Intelligent analysis and processing method and system for gas consumption history data

Abstract

The application provides a gas consumption history data intelligent analysis processing method and system, relates to the gas operation management technical field, and the method comprises the following steps: collecting and preprocessing multi-source data of a target station, obtaining a station and instrument associated data set, and performing analysis and fusion to obtain a comprehensive state feature set; based on the comprehensive state feature set and the instrument physical link configuration relationship, a pre-defined collection logic mode is matched to obtain a mode code. Through the construction of a full link from data integration, intelligent evaluation, controlled execution to closed-loop audit, the application realizes the systematic improvement of data processing quality, decision adaptability, business response efficiency and operation traceability.

Description

Technical Field

[0001] This invention relates to the field of gas operation and management technology, and in particular to a method and system for intelligent analysis and processing of historical gas consumption data. Background Technology

[0002] In gas transmission, distribution, and operation management, regular calibration and maintenance of critical metering equipment such as flow meters are crucial for ensuring fair trade settlements and safe pipeline operation. Currently, the industry generally relies on data acquisition and monitoring systems, asset management systems, and independent work order systems to support related operations. However, in terms of comprehensively utilizing the massive amounts of historical and real-time data generated by these systems to intelligently drive calibration decisions and operation and maintenance processes, existing technical solutions have the following shortcomings at the data processing level:

[0003] First, multi-source data is isolated and scattered across independent systems such as operation monitoring and asset management. This data is heterogeneous and lacks deep integration. Assessing instrument status often relies on single static parameters (such as fixed calibration cycles), failing to integrate and structure multi-dimensional dynamic parameters such as the site's operating environment, cumulative instrument workload, and calibration cycle margin. This can lead to one-sided feature extraction and an inability to accurately depict the true health status of equipment. Second, status assessment methods are overly simplistic, typically relying on manual experience to set fixed thresholds or perform linear judgments. They lack in-depth analysis of the non-linear distribution characteristics of multiple parameters in space, failing to quantify health through spatial partitioning and feature extraction, resulting in… Rigid and passive decision-making may lead to over-maintenance or under-maintenance. Secondly, the decision-making and execution links are disconnected. After the verification reminder is generated, the creation and processing of work orders are often disconnected from the intelligent decision-making at the front end. Especially for critical operations such as closing reminders, there is a lack of rule-based automated compliance verification mechanisms, which may lead to decisions being arbitrarily bypassed and weak closed-loop control. Finally, access control remains at the coarse-grained page access level and is not finely associated with specific decision instructions and interface elements. Moreover, the complete operation link from decision to status synchronization lacks end-to-end structured audit traces, making it difficult to trace problems and potentially failing to meet the security management requirements of high-reliability operations. Summary of the Invention

[0004] The technical problem to be solved by this invention is to provide a method and system for intelligent analysis and processing of historical gas consumption data. By constructing a full-link system from data integration, intelligent assessment, controlled execution to closed-loop auditing, it achieves a systematic improvement in data processing quality, decision adaptability, business response efficiency and operational traceability.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] Firstly, a method for intelligent analysis and processing of historical gas consumption data, the method comprising:

[0007] Multi-source data acquisition and preprocessing are performed on the target site to obtain a set of data related to the site and the instrument. This data is then parsed and fused to obtain a comprehensive status feature set. Based on the comprehensive status feature set and the physical link configuration relationship of the instrument, a predefined acquisition logic pattern is matched to obtain the pattern code.

[0008] Based on pattern coding, the preset site runtime, remaining days of the verification cycle, and cumulative workload of the instrument are extracted as a multi-dimensional status assessment parameter set; based on the multi-dimensional status assessment parameter set, parameter space area division and feature extraction are performed to obtain the instrument health index; the instrument health index is used to dynamically weight and correct the activation condition threshold of the verification reminder to obtain the instrument verification decision instruction, and the interface control strategy is generated according to the metering management authority.

[0009] Based on the instrument calibration decision command and the associated interface control strategy, configure the interactive terminal operation interface to respond to the interactive terminal operation: if it is an enable operation, generate a calibration reminder record and link to create a maintenance work order; if it is a disable operation and its disable reason is verified, clear the uncompleted reminder record and work order associated with the instrument, and generate an instrument reminder status update command.

[0010] Based on the instrument's reminder status update command, and according to the latest instrument status and site configuration information carried in the command, the device status remote signaling frame is obtained and pushed to the remote monitoring platform to obtain its synchronization response message; based on the synchronization response message, an audit trail log containing the complete operation chain is generated.

[0011] Secondly, the intelligent analysis and processing system for historical gas consumption data includes:

[0012] The data fusion module is used to collect and preprocess multi-source data from the target site to obtain a set of data related to the site and the instrument. This data is then parsed and fused to obtain a comprehensive status feature set. Based on the comprehensive status feature set and the physical link configuration relationship of the instrument, a predefined acquisition logic pattern is matched to obtain the pattern code.

[0013] The intelligent assessment module is used to extract preset site runtime, remaining days of the verification cycle, and cumulative workload of the instrument as a multi-dimensional status assessment parameter set based on pattern coding; perform parameter space region division and feature extraction based on the multi-dimensional status assessment parameter set to obtain the instrument health index; use the instrument health index to dynamically weight and correct the threshold of the verification reminder to obtain the instrument verification decision instruction, and generate interface control strategy according to the metering management authority.

[0014] The interactive control module is used to configure the interactive terminal operation interface based on the instrument calibration decision command and the associated interface control strategy to respond to the interactive terminal operation: if it is an enable operation, a calibration reminder record is generated and a maintenance work order is created in conjunction with it; if it is a disable operation and its disable reason is verified, the unfinished reminder records and work orders associated with the instrument are cleared, and an instrument reminder status update command is generated.

[0015] The closed-loop audit module is used to obtain the device status remote signaling frame based on the instrument's reminder status update command, according to the latest instrument status and site configuration information carried in the command, and push it to the remote monitoring platform to obtain its synchronization response message; based on the synchronization response message, an audit trace log containing the complete operation link is generated.

[0016] Thirdly, a computing device, comprising:

[0017] One or more processors;

[0018] A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method.

[0019] Fourthly, a computer-readable storage medium storing a program that, when executed by a processor, implements the method.

[0020] The above-described solution of the present invention has at least the following beneficial effects:

[0021] Multi-source data acquisition and preprocessing integrate scattered information, allowing the comprehensive state feature set to encompass more comprehensive and effective information; data parsing and fusion enhance data relevance and usability, reducing data redundancy; matching predefined acquisition logic patterns standardizes data processing workflows, ensuring consistency and standardization in subsequent analysis; extracting multi-dimensional state assessment parameter sets achieves systematic coverage of key assessment dimensions, providing more sufficient assessment basis; parameter space region division and feature extraction enhance the focusing effect of key state information and improve the targeting of feature extraction; a dynamic weighted correction mechanism deeply adapts activation condition thresholds to the real-time status of instruments, optimizing the rationality of decision commands; combining metering management permissions to generate interface control strategies achieves synergy between data processing and permission management; and configuring standardized interactive terminal operation interfaces... To reduce data loss during operation response and improve efficiency, the system integrates operation reminders and maintenance work orders, enabling seamless data and business process integration and enhancing data utility. It also disables the linkage between operation reason verification and data cleanup to ensure accurate removal of incomplete records and work orders, preventing invalid data residue. Targeted processing of different operation types makes data processing more focused and improves overall process efficiency. Standardized device status remote signaling frame encapsulation ensures consistency and integrity of cross-system data transmission, improving data interaction reliability. Push mechanisms and synchronous response message acquisition enable closed-loop management of data transmission, ensuring timely status information synchronization. Audit trail logs integrate complete operation chain data, enabling full traceability of data flow. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating the intelligent analysis and processing method for historical gas consumption data provided in an embodiment of the present invention.

[0023] Figure 2 This is a schematic diagram of an intelligent analysis and processing system for historical gas consumption data provided in an embodiment of the present invention. Detailed Implementation

[0024] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0025] like Figure 1 As shown, embodiments of the present invention propose an intelligent analysis and processing method for historical gas consumption data, the method comprising the following steps:

[0026] Step 100: Perform multi-source data acquisition and preprocessing on the target site to obtain a set of site and instrument related data, and then parse and fuse it to obtain a comprehensive status feature set; based on the comprehensive status feature set and the instrument physical link configuration relationship, match the predefined acquisition logic mode to obtain the mode code;

[0027] Step 200: Based on pattern coding, extract the preset site runtime, remaining days of the verification cycle, and cumulative workload of the instrument as a multi-dimensional status assessment parameter set; perform parameter space region division and feature extraction based on the multi-dimensional status assessment parameter set to obtain the instrument health index; use the instrument health index to dynamically weight and correct the activation condition threshold of the verification reminder to obtain the instrument verification decision instruction, and generate the interface control strategy according to the metering management authority.

[0028] Step 300: Based on the instrument calibration decision command and the associated interface control strategy, configure the interactive terminal operation interface to respond to the interactive terminal operation: if it is an enable operation, generate a calibration reminder record and link to create a maintenance work order; if it is a disable operation and its disable reason is verified, clear the uncompleted reminder record and work order associated with the instrument, and generate an instrument reminder status update command.

[0029] Step 400: Based on the instrument's reminder status update command, and according to the latest instrument status and site configuration information carried in the command, obtain the device status remote signaling frame and push it to the remote monitoring platform to obtain its synchronization response message; based on the synchronization response message, generate an audit trail log containing the complete operation chain.

[0030] In this embodiment of the invention, multi-source site and instrument data are integrated to enrich the dimensions of status description; a structured comprehensive status feature set is formed through parsing and fusion to improve data usability; a preset acquisition logic mode is matched to achieve an orderly conversion of data to mode encoding, providing a unified and standardized data foundation for subsequent evaluation; site runtime, remaining days of the verification cycle, and cumulative workload of the instrument are selected as core evaluation parameters to ensure the relevance of the evaluation dimensions; the health status of the instrument is quantified through parameter space region division and feature extraction; the threshold for enabling verification reminders is dynamically weighted and corrected to ensure that the verification judgment closely matches the actual operating status of the instrument; and a dedicated operation interface for the interactive terminal is configured to improve operation. The system ensures accurate response; it generates verification reminder records and maintenance work orders in conjunction with the operation, achieving efficient integration of reminders and maintenance; it clears associated incomplete records and work orders after verification when the operation manager is closed, ensuring the rigor of status updates; it generates instrument reminder status update instructions, synchronizes the latest status information, and maintains data consistency; it generates equipment status remote signaling frames based on the latest instrument status and site configuration information, ensuring the real-time performance of data from the remote monitoring platform; it obtains synchronous response messages from the remote monitoring platform to confirm the validity of data transmission; it generates audit trail logs containing the complete operation chain, achieving full-process traceability; and it retains operation records to provide data support for subsequent equipment management and optimization.

