Metering instrument remote cooperative verification and communication management system and method

Abstract

The invention discloses a remote cooperative verification and communication management system and method for a metering instrument, and relates to the technical field of instrument intellectualization. The method comprises the following steps: acquiring environment data and original instrument data of a plurality of metering stations through an Internet of Things communication protocol, and transmitting the original data to a remote communication management center for protocol adaptation processing to generate standard data frames in a unified format; further performing semantic standardization on the standard data frame to obtain a data set for verification; and constructing a cross-site mutual parameter verification model based on the data set and the environmental data, calculating a deviation value of each metering instrument, determining a cooperative verification priority index of the metering instruments in combination with a historical error change trend, generating a remote cooperative verification list containing verification task distribution information, and sending the remote cooperative verification list to a server. And the information is issued to each site for execution through an Internet of Things communication protocol. The remote automatic verification scheduling and data consistency guarantee of heterogeneous instruments is realized, and the efficiency and reliability of multi-site collaborative verification are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of instrument intelligence, in particular to a metrological instrument remote collaborative verification and communication management system and method. BACKGROUND

[0002] Metrological instruments are important foundations for ensuring the stable operation of industrial production, scientific research and public metrological standard systems. The accuracy and timeliness of their verification work directly affect the reliability of related product quality control and metrological traceability. In recent years, with the advancement of industrial site informatization and intelligentization, a large number of metrological instruments are distributed in metrological sites at different geographical locations, and the realization of remote communication and data acquisition through the Internet of Things (IoT) has become a development trend. Existing technologies focus on the verification management of instruments from a single site or a single manufacturer, and perform verification tasks based on fixed scheduling plans by remotely collecting instrument data, which to some extent reduces the amount of on-site manual work.

[0003] However, in practical applications, instruments distributed in multiple sites are usually from different manufacturers, and the communication protocols and data formats differ greatly, resulting in compatibility problems in the unified processing of raw instrument data at the remote communication management center, which in turn leads to the inability to reliably and automatically obtain and process key data for evaluating the health status of the instruments, causing instruments with potential abnormalities to be unable to be identified and prioritized for verification in a timely manner, and stable state instruments to occupy valuable verification resources periodically according to the plan. Ultimately, the efficiency and reliability of the overall verification work are restricted, and intelligent optimization of resources cannot be achieved.

[0004] Therefore, how to generate a reasonable remote collaborative verification list under the environment of multi-site and multi-manufacturer metrological instrument access has become a technical problem that needs to be solved. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a metrological instrument remote collaborative verification and communication management system and method.

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

[0007] In a first aspect, the present application discloses a metrological instrument remote collaborative verification and communication management method, comprising the following steps:

[0008] Obtain environmental data distributed in multiple metrological sites and raw instrument data of multiple metrological instruments through an IoT communication protocol;

[0009] 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;

[0010] receive the standard data frame, and perform semantic standardization processing to obtain a data set for verification;

[0011] input the data set and the environment data into a pre-constructed cross-site interdependence verification model to obtain a bias value of each metrological instrument;

[0012] obtain a historical error change trend of each metrological instrument from a database, 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;

[0013] issue the remote collaborative verification list to each metrological site through the IoT communication protocol.

[0014] In a second aspect, the present application discloses a metrological instrument remote collaborative verification and communication management system using the metrological instrument remote collaborative verification and communication management method, comprising:

[0015] an IoT access gateway configured to obtain environment data distributed in multiple metrological sites and raw instrument data of multiple metrological instruments through an IoT communication protocol;

[0016] a protocol adaptation module configured 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;

[0017] a semantic standardization module configured to receive the standard data frame and perform semantic standardization processing to obtain a data set for verification;

[0018] a cross-site verification module configured to input the data set and the environment data into a pre-constructed cross-site interdependence verification model to obtain a bias value of each metrological instrument;

[0019] a priority calculation module configured to obtain a historical error change trend of each metrological instrument from a database, 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;

[0020] a communication issuing module configured to issue the remote collaborative verification list to each metrological site through the IoT communication protocol.

[0021] Compared with the prior art, the present application has the following beneficial effects:

[0022] 1、The present application solves the problem of access difficulty caused by differences in communication protocols and data field definitions of instruments of different manufacturers and different models by protocol adaptation and semantic standardization processing of original instrument data, so that the system can 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、The present application can timely discover the instrument state with potential abnormality by constructing a cross-site mutual reference verification model and analyzing in combination with environmental data, thereby reducing the influence of data deviation caused by environmental differences, improving the consistency and reliability of measurement results, and avoiding the misjudgment and delay risk caused by traditional manual comparison.

[0024] 3、The present application generates a verification priority index of the measurement instrument based on the historical error change trend, and forms a remote collaborative verification list, so that the verification resource allocation is more reasonable, which helps to preferentially process the instruments with larger error fluctuation, and improves the intelligent level and flexibility of overall verification scheduling, and overcomes the defect that the traditional periodic scheduling mode does not respond to the change of instrument running state in time. BRIEF DESCRIPTION OF DRAWINGS

[0025] The disclosure of the present application will be described with reference to the accompanying drawings. It should be understood that the drawings are only for illustrative purposes, and are not intended to limit the scope of protection of the present application. In the drawings, the same reference numerals are used to refer to the same parts. Among them:

[0026] Figure 1 is a step flowchart of the present application;

[0027] Figure 2 is a protocol adaptation and semantic standardization flowchart;

[0028] Figure 3 is a mutual reference verification model calculation diagram of the present application;

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

[0030] Figure 5 is a complete architecture diagram of the system of the present application. DETAILED DESCRIPTION

[0031] It is easy to understand that, according to the technical scheme of the present application, those skilled in the art can propose a plurality of structure modes and implementation modes which can be replaced with each other without changing the essential spirit of the present application. Therefore, the following specific embodiments and drawings are only exemplary descriptions of the technical scheme of the present application, and should not be regarded as the whole or as a limitation or restriction on the technical scheme of the present application.

