" in the third preset correspondence means that each specific maintenance type uniquely corresponds to a third error value, thereby ensuring the uniqueness and accuracy of the determined maintenance error value.

[0112] In practical applications, obtaining the third pre-defined correspondence can be achieved in several ways. For example, based on a large amount of historical verification and calibration data, the changing trends of the weighing instrument's error after different maintenance operations can be analyzed, and this correspondence can be established using statistical methods. Alternatively, an expert system can be used, allowing experienced verification personnel to assess and quantify the errors that may be introduced by various maintenance types based on their professional knowledge and experience. The purpose is to provide an objective and quantifiable maintenance error value for subsequent error correction.

[0113] This application's solution addresses the subjectivity and inaccuracy issues in determining maintenance error values ​​in the basic solution by introducing a third preset correspondence between maintenance types and third error values. Specifically, when the maintenance record of the weighing instrument is obtained, the specific maintenance type is first identified from the record. Then, using the pre-established third preset correspondence, the identified maintenance type is matched with a preset third error value. Since this correspondence is based on historical data, expert experience, or experimental test results, the third error value determined in this way has high objectivity and quantifiability. Therefore, using this quantified third error value as the maintenance error value of the weighing instrument can more accurately reflect the potential impact of maintenance operations on the instrument's performance, thus providing a more reliable basis for subsequent error reading correction.

[0114] Through the above technical solution, this application enables the precise quantification and standardized determination of weighing instrument maintenance error values. Compared to the potentially vague or subjective maintenance error value assessments in basic solutions, this solution establishes a third preset correspondence between maintenance types and third error values, making the acquisition of maintenance error values ​​more objective, repeatable, and consistent. This significantly improves the accuracy of correction error readings, thereby enhancing the overall reliability of intelligent analysis of weighing instrument verification and calibration data. By more accurately quantifying the impact of maintenance operations on weighing instrument performance, it is possible to more effectively identify and distinguish between errors caused by maintenance and errors caused by drift in the weighing instrument's own performance, thus providing more accurate data support for weighing instrument maintenance decisions and performance evaluation.

[0115] This application further proposes the following steps for determining the performance drift data of a weighing instrument based on multiple correction error readings:

[0116] Based on the acquisition time of each correction error reading among multiple correction error readings, a moving average filter is applied to the multiple correction error readings to obtain multiple smoothed error readings; linear regression analysis is performed on the multiple smoothed error readings to obtain a linear fitting line; the slope of the linear fitting line is used as performance drift data.

[0117] Specifically, moving average filtering is a commonly used digital signal processing technique. Its purpose is to smooth data by calculating the average value of data within a certain window in a data sequence, thereby eliminating short-term fluctuations and random noise and highlighting the long-term trend of the data. The window size of the moving average filter can be adjusted according to the actual application scenario and data characteristics; for example, it can be set to three, five, or more consecutive correction error readings. By applying moving average filtering to the correction error readings, a series of smoothed error readings can be obtained, which can more clearly reflect the true trend of the weighing instrument's performance.

[0118] Furthermore, linear regression analysis is a statistical method used to establish a linear relationship model between a dependent variable (in this case, the smoothed error reading) and one or more independent variables (in this case, the time of data collection). Its purpose is to describe the trend between data points by fitting a straight line (i.e., the linear regression line). By performing linear regression analysis on the smoothed error reading, a linear regression line can be obtained, which can intuitively represent the overall trend of the weighing instrument's error changing over time. The slope of the linear regression line is a key parameter for measuring this trend. The sign and magnitude of the slope directly reflect the direction and rate of drift in the weighing instrument's performance. For example, a positive slope indicates that the weighing instrument's error increases with time, a negative slope indicates that the error decreases with time, and a larger absolute value of the slope indicates a faster drift rate. Therefore, using the slope of the linear regression line as performance drift data can quantify the degree to which the weighing instrument's performance changes over time.

