Remote collaborative verification and communication management system and method for metrology instruments

Abstract

The application discloses a metrological instrument remote cooperative verification and communication management system and method, and relates to the technical field of instrument intelligentization. Environmental data and original instrument data of multiple metrological sites are collected through an Internet of Things communication protocol, and the original data is transmitted to a remote communication management center for protocol adaptation processing to generate a standard data frame in a unified format. The standard data frame is further subjected to semantic standardization to obtain a data set for verification. A cross-site mutual reference verification model is constructed based on the data set and the environmental data, the bias value of each metrological instrument is calculated, and the cooperative verification priority index of the metrological instrument is determined in combination with the historical error change trend to generate a remote cooperative verification list containing verification task allocation information, which is then issued to each site for execution through the Internet of Things communication protocol. The remote automated verification scheduling of heterogeneous instruments and the data consistency guarantee are realized, and the efficiency and reliability of multi-site cooperative verification are improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent instrument technology, specifically to a remote collaborative verification and communication management system and method for measuring instruments. Background Technology

[0002] Metrological instruments are a crucial foundation for ensuring the stable operation of industrial production, scientific research, and the public metrological standards system. The accuracy and timeliness of their verification work directly affect the reliability of product quality control and metrological traceability. In recent years, with the advancement of informatization and intelligentization in industrial sites, a large number of metrological instruments are distributed across metrology stations in different geographical locations. Remote communication and data acquisition via the Internet of Things (IoT) has become a development trend. Existing technologies mostly focus on the verification management of instruments at single sites or from single manufacturers. By remotely collecting instrument data and executing verification tasks based on fixed scheduling plans, the amount of manual work on-site has been reduced to some extent.

[0003] However, in practical applications, instruments distributed across multiple sites often come from different manufacturers, with significant differences in communication protocols and data formats. This leads to compatibility issues in the unified processing of raw instrument data at the remote communication management center. Consequently, it becomes impossible to reliably and automatically acquire and process critical data used to assess instrument health status. Instruments with potential anomalies cannot be identified and prioritized for calibration due to a lack of effective data support, while stable instruments may periodically consume valuable calibration resources according to schedule. Ultimately, this restricts the efficiency and reliability of the overall calibration work, hindering the intelligent optimization and allocation of resources.

[0004] Therefore, how to generate a reasonable remote collaborative verification list in an environment with multiple sites and multiple manufacturers of measuring instruments has become an urgent technical problem to be solved. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a remote collaborative verification and communication management system and method for measuring instruments.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] In a first aspect, the present invention discloses a method for remote collaborative verification and communication management of measuring instruments, comprising the following steps:

[0008] The system acquires environmental data distributed across multiple metering stations and raw instrument data from multiple metering instruments via IoT communication protocols.

[0009] The raw instrument data is sent to the remote communication management center, where protocol adaptation processing is performed to generate a standard data frame in a unified format.

[0010] The standard data frame is received and semantically normalized to obtain a dataset for testing.

[0011] The dataset and environmental data are input into a pre-built cross-site cross-parameter verification model to obtain the deviation values ​​of each measuring instrument.

[0012] The historical error change trends of each measuring instrument are obtained from the database. Based on the deviation value and the historical error change trends, the collaborative verification priority index of each measuring instrument is calculated, and a remote collaborative verification list containing verification task allocation information is generated.

[0013] The remote collaborative verification list is distributed to each metering station via the IoT communication protocol.

[0014] Secondly, this invention discloses a remote collaborative verification and communication management system for measuring instruments. The aforementioned remote collaborative verification and communication management method for measuring instruments includes:

[0015] The IoT access gateway is used to acquire environmental data distributed across multiple metering stations and raw instrument data from multiple metering instruments through the IoT communication protocol.

[0016] The protocol adaptation module is used to send the raw instrument data to the remote communication management center, perform protocol adaptation processing on the raw instrument data, and generate a standard data frame in a unified format.

[0017] The semantic standardization module is used to receive the standard data frame and perform semantic standardization processing to obtain a dataset for testing.

[0018] The cross-site verification module is used to input the dataset and the environmental data into a pre-built cross-site cross-parameter verification model to obtain the deviation values ​​of each measuring instrument.

[0019] The priority calculation module is used to obtain the historical error change trend of each measuring instrument from the database, calculate the collaborative verification priority index of each measuring instrument based on the deviation value and the historical error change trend, and generate a remote collaborative verification list containing verification task allocation information.

[0020] The communication distribution module is used to distribute the remote collaborative verification list to each metering station through the IoT communication protocol.

[0021] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0022] 1. This invention solves the access difficulties caused by differences in communication protocols and data field definitions of instruments from different manufacturers and models by performing protocol adaptation and semantic standardization on the original instrument data. This enables the system to receive and process multi-source heterogeneous measurement data in a unified format, thereby significantly reducing the complexity of multi-site data integration and improving the real-time performance and accuracy of data exchange.

[0023] 2. By constructing a cross-site cross-parameter verification model and combining it with environmental data analysis, this invention can promptly detect instrument states with potential anomalies, reduce the impact of data deviations caused by environmental differences, thereby improving the consistency and reliability of measurement results and avoiding the risks of misjudgment and delays caused by traditional reliance on manual comparison.

[0024] 3. This invention generates calibration priority indicators for measuring instruments based on historical error change trends and forms a remote collaborative calibration list, making the allocation of calibration resources more reasonable. It helps to prioritize instruments with large error fluctuations, improves the intelligence level and flexibility of the overall calibration scheduling, and overcomes the shortcomings of the traditional periodic scheduling mode in responding to changes in instrument operating status in a timely manner. Attached Figure Description

[0025] The disclosure of this invention is illustrated with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. In the drawings, the same reference numerals are used to refer to the same parts. Wherein:

[0026] Figure 1 This is a flowchart of the steps of the present invention;

[0027] Figure 2 Flowchart for protocol adaptation and semantic standardization;

[0028] Figure 3 This is a calculation diagram of the cross-parameter verification model of the present invention;

[0029] Figure 4 This is a system module connection diagram of the present invention;

[0030] Figure 5 This is a complete system architecture diagram of the present invention. Detailed Implementation

[0031] It is readily understood that, based on the technical solution of this invention, those skilled in the art can propose various interchangeable structural methods and implementations without altering the essential spirit of the invention. Therefore, the following detailed embodiments and accompanying drawings are merely illustrative examples of the technical solution of this invention and should not be considered as the entirety of the invention or as limitations or restrictions on the technical solution of this invention.

[0032] In existing technologies, the status monitoring and deviation verification of measuring instruments largely rely on manual calibration or periodic on-site verification, which is insufficient to meet the real-time and intelligent requirements of multi-site collaborative verification. Measuring instruments distributed in different areas are typically maintained independently by each site, and their error calibration is mainly achieved through manual sampling comparison or periodic traceability. However, these methods suffer from reliance on manpower, long cycles, and difficulty in synchronization, especially in application scenarios with a large number of instruments or complex operating conditions. Traditional methods struggle to provide high-frequency, automated deviation judgment criteria. Furthermore, due to differences in environmental conditions such as temperature, humidity, and air pressure among instruments, even instruments of the same model will exhibit fluctuations in results measured within the same time period. Without a dynamic compensation mechanism for environmental factors, systematic errors will inevitably be introduced. In addition, existing communication management architectures often employ fixed protocols or one-way data upload modes, lacking universal protocol adaptation and data semantic consistency mechanisms, resulting in low efficiency in multi-source data fusion and intelligent judgment.

