Method and system for dynamic calibration of measuring instruments based on load index and precision decay

Abstract

This invention discloses a dynamic calibration method and system for measuring instruments based on load indicators and accuracy decay, belonging to the field of intelligent management technology for measuring instruments. This invention solves the problems of fixed-cycle calibration, unquantified multi-dimensional load losses, inability to predict accuracy deviations, and lack of accurate recommendations for borrowing in existing technologies. This invention collects usage events, load intensity, and historical evaluation data of measuring instruments to calculate a comprehensive load score, constructs an accuracy decay sequence, fits a curve to obtain a decay trend score, and generates a comprehensive accuracy score through weighted fusion. Based on the decay curve, it predicts the time of deviation and dynamically calculates the calibration cycle. When instruments are borrowed, a recommendation list is output based on the comprehensive accuracy score. This invention achieves individualized dynamic adjustment of the calibration cycle of measuring instruments, reduces the cost of over-calibration, improves metrological reliability, and provides intelligent borrowing recommendations and quantitative disposal decision support, making it suitable for the refined management of large-scale measuring instruments in industrial settings.

Description

Technical Field

[0001] This invention relates to the field of intelligent management technology for measuring instruments, specifically to a dynamic calibration method and system for measuring instruments based on load indicators and accuracy decay. Background Technology

[0002] Measuring instruments are core infrastructure equipment for ensuring accurate and reliable data in industrial production, quality inspection, and scientific experiments. Regular calibration is a necessary means to maintain their metrological performance. However, the current industry generally adopts a "one-size-fits-all" fixed-period calibration model, which does not take into account the individual differences in usage frequency, working hours, and load intensity of different instruments. This leads to unnecessary human and material costs and downtime due to over-calibration of low-load instruments, while high-load instruments may experience accuracy deviations due to untimely calibration, thus triggering metrological risks.

[0003] Existing metrology management systems often simply record usage frequency and calibration time, failing to integrate multi-dimensional load data to quantify actual instrument wear and tear. They also lack accuracy degradation models based on calibration history, making it impossible to predict deviation times and accuracy degradation trends, leaving systems reliant on passive waiting for inspections. Furthermore, in scenarios involving multiple personnel sharing equipment, it's impossible to distinguish current accuracy differences among instruments of the same specification. Random borrowing by operators can easily lead to the use of low-precision instruments in critical processes. The lack of quantification for rapidly degrading instruments also allows for subjective decisions by management personnel, potentially resulting in waste or safety hazards. These systems are no longer adequate to meet the urgent needs of refined and dynamic management of metrology instruments in the context of industrial intelligent transformation. Summary of the Invention

[0004] The purpose of this invention is to provide a dynamic calibration method and system for measuring instruments based on load index and accuracy decay, thereby solving the problems mentioned in the background art.

[0005] This invention is achieved through the following technical solution: A dynamic calibration method for measuring instruments based on load index and accuracy decay includes the following steps: S1. Collect three types of raw data: usage event data of measuring instruments, load intensity data, and historical assessment data; S2. Calculate the usage frequency and effective usage time based on the usage event data of individual appliances, calculate the load intensity based on the load intensity data, and obtain a comprehensive load score by weighting and normalizing the usage frequency, effective usage time and load intensity. S3. Construct an accuracy decay sequence based on historical evaluation data, use linear regression to fit the accuracy decay curve to obtain the decay rate of a single device, and calculate the decay trend score based on the decay rate of a single device; S4. The comprehensive load score and the attenuation trend score are weighted and fused according to the preset weights to obtain the comprehensive accuracy score of the instrument; S5. Based on the accuracy decay curve, predict the out-of-tolerance time of the instrument, and combine the basic assessment cycle and safety margin to dynamically calculate and suggest the assessment cycle; S6. When receiving an equipment borrowing request, filter out candidate equipment that matches the type and specifications, is available in the current state, and is within the validity period of the evaluation. Then, sort the equipment by comprehensive accuracy score from high to low and output a borrowing recommendation list.

[0006] Furthermore, The effective usage time is calculated as follows: first, calculate the effective usage time of a single session within the period, and use the difference between the end time and start time of a single session to represent it, and filter out false trigger records where the difference is ≤ the effective threshold. Then, the effective usage time is obtained by summing up all the single effective usage times within the period.

[0007] Furthermore, Usage frequency is the ratio of the total number of uses within a cycle to the number of days in the cycle; load intensity is the arithmetic mean of the average of all single average load rates within a cycle and the maximum of all single peak load rates within a cycle.