[0032] In the prior art, the state detection and deviation checking of the metering instrument depend on manual calibration or periodic on-site verification, which is difficult to meet the real-time and intelligent needs of multi-site collaborative verification. The metering instruments distributed in different areas are usually maintained independently by each site, and their error calibration is mainly realized through manual sampling comparison or periodic traceability. However, such methods have problems of relying on manpower, long cycle, and difficulty in synchronization, especially in application scenarios with a large number of instruments or complex running states, the traditional methods are difficult to provide high-frequency and automatic deviation judgment basis. At the same time, due to the differences in environmental temperature, humidity, air pressure and other natural conditions of each instrument, even if they are the same type of equipment, the results measured in the same time period also have fluctuations, and without a dynamic compensation mechanism for environmental factors, systematic errors will inevitably be introduced. In addition, the existing communication management architecture mostly uses fixed protocols or one-way data upload mode, lacks a general protocol adaptation and data semantic consistency mechanism, resulting in low efficiency of multi-source data fusion and intelligent judgment.

[0033] The inventors construct a remote metering instrument verification method that integrates semantic standardization, environmental compensation and dynamic collaborative judgment mechanism, and improve the intelligent identification ability of the system for distributed instrument state abnormalities. Through the IoT communication protocol, the original data and environmental parameters of each metering site instrument are collected in real time, and through the protocol adaptation algorithm library, various communication formats are converted into standard data frames of a unified structure, solving the problem of inconsistent data formats of devices from different manufacturers. Then, combined with the preset semantic mapping table in the database, all kinds of fields are uniformly named and dimensionally converted to form a semantic standardized data set with cross-device universality. On this basis, the instrument identifier is matched and classified with the database to construct a mutual instrument group composed of instruments of the same type or range, and the original measurement values are corrected by time alignment and environmental compensation formula, and then input into the cross-site mutual verification model. The model considers the historical performance indicators and current measurement deviation of the equipment, and outputs the current stable deviation value of each instrument through the weighted average and confidence interval judgment mechanism, thereby realizing automatic deviation judgment driven by data.

[0034] During the research process, it was found that the relative deviation between multiple instrument groups is influenced by environmental parameters and historical usage state of the instruments, and the introduction of weight factors and dynamic updating mechanism can effectively improve the stability and abnormal identification accuracy of the model. When constructing the mutual instrument group, only relying on the same type clustering cannot fully reflect the differences in long-term running conditions of the devices. Therefore, the inventors further introduce multi-dimensional indicators such as historical usage frequency, fluctuation variance and traceability level to form a weight factor, which is used to dynamically adjust the contribution proportion of each device to the reference value in the weighted average process. Especially in the case of sudden changes in environmental conditions or abnormal drift of some devices, this mechanism can effectively suppress the influence of extreme value data on the overall judgment result. In addition, the device data deviating from the interval is marked as abnormal, effectively improving the robustness of the system in the environment of complex multi-source data.

[0035] Compared with the prior art, the cross-site mutual reference verification method and the dynamic feedback mechanism significantly improve the intelligent level and practicability of the remote verification of the measuring instrument. The whole-process verification architecture integrated with the protocol adaptation algorithm library and the cross-site mutual reference verification model has the advantages of data processing automation, deviation judgment intelligence, and result output stabilization. The core innovation is to use the environmental compensation and model weight dynamic adjustment mechanism to unify the multi-source measurement results in the comparable range, realize real-time deviation evaluation and verification task allocation without manual intervention. Especially when used with the remote verification task execution engine, the scheme can form a closed-loop control process of software and hardware linkage, providing a key support means for the intelligent measurement system construction.

[0036] After introducing the basic idea of the present application, the embodiments of the present application will be specifically introduced below with reference to the drawings.

[0037] Embodiment one:

[0038] As shown in Figure 1 , the remote collaborative verification and communication management method of the measuring instrument comprises the following steps:

[0039] Obtain environmental data distributed in multiple measurement sites and original instrument data of multiple measuring instruments through an IoT communication protocol;

[0040] Send the original instrument data to a remote communication management center, perform protocol adaptation processing on the original instrument data, and generate a standard data frame in a unified format;

[0041] Receive the standard data frame and perform semantic standardization processing to obtain a data set for verification;

[0042] Input the data set and the environmental data into a pre-constructed cross-site mutual reference verification model to obtain the deviation value of each measuring instrument;

[0043] Obtain the historical error trend of each measuring instrument from a database, calculate the collaborative verification priority index of each measuring instrument according to the deviation value and the historical error trend, and generate a remote collaborative verification list containing verification task allocation information;

[0044] Distribute the remote collaborative verification list to each measurement site through the IoT communication protocol.

[0045] The working principle of the present application is as follows: first, the IoT edge collection terminal deployed in multiple metering sites accesses various types of metering instruments, and through the interface module compatible with multiple industrial communication protocols, the original instrument data of each metering instrument is obtained, and the environmental parameter information of the site is collected in real time, including temperature, humidity, air pressure and other key influencing factors. These environmental data are packaged in a unified format and sent to the remote communication management center together with the original instrument data for centralized processing.

[0046] In the communication management center, the system calls the protocol adaptation algorithm library to format analyze and field map the data from different instruments and different communication protocols, and uniformly converts the original heterogeneous data into a standard data frame with consistent structure. The standard data frame contains fields such as instrument identification, collection time, measurement value, state information, range unit, etc., providing a consistent data basis for subsequent semantic processing and model calculation.

[0047] After receiving the standard data frame, semantic standardization processing is performed to unify the field semantics in different source data. For example, different device manufacturers may use different names or units for the same measurement field, and the system performs unified naming, unit conversion and state code standardization through pre-defined semantic mapping rules, and finally forms a structured and standardized data set. This data set is the input data required for cross-site verification.

[0048] The constructed data set and the synchronously collected environmental parameter data are input into the pre-constructed cross-site mutual verification model. The model constructs a mutual instrument group according to the instrument model, range category and use scenario, and performs time sequence alignment and environmental compensation on the instrument measurement values of different sites. Combined with the measurement consistency of multi-site instruments, the relative deviation value of each metering instrument is calculated through weighted average, least square fitting and abnormal point identification algorithms, and the deviation detection result is output.

