Pipeline integrity management system based on multiple detection item configuration and data checking

Abstract

The present application relates to a pipeline integrity management system based on multi-detection item configuration and data checking, wherein the system comprises a data acquisition unit, a data processing unit, a detection item configuration and task allocation unit, a data checking and report generation unit; multi-dimensional original data is acquired based on pipeline detection requirements; the multi-dimensional original data is classified and statistically processed and standardized in format to generate a structured detection data set, while personnel qualifications and detection item requirements are matched to obtain inspection team information and available identification is generated by tracking equipment status; effective data is screened based on checking results, detection items are identified, configuration lists are dynamically adapted in combination with pipeline information, task allocation and resource allocation are combined to obtain dispatching schemes and equipment scheduling plans, and directional supplementary sampling instructions are generated for abnormal items; the structured data set and the task scheme are cross-checked to automatically generate a standardized report and obtain a complete management archive. The pipeline integrity management based on multi-detection item configuration and data checking is realized.

Description

Technical Field

[0001] This invention belongs to the field of pipeline inspection and integrity management technology, specifically relating to a pipeline integrity management system based on multi-inspection item configuration and data verification. Background Technology

[0002] Currently, pipeline integrity management still has the following areas for improvement: Against the backdrop of the rapid development of the pipeline transportation industry, pipeline integrity management has become a core element in ensuring transportation safety and reducing operational risks. However, existing pipeline inspection and management systems still have many pain points that urgently need to be addressed: The configuration of testing items lacks flexibility. Traditional systems often use fixed testing item templates, failing to dynamically adapt to differences in pipe type (PE pipe, steel pipe), project scenario (comprehensive inspection, specialized inspection), and testing standards. This frequently results in redundant testing items or missing key items, leading to wasted testing resources or inaccurate evaluation results. The data acquisition process suffers from multi-source heterogeneity issues. The basic pipe parameters entered on the PC are not in the same format as the field environment data collected on the mobile device. The lack of effective integrity verification and traceability mechanisms leads to frequent data loss and errors, severely impacting the reliability of subsequent analysis.

[0003] The allocation of personnel and equipment resources is unreasonable. The failure to accurately match the requirements of professional qualifications for testing items, personnel skill profiles, equipment operating status and maintenance cycles has led to problems such as mismatch between personnel skills and task requirements and frequent equipment failures, which has reduced testing efficiency. The data verification methods are simplistic and rely heavily on manual spot checks, making it difficult to achieve in-depth verification of all data. Abnormal data identification is lagging and the root cause cannot be accurately located, which fails to meet the strict quality requirements of testing data.

[0004] The report generation and approval process is cumbersome, requiring manual integration of multi-source data and manual filling of report templates, which easily leads to problems such as inconsistent formats and missing content. The approval process lacks digital support, resulting in low efficiency in compliance archiving. Furthermore, there is a lack of targeted re-collection mechanisms for abnormal data discovered during the testing process, and the data closed-loop management capability is insufficient. In addition, existing systems mostly focus on a single testing link, failing to achieve integrated management of the entire process from data collection, testing configuration, task assignment, data verification to report archiving. Data is fragmented at each stage, making it difficult to support a comprehensive assessment and long-term tracking of pipeline integrity.

[0005] To address the aforementioned technical issues, this invention proposes a pipeline integrity management system that supports multi-item configuration and data verification. Through modular design, intelligent algorithms, and a full-process collaborative mechanism, it achieves dynamic adaptation of inspection items, accurate data verification, optimized resource allocation, and automated report generation, thereby comprehensively improving the efficiency and reliability of pipeline integrity management. Summary of the Invention

[0006] To address the aforementioned problems in the existing technology, this invention provides a pipeline integrity management system based on multi-detection item configuration and data verification; The objective of this invention can be achieved through the following technical solutions: Data acquisition unit, data processing unit, detection item configuration and task assignment unit, data verification and report generation unit; The data acquisition unit is used to acquire multi-dimensional raw data based on pipeline inspection business needs; The data processing unit performs classification statistics and format standardization processing on the multi-dimensional raw data to obtain a structured test dataset; it performs permission matching by combining personnel qualification data and test item requirements to obtain project inspection team information; it tracks the status of equipment operation status data to generate equipment availability status identifiers; and it compares and verifies the structured test dataset with preset verification rules to obtain verification results. The detection item configuration and task assignment unit filters valid detection data based on the verification results and identifies the corresponding detection items to obtain a detection item dataset with identification features; it performs dynamic adaptation configuration of detection items based on the detection item dataset and corresponding pipeline information to obtain a multi-detection item optimized configuration list; it obtains a detection task assignment scheme and equipment scheduling plan by allocating corresponding tasks and resources; and it re-collects abnormal items in the detection item dataset to generate a targeted re-collection task instruction. The data verification and report generation unit performs cross-validation on the structured detection dataset and the detection task assignment scheme to obtain data verification results; using the data verification results and a preset report template, it automatically fills in the report content and standardizes the format to obtain a standardized detection report; based on the approval process, it performs corresponding operations on the standardized detection report to obtain a compliant report file; and uses project archiving rules to classify and archive the compliant report file to obtain a complete management archive.