[0008] Furthermore, The weighted normalization of the comprehensive load score is as follows: Load Score =100×(w1×F norm +w2×T norm +w3×L norm ), where F norm To normalize the frequency of use, T norm To normalize the effective usage time, L norm To normalize load intensity, the comprehensive load score is calculated. Score The value range is 0-100, and w1, w2, and w3 are weighted weights.

[0009] Furthermore, The specific steps for constructing the accuracy decay sequence are as follows: extract the evaluation date Dn and evaluation error value En from the evaluation records of the instrument in each evaluation, remove abnormal records whose evaluation error value exceeds the preset abnormal threshold, and invalid records whose evaluation date is earlier than the manufacturing date of the instrument, and then form an ordered sequence {(D1,E1),(D2,E2),...,(Dn,En)} according to the evaluation date; Using an ordered sequence as input, a linear regression method is used to fit the accuracy decay curve. With the number of days t since the first assessment as the independent variable and the assessment error value E as the dependent variable, the decay curve formula E(t) = a × t + b is obtained, where a is the decay rate of a single instrument, a positive value indicates a decrease in accuracy, b reflects the initial accuracy state of the instrument, and E(t) is the instrument error value corresponding to any time t.

[0010] Furthermore, Decline trend score DegScore The calculation method is as follows: first calculate the normalized decay rate a norm =a / a max Where a is the attenuation rate of a single device, a max The maximum allowable attenuation rate for appliances of the same category; then according to the formula Deg Score =max(0,100-k×a norm ) Calculate the decay trend score, where k is the preset score amplification factor, with a default value of 100; Where the calculated normalized decay rate a norm When the value is greater than 1, directly take a. norm =1, corresponding to a decay trend score of 0; when a<0, it is judged as data abnormality, and the average decay rate of the same type of equipment is used for calculation.

[0011] Furthermore, The weighted fusion of the overall accuracy score is as follows: Prec Score =β×Load Score +γ×Deg Score , where β is the load score weight and γ is the decay trend score weight.

[0012] Furthermore, The dynamic calculation suggests that the evaluation cycle specifically includes: solving E(t) = E based on the accuracy decay curve. max t at time max E max t represents the maximum allowable error of the instrument. max The estimated time out of tolerance from the initial assessment; calculate T. recommend =min(T base ,t max -t now -t margin ), where T base Based on the evaluation cycle, t now t represents the current time. margin For safety margin; T recommend Map to the preset cycle level and output the corresponding recommended evaluation cycle.

[0013] Furthermore, When the calculated recommended evaluation period is less than the minimum preset period, the system automatically marks the status of the appliance as "to be evaluated", pushes a repair or replacement prompt to the metering administrator, and calls the cost comparison model to calculate the costs of the three options of continued metering, repair, and replacement, and outputs the conclusion of the most cost-effective disposal.

[0014] Furthermore, A dynamic calibration system for measuring instruments based on load index and accuracy decay includes: The data acquisition module is used to collect usage event data, load intensity data, and historical evaluation data of the measuring instruments; The load index calculation module, connected to the data acquisition module, is used to calculate the normalized usage frequency, normalized effective usage duration, and normalized load intensity, and to synthesize a comprehensive load score. The accuracy decay analysis module, connected to the data acquisition module, is used to construct the accuracy decay sequence, fit the accuracy decay curve, calculate the decay rate, and calculate the decay trend score. The weighted fusion engine is connected to the load index calculation module and the accuracy attenuation analysis module respectively, and is used to obtain a comprehensive accuracy score through weighted fusion. The period dynamic adjustment module, connected to the accuracy decay analysis module, is used to predict the out-of-tolerance time and calculate the recommended evaluation period; The intelligent recommendation module, connected to the weighted fusion engine, is used to filter candidate devices and sort them according to the comprehensive accuracy score to output a borrowing recommendation list; The disposal suggestion module, connected to the cycle dynamic adjustment module, is used to push disposal prompts and call the cost comparison model to output disposal conclusions when the suggested evaluation cycle is less than the minimum preset cycle.

[0015] The beneficial effects of this invention are as follows: 1. This invention enables dynamic and individualized adjustment of the calibration cycle of measuring instruments, completely abandoning the traditional "one-size-fits-all" fixed-cycle calibration mode. By integrating multi-dimensional usage load data and accuracy decay trends of instruments, a calibration cycle adapted to the actual usage state is generated for each instrument. This avoids the waste of manpower and material resources and production downtime losses caused by over-calibration of low-load instruments, and can also shorten the calibration interval of high-load and rapidly decaying instruments in a timely manner. It effectively prevents accuracy deviation problems caused by untimely calibration, and significantly improves the economy and reliability of metrological management.