[0031] In a preferred embodiment of the present invention, step 100 involves multi-source data acquisition and preprocessing of the target site to obtain a set of site and instrument-related data, followed by parsing and fusion to obtain a comprehensive status feature set. Based on the comprehensive status feature set and the instrument physical link configuration relationship, a predefined acquisition logic pattern is matched to obtain a pattern code, including:

[0032] Step 101 involves synchronously collecting real-time telemetry data and static attribute data of the instruments from the operation monitoring data source and asset management data source of the target site, respectively. Specifically, this includes: First, identifying the two core data sources corresponding to the target site: the operation monitoring data source and the asset management data source. The operation monitoring data source stores real-time telemetry data of the instruments, including but not limited to dynamic parameters such as real-time temperature, pressure, instantaneous flow rate, cumulative flow rate, and operating status signals. The asset management data source stores static attribute data of the site, including fixed parameters such as instrument model, serial number, installation date, rated range, calibration cycle standard, installation location information, and area. Parallel data retrieval is performed from both data sources through preset data interfaces, such as RS485 communication interfaces and network transmission protocol interfaces. Simultaneously, the timestamp of data collection is recorded to ensure consistency between real-time telemetry data and static attribute data in the time dimension. This lays the foundation for time synchronization in subsequent data association processing and effectively solves the problem of association failure caused by asynchronous multi-source data collection.

[0033] Step 102 involves parsing the site identifier and instrument identifier from the real-time telemetry data and static attribute data of the instrument. Specifically, this includes extracting key information for unique identification from the data fields or metadata information of the instrument and the static attribute data of the site to form the site identifier and instrument identifier. The site identifier can be a feature identifier composed of the unique code, name, and installation location of the target site, and the instrument identifier can be the serial number, factory number, or unique device code assigned by the system. During the parsing process, by traversing the key fields of the data, such as the site number, instrument SN code, and device code, information that conforms to the preset identification rules is filtered out to ensure that each piece of collected data can be clearly attributed to the specific target site and the corresponding instrument, thereby solving the problem of ambiguous attribution and inaccurate matching of multi-source data and providing a core matching basis for subsequent data association.

[0034] Step 103: Based on the site identifier and instrument identifier, clean, standardize, and perform association mapping processing on the real-time telemetry data of the instrument and the static attribute data of the site to obtain a set of site and instrument associated data. Specifically, based on the parsed site identifier and instrument identifier, firstly, clean the real-time telemetry data of the instrument. Specifically, remove abnormal data that exceeds the reasonable value range, complete missing data in key fields, and remove duplicate and redundant data. Among them, abnormal data includes abnormal values ​​such as pressures far exceeding the instrument's rated pressure, and missing data includes short-term missing data such as data interpolated from adjacent time points. The data is first collected, and redundant data is like repeatedly uploaded data with the same timestamp. Then, the cleaned data is standardized, and data with different formats and units are uniformly converted into preset standards, such as unifying the temperature unit to degrees Celsius, the pressure unit to kilopascals, the flow rate unit to cubic meters per hour, and the date format to year-month-day-hour-minute-second. Finally, through precise matching of station identifiers and instrument identifiers, a one-to-one correspondence is established between real-time telemetry data and station static attribute data, forming a structured set of station and instrument-related data, realizing the effective connection of originally isolated and scattered multi-source heterogeneous data.

[0035] Step 104 involves parsing the associated data set of gas stations and meters, identifying and outputting feature vectors for both the station and meter dimensions. Specifically, this includes: based on the associated data set, first determining a preset feature dimension division rule. This rule is based on the core operational management needs of gas stations and meters, and is pre-defined with reference to industry standards, historical operational data, and equipment technical parameters. First, the data is divided into station and meter dimensions according to its ownership subject. Then, feature categories are further subdivided according to data attributes. Simultaneously, the priority and order of each feature are determined, forming a standardized division rule. This rule has been verified for scenario adaptability across different station types and meter models to ensure the targeted and comprehensive nature of feature extraction. Data is then parsed according to this preset rule to extract the station and meter dimensions respectively. The key feature parameters of the degree are analyzed and feature vectors are constructed. The parsing process of the site dimension feature vector is as follows: parameters reflecting the site's operating environment and basic configuration are selected from the associated data, including the environmental category of the installation location, the level of surrounding interference factors, the physical link connection method, and the power supply mode. These parameters are arranged in a preset order to form an ordered site dimension feature vector. The parsing process of the instrument dimension feature vector is as follows: parameters reflecting the instrument's own operating status and attributes are selected from the associated data, including the cumulative running time, cumulative workload, historical verification records, and statistical values ​​of real-time operating parameters (temperature, pressure, flow rate). These parameters are also arranged in a preset order to form an ordered instrument dimension feature vector. The refined extraction of associated data is achieved through dimension splitting, providing structured basic data for subsequent feature fusion.

[0036] Step 105 involves fusing the site-dimensional feature vector and the instrument-dimensional feature vector to obtain a comprehensive state feature set. Specifically, this includes: fusing the site-dimensional and instrument-dimensional feature vectors using a feature integration strategy. First, the validity of each parameter in the two feature vectors is verified to ensure no invalid parameters are mixed in. Then, appropriate weights are assigned based on the importance of the parameters; for example, the weight of the instrument's cumulative workload is higher than that of secondary site environment parameters. Next, the two vectors are combined into a complete feature sequence through feature concatenation. Finally, redundant features that may appear during the fusion process, such as duplicate identifier parameters, are removed to form a comprehensive state feature set covering site environment attributes, instrument static parameters, and instrument dynamic operating status. This fusion process achieves a deep integration of static attributes and dynamic operating data, solving the problem of a one-sided characterization of equipment status by a single-dimensional feature, and ensuring that the feature set can comprehensively reflect the true status of the target site and instruments.

[0037] Step 106: Combine the comprehensive status feature set with the instrument physical link configuration relationship into a matching query vector. Specifically, this includes: introducing the instrument physical link configuration relationship based on the comprehensive status feature set. This configuration relationship includes key information such as the connection interface type between the instrument and the station, communication protocol specifications, data transmission path, and linkage logic with other devices. The preset vector construction rules are based on the core requirements of the data acquisition logic pattern matching, combined with the technical parameter specifications of the instrument and the station, data transmission protocol requirements, and historical matching experience. The priority order of parameter combinations is determined, placing core parameters that have a significant impact on pattern matching at the beginning of the vector. Key operational parameters, such as those in the communication protocol specifications and comprehensive status feature set, are arranged sequentially, followed by secondary auxiliary parameters. The field length, data type, and position of each parameter within the vector are fixed to ensure a consistent and comparable vector structure across different scenarios. Subsequently, following these pre-defined rules, the feature parameters in the comprehensive status feature set are systematically combined with relevant parameters related to the physical link configuration, ensuring each parameter occupies a fixed position within the vector, forming a structurally unified and dimensionally clear matching query vector. This process, by integrating feature information and configuration information, provides a comprehensive and standardized query basis for subsequent queries of predefined collection logic patterns, ensuring the accuracy of pattern matching.

[0038] Step 107: Based on the matching query vector, traverse and query the predefined collection logic mode rule library to determine the corresponding target collection logic mode. Specifically, this includes: The predefined collection logic mode rule library is predefined based on the core needs of gas operation and management, combined with industry metering standards, technical parameters of different types of instruments, site operating environment classification, and historical operation data. First, scenarios are classified according to core dimensions such as instrument model, operating condition, and site environment. For each scenario, the corresponding collection frequency, data processing method, parameter threshold range, and other key configurations are determined. Then, the configuration of each scenario is transformed into a feature vector consistent with the dimension of the subsequent matching query vector, ensuring that each group of modes in the rule library has a structured and matchable feature form, and its adaptability and rationality have been verified through multi-scenario testing. The rule library stores multiple sets of preset collection logic modes, each group of modes corresponding to specific application scenarios, including collection frequencies and data processing methods corresponding to different instrument models, different operating conditions, and different site environments. Each group of modes has been transformed into a feature vector consistent with the dimension of the matching query vector, and also includes the threshold range corresponding to each feature parameter.

[0039] Based on the matching query vector, firstly, each group of acquisition logic modes in the rule base is traversed sequentially, and the feature vector corresponding to each mode is extracted. Then, the vector space similarity algorithm is fused for calculation. First, the product of the matching query vector and the feature vector of the current mode is calculated element by element and summed to obtain the dot product of the two vectors. Then, the square root of the sum of the squares of each element of the two vectors is calculated to obtain their respective magnitudes. The dot product is divided by the product of the two magnitudes to obtain the vector space similarity value. The larger the value, the higher the fit between the two vectors. On this basis, each parameter in the query vector is compared with the feature parameter threshold range of the corresponding mode. The proportion of parameters that meet the threshold requirements to the total number of parameters is counted to obtain the parameter matching degree. The vector space similarity value and the parameter matching degree are weighted and summed according to preset weights to obtain the comprehensive matching value. When the comprehensive matching value of a certain mode reaches the preset threshold, the mode is determined to be the target acquisition logic mode that is compatible with the target site and instrument. Through the fusion calculation of the vector space similarity algorithm and the parameter threshold comparison, the accuracy of mode matching is further improved, ensuring that subsequent data processing and acquisition logic are more in line with the personalized needs of actual application scenarios.

[0040] Step 108: Generate a unique pattern code based on the target acquisition logic mode. Specifically, this includes: For the determined target acquisition logic mode, a preset coding rule is established based on the business scenario requirements of the gas operation data acquisition and reporting system, combined with the classification characteristics of the acquisition logic mode, the recognizability of core parameters, and the convenience of subsequent data flow and retrieval. This rule determines that the code adopts a structured form combining letters and numbers, ensuring both readability and unique identification, while also adapting to the system's functional requirements for rapid pattern recognition and retrieval. A unique pattern code is generated according to this preset rule, where the prefix is ​​assigned a fixed letter based on the type of acquisition logic mode, such as assigning a fixed letter to the real-time acquisition mode. The letter S is assigned to the periodic acquisition mode, and the letter P is assigned to the middle part. The corresponding number is assigned according to the core parameters of the mode. For example, the number 01 corresponds to the acquisition frequency of 1 hour, and the number 02 corresponds to the data processing method of mean calculation. The suffix part is the unique serial number of the mode in the rule base, which is arranged in the order of mode entry into the database. After the code is generated, the uniqueness is checked by traversing all existing mode codes in the rule base to confirm that the code has not been used by other modes. If there are duplicates, the suffix serial number is adjusted until it is unique. Finally, the unique mode code is output. This code can be used as the core identifier for calling the target acquisition logic mode in subsequent steps such as state evaluation and decision execution, simplifying the pattern recognition link in the data flow process and ensuring the efficient connection of the entire process.