[0119] This application's solution effectively solves the problem of inaccurate drift judgment that may result from directly using the original corrected error readings by introducing moving average filtering and linear regression analysis. First, the moving average filtering step effectively filters out random noise and short-term fluctuations in the corrected error readings, making the data sequence smoother and thus more clearly showing the long-term trend of the weighing instrument's performance. Second, performing linear regression analysis on the smoothed error readings after filtering objectively establishes a linear relationship between error and time and fits a linear fitting line. The slope of this linear fitting line, as performance drift data, can accurately quantify the rate and direction of change in the weighing instrument's performance over time, avoiding the bias of subjective judgment. It is precisely this combination of data smoothing and trend quantification that makes the assessment of weighing instrument performance drift more accurate and reliable.

[0120] The above technical solution effectively reduces the impact of random noise and short-term fluctuations in the correction error readings on performance drift judgment, making the determination of performance drift data more accurate and stable. This solution provides an objective method for quantifying the performance drift of weighing instruments, helping to more accurately assess the aging degree and long-term stability of the instruments, thus providing a more reliable basis for instrument maintenance and calibration, and improving the overall reliability and effectiveness of intelligent analysis of weighing instrument verification and calibration data.

[0121] This application further proposes an intelligent analysis method for weighing instrument verification and calibration data. Its error analysis steps include judging performance drift data and acquisition error values ​​to determine whether the weighing instrument has aging error or abnormal error reading acquisition.

[0122] The aforementioned intelligent analysis method for weighing instrument verification and calibration data, when performing error analysis on the weighing instrument based on the collected error values ​​and performance drift data, specifically includes:

[0123] Determine if the performance drift data is greater than the preset performance drift threshold; if so, determine if the weighing instrument has aging error; determine if the acquisition error value is greater than the preset acquisition error threshold; if so, determine if the error reading acquisition of the weighing instrument is abnormal.

[0124] Specifically, performance drift data refers to the trend of performance parameters of a weighing instrument over a period of time, reflecting the wear, aging, or material fatigue of its internal components. The preset performance drift threshold is a critical value determined based on factors such as the instrument's design life, operating environment, historical data, and industry standards. It defines the normal range of performance drift. When performance drift data exceeds this threshold, it indicates that the weighing instrument may have entered its aging stage and requires appropriate maintenance or replacement.

[0125] The acquisition error value refers to the deviation introduced during the error reading acquisition process, which may originate from human error, environmental interference, or insufficient image recognition accuracy. The preset acquisition error threshold is an upper limit set based on factors such as the actual application scenario, data acquisition method, and data accuracy requirements, used to evaluate the reliability of the acquisition process. When the acquisition error value exceeds this threshold, the acquisition process is considered abnormal, potentially leading to deviations in subsequent analysis results, necessitating inspection and optimization of the acquisition process.

[0126] This application's solution addresses the lack of specificity in error analysis results in the basic scheme by introducing threshold judgments for performance drift data and acquisition error values. When performance drift data is judged to exceed a preset performance drift threshold, this directly indicates a potential aging problem in the weighing instrument. This judgment mechanism enables early warning of performance degradation, allowing for timely preventative maintenance and extending the instrument's lifespan. Simultaneously, when the acquisition error value is judged to exceed a preset acquisition error threshold, it clearly indicates an anomaly in the error reading acquisition process. This helps quickly pinpoint the source of the problem, such as improper human operation, harsh environmental conditions, or image recognition system malfunction, enabling targeted improvement measures to ensure the accuracy and reliability of subsequent data. Through this dual judgment mechanism, this application's solution provides more instructive error analysis results, fundamentally improving the intelligence level of weighing instrument verification and calibration data analysis.

[0127] The aforementioned technical solutions enable a more detailed diagnosis of the sources of error in weighing instruments, distinguishing between performance degradation caused by the aging of the instrument itself and errors introduced by abnormalities in the data acquisition process. This ability to differentiate allows maintenance personnel to take targeted measures based on the specific type of error. For example, they can replace or overhaul aging weighing instruments, or optimize the acquisition environment or improve acquisition methods for data acquisition anomalies. This improves the efficiency and accuracy of verification and calibration work, avoids blind maintenance or repeated calibration, and significantly reduces operating costs.