[0016] 2. This invention constructs a multi-dimensional load quantification assessment system, which breaks through the limitation of existing technologies that only measure appliance wear by the number of uses. It comprehensively considers four core indicators: usage frequency, effective usage time, average load rate, and peak load rate, fully covering the appliance's usage frequency, continuous working time, daily continuous wear and tear, and extreme impact wear. At the same time, it uses a benchmark of similar appliances for normalization, realizing a fair horizontal comparison of load levels among appliances of the same specifications, and providing a scientific and accurate quantitative basis for comprehensive accuracy assessment.

[0017] 3. This invention establishes a precision decay prediction model based on historical calibration data. By extracting the dates and error values ​​of each calibration to construct a time-ordered sequence, and using a linear regression method to fit the precision decay curve, it can accurately quantify the precision decay rate of the instrument and predict the future out-of-tolerance time. This realizes the transformation from a "passive due calibration" to a "proactive prediction and early warning" management mode. Combined with a preset safety margin mechanism, it can avoid metrological risks in advance and ensure the continuity and accuracy of the production and testing process.

[0018] 4. This invention achieves intelligent and precise recommendation for the borrowing of measuring instruments. By weighted fusion of comprehensive load score and attenuation trend score, a unified comprehensive accuracy score is generated. When an operator initiates a borrowing request, the system automatically filters out candidate instruments that meet the requirements and outputs a recommendation list in descending order of comprehensive accuracy. This ensures that instruments with higher accuracy and better reliability are prioritized for allocation to key measuring links, completely solving the problem of misuse of low-precision instruments in the traditional random borrowing mode and effectively improving the overall quality of measuring data. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the overall logic flow of the present invention; Figure 2 This is a schematic diagram of the system architecture of the present invention. Detailed Implementation

[0020] The present invention will be further described in detail below with reference to the embodiments and accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0021] See the example. Figures 1 to 2 : A dynamic calibration method for measuring instruments based on load index and accuracy decay includes the following steps: S1. Collect three types of raw data: usage event data of measuring instruments, load intensity data, and historical assessment data; S2. Calculate the usage frequency and effective usage time based on the usage event data of individual appliances, calculate the load intensity based on the load intensity data, and obtain a comprehensive load score by weighting and normalizing the usage frequency, effective usage time and load intensity. S3. Construct an accuracy decay sequence based on historical evaluation data, use linear regression to fit the accuracy decay curve to obtain the decay rate of a single device, and calculate the decay trend score based on the decay rate of a single device; S4. The comprehensive load score and the attenuation trend score are weighted and fused according to the preset weights to obtain the comprehensive accuracy score of the instrument; S5. Based on the accuracy decay curve, predict the out-of-tolerance time of the instrument, and combine the basic assessment cycle and safety margin to dynamically calculate and suggest the assessment cycle; S6. When receiving an equipment borrowing request, filter out candidate equipment that matches the type and specifications, is available in the current state, and is within the validity period of the evaluation. Then, sort the equipment by comprehensive accuracy score from high to low and output a borrowing recommendation list.

[0022] Furthermore, The effective usage time is calculated as follows: first, calculate the effective usage time of a single session within the period, and use the difference between the end time and start time of a single session to represent it, and filter out false trigger records where the difference is ≤ the effective threshold. Then, the effective usage time is obtained by summing up all the single effective usage times within the period.

[0023] To prevent inflated usage time caused by sensor malfunctions and short-term misoperations, and to ensure that the effective usage time accurately reflects the actual working wear of the instrument, avoiding the inclusion of ineffective working time in load statistics, in one embodiment, an effective threshold of 1 minute is set. An electronic balance generated 22 usage records in 30 days, of which 3 were triggered by accidental sensor contact by personnel (durations of 23 seconds, 47 seconds, and 52 seconds, respectively). The remaining 19 effective usage times were 8 minutes, 12 minutes, 15 minutes, etc. After the system automatically filters out the 3 malfunction records, the accumulated effective usage time within the period is 217 minutes. If no malfunction filtering step is set, the total time will be incorrectly calculated as 218 minutes and 2 seconds. Although the difference in individual values ​​is small, for high-frequency precision instruments, long-term accumulation will lead to deviations in load scoring, thereby affecting the accuracy of subsequent accuracy assessments.