[0041] In this embodiment of the invention, data from both operational monitoring and asset management sources are collected simultaneously, covering dynamic operational data and static basic data, providing comprehensive and complete data support for subsequent processing; synchronous acquisition of multi-source data ensures data timeliness and consistency; the attribution relationship between data and specific stations and instruments is determined to avoid data confusion, and a core identifier for data association is established, providing a key basis for subsequent data integration and mapping; invalid and redundant data are cleaned and removed, and data formats are standardized to improve data quality; effective connection of data from different sources is achieved through association mapping, forming a structured set of associated data and enhancing data usability; data dimensions are broken down, transforming complex associated data into targeted feature vectors; and the attribution relationship between stations and instruments is determined. The core characteristics of each instrument create conditions for subsequent multi-dimensional analysis and fusion processing; integrating the characteristic information of stations and instruments breaks down data dimensional barriers; forming a comprehensive feature set that fully reflects the operating status of equipment, providing a comprehensive basis for pattern matching; combining comprehensive features with physical link configuration simplifies query dimensions; constructing accurate matching query criteria improves the targeting and efficiency of subsequent pattern matching; relying on a predefined rule base to achieve efficient traversal query, ensuring the standardization of pattern matching; accurately locating the target acquisition logic pattern, making the matching results fit the actual application scenario; generating a unique pattern code, providing a unified identifier for the target acquisition logic pattern; facilitating rapid identification and retrieval in subsequent steps, simplifying the data flow process, and ensuring processing continuity.

[0042] In a preferred embodiment of the present invention, step 200 above involves extracting preset site runtime, remaining days of the verification cycle, and cumulative workload of the instrument as a multi-dimensional status assessment parameter set based on pattern encoding; performing parameter space region division and feature extraction based on the multi-dimensional status assessment parameter set to obtain the instrument health index; dynamically weighting and correcting the activation condition threshold of the verification reminder using the instrument health index to obtain the instrument verification decision instruction; and generating an interface control strategy based on metering management permissions, including:

[0043] Step 201: Using the pattern code as the query key, retrieve the pre-configured parameter mapping table to obtain three basic status parameters: site runtime, remaining days of the verification cycle, and cumulative instrument workload. Specifically, the pre-configured parameter mapping table is pre-configured based on the actual needs of gas operation and management, combined with industry standards, different site types, instrument models, and operating scenarios. Different site types include industrial sites, commercial sites, and residential sites; instrument models include turbine flow meters and ultrasonic flow meters; and operating scenarios include high-load operation, regular operation, and low-frequency use. First, various application scenarios are subdivided according to the above classification dimensions. Then, a corresponding pattern code is assigned to each subdivided scenario. At the same time, the specific acquisition rules and association logic for site runtime, remaining days of the verification cycle, and cumulative instrument workload under this scenario are determined to ensure that the mapping table covers all scenarios and the association relationship is accurate. Its adaptability and stability have been verified through multiple rounds of actual testing.

[0044] The pattern code generated in step 108 serves as a unique identifier. This mapping table stores a one-to-one correspondence between each group of pattern codes and their corresponding basic status parameters, and has been pre-calibrated and perfected according to different site types, instrument models, and operating scenarios. Using the pattern code as the query key, the parameter mapping table is traversed through precise matching to quickly locate the entry associated with the target pattern code, and then extract the three core basic status parameters recorded under that entry: site runtime, remaining days of the verification cycle, and cumulative instrument workload. Among them, the site runtime is the cumulative running time of the instrument from the date of installation and use to the current data collection time, the remaining days of the verification cycle is the difference between the next verification date and the current date, and the cumulative instrument workload is the total cumulative metered flow during the instrument's operation. This retrieval process realizes the association between the pattern code and the core evaluation parameters, providing targeted data support for subsequent multi-dimensional status evaluation.

[0045] Step 202: Based on the three basic state parameters, construct a structured multidimensional state assessment parameter set. Specifically, this includes: pre-defined data structuring rules that are combined with the actual needs of gas metering equipment state assessment, referencing the attribute characteristics of the three basic state parameters, and the operational logic of subsequent parameter space construction and feature extraction. These rules determine a fixed order for parameter arrangement, prioritizing them according to their importance to the instrument's health status. They also stipulate that each parameter must be labeled with its corresponding parameter type, unit of measurement, and value range, and that the labeling information must be consistent with industry standards and equipment technical parameters to ensure that the structured data can be directly adapted to subsequent processing procedures. Based on the extracted three basic... The status parameters are integrated and processed according to the preset data structuring rules. Specifically, the site runtime, remaining days of the verification cycle, and cumulative instrument workload are arranged in a fixed order. At the same time, each parameter is labeled with its corresponding parameter type, unit of measurement, and value range, forming an orderly parameter combination. For example, the site runtime is labeled as a time parameter in days, the remaining days of the verification cycle is labeled as a time parameter in days, and the cumulative instrument workload is labeled as a flow parameter in cubic meters. This structuring process makes the three originally scattered parameters form an organic whole, avoids data fragmentation, and lays a standardized and orderly data foundation for subsequent parameter space construction and feature extraction.

[0046] Step 203 involves constructing a three-dimensional parameter space using the three basic state parameters as coordinate axes, and mapping the multi-dimensional state assessment parameter set to the three-dimensional parameter space to obtain a parameter space to be analyzed containing mapped data points. Specifically, this includes: based on the structured multi-dimensional state assessment parameter set, using the site runtime, remaining days of the calibration cycle, and cumulative instrument workload as the X, Y, and Z axes of the three-dimensional coordinate system, respectively, and determining the value range of each coordinate axis; the value range is set according to historical operating data and industry standards to ensure that it can cover the reasonable fluctuation range of the three parameters; subsequently, each data item in the structured parameter set is mapped one by one to the three-dimensional coordinate system according to the value of its corresponding parameter, forming a unique corresponding spatial data point, thereby obtaining a parameter space to be analyzed containing all mapped data points; this process transforms the abstract multi-dimensional parameter relationship into a concrete spatial geometric relationship, breaking down the isolated barriers between parameters and creating an intuitive carrier for in-depth analysis of the intrinsic relationship between parameters.

[0047] Step 204 involves performing spatial meshing on the parameter space to be analyzed, resulting in a meshed parameter space composed of multiple regularized parameter units. Specifically, this includes: for the parameter space to be analyzed, a three-dimensional orthogonal meshing algorithm is integrated. The preset meshing rules are based on the operating characteristics of the gas metering equipment, historical data statistics, the influence weights of various state parameters on instrument health assessment, and the operational requirements for subsequent parameter space analysis and feature extraction. These rules first define reasonable value ranges for the X-axis station running time, the Y-axis remaining days of the calibration cycle, and the Z-axis cumulative workload of the instrument. These value ranges are determined with reference to industry standards and equipment operating limits. Then, the number of intervals is allocated according to the sensitivity requirements of each parameter, with more intervals allocated to parameters that have a more significant impact on the instrument status, ensuring that the interval division can capture the impact of subtle parameter changes on the instrument status. Simultaneously, it is stipulated that the interval division for each coordinate axis is performed independently, and the mesh unit boundaries are parallel to the coordinate axes, ensuring the regularity of the mesh units and the accuracy of parameter mapping. Following this preset meshing... The process involves rule-based grid partitioning. First, the ranges for X-axis station runtime, Y-axis remaining days of calibration cycle, and Z-axis cumulative instrument workload are determined. Then, based on the sensitivity requirements of each parameter, the number of intervals for each of the three coordinate axes is determined. Using the core logic of the 3D orthogonal grid partitioning algorithm, the orthogonality of each coordinate axis is maintained. The interval length for each coordinate axis is then calculated, and equal interval lengths are obtained by dividing the coordinate axis value range by the corresponding number of intervals, ensuring uniform interval specifications on the same coordinate axis. Based on this, the equal intervals of the X, Y, and Z axes are cross-combined according to the orthogonal principle to form multiple clearly defined, uniformly sized, and mutually orthogonal 3D parameter units, ultimately constructing a gridded parameter space. This process, through the synergy of the 3D orthogonal grid partitioning algorithm and preset partitioning rules, further ensures the regularity of the grid units and the accuracy of parameter mapping, effectively decomposing the complex parameter space, simplifying the difficulty of subsequent data point positioning and feature extraction, and improving the standardization and efficiency of data processing.

[0048] Step 205: Based on the gridded parameter space, locate the mapping data points corresponding to the multidimensional state assessment parameter set in the gridded parameter space, determine their respective parameter grid units as target parameter units, specifically including: based on the gridded parameter space, perform precise positioning by integrating a spatial point positioning algorithm. First, extract the specific coordinate values ​​of each spatial data point: X-axis station running time, Y-axis remaining days of calibration cycle, and Z-axis cumulative instrument workload, ensuring that the coordinate values ​​are consistent with the coordinate axis units of the gridded parameter space; then, using the core logic of the spatial point positioning algorithm, first arrange the unit boundaries of the X-axis, Y-axis, and Z-axis in the gridded parameter space in numerical order to form an ordered boundary sequence, and then calculate the position of each coordinate value in the corresponding axis boundary sequence. The index quickly identifies the cell range to which a coordinate value might belong, eliminating the need to traverse all cells individually. Next, the cell ranges corresponding to the three coordinate values ​​of a data point are cross-matched. When the position indices of all three coordinate values ​​point to the same parameter cell, it is further verified whether each coordinate value of the data point falls within the boundary range of that parameter cell. If all conditions are met, the data point is determined to belong to that parameter cell, which is the target parameter cell. The integration of spatial point positioning algorithms improves the efficiency of data point positioning. Simultaneously, the dual verification of coordinate values ​​and cell boundaries further ensures the accuracy of the positioning results, determining the specific range to which each data point belongs, focusing on the core analysis area, avoiding interference from irrelevant parameter cells in feature extraction, and ensuring the relevance and accuracy of subsequent analysis.