[0049] The system retrieves the historical error trend data of the instrument from the historical database, forms a trend curve through exponential smoothing or time series analysis method, and jointly calculates the collaborative verification priority index with the current deviation value. The priority index considers factors such as deviation amplitude, error fluctuation trend and instrument use frequency to generate a structured remote collaborative verification list. The remote collaborative verification list clearly marks the device list that needs to be calibrated first, the type of verification task and the allocation strategy, etc.

[0050] Finally, the system sends the remote collaborative verification list to the corresponding metering site through the IoT communication network. For instruments with automatic verification function, the remote execution interface can be directly called to start the verification process; for devices without this function, the manual verification is notified through the prompt mechanism. The execution result can be fed back to the system to form a closed loop update.

[0051] The application builds an intelligent calibration management method integrating multi-site collaborative processing, high-precision deviation detection, intelligent scheduling and remote execution through protocol adaptation, data standardization, mutual reference verification modeling and trend analysis and other technical links. The application not only overcomes the processing obstacles caused by heterogeneous protocols and data differences, but also effectively solves the technical problems of large dependence on manual work, response lag, lack of priority judgment and other technical problems in the traditional calibration process, and realizes the intelligent, real-time and accurate management of metrological calibration.

[0052] As shown in Figure 2 The application further proposes that the step of performing protocol adaptation processing on the original instrument data to generate a standard data frame in a unified format includes:

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

[0054] The communication interface identifier is matched with a preset 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;

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

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

[0057] In the metrological instrument remote collaborative calibration and communication management method proposed in the application, in order to solve the problem of non-uniformity of communication protocols of multi-source heterogeneous metrological instruments, a protocol adaptation algorithm library is provided in the system to adapt to the message parsing requirements of different types of industrial communication protocols.

[0058] After the remote communication management center receives the original instrument data transmitted from the edge device or the IoT gateway, the system reads the communication interface identifier information and the original data format structure of the message contained therein. The communication interface identifier can include the device interface type (such as RS-485, CAN bus, Ethernet port, etc.) and the protocol characteristic code of the message header, which is used for subsequent matching processing.

[0059] The system matches the extracted communication interface identifier with a preset communication protocol type list, which supports mainstream industrial communication protocols, including but not limited to Modbus (RTU / TCP), Profibus-DP, CAN, HART, RS-485 standard protocol, MQTT protocol, and various fieldbus and industrial Internet of Things protocols. According to the matching result, the system calls the corresponding protocol adaptation algorithm to realize field recognition and data extraction of specific message structure.

[0060] In the protocol adaptation algorithm library, each protocol adaptation algorithm encapsulates the data frame structure analysis rules for a certain type of protocol, including start bit, check bit, field offset, byte order, CRC verification, and other basic analysis logic to ensure compatibility with data specifications output by different manufacturers or different version devices. After matching the protocol type, the system maps the original message according to the corresponding protocol format and performs data analysis, and generates a standard data frame in a unified format.

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

[0062] Instrument identification ID field: used to uniquely identify the device entity, which can include device model, serial number, etc.

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

[0064] Measurement value field: represents the collected original measurement value;

[0065] Status code field: reflects the current device working state or running exception, such as overload, misalignment, power failure, etc.

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

[0067] Unit field: clearly defines the physical unit of the measurement value field, such as Pa, V, kg, etc., supports automatic conversion of international units.

[0068] Through the above steps, the system can parse the original instrument data from different measurement sites, different manufacturers, and different communication protocols into a standard data frame with unified structure, clear semantics, and complete fields, providing a data basis for subsequent data standardization, model input processing, and bias value calculation.

[0069] The protocol adaptation processing method effectively solves the technical problems of parsing incompatibility, field missing, structure mismatch and the like of the traditional system when facing heterogeneous instrument data sources by introducing a multi-protocol identification matching mechanism and a structure design of a protocol adaptation algorithm library. The system can realize plug-and-play protocol connection of multiple types of equipment in a complex industrial field, automatically identify the protocol type and complete data parsing without manual intervention. Through a unified output form of a standard data frame, seamless connection of the data acquisition layer and the management layer is realized, greatly improving the system expansibility, compatibility and data processing efficiency, and providing a solid support for building a unified, real-time and highly reliable metrological verification data platform.

[0070] The application further proposes that the specific steps of performing semantic standardization processing on the standard data frame to obtain a data set for verification include:

[0071] The field name and the field value in the standard data frame are semantically unified by calling the semantic mapping table stored in the database to generate a semantically mapped field; the semantic mapping table records a one-to-one correspondence relationship between the field name, the field value of the instrument of different manufacturers and the standard semantic field;

[0072] The semantically mapped field is organized in the form of a key-value pair, the measurement value of the field is unified as the International System of Units, and a unified set of state codes is used for the state flag of the field;

[0073] The processed data is stored as a semantic standardized data set, wherein each record includes a field name, a value and a unit identifier.

[0074] Specifically, this step first calls the semantic mapping table stored in the database in advance by the remote communication management center, and the mapping table records a one-to-one correspondence relationship between the field name, the field value range, the state description method and the like used by different instrument manufacturers and the system general standard semantics. The establishment of the mapping relationship is based on industry standards (such as JJF, GB, IEC and the like) and analysis and induction of the communication protocol field structure of mainstream instrument manufacturers. For example, a certain brand names the "measuredValue" field as "measuredValue", and another manufacturer may name it as "output", and the semantic mapping table records the corresponding relationship between them and the standard semantic field "measurement_value".

[0075] ​After receiving the standard data frame, the system queries the semantic mapping table for each field name and field value in turn, and generates the unified semantic field name and standard value range. During processing, the system uses the key-value pair (Key-Value) data structure to reorganize the field content, ensuring indexability and scalability during subsequent model input. For example, the field "measurement value" will be represented as "measurement_value": "12.5", and its unit will be explicitly identified in the "unit": "kPa" field.

[0076] For the measurement value field, the system further converts its numerical content to the International System of Units (SI), including automatic identification of unit suffixes, conversion factor adjustment, and decimal place retention. This ensures that inconsistencies in unit usage at different sites or devices do not affect subsequent cross-device comparisons or model input calculations.