[0033] The inventors have developed a remote metrology instrument verification method that integrates semantic standardization, environmental compensation, and dynamic collaborative judgment mechanisms, enhancing the system's intelligent identification capability for distributed instrument anomalies. The method collects raw instrument data and environmental parameters from each metrology station in real time via an IoT communication protocol. A protocol adaptation algorithm library converts various communication formats into standardized data frames with a unified structure, resolving the issue of inconsistent data formats across different manufacturers. Subsequently, combined with a pre-set semantic mapping table in the database, various fields are uniformly named and their dimensions converted, forming a semantically standardized dataset with cross-device universality. Based on this, instrument identifiers are matched and categorized with the database to construct a cross-parameter instrument group consisting of instruments of the same model or range. The original measured values ​​are corrected using time-series alignment and environmental compensation formulas, and then further input into a cross-site cross-parameter verification model. This model comprehensively considers historical performance indicators and current measurement deviations, and outputs the current stable deviation value of each instrument through a weighted average and confidence interval judgment mechanism, thereby achieving data-driven automatic deviation determination.

[0034] The research revealed that the relative deviation among multiple instrument groups is influenced by both environmental parameters and the historical usage status of the instruments. Introducing weighting factors and a dynamic update mechanism can effectively improve model stability and anomaly identification accuracy. When constructing cross-referenced instrument groups, relying solely on clustering of the same model cannot fully reflect the differences in long-term operating conditions among the equipment. Therefore, the inventors further introduced multi-dimensional indicators such as historical usage frequency, fluctuation variance, and traceability level to form weighting factors, which are used to dynamically adjust the contribution ratio of each device to the reference value during the weighted averaging process. Especially when environmental conditions change abruptly or some equipment drifts abnormally, this mechanism can effectively suppress the impact of extreme value data on the overall judgment result. In addition, marking equipment data that deviates from the range as anomalies effectively improves the robustness of the system in multi-source complex data environments.

[0035] Compared with existing technologies, the cross-site cross-parameter verification method and dynamic feedback mechanism proposed in this invention significantly improve the intelligence and practicality of remote verification of metrological instruments. The end-to-end verification architecture, integrating a protocol adaptation algorithm library and a cross-site cross-parameter verification model, offers advantages such as automated data processing, intelligent deviation judgment, and stable result output. Its core innovation lies in utilizing environmental compensation and dynamic model weight adjustment mechanisms to uniformly calibrate multi-source measurement results to a comparable range, achieving real-time deviation assessment and verification task allocation without manual intervention. Especially when used in conjunction with a remote verification task execution engine, this solution can form a closed-loop control process with integrated hardware and software, providing crucial support for the construction of intelligent metrology systems.

[0036] After introducing the basic concept of the present invention, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0037] Example 1:

[0038] like Figure 1 As shown, the remote collaborative verification and communication management method for measuring instruments includes the following steps:

[0039] The system acquires environmental data distributed across multiple metering stations and raw instrument data from multiple metering instruments via IoT communication protocols.

[0040] The raw instrument data is sent to the remote communication management center, where protocol adaptation processing is performed to generate standard data frames in a unified format.

[0041] Receive standard data frames and perform semantic normalization to obtain a dataset for testing;

[0042] The dataset and environmental data are input into a pre-built cross-site cross-parameter verification model to obtain the deviation values ​​of each measuring instrument.

[0043] The historical error change trends of each measuring instrument are obtained from the database. Based on the deviation value and the historical error change trends, the collaborative verification priority index of each measuring instrument is calculated, and a remote collaborative verification list containing verification task allocation information is generated.

[0044] The remote collaborative verification checklist is distributed to each metrology station via the IoT communication protocol.

[0045] The working principle of this invention is as follows: First, IoT edge acquisition terminals deployed at multiple metering sites connect to various types of metering instruments. Through interface modules compatible with multiple industrial communication protocols, raw instrument data from each metering instrument is acquired. Simultaneously, environmental parameter information for each site, including key influencing factors such as temperature, humidity, and air pressure, is collected in real time. This environmental data is packaged in a unified format and sent along with the raw instrument data to a remote communication management center for centralized processing.

[0046] At the communication management center, the system invokes the protocol adaptation algorithm library to parse and map the formats and fields of data from different instruments and communication protocols, transforming the raw heterogeneous data into a standardized data frame with a consistent structure. The standardized data frame contains fields such as instrument identifier, acquisition time, measurement value, status information, and measurement unit, providing a consistent data foundation for subsequent semantic processing and model calculations.

[0047] Upon receiving a standard data frame, semantic standardization is performed to unify the semantics of fields from different data sources. For example, different equipment manufacturers may use different names or units for the same measurement field. The system uses predefined semantic mapping rules to standardize naming, unit conversion, and status codes, ultimately forming a structured and standardized dataset. This dataset serves as the input data required for cross-site verification.

[0048] The completed dataset and synchronously collected environmental parameter data are input into a pre-built cross-site cross-parameter verification model. This model constructs cross-parameter instrument groups based on parameters such as instrument model, measurement range, and usage scenario, and performs time-series alignment and environmental compensation for instrument measurements from different sites. Combining the measurement consistency of instruments across multiple sites, algorithms such as weighted averaging, least squares fitting, and outlier identification are used to calculate the relative deviation value of each metrology instrument, outputting the deviation detection results.

[0049] The system retrieves historical error trend data for the instrument from the historical database, generates a trend curve using exponential smoothing or time series analysis, and calculates the collaborative verification priority index in conjunction with the current deviation value. The priority comprehensively considers factors such as deviation amplitude, error fluctuation trend, and instrument usage frequency to generate a structured remote collaborative verification list. The remote collaborative verification list clearly marks the equipment requiring priority calibration, the verification task type, and the allocation strategy.

[0050] Finally, the system distributes the remote collaborative verification list to the corresponding metrology stations via the IoT communication network. For instruments with automatic verification capabilities, the verification process can be started directly by calling the remote execution interface; for devices without this capability, a prompting mechanism notifies manual verification. The execution results can be synchronously fed back to the system to form a closed-loop update.

[0051] This invention constructs an intelligent verification management method integrating multi-site collaborative processing, high-precision deviation detection, intelligent scheduling, and remote execution through multiple technical steps such as protocol adaptation, data standardization, cross-parameter verification modeling, and trend analysis. This application not only overcomes the processing obstacles caused by heterogeneous protocols and data differences, but also effectively solves the technical problems of high reliance on manual labor, slow response, and lack of priority judgment in traditional verification processes, thus realizing intelligent, real-time, and precise metrological verification management.

[0052] like Figure 2 As shown, this application further proposes that the steps of performing protocol adaptation processing on the raw instrument data to generate a standard data frame in a unified format include:

[0053] After receiving the raw instrument data, read the communication interface identifier and message format of the raw instrument data;

[0054] The communication interface identifier is matched against a preset list of communication protocol types to generate a matching result; the list of communication protocol types includes at least one or more of the following protocols: Modbus, Profibus, CAN, HART, RS-485, and MQTT.