[0007] As a preferred technical solution of the present invention, the specific process of obtaining multi-dimensional raw data based on pipeline inspection business needs includes: entering basic pipeline parameters through a PC and collecting on-site environmental data through a mobile terminal based on the collection category to obtain an initial raw data set; performing integrity verification based on the initial raw data set, supplementing the collection of missing data, and simultaneously using the data collection timestamp and version identifier to trace and mark the initial raw data to obtain compliant multi-dimensional raw data.

[0008] Specifically, obtaining the structured detection dataset includes: dividing the type features of the multi-dimensional original data, performing format standardization processing based on the division results, and generating standardized subsets; establishing a mapping relationship for the standardized subsets based on preset data association rules; and encapsulating the standardized subsets based on the mapping relationship to obtain the structured detection dataset.

[0009] Specifically, the process of obtaining project inspection team information includes: extracting the required professional qualification types and skill level requirements based on the testing item requirements to obtain personnel qualification data; screening candidate inspection personnel who meet the qualification requirements based on the personnel qualification data to obtain a candidate pool; using the division of labor data and project priority of the candidate pool to perform personnel suitability scoring; and selecting the best combination based on the suitability scoring results to obtain project inspection team information.

[0010] Specifically, the process of tracking the equipment operating status data includes: obtaining equipment status data based on equipment requisition records and real-time operation monitoring data; comparing the status data with the equipment's rated parameter thresholds to determine the equipment's operating status; generating a tracking curve of the equipment's operating status based on the time-series data of equipment status changes; and generating an equipment availability status identifier based on the tracking curve of the equipment's operating status and the equipment maintenance cycle.

[0011] Specifically, obtaining the verification results includes: constructing a three-dimensional verification matrix based on preset verification rules; performing point-by-point penetration verification on the structured detection dataset, and marking the data of abnormal rules through an abnormal data anchoring algorithm; calculating the data anomaly density value based on the type characteristics and frequency of occurrence of the abnormal data; and generating a data verification result with an anomaly level identifier by comparing the data anomaly density value with a preset threshold and combining it with anomaly tracing data.

[0012] Specifically, the process of obtaining the dataset of detection items with identification features includes: based on the data verification results, filtering detection data that conforms to the verification rules through a data purification mechanism; using the detection item identifiers of the detection data to identify the technical parameters and execution standards of the detection items accordingly, generating a binding relationship between the detection items and the data; and using the binding relationship to add data fingerprints and traceability codes to obtain a traceable identification chain.

[0013] Specifically, the process of dynamically adapting and configuring the test items includes: constructing a test item adaptation decision tree based on the pipeline project type and test standards, and obtaining the test item priority and combination rules; dynamically filtering the test items to obtain an initial configuration list; and performing a closed-loop verification of the adaptability between the initial configuration list and the actual test capabilities of the project to obtain an optimized configuration list for multiple test items.

[0014] Specifically, the process of obtaining the testing task assignment scheme and equipment scheduling plan includes: based on the multi-test item optimized configuration list, breaking down the work modules and time nodes of the testing items through a task decomposition mechanism; based on the personnel skill map and workload saturation in the project inspection team information, allocating the work modules to the corresponding inspection personnel through personnel-task matching rules to obtain a preliminary task assignment scheme; matching suitable testing equipment through an equipment scheduling path optimization mechanism to formulate an equipment scheduling plan; and dynamically resolving resource conflicts based on the preliminary task assignment scheme and the equipment scheduling plan, adjusting the task execution order and equipment allocation scheme to obtain the testing task assignment scheme and equipment scheduling plan.

[0015] Specifically, the process of generating the targeted supplementary sampling task instruction includes: locating the anomalies in the verification results through anomaly tracing, locking down the corresponding data information of the anomaly data; and using the type characteristics of the anomalies through the supplementary sampling scheme generation mechanism to obtain the corresponding indicators of the supplementary sampling data and generate the targeted supplementary sampling task instruction.