[0077] At the same time, for the state code field representing the working state of the instrument, the system uniformly replaces it with a set of standard state codes, such as "normal", "ok", "running" and other state values, which are unified as state code "00", and "fault", "alarm" are classified as "01", etc. This ensures that the state semantics have consistent semantic orientation between different manufacturers' devices.

[0078] Finally, the system organizes all field contents that have been semantically mapped and unit standardized into structured records, each record including at least the field name, corresponding numerical value, and unit identification, and stores them as a record in the semantic standardized dataset. This dataset supports structured storage (such as tables, JSON (JavaScript Object Notation) objects) and interfaces with subsequent cross-reference verification models as input data sources.

[0079] Through the above semantic standardization processing mechanism, the present application effectively solves the heterogeneous semantic problems existing in field expression, unit system usage, and state representation of instruments from different measurement sites and different manufacturers. Unlike traditional systems that rely on unified hardware platforms or manual data cleaning, this scheme realizes automatic semantic fusion capability across manufacturers and protocols through a field standardization mechanism driven by a semantic mapping table, providing a high-quality data source with consistent structure, unified units, and clear semantics for subsequent calibration model data input. This method significantly improves the system's adaptability and data availability in a multi-source complex measurement environment, enhancing the intelligence and automation level of collaborative calibration tasks.

[0080] As shown in Figure 3 the present application further proposes that the specific steps of inputting the dataset and environmental data into the pre-built cross-site cross-reference verification model to obtain the bias value of each measurement instrument include:

[0081] The semantic standardized dataset and corresponding environmental data from multiple metrology sites are obtained as cross-site mutual verification model input data.

[0082] The cross-site mutual verification model establishes a mapping relationship with the instrument database based on the identification information of each metrology instrument in the dataset, and divides the mutual instrument group according to the model or range category. The cross-site mutual verification model will map and match with the instrument database according to the unique identification information (ID field) of each metrology instrument in the dataset, so as to obtain the model, range category and historical performance data of the instrument. According to the same standard of model or range category, the metrology instruments of each site are divided into multiple mutual instrument groups. This grouping method can ensure that the instruments in the same group have comparability in range, resolution, accuracy level, etc., and provides data basis for subsequent mutual reference.

[0083] The measurement data from different sites in each mutual instrument group is time-aligned according to a unified timestamp; it is ensured that the data collected at different places are strictly matched in the time dimension, avoiding time sequence misplacement caused by data collection delay or upload time difference, so as to ensure the accuracy of cross-site data comparison.

[0084] Combined with the environmental data such as temperature (T), humidity (H), air pressure (P) of each site, the environmental compensation value ΔC is calculated and corrected, and the compensation formula is:

[0085]

[0086] Wherein, T, H, P are the environmental parameters collected on site, , , are reference environmental values, and a, b, c are sensitivity coefficients of temperature, humidity and air pressure respectively; the value range of a / b / c is 0.05-0.2%, the sensitivity coefficient calibration obtains the basic sensitivity parameter through laboratory environmental gradient experiment, and dynamically corrects it according to the on-site historical data by using the least square method; the compensation value ΔC is used to correct the influence of environmental difference on the measurement value, so as to eliminate the systematic deviation caused by climate or environmental difference.

[0087] After obtaining the compensated measurement value, the average difference of the compensated measurement value of each metrology instrument and other members in its mutual instrument group is calculated to obtain the relative deviation value.

[0088] In order to further improve the stability and anti-exceptional interference ability of the calculation result, the least square method is performed on all mutual instrument groups for convergence processing to weaken the influence of accidental abnormal value on the overall deviation estimation. The cross-site mutual verification model outputs the stable deviation estimation value of each instrument as the final deviation result. ​

[0089] wherein the cross-site mutual reference calibration model refers to a mathematical model for data mutual reference, bias comparison and precision verification, which is established according to simultaneous period measurement data and corresponding environmental data of a group of metrological instruments with same or compatible metrological parameters (such as measurement range, unit, type) selected from multiple geographically dispersed metrological sites. The cross-site mutual reference calibration model is essentially a distributed collaborative calibration mechanism based on cross-site comparison of similar instruments and environmental compensation modeling, which can construct a logically consistent bias analysis model through mutual reference data of multiple instruments without relying on standard instruments, and support remote intelligent verification scheduling.

[0090] The construction of the cross-site mutual reference calibration model includes: selecting instruments of the same type or same functional parameters from different sites to construct a "mutual reference instrument group"; each group contains at least two or more instruments to ensure that the data are comparable; aligning the data collected by each instrument based on a unified timestamp; simultaneously converting the units and standardizing the semantics of all measurement values; finally calculating the environmental impact through the compensation value ΔC, using the difference between the measurement values of each instrument and other instruments in its mutual reference instrument group, deducting ΔC, calculating the bias, and statistically converging (such as least squares method) multiple bias values to obtain the final bias result as the stable bias conclusion.

[0091] Due to the differences in verification conditions (such as environment, standard source) between sites, the cross-site mutual reference calibration model bridges the standard differences through multi-source instrument mutual reference, and realizes relative verification without relying on standard instruments: through data comparison between similar instruments, the dependence on high-level standard instruments is reduced; supporting remote distributed verification and scheduling: providing data support for subsequent verification priority calculation, task list generation, etc., and improving the automation degree of collaborative verification.

[0092] Through the calculation process of the above cross-site mutual reference calibration model, the application realizes the full-link processing of time alignment, environmental difference compensation and bias value convergence optimization in the bias calculation of multi-source heterogeneous instrument data, significantly improving the accuracy and robustness of cross-regional metrological data comparison. Compared with the traditional method of relying on single-point calibration or manual comparison, this scheme can automatically fuse real-time detection data from multiple sites, dynamically eliminate the interference of environmental factors and occasional abnormalities, thereby providing high-credibility, low-uncertainty bias evaluation results for remote collaborative verification. This not only improves the scientificity of verification task allocation, but also provides a solid data foundation for the whole life cycle quality monitoring of metrological instruments.

[0093] The application further proposes that the cross-site mutual reference calibration model further includes:

[0094] The metadata mapping table is recorded by the unique identification, model, range, accuracy level, traceability level and factory calibration information of each metrology instrument. The mapping table provides structured basic information support for subsequent instrument grouping and data comparison.