[0055] The message format is parsed into a standard data frame with a unified field structure by calling the protocol adaptation algorithm corresponding to the matching result;

[0056] The protocol adaptation algorithm is adapted to the communication protocol; the standard data frame includes the instrument identification ID field, timestamp field, measurement value field, status code field, range field, and unit field.

[0057] In the remote collaborative verification and communication management method for measuring instruments proposed in this invention, in order to solve the problem of inconsistent communication protocols for multi-source heterogeneous measuring instruments, the system is equipped with a protocol adaptation algorithm library to adapt to the message parsing requirements of different types of industrial communication protocols.

[0058] After receiving raw instrument data from edge devices or IoT gateways at the remote communication management center, the system reads the communication interface identification information and the raw data format structure of the message contained therein. The communication interface identification may include the device interface type (such as RS-485, CAN bus, Ethernet port, etc.) and the protocol signature in the communication message header, which is used for subsequent matching processing.

[0059] The system matches the extracted communication interface identifiers one by one with a preset list of communication protocol types. This list supports mainstream industrial communication protocols, including but not limited to: Modbus (RTU / TCP), Profibus-DP, CAN, HART, RS-485 standard protocol, MQTT protocol, and other fieldbus and industrial IoT protocols. Based on the matching results, the system calls the corresponding protocol adaptation algorithm to identify fields and extract data for specific message structures.

[0060] In the protocol adaptation algorithm library, each protocol adaptation algorithm encapsulates data frame structure parsing rules for a specific type of protocol, including basic parsing logic such as start bit, check bit, field offset, byte order, and CRC verification, ensuring compatibility with data specifications output by devices from different manufacturers or versions. After matching the protocol type, the system maps fields and parses the data in the original message according to the corresponding protocol format, and generates a standard data frame in a unified format.

[0061] The standard data frame follows the data structure defined by this system and includes at least the following fields:

[0062] Instrument Identifier ID field: Used to uniquely identify the device entity, and may include device model, serial number, etc.;

[0063] Timestamp field: Represents the data collection time, supporting millisecond-level precision alignment;

[0064] Measurement value field: Represents the raw measurement values ​​collected;

[0065] Status code field: reflects the current working status or abnormal operation of the device, such as overload, failure to calibrate, power failure, etc.

[0066] Range field: Indicates the effective measurement range of the current measurement channel;

[0067] Unit field: The physical unit of the measurement value field is explicitly defined, such as Pa, V, kg, etc., and automatic conversion to the International System of Units is supported.

[0068] Through the above steps, the system can parse raw instrument data from different metrology stations, manufacturers, and communication protocols into standard data frames with unified structure, clear semantics, and complete fields, providing a data foundation for subsequent data standardization, model input processing, and deviation calculation.

[0069] The aforementioned protocol adaptation method, through the introduction of a multi-protocol identification and matching mechanism and a protocol adaptation algorithm library, effectively solves the technical challenges of traditional systems when facing heterogeneous instrument data sources, such as parsing incompatibility, missing fields, and structural mismatch. The system can achieve plug-and-play protocol integration with various types of equipment in complex industrial environments, automatically identifying protocol types and completing data parsing without manual intervention. Through the unified output format of standard data frames, seamless integration between the data acquisition layer and the management layer is achieved, greatly improving system scalability, compatibility, and data processing efficiency, providing solid support for building a unified, real-time, and highly reliable metrological verification data platform.

[0070] This application further proposes that the specific steps for semantically standardizing standard data frames to obtain a dataset for testing include:

[0071] By retrieving the semantic mapping table stored in the database, the field names and values ​​in the standard data frame are semantically unified to generate semantically mapped fields. The semantic mapping table records the one-to-one correspondence between the field names and values ​​of instruments from different manufacturers and the standard semantic fields.

[0072] The semantically mapped fields are organized in key-value pairs, and the measurement values ​​of the fields are unified in the International System of Units (SI). A unified set of status codes is used for the status flags of the fields.

[0073] The processed data is stored as a semantically normalized dataset, where each record includes a field name, a value, and its unit identifier.

[0074] Specifically, this step begins with the remote communication management center accessing a semantic mapping table pre-stored in the database. This table records a one-to-one correspondence between the field names, value ranges, and status descriptions used by different instrument manufacturers and the system's general standard semantics. The mapping is established based on industry standards (such as JJF, GB, IEC, etc.) and the analysis and summarization of the communication protocol field structures of mainstream instrument manufacturers. For example, one brand names the "measuredValue" field as "...". Another vendor might name it "output", and the semantic mapping table records their correspondence with the standard semantic field "measurement_value".

[0075] Upon receiving a standard data frame, the system sequentially queries the semantic mapping table for each field name and value, generating unified semantic field names and standard value ranges. During processing, the system reorganizes the field content using a key-value pair data structure to ensure indexability and scalability in subsequent model input. For example, the field "measurement value" will be represented as "measurement_value": "12.5", with its unit explicitly identified in the "unit": "kPa" field.

[0076] For measurement value fields, the system further converts the numerical content to the International System of Units (SI), including automatic recognition of unit suffixes, adjustment of conversion factors, and preservation of decimal places. This ensures that inconsistencies in unit usage across different sites or devices will not affect subsequent cross-device comparisons or model input calculations.

[0077] Meanwhile, for the status code field that indicates the working status of the instrument, the system uniformly adopts a set of standard status codes for replacement. For example, status values ​​such as "normal", "ok", and "running" are uniformly classified as status code "00", and "fault" and "alarm" are classified as "01", etc., to ensure that the status semantics have consistent semantic orientation across devices from different manufacturers.

[0078] Ultimately, the system organizes all semantically mapped and unit-standardized field content into structured records. Each record includes at least a field name, its corresponding value, and its unit identifier, and stores it as a single record in the semantically standardized dataset. This dataset supports structured storage (such as tables and JSON (JavaScript Object Notation) objects) and serves as the input data source for subsequent cross-reference validation models.

[0079] Through the aforementioned semantic standardization mechanism, this application effectively addresses the heterogeneous semantic issues arising from different metrology sites and instruments from different manufacturers in terms of field expression, unit system usage, and status representation. Unlike traditional systems that rely on a unified hardware platform or manual data cleaning, this solution achieves automatic semantic fusion across manufacturers and protocols through a semantic mapping table-driven field standardization mechanism. This provides a high-quality data source with consistent structure, unified units, and clear semantics for subsequent verification model data input. This method significantly improves the system's adaptability and data availability in complex multi-source metrology environments, enhancing the intelligence and automation level of collaborative verification tasks.

[0080] like Figure 3 As shown, this application further proposes that the specific steps for obtaining the deviation values ​​of each measuring instrument by inputting the dataset and environmental data into a pre-built cross-site cross-parameter verification model include:

[0081] Obtain semantically standardized datasets and corresponding environmental data from multiple metrology sites as input data for cross-site cross-parameter verification models;

[0082] The cross-site cross-reference verification model establishes a mapping relationship between the identification information of each measuring instrument in the dataset and the instrument database, dividing the instruments into cross-reference instrument groups according to model or range category. The model uses the unique identification information (ID field) of each measuring instrument in the dataset to match the data with the instrument database, thereby obtaining metadata such as the instrument's model, range category, and historical performance data. Based on the same standard of model or range category, the measuring instruments at each site are divided into multiple cross-reference instrument groups. This grouping method ensures that instruments within the same group are comparable in terms of range, resolution, and accuracy class, providing a data foundation for subsequent cross-reference.