[0128] In some preferred embodiments, assuming that during continuous verification and calibration of a weighing instrument, linear regression analysis of multiple correction error readings yields a performance drift of 0.05 units / month, and the acquisition error calculated based on the data collector's heart rate and blink rate is 0.02 units, then the system sets the preset performance drift threshold to 0.03 units / month and the preset acquisition error threshold to 0.015 units.

[0129] First, the system determines whether the performance drift value of 0.05 is greater than the preset performance drift threshold of 0.03. Since 0.05 > 0.03, the system determines that the weighing instrument has aging error. This indicates that the weighing instrument may be experiencing a gradual decline in performance due to wear and tear on its internal components caused by long-term use.

[0130] Secondly, the system checks whether the acquisition error value of 0.02 is greater than the preset acquisition error threshold of 0.015. Since 0.02 > 0.015, the system determines that there is an anomaly in the acquisition error reading of the weighing instrument. This may mean that the acquisition personnel were highly fatigued during the data acquisition process, or that factors such as ambient lighting affected the acquisition accuracy.

[0131] Based on the above assessment, the system can clearly point out that the weighing instrument not only has its own aging problem, but also that the process of collecting its verification and calibration data has reliability risks, thus providing clear and specific guidance for subsequent maintenance decisions.

[0132] This application proposes an intelligent analysis system for weighing instrument verification and calibration data, including: an acquisition device and a processing device;

[0133] The instrument is equipped with an acquisition device for acquiring multiple error readings, acquisition error values ​​of the error readings, and maintenance records of the instrument during the verification and calibration process; a processing device for correcting the error readings based on the acquisition error values ​​and maintenance records to obtain corrected error readings; a processing device for determining the performance drift data of the instrument based on multiple corrected error readings; and a processing device for performing error analysis on the instrument based on the acquisition error values ​​and performance drift data.

[0134] This application constructs an acquisition device and a processing device to realize the intelligent acquisition, correction and analysis of multi-source error information, thereby more accurately reflecting the true performance of the weighing instrument and further analyzing its performance drift and potential anomalies, effectively improving the accuracy and reliability of weighing instrument verification and calibration data analysis.

[0135] This system can be implemented in various environments, such as weighing instrument calibration laboratories, production workshops, or on-site calibration points. Data can be acquired through various methods, including manual entry via acquisition devices, automatic sensor acquisition, and image recognition, and then processed and analyzed by processing devices.

[0136] The intelligent analysis system for weighing instrument verification and calibration data proposed in this application is based on the collaborative work of the acquisition device and the processing device to achieve comprehensive consideration and correction of multi-source errors, thereby achieving more accurate performance evaluation of weighing instruments.

[0137] Specifically, the acquisition device can be configured in various forms to acquire multiple error readings, acquisition error values ​​of the error readings, and maintenance records of the weighing instrument during the verification and calibration process. For example, the acquisition device can be a standalone hardware module integrating a data interface, sensor interface, and storage unit for direct connection to the weighing instrument or external sensors to acquire data. In another implementation, the acquisition device can be a software module running on a general-purpose computer or server, receiving data from other data sources (such as manual data entry systems, image recognition systems, or maintenance management systems) via a network interface or file system. As a preferred embodiment, the acquisition device may include one or more data acquisition interfaces, such as RS232 serial ports, USB interfaces, or Ethernet interfaces, for communicating with the weighing instrument to acquire error readings. Furthermore, the acquisition device may include a user interface module for receiving manually input acquisition error values ​​or maintenance records. Through the above configuration, the acquisition device can effectively aggregate multiple error readings, acquisition error values ​​of the error readings, and maintenance records of the weighing instrument during the verification and calibration process, providing a comprehensive data foundation for subsequent processing.