[0095] The semantic standardized data sets from multiple metrology sites are received, and the interrelated instrument group structure is constructed according to the instrument identification and type; the interrelated instrument group is divided according to the principle of "same model or same range category priority", and the historical running parameters, site number and maintenance record of each member are attached in the grouping record, to ensure the traceability and repeatability of data comparison.

[0096] After grouping is completed, an initial weight factor is set for each instrument, and the initial weight factor is set according to the historical usage frequency, fluctuation variance or traceability level; for example, the instrument with long-term stable operation and high traceability level is given a higher weight, so as to occupy a larger proportion in the reference value calculation.

[0097] The reference value R is calculated by using the weighted average method for each group of data of the interrelated instrument group, and the formula is: ;

[0098] Among them, is the measurement value of the i th instrument, is the weight of the i th instrument; the reference value R can effectively offset the influence of individual abnormal value on the overall comparison result.

[0099] The instrument value calculation formula after environmental compensation is ;

[0100] Among them, V represents the original measurement value (such as 105.1 t / h), ΔC is the environmental compensation value, T is the range, and represents the upper limit of the instrument measurement (such as 200 t / h).

[0101] According to the reference value R and the measurement value of each instrument , the deviation ΔM is calculated, and the confidence interval CI is constructed combined with the historical variance σ, and the confidence interval CI is set according to the 3σ criterion, that is:

[0102] ;

[0103] If the deviation ΔM exceeds the range of 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 interrelation verification model to support subsequent deviation calculation and dynamic update.

[0105] The inter-comparison instrument group structure, reference value calculation rules, environmental compensation parameters and abnormality determination mechanism are integrated into the cross-site inter-comparison verification model, so that the model can directly call the preset parameters in the running stage, realize the closed-loop processing of high-speed deviation calculation, dynamic updating and abnormality detection.

[0106] The cross-site inter-comparison verification model can fully utilize historical information and weight distribution mechanism during data comparison, reduce the interference risk of abnormal data of a single instrument on the overall result, and timely discover potential deviation abnormalities through confidence interval determination, thereby improving the consistency and traceability reliability of multi-site measurement data. This not only provides a stable mathematical and data structure foundation for subsequent deviation calculation and dynamic correction, but also significantly improves the precision and robustness of the cross-site measurement collaborative verification process.

[0107] The application further proposes that the specific steps of obtaining the historical error trend include:

[0108] The error history records of the measurement instrument are sorted according to the time stamp to form a time series; the historical records include the deviation value obtained by each calibration or inter-comparison comparison and its corresponding time stamp, calibration environment parameters, calibration method and other information. The system first sorts the error records in ascending order according to the time stamp, thereby forming a continuous and calculable error time series, providing basic data input for trend analysis.

[0109] The short-term historical error trend is calculated by the exponential smoothing method, and the calculation formula of the exponential smoothing method is:

[0110] ;

[0111] Among them, is the current error value, is the smoothing sequence value, and a is a value between 0.1 and 0.3. The exponential smoothing method is used to calculate the short-term trend of the above error time series, so as to quickly capture the fluctuation of instrument performance in the task scheduling stage.

[0112] A higher a value can improve the response speed to the latest error change, and a lower a value is more suitable for instruments with relatively stable error changes. The system can automatically select the optimal a value according to the instrument type, working environment and historical stability.

[0113] When modeling the historical error data trend, the system uses the exponential weighted moving average algorithm (EWMA) to process the instrument deviation change sequence. The selection of the a parameter directly affects the sensitivity of the model to short-term fluctuations and the stability of long-term trends.

[0114] In combination with the actual sampling data test results and industry experience, selecting a = 0.1~0.3 can effectively suppress the high-frequency noise in the data while maintaining the trend response capability, and is suitable for most stable environment measurement scenarios. Under this interval, the error prediction residual remains stationary 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, identifies instruments with accelerated trend changes or decreased stability, and uses these characteristic quantities as one of the important input parameters for subsequent calculation of the priority index for collaborative verification. If it is detected that the trend change amplitude of an instrument has approached the preset performance threshold, the task weight of the instrument is increased in the priority calculation to ensure that it can enter the next round of collaborative verification plan in priority.

[0116] The present application extracts the historical error change trend, not only provides dynamic weight input for the task allocation stage, but also can early warn potential performance degradation risk in cross-site verification process. Compared with the existing scheme which only relies on static deviation value, the present application introduces exponential smoothing calculation in the trend analysis stage, so that the system can not only sensitively perceive the latest changes, but also suppress the interference of single abnormal value on trend judgment, thereby ensuring the rationality of the verification plan while improving the stability and reliability of the overall measurement data.

[0117] The present application further proposes that the specific steps of calculating the collaborative verification priority index of the measurement instrument according to the deviation value and the historical error change trend include:

[0118] Collaborative verification priority index The following formula is used:

[0119] ;

[0120] Wherein ΔE is the error change rate, reflecting the dynamic fluctuation degree of the instrument performance, f is the unit time usage frequency of the instrument, used to measure the importance and real-time influence range of the instrument on the measurement system, E is the current deviation value, directly reflecting the absolute measurement accuracy level of the instrument; , β and γ are weight factors determined by regression analysis and stored in the priority calculation rule library, which can be adjusted according to different industries or application scenarios. Based on the historical verification records, a sample set is constructed, the minimum false rejection rate is taken as the objective function, and the optimal values of λ / β / γ are solved by gradient descent method.

[0121] After the above formula operation is completed, the priority calculation module sorts the PI value of each instrument to generate a high-to-low collaborative verification task priority list. For instruments with a PI value higher than a set threshold, they will be marked as "high priority" and verification instructions will be issued preferentially in the task allocation stage to shorten the response time of potential risk equipment; while for low-priority equipment, the verification can be delayed to the next verification period to optimize the overall resource scheduling efficiency.