[0016] Specifically, the process of automatically filling in and standardizing the report content includes: extracting the corresponding report content through information extraction; filling in the report content through a report filling mechanism based on the field mapping rules of the preset report template; and checking the completeness and logical consistency of the filled content using a report integrity verification matrix to obtain a standardized test report.

[0017] Specifically, the process of obtaining the compliance report document includes: pushing the standardized testing report through report circulation and conducting hierarchical review; obtaining an approval report through intelligent revision of the report based on the review comments; a collaborative process of electronic signature based on the approval report; and obtaining the compliance report document by performing consistency verification on the electronic signature through compliance verification.

[0018] The beneficial effects of this invention are as follows: through a dynamic adaptation configuration mechanism, it achieves precise matching between test items and project types, test standards, and actual capabilities, avoids redundancy or absence of test items, improves the pertinence and feasibility of the test plan, and reduces test costs.

[0019] Multi-terminal collaborative data collection, full-volume in-depth verification, and traceability chain construction ensure data integrity, accuracy, and traceability, solving the problems of chaotic data collection and insufficient verification in traditional data collection, and providing reliable data support for pipeline integrity assessment.

[0020] Intelligent matching of personnel and tasks and dynamic scheduling of equipment enable the optimal allocation of human and equipment resources, reduce resource conflicts and waste, improve the efficiency of testing tasks, and shorten project cycles.

[0021] From data collection, verification, and task assignment to report generation, approval, and archiving, the entire process is digitally collaborative, reducing manual intervention, minimizing human error, and improving management efficiency; the targeted supplementary collection mechanism ensures data closure and further improves the accuracy of test results.

[0022] Standardized report generation, tiered approval, and electronic signature collaboration ensure consistent report formats, compliant content, and legal signatures, meeting industry regulatory and customer requirements. The categorized archiving mechanism facilitates report management and retrieval, supporting long-term tracking of pipeline integrity. Attached Figure Description

[0023] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0024] Figure 1 This is a flowchart illustrating the pipeline integrity management system based on multi-detection item configuration and data verification according to the present invention. Figure 2 This is a flowchart of the detection item configuration and task assignment unit in this invention. Detailed Implementation

[0025] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0026] Please see Figure 1-2 The pipeline integrity management system based on multi-detection item configuration and data verification includes: a data acquisition unit, a data processing unit, a detection item configuration and task assignment unit, and a data verification and report generation unit. The data acquisition unit is used to acquire multi-dimensional raw data based on pipeline inspection business needs; The data processing unit performs classification statistics and format standardization processing on the multi-dimensional raw data to obtain a structured test dataset; it performs permission matching by combining personnel qualification data and test item requirements to obtain project inspection team information; it tracks the status of equipment operation status data to generate equipment availability status identifiers; and it compares and verifies the structured test dataset with preset verification rules to obtain verification results. The detection item configuration and task assignment unit filters valid detection data based on the verification results and identifies the corresponding detection items to obtain a detection item dataset with identification features; it performs dynamic adaptation configuration of detection items based on the detection item dataset and corresponding pipeline information to obtain a multi-detection item optimized configuration list; it obtains a detection task assignment scheme and equipment scheduling plan by allocating corresponding tasks and resources; and it re-collects abnormal items in the detection item dataset to generate a targeted re-collection task instruction. The data verification and report generation unit performs cross-validation on the structured detection dataset and the detection task assignment scheme to obtain data verification results; using the data verification results and a preset report template, it automatically fills in the report content and standardizes the format to obtain a standardized detection report; based on the approval process, it performs corresponding operations on the standardized detection report to obtain a compliant report file; and uses project archiving rules to classify and archive the compliant report file to obtain a complete management archive.

[0027] As a preferred technical solution of the present invention, the specific process of obtaining multi-dimensional raw data based on pipeline inspection business needs includes: entering basic pipeline parameters through a PC and collecting on-site environmental data through a mobile terminal based on the collection category to obtain an initial raw data set; performing integrity verification based on the initial raw data set, supplementing the collection of missing data, and simultaneously using the data collection timestamp and version identifier to trace and mark the initial raw data to obtain compliant multi-dimensional raw data.