[0138] The processing device can be configured to perform core functions such as data correction, performance drift determination, and error analysis. For example, the processing device can be a high-performance central processing unit (CPU) or graphics processing unit (GPU), coupled with memory and storage devices, to run pre-defined algorithm programs. In some implementations, the processing device can be a distributed computing system, with multiple computing nodes collaboratively completing complex analysis tasks. As a specific implementation, the processing device can be an embedded system integrated within the weighing instrument or calibration equipment to process data in real time. The processing device may also include a database module for storing raw data, corrected data, performance drift data, and analysis results. With the above configuration, the processing device can correct the error readings based on the acquisition error values ​​and maintenance records provided by the acquisition device, obtaining corrected error readings. Further, the processing device can determine the performance drift data of the weighing instrument based on multiple corrected error readings. Finally, the processing device can perform error analysis on the weighing instrument based on the acquisition error values ​​and performance drift data. The detailed implementation of these processing steps is similar to the corresponding steps described in the above method embodiments and will not be repeated here.

[0139] The core innovation of this application lies in providing a systematic solution that, through the collaboration of an acquisition device and a processing device, achieves the acquisition of multi-source error information, the correction of error readings, and multi-dimensional error analysis. The acquisition device comprehensively and accurately collects multiple error readings, error values, and maintenance records from the weighing instrument during the verification and calibration process, laying the foundation for subsequent precise analysis. The processing device then corrects the original error readings, effectively eliminating errors introduced by factors other than the weighing instrument itself, such as manual operation, automated acquisition environment, or system maintenance, thus more accurately reflecting the true performance of the weighing instrument. This systematic correction process is generally lacking in existing technologies, avoiding misjudgments caused by data noise and benchmark offsets.

[0140] Furthermore, this system uses a processing device to determine the performance drift data of the weighing instrument based on the corrected error readings, and combines the acquired error value and performance drift data to perform error analysis on the weighing instrument. This multi-dimensional and hierarchical system analysis capability enables the system to effectively distinguish between aging errors and abnormal error reading acquisition. For example, when performance drift data shows that the weighing instrument is showing an aging trend, if the acquired error value is low, the system can accurately determine that it is an aging problem of the weighing instrument itself; while if the acquired error value is high, it may indicate a problem in the data acquisition process. This refined error analysis capability is difficult to achieve with existing technologies. Existing systems often treat all observed errors as the same "error," making it impossible to accurately identify the source of the error, thus affecting the accuracy of maintenance decisions.

[0141] In summary, this application effectively solves the problems of inaccurate data analysis and unclear error sources in the prior art by constructing an intelligent analysis system for acquisition and processing devices, significantly improving the accuracy and reliability of weighing instrument verification and calibration data analysis, and providing strong technical support for the precise maintenance and risk warning of weighing instruments.

[0142] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for intelligent analysis of weighing instrument verification and calibration data, characterized in that, include: Acquire multiple error readings of the weighing instrument during the verification and calibration process, the acquisition error value of the error readings, and the maintenance records of the weighing instrument; The error reading is corrected based on the collected error value and the maintenance record to obtain the corrected error reading; The performance drift data of the weighing instrument is determined based on multiple corrected error readings; The weighing instrument is subjected to error analysis based on the acquisition error value and the performance drift data.

2. The intelligent analysis method for weighing instrument verification and calibration data according to claim 1, characterized in that, When the error reading is manually collected by the data collector, the acquisition error value of the error reading is obtained, including: The heart rate and blink rate of the data acquisition personnel during the acquisition of the error readings; The fatigue index of the data collector is determined based on the heart rate and the blink rate. Obtain a first preset correspondence; the first preset correspondence includes a one-to-one correspondence between multiple fatigue index ranges and multiple first error values; The first error value corresponding to the fatigue index range in the first preset correspondence is taken as the acquisition error value.