[0122] Through the above priority calculation mechanism, the application realizes dynamic sorting based on risk and impact in the task scheduling stage. Compared with the existing priority calculation method based on fixed period or single error value, the application can consider the deviation size, change trend and equipment usage intensity at the same time, thereby significantly improving the scientificity and timeliness of verification task allocation and ensuring the overall accuracy and stability of the measurement system.

[0123] The application further proposes that the remote collaborative verification and communication management method of the measurement instrument further comprises a remote automatic verification program, and the specific steps of the remote automatic verification program comprise:

[0124] After receiving the remote collaborative verification list through the IoT communication protocol, the verification task execution engine is called;

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

[0126] 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 measurement instrument collected during remote execution;

[0127] When it is detected that the instrument does not support automatic verification, an artificial verification prompt is sent to the site operation terminal, and the task state is marked as an artificial execution state.

[0128] First, after each measurement site receives the remote collaborative verification list issued by the communication management center through the IoT communication protocol, the system automatically calls the verification task execution engine deployed locally or in the cloud. The engine will parse the instrument identification, verification task type and execution parameters in the verification list in the initialization stage, and adjust the task execution order according to the task priority index to ensure that high-risk or high-impact instruments are verified first.

[0129] The verification task execution engine is deployed in an embedded server local to the metrology site, and has functions such as task receiving, process scheduling, instrument operation instruction sending, and result returning. The engine is constructed based on a unified control logic programming language, and its core scheduler automatically selects the instrument to be verified according to the task priority index, and sends instructions to the corresponding instrument through serial communication or an industrial control bus to complete actions including zero point calibration, calibration point acquisition, state acquisition, etc. If the engine identifies that the instrument does not have automatic verification capability, it triggers a manual assistance interface and sends operation instructions to a local human-computer interaction terminal, which continues the process after being confirmed by a human, ensuring the compatibility of software and hardware systems.

[0130] The verification task execution engine performs verification operations according to a preset verification task process, which includes instrument zero point calibration, calibration point measurement, and error analysis. In the zero point calibration link, the system automatically outputs a reference signal, and calculates the zero point offset by comparing the instrument response value with the theoretical zero point value; in the calibration point measurement link, the standard input is applied in sequence according to the preset multi-point calibration curve, the instrument response data is acquired, and real-time comparison with the standard value is performed to obtain the deviation at different range points; in the error analysis link, the system matches the deviation results of each calibration point with the preset tolerance standard to generate a complete error analysis report.

[0131] During the verification process, the system acquires the instrument response data in real time through a high-speed data acquisition module, and performs noise filtering, outlier rejection, and time synchronization processing to ensure the comparison accuracy with the standard value. The difference between the processed data and the standard value is used to calculate the verification indicators in real time, and a verification result file is generated, which can adopt a unified XML / JSON structure and include instrument identification, verification time, results of each verification step, final conclusion, and execution log. The verification result file is encrypted and returned to the communication management center for archiving and subsequent analysis.

[0132] When the verification task execution engine detects that the target instrument does not have the interface capability for remote automatic verification (such as lacking remote control command support or key parameters cannot be read through communication) during the initialization process or the verification process, the system immediately sends a manual verification prompt to the operation terminal of the metrology site, and marks the task status as "manual execution" in the task list, to ensure timely intervention of manual intervention and the integrity of the verification task.

[0133] Through the above remote automatic verification procedure, the present application realizes full automation of the verification process and closed-loop return of the results based on deviation analysis and task priority sorting, reduces the proportion of manual participation, and improves the verification efficiency and consistency. At the same time, it automatically switches to manual mode when the device conditions are insufficient, ensuring the continuity and coverage of the verification task, effectively improving the overall operation reliability and management level of the metrology system under cross-site and cross-device conditions.

[0134] The application further proposes that the metrological instrument remote collaborative verification and communication management method further comprises incremental updating, and the specific steps of the incremental updating comprise:

[0135] receiving instrument verification data and state information after completion of verification, and storing the instrument verification data to a database;

[0136] updating a weight factor of a cross-site mutual verification model according to the verification data , and updating a historical error trend in an incremental learning manner; when a residual error change rate is greater than 5%, triggering the incremental learning, updating an error sequence of the historical error trend in a sliding window mechanism, and the window length N is greater than or equal to 30 groups, and recalculating an exponential smoothing value after each new data is added ;

[0137] redeploying the updated historical error trend to calculate a collaborative verification priority index of each metrological instrument.

[0138] Specifically, after completion of a verification task, whether the task is executed through a remote automatic verification program or completed in a manual verification mode, the system receives instrument verification data and state information from each metrological site. The verification data comprises a zero point deviation, a multi-point error value within a range, a fitting parameter of an error curve, a verification date and an environmental condition, and the state information comprises a working state flag of the instrument, a verification completion flag, an abnormal event record and the like. After the above information is received, it is written into a central database in a unified data format, and is stored in association with historical records of the instrument, thereby forming a traceable full life cycle verification data chain.

[0139] The system calls a cross-site mutual verification model to update weights, adjusts a weight factor of a corresponding instrument in the model according to newly stored verification data. The correction logic of the weight factor comprehensively considers a deviation level of this verification, a stability of a verification result and a relative consistency with other instruments in a mutual verification instrument group, so as to ensure that the model can more accurately reflect a credibility of each instrument when calculating a reference value in the next round. Meanwhile, for updating of a historical error trend, the system processes in an incremental learning manner - instead of retraining all historical data, the newly added error data is integrated into an existing trend calculation model in a time sequence increment, so that the calculation efficiency is maintained while the continuous optimization of trend prediction is realized.

[0140] After completion of the weight updating of the model and the correction of the historical trend, the system takes the latest deviation value and the updated historical error trend as inputs, and recalculates a collaborative verification priority index of each metrological instrument. This process ensures that the priority evaluation can reflect the latest verification result and performance change in real time, so that subsequent verification task scheduling is more in line with a global optimal strategy.

[0141] To enhance the intelligence of the bias trend modeling, the system introduces an adaptive update mechanism in the weight factor (e.g., a). This mechanism uses the error residual after each round of verification as a feedback signal input, and uses the Bayesian optimization method to fine-tune the factor value, thereby minimizing the deviation of the historical trend from the actual error.