[0028] In this embodiment, a multi-terminal collaborative model is adopted to collect multi-dimensional raw data based on the needs of pipeline inspection. Inspection personnel input basic pipeline parameters via PC, including core static information such as pipeline material, diameter, laying method, design pressure, service life, and pipeline number, according to the system's preset collection categories. They also collect dynamic environmental data via mobile devices, such as soil type, distribution of surrounding structures, on-site temperature and humidity, meteorological conditions, and surrounding topography, forming an initial raw data set. The system automatically performs integrity checks on the initial data, identifying missing key data items and triggering pop-up prompts to guide inspection personnel to supplement the data through mechanisms such as field non-empty checks and logical correlation checks (e.g., the pipeline service life must not exceed the laying period). Throughout the collection process, the system automatically adds a unique collection timestamp, collection device number, and collection personnel account information to each piece of raw data, establishing a full-link traceability marker and forming compliant, standardized, and complete multi-dimensional raw data.

[0029] Specifically, obtaining the structured detection dataset includes: dividing the type features of the multi-dimensional original data, performing format standardization processing based on the division results, and generating standardized subsets; establishing a mapping relationship for the standardized subsets based on preset data association rules; and encapsulating the standardized subsets based on the mapping relationship to obtain the structured detection dataset.

[0030] In this embodiment, data is categorized into several types based on data type characteristics, such as pipeline basic parameters, on-site environment, and equipment operation. Then, it is standardized according to preset system format standards (e.g., dates are standardized to year-month-day format, numerical data retains a uniform number of decimal places, and text data uses industry-standard terminology), generating standardized subset datasets for each type. Based on preset data association rules, mapping relationships are established between subset datasets. For example, pipeline material (steel pipe) is associated with testing methods (ultrasonic testing), and equipment model (ultrasonic testing instrument); pipeline laying method (buried) is associated with on-site environment (soil type), and anti-corrosion layer testing requirements. Finally, based on these mapping relationships, the standardized subset datasets are encapsulated to form a structured testing dataset with a clear structure and well-defined relationships.

[0031] Specifically, the process of obtaining project inspection team information includes: extracting the required professional qualification types and skill level requirements based on the testing item requirements to obtain personnel qualification data; screening candidate inspection personnel who meet the qualification requirements based on the personnel qualification data to obtain a candidate pool; using the division of labor data and project priority of the candidate pool to perform personnel suitability scoring; and selecting the best combination based on the suitability scoring results to obtain project inspection team information.

[0032] In this embodiment, the system first extracts the required professional qualification types (such as pipeline inspection qualification, non-destructive testing qualification, corrosion prevention testing qualification, etc.) and skill level requirements (such as intermediate inspector, senior inspector, inspection supervisor, etc.). It then filters out qualified candidate inspectors from the personnel qualification database to construct a candidate pool. Combining the candidate pool's division of labor data (such as areas of expertise, past experience in similar projects, and historical task completion quality scores) with project priorities (such as emergency maintenance projects, routine inspection projects, and annual inspection projects), a suitability scoring algorithm is used to comprehensively score candidates based on dimensions such as professional matching, experience fit, and workload. Based on the scoring results, the system selects the best candidates to form the project inspection team, including the team leader, inspectors, safety officer, and equipment custodian. The system clarifies the responsibilities and authority of each member, forming complete project inspection team information to ensure accurate matching of personnel skills with inspection needs.

[0033] Specifically, the process of tracking the equipment operating status data includes: obtaining equipment status data based on equipment requisition records and real-time operation monitoring data; comparing the status data with the equipment's rated parameter thresholds to determine the equipment's operating status; generating a tracking curve of the equipment's operating status based on the time-series data of equipment status changes; and generating an equipment availability status identifier based on the tracking curve of the equipment's operating status and the equipment maintenance cycle.

[0034] In this embodiment, based on equipment requisition records (including requisition personnel, requisition time, associated projects, and requisition status) and real-time operational monitoring data (such as equipment operating voltage, operating temperature, detection accuracy deviation, and runtime), the system acquires equipment status data in real time. This status data is compared with the equipment's rated parameter thresholds (such as rated operating temperature range, allowable accuracy deviation value, and maximum continuous runtime) to determine the equipment's operating status (normal, abnormal, requiring maintenance, or faulty). By analyzing the time-series data of equipment status changes, an equipment operating status tracking curve is generated, visually presenting the equipment's status fluctuations over a period of time and identifying potential equipment failure risks. Combined with equipment maintenance cycles (such as monthly maintenance, quarterly maintenance, and annual overhaul), the remaining available time of the equipment is predicted, ultimately generating an equipment availability status identifier (such as available, requiring maintenance, faulty, or scrapped), providing accurate data support for subsequent equipment scheduling.