[0083] For measurement data from different sites in each set of cross-parameter instruments, time sequence alignment is performed according to a unified timestamp; this ensures that data collected from different locations are strictly matched in the time dimension, avoiding time sequence misalignment caused by data acquisition delays or upload time differences, thereby ensuring the accuracy of cross-site data comparison.

[0084] Based on environmental data such as temperature (T), humidity (H), and air pressure (P) at each station, the environmental compensation value ΔC is calculated and corrected. The compensation formula is as follows:

[0085] ;

[0086] Among them, T, H, and P are environmental parameters collected on-site. , , For reference environmental values, a, b, and c are the sensitivity coefficients for temperature, humidity, and air pressure, respectively; the values ​​of a / b / c range from 0.05% to 0.2%. The sensitivity coefficients are calibrated by obtaining basic sensitivity parameters through laboratory environmental gradient experiments and dynamically corrected using the least squares method based on historical field data; the compensation value ΔC is used to correct the influence of environmental differences on the measured values ​​to eliminate systematic biases caused by climate or environmental differences.

[0087] After obtaining the compensated measurement value, calculate the difference between the average value of the compensated measurement value of each measuring instrument and the other members in its cross-reference instrument group to obtain its relative deviation value.

[0088] To further improve the stability and resilience of the calculation results, the least squares method was used to converge the deviation values ​​of all cross-parameter instrument groups, thereby reducing the impact of occasional outliers on the overall deviation estimation. The cross-site cross-parameter verification model outputs the stable deviation estimates of each instrument as the final deviation result.

[0089] The Cross-site Mutual Reference Calibration Model (CMUCA) refers to a mathematical model established by selecting a group of metrology instruments with the same or compatible metrological parameters (such as measurement range, unit, and type) from multiple geographically dispersed metrology stations. Based on their simultaneous measurement data and corresponding environmental data, the model is used for data cross-reference, deviation comparison, and accuracy verification. Essentially, the CMUCA is a distributed collaborative calibration mechanism based on cross-site comparison of similar instruments and environmental compensation modeling. It can construct a logically consistent deviation analysis model through multi-instrument cross-reference data without relying on standards, supporting remote intelligent verification scheduling.

[0090] The construction of the cross-site cross-parameter verification model includes: selecting instruments of the same model or with the same functional parameters from different sites to construct a "cross-parameter instrument group"; each group contains at least two instruments to ensure data comparability; aligning the data collected by each instrument based on a unified timestamp; performing unit conversion and semantic standardization on all measured values; finally, calculating the environmental impact through a compensation value ΔC, subtracting ΔC from the measured value difference between each instrument and other instruments in its cross-parameter instrument group to calculate the deviation, and statistically converging multiple deviation values ​​(such as the least squares method) to obtain the final deviation result as a stable deviation conclusion.

[0091] Since there may be differences in verification conditions between sites (such as environment and standard source), the cross-site cross-parameter verification model bridges the standard differences through cross-parameter verification of multiple source instruments, and can achieve relative verification without relying on standard instruments: by comparing data between similar instruments, the dependence on high-level standard instruments is reduced; it supports remote distributed verification and scheduling: providing data support for subsequent verification priority calculation, task list generation, etc., and improving the automation level of collaborative verification.

[0092] Through the calculation process of the aforementioned cross-site cross-parameter verification model, this application achieves end-to-end processing of time alignment, environmental difference compensation, and deviation value convergence optimization in the deviation calculation of multi-source heterogeneous instrument data, significantly improving the accuracy and robustness of cross-regional metrological data comparison. Compared with traditional methods relying on single-point calibration or manual comparison, this scheme can automatically integrate real-time detection data from multiple sites, dynamically eliminating interference from environmental factors and occasional anomalies, thereby providing highly reliable and low-uncertainty deviation assessment results for remote collaborative verification. This not only improves the scientific nature of verification task allocation but also provides a solid data foundation for the full life-cycle quality monitoring of metrological instruments.

[0093] This application further proposes that the cross-site cross-parameter verification model also includes:

[0094] A metadata mapping table is established by initializing the instrument database. This table records metadata such as the unique identifier, model, measuring range, accuracy class, traceability class, and factory calibration information of each measuring instrument. This mapping table provides structured basic information support for subsequent instrument grouping and data comparison.

[0095] The system receives semantically standardized datasets from multiple metrology stations and constructs a cross-parameter instrument group structure based on instrument identification and type. The cross-parameter instrument groups are divided according to the principle of "priority for the same model or the same range category," and the historical operating parameters, station number, and maintenance records of each member are attached to the grouping record to ensure traceability and repeatability when comparing data.

[0096] After grouping, an initial weighting factor is set for each instrument. The initial weighting factor is set according to its historical usage frequency, fluctuation variance, or traceability level. For example, instruments that have been operating stably for a long time and have a high traceability level will be given a higher weight so that they will account for a larger proportion in the calculation of reference values.

[0097] For each group of data from the cross-parameter instrument group, the reference value R is calculated using the weighted average method, and the formula is: ;

[0098] in, Let i be the measured value of the i-th instrument. Let R be the weight of the i-th instrument; the reference value R can effectively offset the impact of individual outliers on the overall comparison results.

[0099] The formula for calculating the instrument value after environmental compensation is as follows: ;

[0100] Where V represents the original measurement value (e.g., 105.1 t / h), ΔC is the environmental compensation value, and T is the range, representing the upper limit of the instrument's measurement (e.g., 200 t / h).

[0101] Based on the reference value R and the measured values ​​of each instrument Calculate the deviation ΔM, and combine it with the historical variance σ to construct the confidence interval CI. The confidence interval CI is set according to the 3σ criterion, that is:

[0102] ;

[0103] If the deviation ΔM exceeds the confidence interval CI, the instrument is marked as an abnormal deviation instrument.

[0104] The above structure and processing parameters are integrated into the cross-site cross-parameter verification model, which supports subsequent deviation calculation and dynamic updates.

[0105] The cross-site cross-parameter instrument group structure, reference value calculation rules, environmental compensation parameters, and anomaly detection mechanism are integrated into the cross-site cross-parameter verification model, enabling the model to directly call these preset parameters during the operation phase, and realize closed-loop processing of high-speed deviation calculation, dynamic update, and anomaly detection.

[0106] This application utilizes a cross-site cross-parameter verification model to fully leverage historical information and weight allocation mechanisms during data comparison, reducing the risk of interference from anomalous data from a single instrument on the overall results. Furthermore, it promptly identifies potential deviations through confidence interval determination, enhancing the consistency and traceability reliability of multi-site metrological data. This not only provides a stable mathematical and data structure foundation for subsequent deviation calculation and dynamic correction but also significantly improves the accuracy and robustness of the cross-site metrological collaborative verification process.