[0049] Step 206: For the target parameter unit, analyze the spatial distribution characteristics of the mapped data points within it, and calculate the spatial distribution features. These features include the distance offset of the mapped data points relative to each coordinate axis reference plane, and their distribution clustering degree within the boundary of the target parameter unit. Specifically, this includes: for the target parameter unit, further analyze the spatial distribution characteristics of the mapped data points to uncover the nonlinear distribution relationship between multiple parameters, providing support for instrument health status assessment; on the one hand, calculate the distance offset of the data points relative to each coordinate axis reference plane. First, determine the determination logic of each coordinate axis reference plane. The reference plane for the X-axis station runtime is calibrated based on the industry's standard cumulative normal operating time for similar instruments and historical reliable operating data, and its value is a reasonable proportion of the cumulative operating time corresponding to the instrument's design life; the Y-axis calibration cycle... The reference plane for the remaining days is set based on the instrument calibration cycle standard and maintenance experience, and its value is a safe remaining days threshold to ensure the accuracy of instrument measurement. The reference plane for the Z-axis instrument cumulative workload is determined based on the instrument's rated range and the average workload distribution of similar users, and its value is a reasonable threshold for the instrument's rated workload. Taking the X-axis reference plane as an example, the X-axis reference value corresponding to this plane is extracted, and then the X-axis coordinate value of the target data point is obtained. The absolute difference between the two is calculated, and this difference is the distance offset in the X-axis direction. The distance offset in the Y-axis direction is obtained by the absolute difference between the remaining days of the calibration cycle of the target data point and the Y-axis reference value. The distance offset in the Z-axis direction is obtained by the absolute difference between the instrument cumulative workload of the target data point and the Z-axis reference value. The distance offsets in these three directions reflect the degree to which the data point deviates from the normal reference state in each dimension.

[0050] On the other hand, to calculate the distribution clustering degree of data points within the boundary of the target parameter unit, firstly, the theoretical maximum number of data points that the target parameter unit can accommodate is determined. This value is calculated based on the spatial volume of the parameter unit, the interval length of each coordinate axis, and the average distribution density of data points in the historical similar parameter space. At the same time, fine-tuning and calibration are performed in combination with the accuracy requirements of instrument status assessment. Then, the total number of mapped data points actually contained within the target parameter unit is counted, and the ratio of the actual total number of data points to the theoretical maximum number of data points is calculated. The result is the distribution clustering degree. The higher the value of the distribution clustering degree, the higher the frequency of the instrument's operating state under the parameter combination corresponding to the target parameter unit, and the more obvious the reference value of this state characteristic for instrument health assessment. Through the above specific calculations of distance offset and distribution clustering degree, the spatial distribution characteristics of data points are comprehensively extracted, and the instrument status information under multi-parameter nonlinear distribution is fully explored, providing a deep and targeted basis for the subsequent quantitative calculation of the instrument health index.

[0051] Step 207: Based on the spatial distribution characteristics, a weighted aggregation calculation is performed to obtain the instrument health index. Specifically, this includes: based on the spatial distribution characteristics, a preset weight allocation rule is established, combining the core operational management needs of gas metering equipment, historical fault data statistics, analysis of the actual impact of each spatial distribution characteristic on the instrument health status, and industry-wide instrument maintenance experience. This rule determines that the weight of distribution clustering is higher than that of distance offset, because distribution clustering reflects the frequency of occurrence of similar operating states and has a more significant predictive value for instrument health trends, while distance offset mainly reflects the degree of deviation from the benchmark in a single dimension. The weight values ​​are determined through comparative experiments under multiple sets of different site environments, different instrument models, and different operating conditions. The system is designed to ensure that the weight allocation is adaptable to various application scenarios and accurately quantifies the impact of each feature on the health status. According to the preset weight allocation rules, corresponding weights are assigned to distance offset and distribution clustering. The weight values ​​are determined through experimental calibration based on the degree of influence of the parameters on the instrument's health status. Then, the distance offset and distribution clustering are multiplied by their corresponding weights to obtain two weighted feature values. These two weighted feature values ​​are summed, and the summation result is normalized so that the final result falls within a preset value range of 0 to 1. This normalized result is the instrument health index. This process transforms multi-dimensional spatial features into a single quantitative indicator through weighted aggregation, achieving a comprehensive and intuitive description of the instrument's health status.

[0052] Step 208 involves processing the instrument health index using a preset conversion function to obtain dynamic weighting coefficients. Specifically, the preset conversion function is based on historical operating data of gas metering equipment, operating characteristics of different instrument models, differences in site environment, and practical experience in calibration and maintenance. The function aims to establish a precise quantitative correspondence between the instrument health index and the dynamic weighting coefficients. Through nonlinear conversion logic, the abstract health status is quantified into specific coefficients that can be directly used for threshold adjustment. At the same time, the range of the conversion result is calibrated to be 0.8 to 1.2 to ensure that the weight adjustment range is within a reasonable range, thus avoiding excessive threshold fluctuations that could affect decision stability while accurately responding to changes in the instrument health status.

[0053] The instrument health index is input into the preset conversion function. The conversion logic is that the higher the health index, the more the corresponding dynamic weight coefficient is biased towards lowering the verification reminder activation threshold; the lower the health index, the more the corresponding dynamic weight coefficient is biased towards raising the verification reminder activation threshold. The generated dynamic weight coefficient is used for subsequent weighted fusion with the benchmark threshold for verification reminder activation conditions. Through the dynamic adjustment of this coefficient, the threshold can be adapted to the current health status of the instrument in real time, solving the problem that fixed thresholds cannot flexibly respond to changes in equipment status. Through the calculation of the conversion function, the instrument health index is converted into a dynamic weight coefficient with a value between 0.8 and 1.2, establishing a quantitative correlation between the health status and the threshold adjustment range, providing adaptive support for the dynamic correction of the activation condition threshold.

[0054] Step 209: The dynamic weighting coefficient is weighted and fused with the preset verification reminder activation condition benchmark threshold to obtain the dynamic correction threshold. Specifically, the preset verification reminder activation condition benchmark threshold is based on industry safety operation standards for gas metering equipment, technical parameter specifications for different instrument models, historical verification and maintenance data statistical analysis, and actual operation failure rate data. During the preset process, the threshold is categorized and calibrated according to site type and instrument model. For different site types such as industrial users and commercial users, and different instrument models such as turbine flow meters and ultrasonic flow meters, the reliable operating parameter ranges and calibration parameters of similar equipment are statistically analyzed. The benchmark threshold is verified through multiple rounds of scenario testing and parameter fine-tuning to ensure its initial adaptability and universality. This benchmark threshold is determined based on industry standards, instrument technical parameters, and historical maintenance data, and is a fixed value. The dynamic weighting coefficient is then weighted and fused with the benchmark threshold. Specifically, the dynamic weighting coefficient is multiplied by the benchmark threshold, and a preset basic correction amount is added. The basic correction amount is set according to different site types and instrument models to fine-tune the fusion result to adapt to specific application scenarios. Through this weighted fusion calculation, a dynamic correction threshold that can reflect the health status of the instrument in real time is obtained, so that the threshold adjustment is deeply linked to the actual operating status of the instrument.

[0055] Step 210: Based on the dynamically corrected threshold, determine the updated activation condition threshold. Specifically, this includes: For the dynamically corrected threshold, firstly, determining the preset effective value range. This range is preset based on industry safety operation standards for gas metering equipment, rated operating parameters of different meter models, historical calibration and maintenance data statistics, and the actual operational needs of different site types (industrial sites, commercial sites, and residential sites). The effective value range is categorized and calibrated according to site type and meter model. For example, the effective value range of turbine flow meters used in industrial sites is set as a reasonable range that combines their high-load operating characteristics. The effective value range of ultrasonic flow meters used in commercial sites is adapted to their normal operating conditions, ensuring that the range covers the equipment. The threshold fluctuations during normal operation can mitigate the risks of over- or under-maintenance. A subsequent verification process involves extracting the specific value of the dynamic correction threshold and comparing it to the valid value range for the corresponding category. If the dynamic correction threshold is higher than the upper limit of the valid value range, it is corrected according to the upper limit; if it is lower than the lower limit, it is corrected according to the lower limit; and if it is within the valid value range, the dynamic correction threshold is directly used. Through this targeted verification, the updated activation condition threshold is finally determined. This threshold integrates the real-time health status of the instrument and conforms to industry standards and actual operational needs, providing a clear, unified, and practical standard for subsequent determination of instrument health status, ensuring the consistency and fairness of threshold comparison calculations.

[0056] Step 211: Based on the updated activation condition threshold and the meter health index, perform a threshold comparison operation to obtain a health status determination identifier. Specifically, this includes: First, standardizing the meter health index and the updated activation condition threshold to unify their numerical precision to two decimal places, ensuring the accuracy of the comparison operation; then, performing a standardized threshold comparison operation. This operation follows a preset comparison rule: if the meter health index is lower than the updated activation condition threshold, the meter is determined to require calibration. This determination corresponds to the core requirement of flow meter calibration in gas operation management and can trigger subsequent calibration reminders and work order creation processes; if the meter health index is higher than or equal to the updated activation condition threshold... If the result is positive, the instrument is determined to be in normal condition. This result indicates that the instrument's operating parameters meet industry standards and equipment technical requirements, and no immediate calibration is required. Based on the comparison results, a corresponding health status determination identifier is output. The identifier has two forms: binary code identifiers where 01 represents calibration required and 00 represents normal condition, and text identifiers directly correspond to either calibration required or normal condition. The two identifier forms can be flexibly switched according to subsequent system call requirements. The output health status determination identifier will be synchronized to the decision module of the flow meter management system and associated with the interface control strategy and work order management service interface. This provides a direct basis for triggering subsequent calibration reminders, operation permission configurations, and other strategies, achieving seamless integration between health status determination and subsequent execution processes.