[0024] Furthermore, Usage frequency is the ratio of the total number of uses within a cycle to the number of days in the cycle; load intensity is the arithmetic mean of the average of all single average load rates within a cycle and the maximum of all single peak load rates within a cycle.

[0025] By combining "average load rate + peak load rate," the system comprehensively covers both daily continuous wear and tear and extreme impact wear, avoiding the deficiency that a single indicator cannot fully reflect the actual stress state of the equipment. In one embodiment, when the operator uses the wrench to tighten the M12 chassis bolts, the wrench's internal sensor collects the torque value in real time at a sampling frequency of 10ms. When the torque value at 3 consecutive sampling points is greater than 5N·m (5% of the rated torque), the system automatically determines that a single operation has begun. When the torque value reaches the preset 80N·m and triggers the wrench's audible and visual prompts, the operator releases the wrench. When the torque value at 5 consecutive sampling points is less than 5N·m, the single operation is determined to have ended. The wrench's microprocessor automatically calculates the average torque of this operation as 65 N·m and the peak torque as 82 N·m, and uploads the data in real time via Bluetooth. Based on the wrench's rated torque, the system calculates the average load rate of this operation as 65 N·m ÷ 100 N·m × 100% = 65%, and the peak load rate as 82 N·m ÷ 100 N·m × 100% = 82%.

[0026] Using a 30-day statistical period, the wrench generated 45 valid operation records. After automatically filtering out two false trigger records with a duration of less than 1 second, the system calculated the average single-operation average load rate within the period to be 62%, and the maximum single-operation peak load rate to be 91%. Therefore, the periodic load intensity of the wrench = (62% + 91%) ÷ 2 = 76.5%. This load intensity data will be transmitted to the load index calculation module and weighted and normalized with the usage frequency (1.5 times / day) and effective usage duration (127 minutes) calculated during the same period to obtain a comprehensive load score of 72 points, providing a data basis for subsequent accuracy attenuation analysis and calibration cycle adjustment. When the peak load rate of a certain operation exceeds 120 N·m (120% of the rated torque), the system automatically marks the operation as "overloaded use" and pushes a warning message to the metering administrator.

[0027] Furthermore, The weighted normalization of the comprehensive load score is as follows: Load Score =100×(w1×F norm +w2×T norm +w3×L norm） F norm To normalize the frequency of use, T norm To normalize the effective usage time, L norm To normalize load intensity, the comprehensive load score is calculated. Score The value range is 0-100, and w1, w2, and w3 are weighted weights.

[0028] In one embodiment, the load index weighting coefficients are set as w1=0.4 (usage frequency), w2=0.4 (effective usage duration), and w3=0.2 (load intensity), and the sum of the three is 1; First, the original indicators were calculated. The wrench generated 45 operation records over 30 days. After automatically filtering out two false trigger records with durations less than 1 second, 43 valid operations were obtained. Therefore, the original usage frequency = 43 times ÷ 30 days ≈ 1.43 times / day. The total effective usage time for all single operations was accumulated to obtain the original effective usage time of 127 minutes. The average load rate of the 43 operations was calculated and averaged to obtain 62%. The maximum value of the peak load rate for all single operations was extracted, yielding 91%. Therefore, the original load intensity = (62% + 91%) ÷ 2 = 76.5%. Subsequently, single-indicator range normalization was performed based on similar benchmarks, and the normalized usage frequency F was calculated. norm =(Current usage frequency of a single appliance - Minimum usage frequency of the same type) ÷ (Maximum usage frequency of the same type - Minimum usage frequency of the same type) = (1.43 - 0) ÷ (2.5 - 0) = 0.572; Normalized effective usage time T norm =(Current effective usage time of a single appliance - Minimum effective usage time of the same type) ÷ (Maximum effective usage time of the same type - Minimum effective usage time of the same type) = (127-0) ÷ (180-0) ≈ 0.706; Normalized load intensity L norm =(Current load intensity of a single appliance - Minimum load intensity of the same type) ÷ (Maximum load intensity of the same type - Minimum load intensity of the same type) = (76.5 - 0) ÷ (100 - 0) = 0.765; Substituting the normalized indicators into the comprehensive load scoring formula, the Load is calculated. Score =100×(0.4×0.572+0.4×0.706+0.2×0.765)=66.42 points; then, based on the calibration records of this wrench, the attenuation rate a=0.006% / day is obtained by fitting the data, and the maximum allowable attenuation rate a for the same category is... max =0.02% / day, normalized decay rate a norm =0.006÷0.02=0.3, Decline trend score (Deg) Score =max(0,100-100×0.3)=70 points, substituting into the comprehensive accuracy scoring formula, we get Prec Score=0.3×66+0.7×70=68.8 points, rounded to 69 points; this comprehensive accuracy score of 69 points will serve as the core basis for the horizontal ranking of torque wrenches of the same specification. When an operator initiates a borrowing request, the system will sort the wrench and other candidate tools of the same specification from high to low according to the comprehensive accuracy score. If its score ranks in the top three, it will be included in the recommended list. At the same time, the system will predict the out-of-tolerance time based on its attenuation curve and dynamically calculate the calibration cycle. If the comprehensive load score exceeds 80 points for three consecutive statistical cycles, its calibration cycle will be automatically shortened by one level and the frequency of daily inspections will be increased. The normalized benchmark will be automatically updated once a month on the 1st, incorporating the operating data of newly added tools of the same model to ensure that the scoring standard always matches the actual use on site.