[0107] This application further proposes that the specific steps for obtaining the historical error change trend include:

[0108] The system sorts the historical error records of measuring instruments according to timestamps to form a time series. The historical records include the deviation values ​​calculated from each verification or cross-reference comparison, along with their corresponding timestamps, verification environment parameters, verification methods, and other information. The system first sorts the error records in ascending order based on timestamps, thus forming a continuous and computable error time series, providing basic data input for trend analysis.

[0109] The exponential smoothing method is used to calculate the short-term historical error trend. The formula for exponential smoothing is as follows:

[0110] ;

[0111] in, This is the current error value. The values ​​are smoothed, with α being a smoothing coefficient between 0.1 and 0.3. Exponential smoothing is used to calculate the short-term trend of the above error time series, enabling rapid capture of fluctuations in instrument performance during the task scheduling phase.

[0112] A higher α value improves the response speed to the latest error changes, while a lower α value is more suitable for instruments with relatively stable error changes. The system can automatically select the optimal α value based on the instrument type, operating environment, and historical stability.

[0113] When modeling trends using historical error data, the system employs an exponentially weighted smoothing algorithm (EWMA) to process instrument deviation variation sequences. The selection of the α parameter directly affects the model's sensitivity to short-term fluctuations and its stability in the long-term trend.

[0114] Based on actual sampling data test results and industry experience, selecting α=0.1~0.3 can effectively suppress high-frequency noise in the data while maintaining trend response capability, making it suitable for most environmentally stable metrology scenarios. Within this range, the error prediction residuals maintain a stable distribution, meeting the stability requirements of the trend prediction model.

[0115] After the smoothing calculation is completed, the system compares the latest short-term trend value with the historical long-term fluctuation curve to identify instruments with accelerating trend changes or declining stability. These characteristics are then used as important input parameters for subsequent calculations of collaborative verification priority indicators. If the trend change of an instrument is detected to be close to the preset performance threshold, the task weight of that instrument is increased in the priority calculation to ensure that it can enter the next round of collaborative verification plan first.

[0116] This application, by extracting historical error change trends, not only provides dynamic weight input for the task allocation stage but also provides early warning of potential performance degradation risks during cross-site verification. Compared with existing schemes that rely solely on static deviation values, this application introduces exponential smoothing calculations in the trend analysis stage. This allows the system to sensitively detect the latest changes while suppressing the interference of single outliers on trend judgment, thereby ensuring the rationality of the verification plan while improving the stability and reliability of the overall metrological data.

[0117] This application further proposes that the specific steps for calculating the collaborative verification priority index of measuring instruments based on deviation values ​​and historical error change trends include:

[0118] Collaborative verification priority indicators Use the following formula:

[0119] ;

[0120] Where: ΔE is the error change rate, which reflects the dynamic fluctuation of the instrument performance; f is the frequency of instrument use per unit time, which is used to measure the importance of the instrument to the metrology system and the real-time range of its influence; and E is the current deviation value, which directly reflects the absolute metrological accuracy level of the instrument. λ, β, and γ are weighting factors determined through regression analysis and stored in the priority calculation rule base, which can be adjusted according to different industries or application scenarios. A sample set is constructed based on historical verification records, and the optimal values ​​of λ / β / γ are solved using the gradient descent method with the objective function of minimizing the false negative rate.

[0121] After completing the above formula calculation, the priority calculation module sorts the PI values ​​of each instrument and generates a collaborative verification task priority list from high to low. Instruments with PI values ​​higher than the set threshold will be marked as "high priority" and verification instructions will be issued first during the task allocation phase to shorten the response time of potentially risky equipment; while low priority equipment can be postponed to the next verification cycle to optimize the overall resource scheduling efficiency.

[0122] Through the aforementioned priority calculation mechanism, this application achieves dynamic ranking based on risk and impact during the task scheduling phase. Compared with existing methods that only determine priority based on fixed periods or single error values, this approach can simultaneously consider deviation magnitude, trend of change, and equipment usage intensity, thereby significantly improving the scientific nature and timeliness of verification task allocation and ensuring the overall accuracy and stability of the metrology system.

[0123] This application further proposes that the remote collaborative verification and communication management method for measuring instruments also includes a remote automatic verification procedure, the specific steps of which include:

[0124] After receiving the remote collaborative verification list via the IoT communication protocol, the verification task execution engine is invoked.

[0125] The verification task execution engine performs the verification according to the verification task process. The verification steps include instrument zero-point calibration, calibration point measurement, and error analysis.

[0126] During the verification process, the instrument response data is collected in real time and compared with the preset standard value to generate a verification result file; the instrument response data is the real-time response data of the measuring instrument collected during the remote execution process;

[0127] When it is detected that the instrument does not support automatic calibration, a manual calibration prompt is sent to the station operation terminal, and the task status is marked as manual execution status.

[0128] First, once each metrology station receives the remote collaborative verification list from the communication management center via the IoT communication protocol, the system automatically invokes the verification task execution engine deployed locally or in the cloud. During the initialization phase, this engine parses the instrument identifier, verification task type, and execution parameters in the verification list, and adjusts the task execution order according to the task priority indicators to ensure that high-risk or high-impact instruments are verified first.

[0129] The verification task execution engine is deployed on an embedded server locally at the metrology site. It features functions such as task reception, process scheduling, sending instrument operation commands, and result feedback. Built on a unified control logic programming language, the engine's core scheduler automatically selects the instrument to be verified based on task priority indicators and sends commands to the corresponding instrument via serial communication or industrial control bus to complete actions including zero-point calibration, calibration point acquisition, and status acquisition. If the engine detects that the instrument lacks automatic verification capabilities, it triggers a manual assistance interface, sending pending operation commands to the local human-machine interface terminal. After manual confirmation, the process continues, ensuring the integration and compatibility of the hardware and software systems.

[0130] The verification task execution engine executes verification operations sequentially according to the preset verification task process, which includes: instrument zero-point calibration, calibration point measurement, and error analysis. In the zero-point calibration stage, the system automatically outputs a reference signal and calculates the zero-point offset by comparing the instrument's response value with the theoretical zero-point value. In the calibration point measurement stage, standard quantities are applied sequentially according to the preset multi-point calibration curve sequence, the instrument's response data is collected, and compared in real time with the standard values ​​to obtain the deviation at different measurement ranges. In the error analysis stage, the system matches the deviation results at each calibration point with the preset tolerance standards to generate a complete error analysis report.

[0131] During the calibration process, the system acquires the instrument's response data in real time through a high-speed data acquisition module. This data undergoes noise filtering, outlier removal, and time synchronization to ensure accurate comparison with standard values. The difference between the processed data and the standard values ​​is used to calculate calibration indicators in real time and generate a calibration result file. This file can use a unified XML / JSON structure and includes the instrument identifier, calibration time, results of each calibration step, final conclusion, and execution log. The calibration result file will be encrypted and sent back to the communication management center for archiving and subsequent analysis.

[0132] When the verification task execution engine detects during initialization or verification process that the target instrument does not have the interface capability for remote automatic verification (e.g., lack of remote control command support or inability to read key parameters through communication), the system will immediately send a manual verification prompt to the operation terminal of the metrology station and mark the task status as "manual execution" in the task list to ensure timely intervention and the integrity of the verification task.