[0142] The update logic adopts the following strategy: set the initial a = 0.2, when the prediction error is significantly higher than the upper threshold of the model residual for three consecutive rounds, trigger the parameter adjustment process, re-estimate the weight factor, so that the model dynamically adapts to changes in different instruments or environmental conditions.

[0143] Through this weight learning mechanism, the system has long-term adaptive evolution capability, thereby improving the generalization ability and long-term stability of the remote collaborative verification model.

[0144] Through the above incremental update mechanism, the application realizes the dynamic adaptation of the cross-site mutual reference verification model and the task priority evaluation system. Compared with the traditional periodic batch update mode, the present scheme can optimize the core model parameters immediately after each verification task is completed, greatly improving the response speed of the system to changes in instrument performance, ensuring that under the condition of continuous operation of multiple sites and multiple batches of measurement tasks, the verification resources can always be allocated to the instruments that need calibration the most, improving the overall efficiency and long-term stability of the verification system.

[0145] The following is an implementation case of a remote collaborative verification and communication management method for a power generation group cross-factory measurement instrument:

[0146] Three coal-fired power plants (A plant, B plant, and C plant) under a certain energy group need to carry out quarterly verification on distributed steam flow meters, flue gas analyzers, and water quality monitoring instruments. The traditional mode requires 6 verification personnel to spend 2 weeks to complete, and due to environmental differences (boiler room temperature 52°C vs. water treatment workshop 28°C), the data comparability is poor. After applying the technical solution, the following process is used to realize remote collaborative management:

[0147] 1. Heterogeneous instrument data collection and standardization:

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

[0149] Gateway identifies Rosemount interface identifier RS485_ModRTU, calls 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] Convert "Siemens unit Vol" to standard semantic oxygen concentration through the mapping table, and unify the dimension to vol%, output data set {ID: C-GAS-07, Param: oxygen concentration, Value: 12.3, Unit: vol%}.

[0151] 2. Cross-site inter-reference verification core process:

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

[0153] The same type of steam flowmeter (E+H FMR50) constitutes an inter-reference instrument group: A factory group member weight w=0.7 (qualified in the last three inspections); B factory group member w=0.9 (new equipment); C factory group member w=0.5 (historical fluctuations are large);

[0154] Calculate the weighted reference value:

[0155] ;

[0156] (2.2) Double environmental compensation:

[0157] B plant instrument environment: temperature T=46℃, humidity H=85% (boiler room), call sensitivity parameters (E+H FMR50): a=0.15% / ℃, b=0.08% / %RH; The sensitivity parameters are the laboratory calibration results;

[0158] Calculate the compensation calculation of a single instrument: ΔC = 0.15×(46-25) + 0.08×(85-60) = 3.15% +2.0% = 5.15%; The 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) x 100% = 3.95%;

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

[0161] Conclusion: Deviation state is OVER_DEVIATION (over threshold).

[0162] 3. Intelligent scheduling and remote execution:

[0163] Priority of C plant flue gas analyzer: PI = 0.4x|3.95%| (current deviation) + 0.3x0.82 (error week-on-week up 18%) + 0.3x0.9 (high frequency of use) = 1.58 + 0.246 + 0.27 = 2.096;

[0164] Full plant area ranking: C plant flue gas analyzer > A plant pressure transmitter > B plant water quality monitor;

[0165] Issue task {device ID: C-GAS-07, task: zero point calibration + range verification} to C plant, and the verification engine automatically executes: introduce standard gas (5.0% O2); collect response value 5.12%, error 0.12% (consistent with ≤0.5%); generate report: {calibration date: 2025-08-08, error curve: 0.12% @ 5%, 0.31% @ 10%}.

[0166] 4. Model closed-loop optimization:

[0167] Update C plant instrument weight: = 0.5x1.1 (calibration qualified) = 0.55;

[0168] Historical trend incremental learning, update historical error trend = 0.2x0.12% + (1-0.2)x0.15% = 0.144% (smoothed week error trend, smoothing coefficient a = 0.2).

[0169] This embodiment realizes the standardized integration of multi-site environment and measurement data by unifying the data interfaces of multiple protocols and multiple vendor measurement instruments, solves the problems of traditional data islands and inconsistent standards. The cross-site mutual verification model combines environmental compensation and statistical optimization to effectively correct instrument deviation and improve the accuracy and reliability of verification results. The priority index calculation ensures that limited verification resources are reasonably allocated, highlights the key points, and optimizes the verification efficiency. Remote automated verification reduces the need for on-site manual operation, reduces labor costs and human errors. Through dynamic model updating and incremental learning, the system has self-adaptive ability, adapts to changes in instrument performance and fluctuations in the use environment, significantly enhances the intelligent level and reliability of measurement management, and promotes the practicality and popularization of remote collaborative verification technology of measurement instruments.

[0170] Example Two:

[0171] As Figure 4 andFigure 5 The meter remote collaborative verification and communication management system uses the meter remote collaborative verification and communication management method described above, and comprises:

[0172] An IoT access gateway is configured to acquire environmental data distributed in multiple meter sites and raw instrument data of multiple meters through an IoT communication protocol.

[0173] A protocol adaptation module is configured 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.

[0174] A semantic standardization module is configured to receive the standard data frame and perform semantic standardization processing to obtain a data set for verification.

[0175] A cross-site verification module is configured to construct a cross-site mutual verification model with the data set and the environmental data, and obtain a bias value of each meter.

[0176] A priority calculation module is configured to acquire a historical error change trend of each meter from a database, calculate a collaborative verification priority index of each meter according to the bias value and the historical error change trend, and generate a remote collaborative verification list containing verification task allocation information.

[0177] A communication issuing module is configured to issue the remote collaborative verification list to the multiple meter sites through the IoT communication protocol.

[0178] The IoT access gateway comprises an industrial communication processor, a wireless / wired communication interface (such as a 4G / 5G module, an Ethernet port, and an RS-485 interface), a data buffer memory, and a protocol stack firmware. The IoT access gateway is configured to acquire raw instrument data of environmental data acquisition terminals (temperature sensors, humidity sensors, and barometers) and multiple meters (flow meters, pressure gauges, and thermometers) distributed in multiple meter sites through an IoT communication protocol such as MQTT, Modbus, and HART, and perform data buffering and preliminary verification.