[0057] Step 212: If the health status indicator indicates a verification-required state, the verification activation strategy is triggered, resulting in an instrument verification decision instruction with the status "Verification Activation Reminder"; if the health status indicator indicates a normal state, the status maintenance strategy is triggered, resulting in an instrument verification decision instruction with the status "Maintain Current State". Specifically, if the health status indicator indicates a verification-required state, the system automatically triggers a preset verification activation strategy. This strategy is based on the gas flow meter operation and management requirements, combined with industry verification standards, instrument type, user type, and historical verification data. The instrument type includes turbine flow meters, ultrasonic flow meters, etc., and the user type includes industrial users, commercial users, etc. For both industrial and residential users, the core logic for triggering verification reminders and the rules for generating reminder content should be determined. The triggering logic needs to comprehensively consider the extent to which the health index falls below the threshold, the cumulative running time of the meter, the number of days overdue for inspection, and the user's gas consumption. Industrial users, due to their larger gas consumption and the significant impact on metering accuracy, have a higher triggering priority than commercial and residential users. For those overdue for inspection for more than 15 days, an emergency verification process should be triggered. The rules for generating reminder content need to integrate key information of the target meter, including the site name, site number, meter manufacturer, meter model, serial number, current number of overdue days, last verification date, next verification date, and the specific value of the health index, to ensure that the reminder content is complete and intuitive.

[0058] Based on this strategy, a meter calibration decision instruction is generated with the calibration reminder enabled. In addition to the target meter identifier, calibration priority, and suggested calibration time limit, the instruction also includes key information such as the dispatch type (periodic calibration, emergency calibration), the completion time limit (set based on the number of overdue days: 7 working days for overdue periods within 15 days, and 3 working days for overdue periods exceeding 15 days), the associated remote transmission device number, and user contact information. Specifically, the dispatch type is either periodic calibration or emergency calibration, and the completion time limit is set based on the number of overdue days: 7 working days for overdue periods within 15 days, and 3 working days for overdue periods exceeding 15 days. If the health status indicator shows a normal state, the system automatically triggers a preset status maintenance strategy. This strategy is based on the technical characteristics of the meter model, historical operational stability data, and the most recent... The system determines the duration of the status maintenance based on the most recent calibration result and user type. For industrial flow meters, the maintenance period is set to 30 days due to higher operating loads, while for commercial and residential flow meters, it is set to 60 days. The system also determines the monitoring frequency of operating data during the status maintenance period, such as monitoring industrial flow meters every 12 hours and commercial and residential flow meters every 24 hours. Based on this strategy, a calibration decision instruction is generated to maintain the current status. In addition to the target instrument identifier and the status maintenance duration, the instruction includes the most recent calibration record (calibration unit, calibration number, calibration accuracy data), current operating parameter (temperature, pressure, instantaneous flow) statistics, and status review trigger conditions (such as automatic review triggered when operating parameter fluctuations exceed ±10%). This ensures precise linkage between the judgment result and the decision instruction, guaranteeing the relevance and effectiveness of the decision instruction.

[0059] Step 213: Based on metering management permissions, the meter calibration decision instructions for enabling calibration reminders and maintaining the current state are processed using permission logic to obtain the associated interface control strategy for controlling the interactive terminal. Specifically, this includes: a pre-established hierarchical metering management permission system, pre-established based on the business processes of gas operation management, job responsibilities, work order approval standards, and system safety operation requirements. Referencing the system registration and login permission configuration requirements, report approval processes, and actual scenarios of meter operation execution and management, the system determines the personnel scope and operational boundaries corresponding to each level of permission. The administrator level includes personnel from the group's safety operation department, the company manager, and the deputy manager in charge of the work; the department head level includes the director of the company's safety operation department and the deputy director in charge of the work; and the ordinary operator level includes company report clerks, front-line maintenance personnel, and metering management personnel. This system is bound to the OA account. During registration, the corresponding permissions must be applied for according to the actual position. After the superior reviews and confirms that the permissions match the position, the account is activated. Simultaneously, the data access scope and operational restrictions for each level of permission are determined to avoid unauthorized operations and ensure that the system meets the compliance requirements and efficient operation needs of gas metering management. This system determines the operational permission scope corresponding to different permission levels, including multiple levels of permissions such as administrator, department head, and ordinary operator.

[0060] Based on this hierarchical permission system, permission logic is applied to instrument calibration decision commands that enable calibration reminders and those that maintain the current state: For command commands that enable calibration reminders, administrators can perform operations such as confirming execution, modifying suggested calibration time limits, assigning work order recipients, initiating work dispatch operations, and deleting invalid reminders; department heads can perform operations such as confirming execution, reviewing work order information, and approving work dispatch plans; ordinary operators only have permissions to view reminder content, basic instrument information, calibration priority, and suggested calibration time limits, but no permission to modify or execute any operations. For command commands that maintain the current state, ordinary operators have permissions to view the command content, instrument operating status data, and the basis for maintaining the state; administrators have permissions to review the decision results, adjust the state maintenance duration, trigger reassessment, and modify the monitoring frequency during the state maintenance period; department heads have permissions to view review records and confirm the state maintenance plan. Based on the permission logic processing results, a control interaction terminal is generated. The strategy implements a unified interface control policy, which sets rules for controlling interface elements based on different decision commands and permission levels. This includes controls for showing or hiding interface buttons and enabling or disabling operation permissions. For decision commands to enable verification reminders, administrators and department heads display a "Dispatch" button, a "Verification Priority Adjustment" dropdown, a "Suggested Verification Time Limit Modification" box, and a "Confirm Execution" button on their terminals, while hiding the "View Only" button. Regular operators' terminals only display the "View" button and hide all operation-related buttons. For decision commands to maintain the current state, regular operators' terminals only display a "Status Information View" button, administrators' terminals additionally display a "Review" button, a "Maintainment Duration Adjustment" box, and a "Reassess" button, while department heads' terminals display a "Confirm" button and a "Review Record View" button. The policy also includes operation log recording rules, automatically recording the operator's account, operation time, and operation content for all permission-related interface operations. This achieves deep integration of decision commands and permission management, ensuring the compliance and security of interface operations.

[0061] In this embodiment of the invention, based on the unique identifier attribute of the pattern encoding, precise matching is achieved by retrieving a pre-configured parameter mapping table, quickly extracting three core basic parameters: site runtime, remaining days of the verification cycle, and cumulative instrument workload. These three dispersed basic state parameters are integrated into a structured multi-dimensional state assessment parameter set, standardizing the data organization. This allows the multi-dimensional parameters to form an organic whole, avoiding data fragmentation and laying an orderly data foundation for subsequent parameter space construction and feature extraction. A three-dimensional parameter space is constructed using the three basic state parameters as coordinate axes, mapping the structured parameter set to spatial data points. Regularized grid partitioning is performed on the three-dimensional parameter space, transforming the complex parameter space... The data is broken down into standardized parameter units, simplifying the analysis of the parameter space and making subsequent data point location and feature extraction more operational, thus improving the efficiency and standardization of data processing. Mapped data points are precisely located to their corresponding target parameter units, determining the data point's scope. The spatial distribution characteristics of data points within the target parameter units are analyzed in depth, extracting key features such as distance offset and distribution clustering. Through weighted aggregation calculations, multi-dimensional spatial distribution features are transformed into a single quantified instrument health index. This achieves a comprehensive quantitative description of the instrument's status, making complex multi-feature information easier to interpret and apply, and providing a concise and effective quantitative basis for subsequent threshold correction and decision command generation.

[0062] The instrument health index is converted into dynamic weighting coefficients using a preset conversion function; a quantitative correlation is established between health status and threshold adjustment, enabling the weighting coefficients to accurately reflect the actual instrument status and providing strong quantitative support for the dynamic correction of subsequent activation condition thresholds; the dynamic weighting coefficients are weighted and fused with the baseline threshold to obtain the dynamic correction threshold; a deep binding between the threshold and the instrument health status is achieved, making the threshold adjustment more closely match the actual operating conditions of the instrument, avoiding the rigidity and limitations of fixed thresholds, and improving the adaptability of the thresholds; the updated activation condition thresholds are determined, providing a clear and unified standard for judging the instrument health status; a health status judgment identifier is obtained through standardized threshold comparison calculations; and the status judgment process is simplified. This ensures the objectivity and accuracy of the judgment results, quickly determines the current status attributes of the instrument, and provides a direct and reliable basis for subsequent strategy triggering; it triggers the corresponding start verification strategy or status maintenance strategy based on the health status judgment identifier, and generates targeted instrument verification decision instructions; it achieves precise linkage between judgment results and decision strategies, ensuring that decision instructions are highly consistent with the actual state of the instrument, and improves the pertinence and effectiveness of decision-making; it processes different decision instructions according to metering management permissions, and generates associated interface control strategies; it achieves deep integration of decision instructions and permission management, ensuring the compliance and security of interface control operations, while ensuring that control strategies are precisely matched with specific management permissions, and improving the orderliness of operation management.

[0063] In a preferred embodiment of the present invention, step 300 above, based on the instrument calibration decision command and the associated interface control strategy, configures the interactive terminal operation interface to respond to the interactive terminal operation: if it is an enable operation, a calibration reminder record is generated and a maintenance work order is created in conjunction with it; if it is a disable operation and its disablement reason is verified, the unfinished reminder records and work orders associated with the instrument are cleared, and an instrument reminder status update command is generated, including:

[0064] Step 301: Receive the instrument calibration decision instruction and the associated interface control strategy; based on the associated interface control strategy, configure the state of controls related to the calibration reminder function in the interactive terminal operation interface to determine their display state and operation permission, thereby instantiating an interactive terminal operation interface instance. Specifically, this includes: First, receiving the instrument calibration decision instruction and the associated interface control strategy, wherein the interface control strategy has determined the operation rules of the interactive terminal controls corresponding to different metering management permissions; based on the interface control strategy, configuring the state of core controls related to the calibration reminder function in the interactive terminal operation interface, including a calibration reminder on button, a close button, and a reason input button. The configuration includes controls such as boxes and submit buttons. During the configuration process, the display status of each control is set according to the control strategy, that is, controls that meet the permissions are set to visible, and those that do not meet the permissions are set to hidden. At the same time, operation permission permissions are set, that is, operators with corresponding permissions can trigger control operations, and those without permissions are restricted from operating. For example, for the decision instruction to enable verification reminders, the enable button is configured to be visible and clickable for administrators and department heads, and the close button is configured to be hidden. For the decision instruction to maintain the current state, the button is configured to be visible but not editable for ordinary operators. Through the above configuration, the precise binding of controls with decision instructions and management permissions is achieved, and finally, a functionally adapted and permission-compliant interactive terminal operation interface instance is instantiated.