[0133] Through the aforementioned remote automated verification procedure, this application, based on deviation analysis and task prioritization, achieves fully automated execution of the verification process and closed-loop result feedback, reducing the proportion of manual intervention and improving verification efficiency and consistency. Simultaneously, it automatically switches to manual mode when equipment conditions are insufficient, ensuring the continuity and coverage of verification tasks, and effectively improving the overall operational reliability and management level of the metrology system under cross-site and cross-equipment conditions.

[0134] This application further proposes that the remote collaborative verification and communication management method for measuring instruments also includes incremental updates, the specific steps of which include:

[0135] Receive instrument calibration data and status information after calibration is completed, and store the instrument calibration data in the database;

[0136] Update the weight factors of the cross-site cross-parameter verification model based on the verification data. Incremental learning is used to update the historical error trend. When the residual change rate is >5%, incremental learning is triggered, and a sliding window mechanism is used to update the error sequence of the historical error trend. The window length N ≥ 30 groups, and the exponential smoothing value is recalculated after each new data. ;

[0137] The updated historical error trends are redeployed to calculate the collaborative verification priority index for each measuring instrument.

[0138] Specifically, after a calibration task is completed, regardless of whether it was performed via a remote automated calibration program or a manual calibration mode, the system will receive instrument calibration data and status information from various metrology stations. The calibration data includes zero-point deviation, multi-point error values ​​within the measurement range, error curve fitting parameters, calibration date, and environmental conditions; the status information includes the instrument's operating status indicators, calibration completion indicators, and abnormal event records. Upon receipt, this information is written into a central database according to a unified data format and stored in association with the instrument's historical records, forming a traceable, full-lifecycle calibration data chain.

[0139] The system calls the cross-site cross-parameter verification model to update the weights, adjusting the weight factors of the corresponding instruments in the model based on the newly stored verification data. The correction logic of the weight factors comprehensively considers the deviation level of this verification, the stability of the verification results, and the relative consistency with other instruments in the cross-parameter instrument group, ensuring that the model can more accurately reflect the reliability of each instrument when calculating the reference value in the next round. At the same time, for updating the historical error change trend, the system adopts an incremental learning approach—instead of retraining all historical data, it integrates the newly added error data into the existing trend calculation model in a time series increment, thereby achieving continuous optimization of trend prediction while maintaining computational efficiency.

[0140] After updating the model weights and correcting historical trends, the system uses the latest deviation value and the updated historical error trend as input to recalculate the collaborative verification priority index for each metrology instrument. This process ensures that the priority assessment can reflect the latest verification results and performance changes in real time, making the scheduling of subsequent verification tasks more consistent with the globally optimal strategy.

[0141] To enhance the intelligence of deviation trend modeling, the system introduces an adaptive update mechanism into the weighting factors (such as α). This mechanism uses the error residuals after each round of verification as the feedback signal input and employs Bayesian optimization to fine-tune the factor values, thereby minimizing the deviation between the historical trend and the actual error.

[0142] The update logic adopts the following strategy: set the initial α=0.2, when the prediction error of three consecutive rounds is significantly higher than the upper limit threshold of the model residual, the parameter adjustment process is triggered to re-estimate the weight factors, so that the model can dynamically adapt to changes in different instrument or environmental conditions.

[0143] Through this weighted learning mechanism, the system possesses long-term adaptive evolution capabilities, thereby enhancing the generalization ability and long-term stability of the remote collaborative verification model.

[0144] Through the aforementioned incremental update mechanism, this application achieves dynamic adaptation of the cross-site cross-parameter verification model and the task priority evaluation system. Compared with the traditional periodic batch update mode, this solution can immediately optimize the core model parameters after each verification task, significantly improving the system's response speed to changes in instrument performance. This ensures that under the condition of continuous operation of multi-site, multi-batch metrology tasks, verification resources can always be prioritized for allocation to the instruments that most need calibration, thereby improving the overall efficiency and long-term stability of the verification system.

[0145] The following is a case study of a remote collaborative verification and communication management method for measuring instruments implemented across different plant areas in a thermal power generation group:

[0146] A certain energy group's three coal-fired power plants (Plant A, Plant B, and Plant C) require quarterly calibration of distributed steam flow meters, flue gas analyzers, and water quality monitors. The traditional method requires dispatching six calibration personnel and taking two weeks to complete the task, and data comparability is poor due to environmental differences (boiler room temperature 52℃ vs. water treatment workshop 28℃). After applying this technical solution, remote collaborative management is achieved through the following process:

[0147] 1. Data acquisition and standardization of heterogeneous instruments:

[0148] Boiler area of ​​Plant A: Rosemount 3051S pressure transmitter (Modbus protocol) real-time output {Tag:PT101,Value:6.83MPa, Unit:bar}; Desulfurization tower of Plant C: Siemens S7-400 flue gas analyzer (Profibus protocol) uploading {Address:DB10.DBD24,Value:12.3% O2}

[0149] The gateway identifies the Rosemount interface identifier RS485_ModRTU, calls the Modbus parsing algorithm for protocol adaptation, and generates a standard frame: {ID: A-PT-101, Time:2025-08-08 14:30:02.356, Value:68.3, Status:0x01, Range:0-100, Unit:bar};

[0150] The "Siemens unit %Vol" is converted to standard semantic oxygen concentration using a mapping table, with the unified unit being vol%. The output dataset is {ID:C-GAS-07, Param:Oxygen concentration, Value:12.3, Unit:vol%}.

[0151] 2. Core process of cross-site parameter verification:

[0152] (2.1) Dynamic grouping and reference value calculation:

[0153] Steam flow meters of the same model (E+H FMR50) form a cross-parameter instrument group: Group member A has a weight w=0.7 (passed the last 3 calibrations); Group member B has w=0.9 (newly installed equipment); Group member C has w=0.5 (large historical fluctuations).

[0154] Calculate the weighted reference value:

[0155] ;

[0156] (2.2) Dual environmental compensation:

[0157] Instrument environment at Plant B: Temperature T=46℃, Humidity H=85% (boiler room), sensitivity parameters (E+H FMR50): a=0.15% / ℃, b=0.08% / %RH; sensitivity parameters are laboratory calibration results;

[0158] Calculation of compensation for a single instrument: ΔC = 0.15×(46-25) + 0.08×(85-60) = 3.15% +2.0% = 5.15%; Instrument value after environmental compensation V'=105.1+(0.0515×200)= 110.5t / h;

[0159] (2.3) Relative deviation after compensation: |110.5 - 102.6| / 200 (range) × 100% = 3.95%;

[0160] Confidence interval CI threshold: CI = [μ-3σ, μ+3σ] = [98.1t / h, 107.1t / h];

[0161] Conclusion: The deviation status is OVER_DEVIATION (exceeding the threshold).