[0029] The reason why it is necessary to use the benchmark of similar appliances rather than the historical data of individual appliances is that appliances of the same model have completely identical design life, material strength, and rated parameters. The correspondence between accuracy decay and load intensity is comparable. If the benchmark of individual appliances is used, new appliances will be scored too high due to insufficient historical data, and old appliances will be scored too low due to high historical loads. This will completely lose the horizontal comparability between different appliances and will not be able to support the intelligent recommendation function. The historical data of individual appliances can only be used to monitor their load change trend longitudinally and does not participate in horizontal scoring and ranking.

[0030] Furthermore, The specific steps for constructing the accuracy decay sequence are as follows: extract the evaluation date Dn and evaluation error value En from the evaluation records of the instrument in each evaluation, remove abnormal records whose evaluation error value exceeds the preset abnormal threshold, and invalid records whose evaluation date is earlier than the manufacturing date of the instrument, and then form an ordered sequence {(D1,E1),(D2,E2),...,(Dn,En)} according to the evaluation date; Using an ordered sequence as input, a linear regression method is used to fit the accuracy decay curve. With the number of days t since the first assessment as the independent variable and the assessment error value E as the dependent variable, the decay curve formula E(t) = a × t + b is obtained, where a is the decay rate of a single instrument, a positive value indicates a decrease in accuracy, b reflects the initial accuracy state of the instrument, and E(t) is the instrument error value corresponding to any time t.

[0031] Abnormal calibration data and invalid historical records were removed to ensure the reliability of the fitted input data. The logic of linear regression based on time-ordered sequences was clarified to ensure that the fitted accuracy decay curve accurately reflects the change in instrument error over time. For example, a pressure transmitter had 6 calibration records. One record had an error value of 12.3% (far exceeding the normal range of 0-5%) due to operator error, and another record had a calibration date earlier than the instrument's manufacturing date (manufactured on March 15, 2024, recorded on February 20, 2024). After automatically removing these two invalid records, the system sorted the remaining 4 records in ascending order of evaluation date as {(2024-06-01, 0.8%), (20 The sequence (2024-09-01, 1.5%), (2024-12-01, 2.2%), (2025-03-01, 2.9%)) is fitted to obtain the decay curve E(t) = 0.0078t + 0.8, with a decay rate of 0.0078% / day. If data cleaning is not performed, the decay rate fitted by the sequence containing abnormal records will reach 0.016% / day, causing the predicted out-of-range time to be advanced by about 180 days, resulting in unnecessary overcalibration.

[0032] Furthermore, Decline trend score Deg Score The calculation method is as follows: first calculate the normalized decay rate a norm =a / a max Where a is the attenuation rate of a single device, a max The maximum allowable attenuation rate for appliances of the same category; then according to the formula Deg Score =max(0,100-k×a norm ) Calculate the decay trend score, where k is the preset score amplification factor, with a default value of 100; Where the calculated normalized decay rate a norm When the value is greater than 1, directly take a. norm =1, corresponding to a decay trend score of 0; when a<0, it is judged as data abnormality, and the average decay rate of the same type of equipment is used for calculation.