[0065] Step 302 involves responding to and acquiring the operation instructions triggered by the interactive terminal for the selected target instrument through the interactive terminal operation interface instance. Specifically, the instantiated interactive terminal operation interface maintains a real-time response state. When a target instrument is selected on the interactive terminal and an operation related to the verification reminder is triggered, the interface immediately captures the operation behavior. The interface identifies the operator's click action and the control identifier corresponding to the input behavior to obtain the core information of the operation instruction, including the unique identifier of the target instrument, the operation trigger time, and the operation type trigger signal. During this process, the interface performs preliminary filtering of the operation instructions, retaining only valid instructions related to the verification reminder function and conforming to the current interface control strategy, and eliminating invalid instructions due to misoperation or inappropriate permissions. This ensures that the acquired operation instructions are targeted and effective, laying the foundation for subsequent instruction parsing.

[0066] Step 303: Perform type parsing on the operation command to obtain the command type identifier. When the command type identifier is an enable verification reminder command, use the identifier of the target instrument, the current timestamp, and the decision basis data in the instrument verification decision command as input to obtain a structured verification reminder record. Specifically, this includes: based on this, performing type parsing on the operation command obtained in step 302, and determining whether the command type identifier is an enable verification reminder command or a disable verification reminder command by identifying the operation type trigger signal carried in the command; when the command type identifier is an enable verification reminder command, use the unique identifier of the target instrument (such as instrument code, serial number), the current system timestamp (accurate to hours, minutes, and seconds), and the instrument verification decision... The decision-making basis data in the instruction (including instrument health index, dynamic correction threshold, and health status judgment identifier) ​​serves as the core input information. Among them, the unique identifier of the target instrument, such as the instrument code and serial number, and the current system timestamp accurate to the hour, minute, and second, are included. The decision-making basis data in the instrument verification decision instruction includes: instrument health index, dynamic correction threshold, and health status judgment identifier. The above input information is systematically integrated according to a preset structured data format to generate a structured verification reminder record containing basic instrument information, operation trigger information, decision-making basis information, and reminder type information. Each field is arranged in a fixed order and labeled with its data type to ensure that the record format is uniform and the information is complete, making it easy to directly call when creating subsequent work orders.

[0067] Step 304: Based on the structured verification reminder record, extract key fields as work order creation parameters, and automatically call the work order management service interface to obtain the maintenance work order. Specifically, this includes: based on the structured verification reminder record, a preset field mapping rule, combined with the parameter requirements of the work order management service, the structured field specifications of the verification reminder record, the core information requirements for industry work order creation, and the preset data interaction standards of each module of the system. This rule determines the one-to-one correspondence between each target field in the structured verification reminder record and the key fields required for work order creation. Among them, the target instrument identifier corresponds to the instrument code or serial number in the reminder record, the site information corresponds to the combination of the site number and the site name, the verification reminder priority corresponds to the priority level in the decision result, the suggested verification completion time limit corresponds to the limited completion time in the reminder record, and the decision basis summary corresponds to the comparison result of the instrument health index and the dynamic correction threshold and the core evaluation parameter summary, ensuring that the extracted key fields can completely cover all the requirements for work order creation and that the format meets the interface requirements.

[0068] The system extracts key fields required for work order creation according to the preset field mapping rules. These key fields include: target instrument identifier, site information, verification reminder priority, suggested verification completion time limit, and a summary of decision basis. The extracted key fields are then encapsulated into standardized work order creation parameters. Through a preset interface call protocol, a call request to the work order management service interface is automatically triggered. After receiving the parameters, the work order management service interface automatically fills in the parameter information according to the built-in work order generation template, generating a maintenance work order containing core content such as work order number, task description, responsible department, and associated reminder record ID. This achieves seamless linkage between verification reminder records and maintenance work orders, enabling work order creation without manual intervention and ensuring the consistency and accuracy of work order information and reminder records.

[0069] Step 305: When the instruction type is identified as a "Close Verification Reminder Instruction," the closing reason text submitted by the interactive terminal along with the closing verification reminder instruction is obtained. Specifically, when the instruction type is identified as a "Close Verification Reminder Instruction," the interactive terminal operation interface automatically activates the reason input box control, prompting the operator to enter the specific reason for closing the reminder. After the operator completes the input, the interface captures and obtains the closing reason text in real time. The text format supports common forms such as Chinese descriptions and numerical supplements. The interface performs preliminary format verification on the text to ensure that there are no special characters interfering and that the text length is within a preset range. At the same time, the interface binds and stores the closing reason text with the corresponding target instrument identifier and operation instruction to ensure that the reason text and the closing operation are uniquely associated, providing complete supporting data for subsequent compliance verification.

[0070] Step 306 involves matching and analyzing the closure reason text against a pre-set rule base of legitimate closure reasons to calculate the compliance index of the closure reason. Specifically, this includes: calling a pre-set rule engine, which is pre-set based on industry standards for gas metering equipment management, enterprise internal flow meter maintenance management requirements, historical closure application review cases, and system operation process requirements. This engine integrates semantic analysis algorithms, keyword matching logic, and weight assignment models to determine the parsing process and core working principle of the closure reason. First, the input closure reason text is pre-processed, removing redundant modifiers related to the closure application and retaining the core expression reflecting the substantive reason for closure. Then, word segmentation and calculation are performed... The algorithm breaks down the core expression into basic vocabulary units, then uses semantic analysis algorithms to extract the logical connections and core semantic features between the words. Simultaneously, it invokes keyword matching logic to accurately compare the decomposed words with feature keywords in the rule base of legitimate closure reasons. Subsequently, a weighted scoring model is used, assigning different weights based on the number of keyword matches and semantic fit. Finally, a weighted sum is used to obtain a preliminary compliance score, which is then cross-validated with multiple sets of reasons in the rule base to ensure that the score objectively reflects the compliance level of the closure reason. This engine has passed multi-scenario closure application case tests and is adapted to various application scenarios such as flow meter verification, repair, and deactivation, ensuring efficient identification of the compliance attributes of closure reasons.

[0071] The pre-set legal shutdown rule base is also based on the above criteria. It first filters common legal shutdown scenarios in the flow meter management process, including the meter has completed calibration in advance, the equipment is in a shutdown state, the fault has been handled on-site, and the status is normal after data anomaly correction. Then, it refines the definition of each legal shutdown reason in combination with industry standards and marks the corresponding characteristic keywords. For example, the meter has completed calibration in advance and corresponds to keywords such as early calibration, calibration completed, and calibration certificate obtained. The equipment is in a shutdown state and corresponds to keywords such as shutdown, suspension of use, and no gas demand. At the same time, the matching weight is set according to the compliance priority and actual application frequency of each reason. After multiple rounds of review and verification by the Group's Safety Operation Department and Metering Management Department, the comprehensiveness and accuracy of the rule base are ensured.

[0072] The system invokes a pre-defined rule engine, which contains a pre-built rule library of legal closure reasons. This library stores multiple sets of legal closure reasons calibrated according to industry standards and corporate management requirements, with each set of legal reasons having corresponding feature keywords and matching weights. The closure reason text is input into the rule engine, which first performs text preprocessing, word segmentation, and semantic extraction according to its pre-defined working principle. Then, it compares the text with each set of reasons in the legal closure reason rule library through keyword matching and semantic analysis. A compliance index is calculated based on the degree of matching. For example, a higher weight score is assigned when the reason text completely matches the feature keywords of a certain legal reason and is semantically consistent; a medium weight score is assigned when the reason text contains some feature keywords and is semantically relevant; and a lower weight score is assigned when there are no matching keywords and the semantics are irrelevant. Finally, the scores of each matching item are weighted and summed according to their corresponding weights to obtain the compliance index of the closure reason. This achieves a quantitative assessment of the closure reason and provides an objective basis for subsequent review decisions on closure applications.

[0073] Step 307: Determine whether the compliance index exceeds a preset verification threshold; if not, return a verification failure message to the interactive terminal operation interface; if it exceeds, determine that the reason for closure has passed verification, and use the identifier of the target meter as the index key to retrieve and delete all verification reminder records with an incomplete status and their associated maintenance work orders. Specifically, this includes: comparing the compliance index with a preset verification threshold, which is based on industry standards for gas metering equipment management, enterprise internal flow meter maintenance management requirements, statistical analysis of historical closure application review data, and the distribution pattern of the compliance index. The process involves first reviewing compliance index data from legal closure applications and those that did not meet legal requirements over the years, then defining the critical ranges for compliant and non-compliant cases. This is followed by combining the review standards and efficiency requirements of the Group's Safety Operations Department and Metrology Management Department to determine core critical values. Subsequently, multiple closure application scenarios with different site types and instrument models are tested and verified, with fine-tuning of the threshold values ​​to ensure accurate differentiation between legal and non-compliant closure reasons, while balancing review rigor and operational convenience. Finally, a fixed verification threshold is determined, which is used to define whether the closure reason is legal and valid.

[0074] If the compliance index does not exceed the verification threshold, the reason for closure is deemed unverified, and the rule engine returns a verification failure message containing the reason for failure to the interactive terminal operation interface, prompting the operator to supplement or modify the reason for closure. If the compliance index exceeds the verification threshold, the reason for closure is deemed verified. Using the unique identifier of the target meter as the index key, the system traverses the verification reminder record database and work order database, retrieves all associated verification reminder records with an incomplete status and their corresponding maintenance work orders, and performs batch deletion operations to ensure that the deletion operation only affects the relevant data of the target meter and does not affect the reminders and work order information of other meters, thus ensuring the accuracy and cleanliness of the system data.

[0075] Step 308: Integrate the latest status of the target instrument after the shutdown operation, the timestamp of the shutdown operation, and the operator information to construct a reminder status update instruction for the instrument. Specifically, this includes: after data cleanup, integrating the latest status of the target instrument after the shutdown operation (i.e., verifying the shutdown status), the system timestamp of the shutdown operation, and the operator's account information and permission level information; according to the preset instruction format requirements, combining the above information in an orderly manner, wherein the latest status is marked as a status code, the timestamp is kept consistent with the system time, and the operator information is associated with a legitimate account in the enterprise user management database; through structured processing, a complete instrument reminder status update instruction is formed, which includes core fields such as the target instrument identifier, status update content, and operation traceability information, ensuring that the instruction data is complete and formatted correctly, providing a reliable data carrier for subsequently pushing status information to the remote monitoring platform.