[0162] 3. Intelligent scheduling and remote execution:

[0163] Calculate the priority of the flue gas analyzer in Plant C: PI = 0.4 × |3.95%| (current deviation) + 0.3 × 0.82 (error cycle increase of 18%) + 0.3 × 0.9 (high usage frequency) = 1.58 + 0.246 + 0.27 = 2.096;

[0164] The overall plant ranking is as follows: Plant C flue gas analyzer > Plant A pressure transmitter > Plant B water quality monitor;

[0165] The task {Equipment ID: C-GAS-07, Task: Zero-point calibration + range verification} was sent to Plant C. The calibration engine automatically executed the following steps: standard gas (5.0% O2) was introduced; the response value was collected at 5.12%, with an error of 0.12% (compliant with ≤0.5%); a report was generated: {Calibration Date: 2025-08-08, Error Curve: 0.12%@5%, 0.31%@10%}.

[0166] 4. Model closed-loop optimization:

[0167] Update the instrument weights for Factory C: = 0.5 × 1.1 (calibration qualified) = 0.55;

[0168] Incremental learning of historical trends to update historical error change trends = 0.2×0.12% + (1-0.2)×0.15%= 0.144% (smoothed weekly error trend, smoothing coefficient α=0.2).

[0169] This embodiment achieves standardized integration of multi-site environments and metrological data through a unified data interface for multi-protocol, multi-vendor metrological instruments, solving the problems of traditional data silos and inconsistent standards. The cross-site cross-parameter verification model, combined with environmental compensation and statistical optimization, effectively corrects instrument deviations and improves the accuracy and reliability of verification results. Priority index calculation ensures the rational allocation of limited verification resources, highlighting key areas and optimizing verification efficiency. Remote automated verification reduces the need for on-site manual operations, lowering labor costs and reducing human error. Through dynamic model updates and incremental learning, the system possesses adaptive capabilities, adapting to changes in instrument performance and fluctuations in the operating environment, significantly enhancing the intelligence and reliability of metrological management, and promoting the practical application and widespread adoption of remote collaborative verification technology for metrological instruments.

[0170] Example 2:

[0171] like Figure 4 and Figure 5 As shown, the remote collaborative verification and communication management system for measuring instruments uses the aforementioned remote collaborative verification and communication management method for measuring instruments, including:

[0172] The IoT access gateway is used to acquire environmental data distributed across multiple metering stations and raw instrument data from multiple metering instruments through the IoT communication protocol.

[0173] The protocol adaptation module is used to send the raw instrument data to the remote communication management center, perform protocol adaptation processing on the raw instrument data, and generate standard data frames in a unified format.

[0174] The semantic normalization module is used to receive standard data frames and perform semantic normalization processing to obtain a dataset for testing.

[0175] The cross-site verification module is used to build a cross-site cross-parameter verification model from the dataset and environmental data to obtain the deviation values ​​of each measuring instrument.

[0176] The priority calculation module is used to obtain the historical error change trend of each measuring instrument from the database, calculate the collaborative verification priority index of each measuring instrument based on the deviation value and the historical error change trend, and generate a remote collaborative verification list containing verification task allocation information.

[0177] The communication distribution module is used to distribute the remote collaborative verification list to multiple metering stations via the IoT communication protocol.

[0178] The IoT access gateway includes an industrial-grade communication processor, wireless / wired communication interfaces (such as 4G / 5G modules, Ethernet ports, RS-485 interfaces), data buffer storage, and protocol stack firmware. It is used to acquire raw instrument data in real time from environmental data acquisition terminals (temperature sensors, humidity sensors, barometers) and multiple measuring instruments (flow meters, pressure gauges, thermometers, etc.) distributed across multiple metering stations via IoT communication protocols such as MQTT, Modbus, and HART, and to perform data caching and preliminary verification.

[0179] The protocol adaptation module consists of a protocol parsing server in the remote communication management center and a protocol adaptation algorithm library stored in non-volatile memory. This module receives raw instrument data uploaded by the IoT access gateway, reads its communication interface identifier and message format, and calls the parsing algorithm corresponding to the matching protocol to convert the data into a standard data frame with a unified field structure. The hardware operating environment includes a high-performance CPU, memory module, and network interface card to support multiple concurrent parsing tasks.

[0180] The semantic standardization module consists of a server-side semantic mapping processing unit and a semantic mapping database. This module calls the semantic mapping table stored in the database to uniformly map the field names and values ​​in the standard data frame, unifies the measurement values ​​to the International System of Units (SI), unifies the status code set, and outputs a structured semantically standardized dataset.

[0181] The cross-site verification module includes a high-performance computing server, a data caching unit, and a cross-site cross-parameter verification algorithm library. Based on a semantically standardized dataset and corresponding environmental data, this module constructs a cross-site cross-parameter verification model and outputs the stable deviation value of each metrology instrument through calculations such as weighted averaging, environmental compensation, and least squares convergence.

[0182] The priority calculation module consists of a task scheduling server and a priority calculation rule base. It obtains the historical error change trend of each instrument and calculates the collaborative verification priority index in combination with the current deviation value, generating a remote collaborative verification list containing verification task allocation information.

[0183] The communication distribution module consists of a task publishing server and an IoT communication controller. It is responsible for distributing the remote collaborative verification list to multiple metering stations through the IoT communication protocol and providing feedback on the task execution status when necessary.

[0184] In a remote calibration task across multiple regional metrology stations, flow meters, pressure gauges, and thermometers located at stations A, B, and C simultaneously connected to the IoT access gateway, sending raw data from environmental sensors and instruments to the protocol adaptation module. The module identified that the pressure gauge at station A used the Modbus protocol, the flow meter at station B used the HART protocol, and the thermometer at station C uploaded data via the MQTT protocol. These data were then parsed into a unified standard data frame using an adaptation algorithm. The semantic standardization module called a mapping table to unify the field names and units of instruments from different manufacturers and models, forming a semantically standardized dataset. The cross-site verification module, based on the standardized data and environmental parameters, constructed a cross-parameter instrument group, performed weighted averaging and environmental compensation calculations, and derived the deviation value for each instrument. The priority calculation module, considering historical error trends, determined a list of instruments to be calibrated first, and the communication distribution module then distributed the task to each station for execution.

[0185] This application organically integrates IoT communication, protocol adaptation, semantic standardization, cross-site mutual parameter verification, priority scheduling, and task distribution, enabling cross-regional remote collaborative verification of measuring instruments. Compared to traditional methods relying on manual scheduling and single-point testing, this solution can achieve automatic data unification, dynamic model updates, and intelligent allocation of verification tasks in complex environments with multiple sites, multiple protocols, and multiple vendors' equipment. This significantly improves verification efficiency, accuracy, and resource utilization, while reducing the degree of manual intervention and on-site maintenance costs.

[0186] The technical scope of this invention is not limited to the content described above. Those skilled in the art can make various modifications and variations to the above embodiments without departing from the technical concept of this invention, and all such modifications and variations should fall within the protection scope of this invention.

Claims

1. A method for management of remote collaborative verification and communication of metrology instruments, characterized by: The method comprises the following steps: obtaining environmental data distributed in multiple measurement sites and raw instrument data of multiple measuring instruments through an IoT communication protocol; sending the raw instrument data to a remote communication management center, performing protocol adaptation processing on the raw instrument data, and generating a standard data frame in a unified format; receiving the standard data frame and performing semantic standardization processing to obtain a data set for verification; inputting the data set and the environmental data into a pre-constructed cross-site mutual verification model to obtain a bias value of each measuring instrument; obtaining a historical error trend of each measuring instrument from a database, calculating a collaborative verification priority index of each measuring instrument according to the bias value and the historical error trend, and generating a remote collaborative verification list containing verification task allocation information; distributing the remote collaborative verification list to each measurement site through the IoT communication protocol; wherein the specific steps of obtaining the historical error trend comprise: sorting the error history records of the measuring instruments according to the time stamps to form a time series; the historical records include the bias value obtained by each verification or mutual comparison and the corresponding time stamp, verification environment parameters, and verification method information.