[0033] By normalizing the attenuation rate and using a linear mapping formula, the attenuation rates of instruments of different categories and accuracy levels are converted into a unified 0-100 score system. This provides a direct and intuitive quantification of the instrument's accuracy stability. Furthermore, the sensitivity of the score to changes in the attenuation rate can be flexibly adjusted using the scoring amplification factor k. For example, the maximum allowable attenuation rate 'a' for pressure transmitters of the same category... max =0.02% / day, set k=100, the attenuation rate of the above pressure transmitter a=0.0078% / day, normalized attenuation rate a norm =0.0078 / 0.02=0.39, the calculated decay trend score is Deg Score=100-100×0.39=61 points; the attenuation rate of another pressure transmitter of the same type is a=0.015% / day, a norm =0.75, Deg Score =25 points; The scoring results clearly show that the accuracy stability of the transmitter with 61 points is significantly better than that of the transmitter with 25 points, providing a reliable quantitative basis for the subsequent calculation of the comprehensive accuracy score.

[0034] If a pressure transmitter of the same type obtains an attenuation rate a = 0.025% / day after fitting calibration data, which is greater than a max =0.02% / day, at this time a norm =1.25>1, the system automatically selects a. norm =1, DegScore=0, and mark the instrument as "severely degraded in accuracy"; if an instrument has a degradation rate of a=-0.003% / day due to an error in the first calibration operation, at which time anorm=-0.15<0, the system judges it as data abnormal, calculates a temporary anorm=0.5 using the average degradation rate of 0.01% / day of the same category, and obtains a temporary degradation trend score of 50 points. After the second effective calibration, the degradation curve is refitted and the score is updated.

[0035] Furthermore, The weighted fusion of the overall accuracy score is as follows: Prec Score =β×Load Score +γ×Deg Score , where β is the load score weight and γ is the decay trend score weight.

[0036] The comprehensive load score, reflecting external usage losses, is organically combined with the attenuation trend score, reflecting internal accuracy degradation. Furthermore, the weighting coefficients highlight the dominant influence of the accuracy degradation trend on the current accuracy of the instrument. For example, in one embodiment, the load score weight β=0.3 and the attenuation trend score weight γ=0.7 are set. The comprehensive load score of the aforementioned pressure transmitter is 64 points, and the attenuation trend score is 61 points. The calculated comprehensive accuracy score Prec is then obtained. Score =0.3×64+0.7×61=61.9 points; another transmitter of the same type had a comprehensive load score of 58 points and a decay trend score of 82 points, Prec Score =0.3×58+0.7×82=74.8 points; Although the first transmitter has a slightly higher operating load, the second transmitter's accuracy decays more slowly and its overall accuracy is higher. Therefore, the second transmitter should be recommended when borrowing it.

[0037] Furthermore, The dynamic calculation suggests that the evaluation cycle specifically includes: solving E(t) = E based on the accuracy decay curve. max t at timemax E max t represents the maximum allowable error of the instrument. max The estimated time out of tolerance from the initial assessment; calculate T. recommend =min(T base ,t max -t now -t margin ), where T base Based on the evaluation cycle, t now t represents the current time. margin For safety margin; T recommend Map to the preset cycle level and output the corresponding recommended evaluation cycle.

[0038] A complete dynamic cycle calculation logic was established, consisting of "over-tolerance time prediction - safety margin reservation - basic cycle constraint - gear mapping output." This logic not only enables individualized adjustment of the calibration cycle based on the accuracy decay model, but also ensures metrological reliability and management convenience through safety margins and basic cycles, effectively solving the problem of over-calibration or under-calibration caused by a fixed cycle. For example, in one embodiment, the calibration cycle T is set... base =12 months (365 days), the maximum allowable error E of the pressure transmitter max =5%, safety margin t margin =30 days, for the transmitter with an attenuation rate of 0.0078% / day, the fitted curve is E(t) = 0.0078t + 0.8, and the over-tolerance time t is obtained by solving. max =(5-0.8) / 0.0078≈538 days; the current time distance from the initial calibration is 180 days, so T is calculated. recommend =min(365,538-180-30)=328 days, mapped to a preset output calibration cycle of 12 months; if another transmitter has an attenuation rate of 0.02% / day, t max =(5-0.8) / 0.02=210 days, t now =180 days, T recommend =min(365,210-180-30)=0 days, which is mapped to <1 month, triggering the subsequent processing procedure.

[0039] Furthermore, When the calculated recommended evaluation period is less than the minimum preset period, the system automatically marks the status of the appliance as "to be evaluated", pushes a repair or replacement prompt to the metering administrator, and calls the cost comparison model to calculate the costs of the three options of continued metering, repair, and replacement, and outputs the conclusion of the most cost-effective disposal.