[0076] In this embodiment of the invention, the display status and operation permissions of the interactive terminal control are configured according to a strategy, and the operation interface is instantiated; the interface is accurately adapted to decision instructions and management permissions to ensure the compliance and relevance of the operation interface and improve the standardization of interface use; the instantiated interface responds to the interactive terminal operation and obtains the operation instructions of the target instrument in a targeted manner; irrelevant instructions are avoided to ensure the timeliness and accuracy of instruction acquisition; the operation instruction type is parsed, and a structured verification reminder record is generated based on the instrument identifier, timestamp, and decision basis data; the data format of the reminder record is standardized, key core information is integrated, and the integrity and traceability of the record are improved; key fields are extracted from the structured reminder record as work order parameters, and the work order management service interface is automatically called; the reminder record and maintenance work order are seamlessly linked to avoid manual input errors and improve the efficiency of work order management. Efficiency and data consistency in single creation; targeted collection of closure reason text submitted by interactive terminals for closure verification reminder instructions; complete capture of the core basis of the closure operation, providing comprehensive and critical data support for subsequent compliance verification; calling the rule engine to match and analyze the closure reason with the legal reason rule base, quantitatively calculating the compliance index, and improving the objectivity and standardization of closure reason verification; determining the validity of the reason based on the compliance index, retrieving and deleting incomplete reminder records and work orders through the instrument identification index; achieving precise data filtering and cleaning, avoiding invalid data residue, and ensuring the consistency and cleanliness of system data; integrating the latest status of the target instrument, operation timestamp, and operator information to construct reminder status update instructions; integrating key operation data from multiple dimensions to form structured update instructions, ensuring the integrity and accuracy of instruction data.

[0077] In a preferred embodiment of the present invention, step 400 above involves obtaining a device status remote signaling frame based on the instrument's reminder status update instruction, according to the latest instrument status and site configuration information carried in the instruction, and pushing it to the remote monitoring platform to obtain its synchronization response message; based on the synchronization response message, generating an audit trail log containing the complete operation chain, including:

[0078] Step 401: Receive and parse the instrument's reminder status update instruction, extracting the latest instrument status data and associated site configuration information carried by it. Specifically, this includes: First, receiving the instrument reminder status update instruction, which integrates the latest status of the target instrument, operation timestamp, and operator information, and conforms to a preset structured standard; parsing the instruction layer by layer, first identifying the unique identifier of the target instrument in the instruction header, such as the instrument code and site number, and then extracting the core data of the instruction body, including the latest instrument status data and associated site configuration information; wherein, the latest instrument status data is a clear identifier for verifying whether the reminder is closed or maintained in the current state, and the associated site configuration information covers basic attributes such as site name, area, instrument model, and installation location; during the parsing process, verifying the integrity of fields and the standardization of data format ensures that the extracted core data is complete and error-free, providing an accurate and complete data source for the encapsulation of subsequent device status remote signaling frames, effectively solving the problem of information omission during multi-system data synchronization.

[0079] Step 402: Based on the preset remote signaling frame encoding rules, the latest instrument status data and associated site configuration information are encapsulated into a standard format device status remote signaling frame. Specifically, this includes: encapsulating the latest instrument status data and associated site configuration information according to the preset remote signaling frame encoding rules. These encoding rules are preset based on gas industry communication standards, the compatibility requirements of the group's SCADA system, the data receiving protocol of the remote monitoring platform, the remote transmission protocol specifications of the flow meter equipment, and historical data transmission stability statistics. During the preset process, the core types of device status data and site configuration information are first identified to determine the data dimensions to be encapsulated, and then the parsing capabilities of the remote monitoring platform are considered. This document defines the field composition, field length, data type, and arrangement order of remote signaling frames. It also aligns with industry binary encoding standards, specifying unique binary status codes for different device states. The document clarifies the ASCII conversion standard for text fields, the format preservation requirements for numeric fields, and the rules for adding unit identifiers. Finally, through transmission tests on multiple brands of flow meters, multiple site environments, and multiple network conditions, the field arrangement order and verification algorithm are optimized to ensure that the encoding rules meet both data transmission efficiency requirements and are adaptable to all scenarios. These encoding rules have been pre-calibrated according to industry communication standards and the data reception requirements of remote monitoring platforms, clearly defining the field composition, field length, data type, and arrangement order of remote signaling frames.

[0080] During encapsulation, the latest instrument status data is first converted into preset binary status codes. For example, a specific binary code corresponds to the verification reminder being turned off, while another binary code corresponds to the maintenance status. Then, the station configuration information is encoded according to field type. For example, text fields are converted to ASCII codes, and numeric fields retain their original format and have unit identifiers added. Subsequently, the status code, the encoded station configuration information, and the data check code are concatenated in the order specified by the encoding rules to form a standard equipment status remote signaling frame with a fixed length and uniform format. The data check code is obtained by summing and verifying the preceding fields and is used to ensure data integrity during the transmission of the remote signaling frame. This encapsulation process realizes the standardized conversion of heterogeneous data, ensuring that the remote monitoring platform can accurately parse it.

[0081] Step 403: Push the device status remote signaling frame to the remote monitoring platform through a preset communication interface. Specifically, this includes: Pushing the encapsulated device status remote signaling frame to the remote monitoring platform through the preset communication interface. The preset communication interface is based on the gas industry data transmission specifications, the compatibility requirements of the group's SCADA system, the communication protocol standards of the remote monitoring platform, historical data transmission stability statistics, and the network environment characteristics of different sites. During the preset process, a transmission protocol conforming to industry standards, such as TCP / IP, is selected based on the security and real-time requirements of data transmission. Then, combined with the receiving configuration of the remote monitoring platform, the corresponding IP address and port number are assigned. A reasonable configuration is set based on the data size of the remote signaling frame, link bandwidth, and transmission delay requirements. The system configures the transmission rate, data verification methods, and timeout thresholds. Then, through multi-site and multi-network condition communication tests, the interface's compatibility and stability are verified. Communication parameters are fine-tuned to ensure the communication link can adapt to transmission requirements in different network environments. Communication parameter configuration with the remote monitoring platform has been completed in advance to ensure efficient and reliable data transmission. During the push process, the system monitors the transmission status in real time. If abnormal situations such as transmission timeout or link interruption occur, a retransmission mechanism is automatically triggered. The number of retransmissions and the interval time are executed according to preset rules. This continues until the remote signaling frame is successfully sent to the remote monitoring platform, or the maximum number of retransmissions is reached and an abnormal log is recorded. This push mechanism ensures that status information can be efficiently and stably synchronized to the remote monitoring platform, solving the problem of poor data transmission between multiple systems.

[0082] Step 404 involves obtaining the synchronization response message returned by the remote monitoring platform after successfully processing the device status remote signaling frame. Specifically, this includes: after receiving the device status remote signaling frame, the remote monitoring platform performs data verification and parsing to confirm that the remote signaling frame format is valid, the data is complete, and matches the target site and instrument. Then, it completes the database update of the latest status information. Subsequently, the platform automatically generates a synchronization response message, which includes core fields such as the target instrument identifier, the status update result identifier (success or failure), and the platform processing timestamp. The platform feedback is received in real time through the aforementioned preset communication interface. Once the synchronization response message is captured, it is immediately received and stored, and the retransmission mechanism is paused. The acquisition of the synchronization response message forms a closed loop of instruction push, platform processing, and feedback reception, ensuring timely confirmation of whether the status update has taken effect and avoiding data inconsistency caused by blind synchronization.

[0083] Step 405 involves parsing the synchronization response message and confirming the status synchronization confirmation identifier. Specifically, this includes: first, locating the verification field in the header of the synchronization response message to verify whether data tampering occurred during message transmission; if the verification passes, continuing to parse the main content; focusing on extracting the status synchronization confirmation identifier field, which is a specific character or code set by the platform, such as 00 representing successful synchronization and 01 representing synchronization failure; by identifying this confirmation identifier, confirming whether the latest instrument status has been successfully updated on the remote monitoring platform; if the identifier indicates synchronization failure, recording the content of the failure reason field to provide a basis for subsequent troubleshooting. This parsing process ensures accurate determination of the status synchronization result, avoiding management loopholes caused by unconfirmed synchronization effects.

[0084] Step 406: Based on the status synchronization confirmation identifier, and combined with the source instruction information of the reminder status update instruction, the push timestamp of the device status remote signaling frame, and the reception timestamp of the synchronization response message, an audit trail log containing the complete operation chain from status update to platform synchronization is obtained. Specifically, this includes: based on the status synchronization confirmation identifier, integrating multi-dimensional key information to generate a structured audit trail log. The integrated information includes: the source instruction information of the reminder status update instruction (including instruction number, generation time, and core decision basis), the push timestamp of the device status remote signaling frame (accurate to the hour, minute, and second), and the reception timestamp of the synchronization response message (accurate to the hour, minute, and second). The system includes: status synchronization confirmation identifier and corresponding synchronization result description; core configuration information of the target instrument and station; operator account and permission level; and arranges the above information in an orderly manner according to a preset log format, assigning a unique log number to each log entry, determining field separators and record formats, forming a complete audit trail log covering the entire operation chain from status update instruction generation, remote signaling frame encapsulation, push, platform processing, and synchronization feedback. This log can be stored long-term and supports retrieval by log number, instrument identifier, time range, and other conditions, enabling full traceability and verification of operational behavior, effectively solving the deficiency of lacking end-to-end structured audit trails in the complete operation chain of existing technologies.

[0085] In this embodiment of the invention, the system receives and parses a status update reminder command, extracts the latest instrument status data and associated site configuration information, ensures the integrity and accuracy of the data source for subsequent data processing, and provides reliable data support for the encapsulation of device status remote signaling frames, encapsulates data according to preset remote signaling frame encoding rules, and transforms scattered status data and configuration information into standard format remote signaling frames, unifies the data transmission format, and ensures data compatibility and readability across different systems, pushes device status remote signaling frames to the remote monitoring platform through a preset communication interface, and ensures the stability and security of data transmission by relying on standardized interfaces, ensuring that remote signaling frames are delivered to the target platform quickly and accurately, and achieving efficient synchronization of status information; and obtains remote monitoring data. The system receives and processes the synchronization response message returned by the control platform; confirms that the remote signaling frame has been successfully received and processed by the platform, forming a closed-loop verification of data transmission, avoiding omissions in status information synchronization, and ensuring the effectiveness of data transmission; it parses the synchronization response message to confirm the status synchronization confirmation identifier; it quickly verifies whether the status update has taken effect on the remote platform, determines the synchronization result, provides key verification basis for subsequent audit log generation, and ensures the accuracy of status synchronization; it integrates the status synchronization confirmation identifier, source command information, push and receive timestamps to generate a complete audit trace log of the operation chain; and it comprehensively retains all process data from status update to platform synchronization, enabling traceability and verification of operational behavior, and improving the compliance and security of operation management.