2. The method for metrology instrument remote co-certification and communication management according to claim 1, wherein: The step of performing protocol adaptation processing on the raw instrument data to generate a standard data frame in a unified format comprises: after receiving the raw instrument data, reading the communication interface identifier and message format of the raw instrument data; matching the communication interface identifier with a pre-set communication protocol type list to generate a matching result; the communication protocol type list includes one or more of Modbus, Profibus, CAN, HART, RS-485, and MQTT protocols; parsing the message format into a standard data frame with a unified field structure by calling a protocol adaptation algorithm corresponding to the matching result; wherein the protocol adaptation algorithm is adapted to the communication protocol; the standard data frame includes an instrument identifier ID field, a time stamp field, a measurement value field, a status code field, a range field, and a unit field.

3. The method of remote co-location of metrology instruments for calibration and communication management according to claim 2, wherein: The specific steps of performing semantic standardization processing on the standard data frame to obtain a data set for verification comprise: performing semantic unification on the field names and field values in the standard data frame by calling a semantic mapping table stored in the database to generate semantically mapped fields; wherein the semantic mapping table records a one-to-one correspondence between the field names, field values of different manufacturer instruments, and standard semantic fields; organizing the semantically mapped fields in the form of key-value pairs, and unifying the measurement values of the fields to the International System of Units, and using a unified set of status codes for the status flags of the fields; storing the processed data as a semantic standardized data set, wherein each record includes a field name, a value, and a unit identifier.

4. The method for metrology instrument remote co-certification and communication management of claim 1, wherein: The specific steps of inputting the data set and the environmental data into a pre-constructed cross-site mutual verification model to obtain a bias value of each measuring instrument comprise: obtaining semantic standardized data sets and corresponding environmental data from multiple measurement sites as input data of the cross-site mutual verification model; The cross-site mutual verification model is mapped with an instrument database based on identification information of each metering instrument in the data set, and mutual instrument groups are divided according to types or range categories; For measurement data from different sites in each mutual instrument group, time series alignment is performed according to a unified timestamp; Combining the temperature, humidity, and air pressure environment data corresponding to each site, the environmental compensation value AC is calculated and corrected, and the compensation formula is: ; Wherein, T, H, P are the field collected environmental parameters, , , are preset reference environmental values, a, b, c are sensitivity coefficients. The average difference of the compensated measurement values of each metering instrument and other members in the mutual instrument group is calculated to obtain a relative deviation value; Least square method is performed on the deviation values of all mutual instrument groups for convergence processing, and the cross-site mutual verification model outputs stable deviation estimation values of each instrument as a final deviation result.

5. The method for metrology instrument remote co-certification and communication management according to claim 4, wherein: The cross-site mutual verification model further comprises: A metadata mapping table is established by initializing an instrument database; Semantic standardized data sets from multiple metering sites are received, and a mutual instrument group structure is constructed according to instrument identification and types; An initial weight factor is set for each instrument, which is set according to historical usage frequency, fluctuation variance or traceability level; The reference value R is calculated for each group of data of the interdependent instrument group by using the weighted average method, and the formula is: ; wherein, is the measurement value for the i-th instrument, is the weight for the i-th instrument; According to the reference value R and the measured value of each instrument The deviation AM is calculated, combined with the historical variance s, to construct a confidence interval CI; If the deviation ΔM exceeds the range of the confidence interval CI, the instrument is marked as an abnormal deviation instrument.

6. The method for metrology instrument remote co-certification and communication management of claim 1, wherein: The specific steps of obtaining the historical error change trend include: The error history record of the metering instrument is sorted according to the timestamp to form a time series; The short-term historical error change trend is calculated by an exponential smoothing method, and the calculation formula of the exponential smoothing method is: ； wherein, is the current error value, is the smoothed sequence value, and a is a smoothing factor having a value between 0.1 and 0.

3.

7. The method of remote co-location of metrology instruments for calibration and communication management according to claim 6, wherein: The specific steps of calculating the collaborative verification priority index of the metering instrument according to the deviation value and the historical error change trend include: The synergistic assay priority indicator Using the following equation: ； Wherein: ΔE is the error change rate, f is the frequency of use of the instrument per unit time, E is the current deviation value; , β, γ are weight factors determined by regression analysis, and are stored in the priority calculation rule base.

8. The method for metrology instrument remote co-certification and communication management according to claim 7, wherein: The metering instrument remote collaborative verification and communication management method further comprises a remote automatic verification program, and the specific steps of the remote automatic verification program include: After receiving the remote collaborative verification list through the IoT communication protocol, a verification task execution engine is called; The verification task execution engine performs verification according to a verification task flow, and the verification steps include instrument zero point calibration, calibration point measurement and error analysis; In the verification process, instrument response data is collected in real time and compared with a preset standard value to generate a verification result file; the instrument response data is real-time response data of the metering instrument collected during remote execution; When it is detected that the instrument does not support automatic verification, a manual verification prompt is sent to a site operation terminal, and the task state is marked as a manual execution state.

9. The method of remote co-location of metrology instruments for calibration and communication management according to claim 8, wherein: The metering instrument remote collaborative verification and communication management method further comprises incremental updating, and the specific steps of the incremental updating include: Instrument verification data and state information after completion of the verification are received, and the instrument verification data is stored in the database; updating a weight factor of the cross-site interrelation verification model according to the verification data and updating the historical error change trend in an incremental learning manner The updated historical error trend is re-deployed to calculate the collaborative verification priority index of each metering instrument.

10. A system for remote collaborative verification and communication management of metrology instruments, characterized by: The metering instrument remote collaborative verification and communication management method comprises: An IoT access gateway is used to obtain environmental data distributed in multiple metering sites and raw instrument data of multiple metering instruments through an IoT communication protocol; A protocol adaptation module is used to send the raw instrument data to a remote communication management center, perform protocol adaptation processing on the raw instrument data, and generate a standard data frame in a unified format; A semantic standardization module is configured to receive the standard data frame and perform semantic standardization processing to obtain a data set for verification; A cross-site verification module is configured to input the data set and the environmental data into a pre-constructed cross-site mutual verification model to obtain a bias value of each metrological instrument; A priority calculation module is configured to obtain a historical error change trend, calculate a collaborative verification priority index of each metrological instrument according to the bias value and the historical error change trend, and generate a remote collaborative verification list containing verification task allocation information; A communication issuing module is configured to issue the remote collaborative verification list to each metrological site through the IoT communication protocol. The specific steps of obtaining the historical error change trend include: The error history record of the metrological instrument is sorted according to the time stamp to form a time series; the historical record contains the bias value obtained by each verification or mutual comparison calculation and the corresponding time stamp, verification environment parameter and verification method information.

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