[0040] When an instrument is about to exceed tolerances, its status is automatically marked and a notification is sent. Simultaneously, a cost comparison model is used to quantitatively evaluate three options: continued measurement, repair, and replacement. This avoids measurement risks or cost waste caused by managers making decisions based solely on experience. For example, the aforementioned T... recommend A pressure transmitter with 0 days of calibration has had two consecutive calibration cycles of ≤1 month. The system automatically marks its status as "Pending Evaluation" and sends a notification to the metrology administrator. The cost comparison model calculates the following: Continuing metrology requires 12 calibrations over the next 12 months, each costing 200 yuan, for a total cost of 2400 yuan; Repair requires a supplier fee of 600 yuan, restoring the calibration cycle to 6 months, requiring 2 calibrations over the next 12 months, for a total cost of 600 + 400 = 1000 yuan; Replacing with a new instrument of the same model requires a purchase cost of 1800 yuan, with a 12-month calibration cycle, for a total cost of 1800 yuan; The final output is "Repair recommended, expected to save 1400 yuan in calibration costs over the next 12 months," indicating the optimal solution.

[0041] Furthermore, A dynamic calibration system for measuring instruments based on load index and accuracy decay includes: The data acquisition module is used to collect usage event data, load intensity data, and historical evaluation data of the measuring instruments; The load index calculation module, connected to the data acquisition module, is used to calculate the usage frequency F, effective usage duration, load intensity, and synthesize a comprehensive load score. The accuracy decay analysis module, connected to the data acquisition module, is used to construct the accuracy decay sequence, fit the accuracy decay curve, calculate the decay rate, and calculate the decay trend score. The weighted fusion engine is connected to the load index calculation module and the accuracy attenuation analysis module respectively, and is used to obtain a comprehensive accuracy score through weighted fusion. The period dynamic adjustment module, connected to the accuracy decay analysis module, is used to predict the out-of-tolerance time and calculate the recommended evaluation period; The intelligent recommendation module, connected to the weighted fusion engine, is used to filter candidate devices and sort them according to the comprehensive accuracy score to output a borrowing recommendation list; The disposal suggestion module, connected to the cycle dynamic adjustment module, is used to push disposal prompts and call the cost comparison model to output disposal conclusions when the suggested evaluation cycle is less than the minimum preset cycle.

[0042] It is understood that the above embodiments are merely exemplary implementations used to illustrate the principles of the present invention, and the present invention is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and essence of the present invention, and these modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. A dynamic calibration method for measuring instruments based on load index and accuracy attenuation, characterized in that, Includes the following steps: S1. Collect three types of raw data: usage event data of the collection equipment, load intensity data, and historical assessment data; S2. Calculate the usage frequency and effective usage time based on the usage event data of individual appliances, calculate the load intensity based on the load intensity data, and obtain a comprehensive load score by weighting and normalizing the usage frequency, effective usage time and load intensity. S3. Construct an accuracy decay sequence based on historical evaluation data, use linear regression to fit the accuracy decay curve to obtain the decay rate of a single device, and calculate the decay trend score based on the decay rate of a single device; S4. The comprehensive load score and the attenuation trend score are weighted and fused according to the preset weights to obtain the comprehensive accuracy score of the instrument; S5. Based on the accuracy decay curve, predict the out-of-tolerance time of the instrument, and combine the basic assessment cycle and safety margin to dynamically calculate and suggest the assessment cycle; S6. When receiving an equipment borrowing request, filter out candidate equipment that matches the type and specifications, is available in the current state, and is within the validity period of the evaluation. Then, sort the equipment by comprehensive accuracy score from high to low and output a borrowing recommendation list.

2. The dynamic calibration method for measuring instruments based on load index and accuracy attenuation according to claim 1, characterized in that, The effective usage time is calculated as follows: First, calculate the effective usage time of a single instance within the period, and use the difference between the end time and start time of a single instance to represent it, and filter out false trigger records where the difference is ≤ the effective threshold; Then, the effective usage time is obtained by summing up all the single effective usage times within the period.

3. The dynamic calibration method for measuring instruments based on load index and accuracy attenuation according to claim 1, characterized in that, Usage frequency is the ratio of the total number of uses within a cycle to the number of days in the cycle; load intensity is the arithmetic mean of the average of all single average load rates within a cycle and the maximum of all single peak load rates within a cycle.