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

[0180] The semantic standardization module is composed of a semantic mapping processing unit on the server side and a semantic mapping database. The module maps the field name and field value in the standard data frame uniformly by calling the semantic mapping table stored in the database, unifies the measurement value into the International System of Units, unifies the state code set, and outputs the structured semantic standardized data set.

[0181] The cross-site verification module includes a high-performance computing server, a data cache unit, and a cross-site mutual verification algorithm library. Based on the semantic standardized data set and the corresponding environmental data, the module constructs a cross-site mutual verification model, and outputs the stable deviation value of each measurement instrument through weighted average, environmental compensation, and least squares convergence calculation.

[0182] The priority calculation module is composed of a task scheduling server and a priority calculation rule library, obtains the historical error trend of each instrument, calculates the cooperative verification priority index in combination with the current deviation value, and generates a remote cooperative verification list containing verification task allocation information.

[0183] The communication delivery module is composed of a task publishing server and an IoT communication controller, and is responsible for delivering the remote cooperative verification list to multiple measurement sites through the IoT communication protocol, and feeding back the task execution status when necessary.

[0184] In a remote verification task of a cross-regional measurement site, the flow meter, pressure gauge, and thermometer located at stations A, B, and C are simultaneously connected to the IoT access gateway, and the original data of the environmental sensor and the instrument are sent to the protocol adaptation module. The protocol adaptation module identifies that the pressure gauge at station A uses the Modbus protocol, the flow meter at station B uses the HART protocol, and the thermometer at station C uploads data through the MQTT protocol, and the data is parsed into a unified standard data frame through an adaptation algorithm. The semantic standardization module calls the mapping table to uniformly process the field name and unit of instruments of different manufacturers and different models, and forms a semantic standardized data set. The cross-site verification module constructs a mutual instrument group based on the standardized data and environmental parameters, performs weighted average and environmental compensation calculation, and obtains the deviation value of each instrument. The priority calculation module determines the instrument list for priority verification in combination with the historical error trend, and the communication delivery module delivers the task to each site for execution.

[0185] The present application integrates IoT communication, protocol adaptation, semantic standardization, cross-site mutual verification, priority scheduling, and task delivery, and realizes the cross-regional remote cooperative verification of measurement instruments. Compared with the traditional manual scheduling and single-point detection method, the present application can realize data automatic unification, model dynamic update, and intelligent allocation of verification tasks in a complex environment with multiple sites, multiple protocols, and multiple manufacturer devices coexisting, significantly improving the verification efficiency, accuracy, and resource utilization, while reducing the degree of human participation and on-site maintenance cost.

[0186] The technical scope of the present application is not limited to the above-described embodiments, and various modifications and changes can be made to the above-described embodiments without departing from the technical idea of the present application, and these modifications and changes should be included in the scope of the present application.

Claims

1. A method for remote collaborative verification and communication management of metrology instruments, characterized in that: 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 correlation 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; issuing the remote collaborative verification list to each measurement site through the IoT communication protocol.

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 preset communication protocol type list to generate a matching result; the communication protocol type list at least 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 timestamp 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 name and field value in the standard data frame by calling a semantic mapping table stored in a database to generate a semantically mapped field; wherein the semantic mapping table records a one-to-one correspondence between the field name, field value and standard semantic field of different manufacturer instruments; organizing the semantically mapped field in the form of key-value pair, and unifying the measurement value of the field to the International System of Units, and adopting a unified set of status codes for the status flag of the field; 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 correlation 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 correlation verification model; the cross-site mutual correlation verification model establishes a mapping relationship with an instrument database based on the identification information of each measuring instrument in the data set, and divides mutual correlation instrument groups according to type or range category; aligning the measurement data from different sites in each mutual correlation instrument group in time sequence according to a unified timestamp; Combining the temperature, humidity, air pressure and other environmental 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. Calculate the average difference of the compensated measurement values of each metrological instrument and other members in its inter-reference instrument group, and obtain its relative deviation value; Perform least squares method convergence processing on the deviation values of all inter-reference instrument groups, and output the stable deviation estimation value of each instrument by the cross-site inter-reference verification model as the final deviation result.

5. The method for metrology instrument remote co-certification and communication management according to claim 4, wherein: The cross-site inter-reference verification model further comprises: Establish a metadata mapping table by initializing an instrument database; Receive semantic standardized data sets from multiple metrological sites, and construct an inter-reference instrument group structure according to instrument identification and type; Set an initial weight factor for each instrument, which is set according to its 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 of the i-th instrument, is the weight of 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, mark the instrument 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: Sort the error history records of the metrological instrument according to the time stamp to form a time series; Calculate the short-term historical error change trend by the 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 metrological 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 of claim 7, wherein: The metrological 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, call the verification task execution engine; The verification task execution engine performs verification according to the verification task flow, and the verification steps include instrument zero point calibration, calibration point measurement, and error analysis; In the verification process, real-time instrument response data is collected and compared with the preset standard value to generate a verification result file; the instrument response data is real-time response data of the metrological instrument collected during remote execution; When it is detected that the instrument does not support automatic verification, send a manual verification prompt to the site operation terminal, and mark the task state 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 metrological instrument remote collaborative verification and communication management method further comprises an incremental update, and the specific steps of the incremental update include: Receive instrument verification data and state information after completing the verification, and store the instrument verification data 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 Re-deploy the updated historical error trend to calculate the collaborative verification priority index of each metrological instrument.

10. A system for remote collaborative verification and communication management of metrology instruments, characterized by: Use the metrological instrument remote collaborative verification and communication management method according to any one of claims 1-9, comprising: An IoT access gateway is configured to obtain environmental data distributed in multiple metrological sites and raw instrument data of multiple metrological instruments through an IoT communication protocol; A protocol adaptation module is configured 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 inter-reference verification model to obtain the deviation value of each metrological instrument; A priority calculation module is configured to acquire a historical error variation trend, calculate a collaborative verification priority index of each metering instrument according to the deviation value and the historical error variation 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 metering site through the IoT communication protocol.

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