[0086] like Figure 2As shown, embodiments of the present invention also provide an intelligent analysis and processing system for historical gas consumption data, including:

[0087] The data fusion module is used to collect and preprocess multi-source data from the target site to obtain a set of data related to the site and the instrument. This data is then parsed and fused to obtain a comprehensive status feature set. Based on the comprehensive status feature set and the physical link configuration relationship of the instrument, a predefined acquisition logic pattern is matched to obtain the pattern code.

[0088] The intelligent assessment module is used to extract preset site runtime, remaining days of the verification cycle, and cumulative workload of the instrument as a multi-dimensional status assessment parameter set based on pattern coding; perform parameter space region division and feature extraction based on the multi-dimensional status assessment parameter set to obtain the instrument health index; use the instrument health index to dynamically weight and correct the threshold of the verification reminder to obtain the instrument verification decision instruction, and generate interface control strategy according to the metering management authority.

[0089] The interactive control module is used to configure the interactive terminal operation interface based on the instrument calibration decision command and the associated interface control strategy to respond to the interactive terminal operation: if it is an enable operation, a calibration reminder record is generated and a maintenance work order is created in conjunction with it; if it is a disable operation and its disable reason is verified, the unfinished reminder records and work orders associated with the instrument are cleared, and an instrument reminder status update command is generated.

[0090] The closed-loop audit module is used to obtain the device status remote signaling frame based on the instrument's reminder status update command, according to the latest instrument status and site configuration information carried in the command, and push it to the remote monitoring platform to obtain its synchronization response message; based on the synchronization response message, an audit trace log containing the complete operation link is generated.

[0091] It should be noted that this system is a system corresponding to the above method. All implementation methods in the above method embodiments are applicable to this embodiment and can achieve the same technical effect.

[0092] Embodiments of the present invention also provide a computing device, including: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0093] Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0094] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for intelligent analysis and processing of historical gas consumption data, characterized in that, The method includes: Step 100: Perform multi-source data acquisition and preprocessing on the target site to obtain a set of site and instrument related data, and then parse and fuse it to obtain a comprehensive status feature set; based on the comprehensive status feature set and the instrument physical link configuration relationship, match the predefined acquisition logic mode to obtain the mode code; Step 200: Based on pattern encoding, extract the preset site runtime, remaining days of the verification cycle, and cumulative instrument workload as a multi-dimensional status assessment parameter set; perform parameter space region division and feature extraction based on the multi-dimensional status assessment parameter set to obtain the instrument health index; use the instrument health index to dynamically weight and correct the activation condition threshold of the verification reminder to obtain the instrument verification decision instruction, and generate an interface control strategy according to the metering management authority, including: using the pattern encoding as the query key to retrieve the pre-configured parameter mapping table and obtain the corresponding three basic status parameters: site runtime, remaining days of the verification cycle, and cumulative instrument workload; based on The three basic state parameters are used to construct a structured multidimensional state evaluation parameter set; using the three basic state parameters as coordinate axes, a three-dimensional parameter space is constructed, and the multidimensional state evaluation parameter set is mapped into the three-dimensional parameter space to obtain a parameter space to be analyzed containing mapped data points; spatial gridding is performed on the parameter space to be analyzed to obtain a gridded parameter space composed of multiple regularized parameter units; based on the gridded parameter space, the mapped data points corresponding to the multidimensional state evaluation parameter set are located in the gridded parameter space, and their respective parameter grid units are determined as target parameter units; for the target parameter unit... The system analyzes the spatial distribution characteristics of the mapped data points within the target parameter unit, calculating spatial distribution features. These features include the distance offset of the mapped data points relative to the reference planes of each coordinate axis, and their distribution clustering degree within the boundary of the target parameter unit. Based on these spatial distribution features, a weighted aggregation calculation is performed to obtain the instrument health index. The instrument health index is then processed using a preset transformation function to obtain dynamic weighting coefficients. These dynamic weighting coefficients are then weighted and fused with a preset verification reminder activation condition benchmark threshold to obtain a dynamic correction threshold. Based on the dynamic correction threshold, an updated activation condition threshold is determined. Based on the updated activation condition threshold and the meter health index, a threshold comparison operation is performed to obtain a health status determination identifier. If the health status determination identifier indicates a verification requirement, a verification activation strategy is triggered, resulting in a meter verification decision instruction with the status of "verification reminder activated". If the health status determination identifier indicates a normal status, a status maintenance strategy is triggered, resulting in a meter verification decision instruction with the status of "maintain current status". Based on metering management permissions, the meter verification decision instruction with the status of "verification reminder activated" and the meter verification decision instruction with the status of "maintain current status" are processed using permission logic to obtain the associated interface control strategy for controlling the interactive terminal. Step 300: Based on the instrument calibration decision command and the associated interface control strategy, configure the interactive terminal operation interface to respond to the interactive terminal operation: if it is an enable operation, generate a calibration reminder record and link to create a maintenance work order; if it is a disable operation and its disable reason is verified, clear the uncompleted reminder record and work order associated with the instrument, and generate an instrument reminder status update command. Step 400: Based on the instrument's reminder status update command, and according to the latest instrument status and site configuration information carried in the command, obtain the device status remote signaling frame and push it to the remote monitoring platform to obtain its synchronization response message; based on the synchronization response message, generate an audit trail log containing the complete operation chain.

2. The intelligent analysis and processing method for historical gas consumption data according to claim 1, characterized in that, Step 100 includes: Real-time telemetry data of instrument operation and static attribute data of the site are collected synchronously from the operation monitoring data source and asset management data source of the target site, respectively. The station identifier and the instrument identifier are parsed from the real-time telemetry data of the instrument and the static attribute data of the station; Based on the site identifier and the instrument identifier, the real-time telemetry data of the instrument and the static attribute data of the site are cleaned, standardized and associated with each other to obtain a set of associated data of the site and the instrument. The site and meter associated data set is parsed to identify and output site-dimensional feature vectors and meter-dimensional feature vectors respectively. The site-dimensional feature vector and the instrument-dimensional feature vector are fused to obtain a comprehensive status feature set; The comprehensive status feature set is combined with the instrument physical link configuration relationship to form a matching query vector; Based on the matching query vector, the predefined collection logic mode rule base is traversed to determine the corresponding target collection logic mode. A pattern code is generated as a unique identifier based on the target acquisition logic pattern.

3. The intelligent analysis and processing method for historical gas consumption data according to claim 2, characterized in that, Step 300 includes: Receive the instrument calibration decision instruction and the associated interface control strategy; based on the associated interface control strategy, configure the status of the controls related to the calibration reminder function in the interactive terminal operation interface to determine their display status and operation permission, thereby instantiating the interactive terminal operation interface instance. Through the interactive terminal operation interface instance, respond to and obtain the operation instructions triggered by the interactive terminal for the selected target instrument; The operation command is parsed to obtain the command type identifier; when the command type identifier is an enable verification reminder command, the target instrument identifier, the current timestamp, and the decision basis data in the instrument verification decision command are used as inputs to obtain a structured verification reminder record; Based on the structured verification reminder records, key fields are extracted as work order creation parameters, and the work order management service interface is automatically called to obtain maintenance work orders.

4. The intelligent analysis and processing method for historical gas consumption data according to claim 3, characterized in that, Step 300 further includes: When the instruction type is identified as a close verification reminder instruction, obtain the closing reason text submitted by the interactive terminal along with the close verification reminder instruction; The text of the reason for closure is matched and analyzed with a pre-set rule base of legitimate reasons for closure, and the compliance index of the reason for closure is calculated. Determine whether the compliance index exceeds the preset verification threshold; if it does not exceed the threshold, return a verification failure message to the interactive terminal operation interface; if it exceeds the threshold, determine that the reason for closing has passed the verification, and use the identifier of the target instrument as the index key to retrieve and delete all verification reminder records with an incomplete status and their associated maintenance work orders. By integrating the latest status of the target instrument after the shutdown operation, the timestamp of the shutdown operation, and the operator information, a reminder status update instruction for the instrument is constructed.

5. The intelligent analysis and processing method for historical gas consumption data according to claim 4, characterized in that, Step 400 includes: Receive and parse the reminder status update command of the instrument, and extract the latest instrument status data and associated site configuration information carried by it; According to the preset remote signaling frame encoding rules, the latest instrument status data and associated site configuration information are encapsulated into a standard format device status remote signaling frame; The device status remote signaling frame is pushed to the remote monitoring platform through a preset communication interface; Obtain the synchronization response message returned by the remote monitoring platform after successfully processing the device status remote signaling frame; Parse the synchronization response message to confirm the status synchronization confirmation identifier; Based on the status synchronization confirmation identifier, and combined with the source instruction information of the reminder status update instruction, the push timestamp of the device status remote signaling frame, and the receive timestamp of the synchronization response message, an audit trail log containing the complete operation chain from status update to platform synchronization is obtained.

6. A smart analysis and processing system for historical gas consumption data, wherein the system implements the method as described in any one of claims 1 to 5, characterized in that, include: The data fusion module is used to collect and preprocess multi-source data from the target site to obtain a set of data related to the site and instruments, and then analyze and fuse the data to obtain a comprehensive status feature set. Based on the comprehensive status feature set and the physical link configuration relationship of the instrument, a predefined acquisition logic pattern is matched to obtain the pattern code; The intelligent assessment module is used to extract preset site runtime, remaining days of the verification cycle, and cumulative workload of the instrument as a multi-dimensional status assessment parameter set based on pattern coding. Based on the multidimensional state assessment parameter set, the parameter space region is divided and features are extracted to obtain the instrument health index; The instrument health index is used to dynamically weight and correct the threshold for enabling calibration reminders, thereby obtaining instrument calibration decision instructions and generating interface control strategies based on metering management permissions. The interactive control module is used to configure the interactive terminal operation interface based on the instrument calibration decision command and the associated interface control strategy, so as to respond to the interactive terminal operation: if it is an enable operation, a calibration reminder record is generated and a maintenance work order is created in conjunction with it. If it is a shutdown operation and the shutdown reason is verified, then clear the unfinished reminder records and work orders associated with the instrument, and generate a reminder status update instruction for the instrument; The closed-loop audit module is used to obtain the device status remote signaling frame based on the instrument's reminder status update command, according to the latest instrument status and site configuration information carried in the command, and push it to the remote monitoring platform to obtain its synchronization response message; Based on the synchronous response message, an audit trail log containing the complete operation chain is generated.

7. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the method as described in any one of claims 1 to 5.

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