4. The dynamic calibration method for measuring instruments based on load index and accuracy attenuation according to claim 3, characterized in that, The weighted normalization of the comprehensive load score is as follows: Load Score =100×(w1×F norm +w2×T norm +w3×L norm ), where F norm To normalize the frequency of use, T norm To normalize the effective usage time, L norm To normalize load intensity, the comprehensive load score is calculated. Score The value range is 0-100, and w1, w2, and w3 are weighted weights.

5. The dynamic calibration method for measuring instruments based on load index and accuracy attenuation according to claim 1, characterized in that, The specific steps for constructing the accuracy decay sequence are as follows: extract the evaluation date Dn and evaluation error value En from the evaluation records of the instrument in each evaluation, remove abnormal records whose evaluation error value exceeds the preset abnormal threshold, and invalid records whose evaluation date is earlier than the manufacturing date of the instrument, and then form an ordered sequence {(D1,E1),(D2,E2),...,(Dn,En)} according to the evaluation date; Using an ordered sequence as input, a linear regression method is used to fit the accuracy decay curve. With the number of days t since the first assessment as the independent variable and the assessment error value E as the dependent variable, the decay curve formula E(t) = a × t + b is obtained, where a is the decay rate of a single instrument, b reflects the initial accuracy state of the instrument, and E(t) is the instrument error value corresponding to any time t.

6. The dynamic calibration method for measuring instruments based on load index and accuracy attenuation according to claim 5, characterized in that, Decline trend score Deg Score The calculation method is as follows: first calculate the normalized decay rate a norm =a / a max Where a is the attenuation rate of a single device, a max The maximum allowable attenuation rate for appliances of the same category; then according to the formula Deg Score =max(0,100-k×a norm ) Calculate the decay trend score, where k is the preset score amplification factor, with a default value of 100; When the calculated normalized decay rate a norm When the value is greater than 1, directly take a. norm =1, corresponding to a decay trend score of 0; when a<0, it is judged as data abnormality, and the average decay rate of the same type of equipment is used for calculation.

7. The dynamic calibration method for measuring instruments based on load index and accuracy attenuation according to claim 1, characterized in that, The weighted fusion of the overall accuracy score is as follows: Prec Score =β×Load Score +γ×Deg Score , where β is the load score weight and γ is the decay trend score weight.

8. The dynamic calibration method for measuring instruments based on load index and accuracy attenuation according to claim 5, characterized in that, The dynamic calculation suggests that the evaluation cycle specifically includes: solving E(t) = E based on the accuracy decay curve. max t at time max E max t represents the maximum allowable error of the instrument. max The estimated time out of tolerance from the initial assessment; calculate T. recommend =min(T base ,t max -t now -t margin ), where T base Based on the evaluation cycle, t now t represents the current time. margin For safety margin; T recommend Map to the preset cycle level and output the corresponding recommended evaluation cycle.

9. The dynamic calibration method for measuring instruments based on load index and accuracy attenuation according to claim 1, characterized in that, When the calculated recommended evaluation period is less than the minimum preset period, the system automatically marks the status of the appliance as "to be evaluated", pushes a repair or replacement prompt to the metering administrator, and calls the cost comparison model to calculate the costs of the three options of continued metering, repair, and replacement, and outputs the conclusion of the most cost-effective disposal.

10. A dynamic calibration system for measuring instruments based on load index and accuracy attenuation, characterized in that, include: The data acquisition module is used to collect usage event data, load intensity data, and historical evaluation data of the measuring instruments; The load index calculation module, connected to the data acquisition module, is used to calculate the normalized usage frequency, normalized effective usage duration, and normalized load intensity, and to synthesize a comprehensive load score. The accuracy decay analysis module, connected to the data acquisition module, is used to construct the accuracy decay sequence, fit the accuracy decay curve, calculate the decay rate, and calculate the decay trend score. The weighted fusion engine is connected to the load index calculation module and the accuracy attenuation analysis module respectively, and is used to obtain a comprehensive accuracy score through weighted fusion. The period dynamic adjustment module, connected to the accuracy decay analysis module, is used to predict the out-of-tolerance time and calculate the recommended evaluation period; The intelligent recommendation module, connected to the weighted fusion engine, is used to filter candidate devices and sort them according to the comprehensive accuracy score to output a borrowing recommendation list; The disposal suggestion module, connected to the cycle dynamic adjustment module, is used to push disposal prompts and call the cost comparison model to output disposal conclusions when the suggested evaluation cycle is less than the minimum preset cycle.

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