3. The intelligent analysis method for weighing instrument verification and calibration data according to claim 2, characterized in that, The fatigue index of the data collector is determined based on the heart rate and blink frequency, including: The absolute value of the difference between the heart rate and the normal heart rate of the data collector under normal conditions is normalized to obtain the heart rate deviation index. The absolute value of the difference between the blink frequency and the blink frequency of the data collector under normal conditions is normalized to obtain the blink frequency deviation index. The average of the heart rate deviation index and the blink frequency deviation index is used as the fatigue index.

4. The intelligent analysis method for weighing instrument verification and calibration data according to claim 1, characterized in that, When the error reading is acquired by an image recognition device through image recognition, obtaining the acquisition error value of the error reading includes: The ambient light intensity value of the environment in which the weighing instrument is located and the edge sharpness of the image acquired by the image recognition device are obtained; The recognition accuracy of image recognition is determined based on the ambient light intensity value and the edge sharpness. Obtain a second preset correspondence; the second preset correspondence includes a one-to-one correspondence between multiple recognition accuracy ranges and multiple second error values; The second error value corresponding to the recognition accuracy range in the second preset correspondence is taken as the acquisition error value.

5. The intelligent analysis method for weighing instrument verification and calibration data according to claim 4, characterized in that, Determining the recognition accuracy of image recognition based on the ambient light intensity value and the edge sharpness includes: The absolute value of the difference between the ambient light intensity value and the preset ambient light intensity value is normalized to obtain the light intensity deviation index. The absolute value of the difference between the edge sharpness and the preset edge sharpness is normalized to obtain the edge sharpness deviation index; The average of the illumination intensity deviation index and the edge sharpness deviation index is used as the recognition accuracy.

6. The intelligent analysis method for weighing instrument verification and calibration data according to claim 1, characterized in that, The error reading is corrected based on the collected error value and the maintenance record to obtain a corrected error reading, including: The maintenance error value of the weighing instrument generated by the maintenance record is determined based on the maintenance record; The difference between the error reading and the first value is used as the corrected error reading; the first value is the sum of the acquisition error value and the maintenance error value.

7. The intelligent analysis method for weighing instrument verification and calibration data according to claim 6, characterized in that, The maintenance record includes the maintenance type, and the maintenance error value of the weighing instrument is determined based on the maintenance record, including: Obtain a third preset correspondence; the third preset correspondence includes a one-to-one correspondence between multiple maintenance types and multiple third error values; The third error value corresponding to the maintenance type in the maintenance record of the third preset correspondence is used as the maintenance error value.

8. The intelligent analysis method for weighing instrument verification and calibration data according to claim 1, characterized in that, The performance drift data of the weighing instrument is determined based on multiple correction error readings, including: Based on the acquisition time of each correction error reading among multiple correction error readings, a moving average filter is applied to the multiple correction error readings to obtain multiple smooth error readings; Linear regression analysis was performed on multiple smoothing error readings to obtain a linear fitting line; The slope of the linear fitting line is used as the performance drift data.

9. The intelligent analysis method for weighing instrument verification and calibration data according to claim 1, characterized in that, Based on the acquisition error value and the performance drift data, an error analysis is performed on the weighing instrument, including: Determine whether the performance drift data is greater than a preset performance drift threshold; If so, it is determined that the weighing instrument has an aging error; Determine whether the acquisition error value is greater than a preset acquisition error threshold; If so, it is determined that there is an abnormality in the acquisition of the error reading of the weighing instrument.

10. A smart analysis system for weighing instrument verification and calibration data, characterized in that, include: Acquisition device and processing device; The acquisition device is used to acquire multiple error readings of the weighing instrument during the verification and calibration process, the acquisition error value of the error readings, and the maintenance record of the weighing instrument. The processing device is used to correct the error reading based on the collected error value and the maintenance record to obtain a corrected error reading; The processing device is used to determine the performance drift data of the weighing instrument based on multiple correction error readings. The processing device is used to perform error analysis on the weighing instrument based on the acquisition error value and the performance drift data.

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