[0035] Specifically, obtaining the verification results includes: constructing a three-dimensional verification matrix based on preset verification rules; performing point-by-point penetration verification on the structured detection dataset, and marking the data of abnormal rules through an abnormal data anchoring algorithm; calculating the data anomaly density value based on the type characteristics and frequency of occurrence of the abnormal data; and generating a data verification result with an anomaly level identifier by comparing the data anomaly density value with a preset threshold and combining it with anomaly tracing data.

[0036] In this embodiment, a three-dimensional verification matrix in a preset verification rule base is used to perform point-by-point penetration verification on the structured detection dataset. Each data point is scanned using an anomaly data anchoring algorithm, marking data that does not conform to the rules, such as data with values ​​exceeding reasonable ranges, logical contradictions, or missing fields. The types and characteristics of the anomaly data (e.g., missing data, numerical errors, logical conflicts) and their frequency of occurrence are statistically analyzed to calculate the data anomaly density value, quantifying the overall data quality. The anomaly density value is compared with a preset threshold, and combined with anomaly source data (e.g., data collectors, data collection equipment, data collection time), a data verification result with anomaly level identifiers (e.g., minor anomaly, moderate anomaly, severe anomaly) is generated, clarifying the severity, distribution range, and source information of the anomaly data.

[0037] Specifically, the process of obtaining the dataset of detection items with identification features includes: based on the data verification results, filtering detection data that conforms to the verification rules through a data purification mechanism; using the detection item identifiers of the detection data to identify the technical parameters and execution standards of the detection items accordingly, generating a binding relationship between the detection items and the data; and using the binding relationship to add data fingerprints and traceability codes to obtain a traceable identification chain.

[0038] In this embodiment, based on the data verification results, anomaly data that does not conform to the verification rules is filtered out through a data purification mechanism, retaining valid test data that meets the requirements of completeness, consistency, and reasonableness. Using the test item identifiers corresponding to the valid test data, the technical parameters of the test items (such as testing accuracy requirements, testing range, and testing frequency) are associated with the execution standards (such as industry specifications and internal enterprise standards), establishing a one-to-one binding relationship between test items and corresponding data, ensuring that the data accurately corresponds to the testing requirements. Based on this binding relationship, a unique data fingerprint and traceability code are added to each piece of data, forming a traceable identification chain, realizing full-process traceability from test items to raw data, the collection process, and verification results, ensuring the verifiability and credibility of the test data.

[0039] Specifically, the process of dynamically adapting and configuring the test items includes: constructing a test item adaptation decision tree based on the pipeline project type and test standards, and obtaining the test item priority and combination rules; dynamically filtering the test items to obtain an initial configuration list; and performing a closed-loop verification of the adaptability between the initial configuration list and the actual test capabilities of the project to obtain an optimized configuration list for multiple test items.

[0040] In this embodiment, a decision tree for matching testing items is constructed based on the pipeline project type (e.g., comprehensive inspection project, special inspection project, annual inspection project) and preset testing standards. This clarifies the priority of various testing items under different project types (e.g., in comprehensive inspection projects, non-destructive testing and corrosion protection layer testing have higher priority than visual inspection) and combination rules (e.g., special inspection projects require a combination of corrosion testing and leak testing). The calculation method for the priority weight of testing items is as follows: , W priority : Priority weight of detection items, K importance Importance coefficient, K urgency Urgency coefficient; Based on the decision tree, the detection items are dynamically filtered to initially select an initial configuration list that meets the basic requirements of the project. The decision tree node determination formula is: , Node decision Decision tree node decision results (output: retain / remove / supplement detection items); T project : Project type characteristic value; S standard Preset detection standard matching value; R ability Actual detection capability matching value; Based on the project's actual testing capabilities, including the types and performance of existing testing equipment, the skill level of inspection personnel, testing cycle requirements, and on-site operating conditions, a closed-loop verification of the initial configuration list is conducted to determine its suitability. Testing items that exceed the actual capability range (such as high-end testing technologies for which there are no corresponding testing equipment) are eliminated, and key testing items that are missing (such as stray current detection under special environments) are added. Finally, an optimized configuration list of multiple testing items that fits the actual project and is both targeted and feasible is formed.

[0041] Specifically, the process of obtaining the testing task assignment scheme and equipment scheduling plan includes: based on the multi-test item optimized configuration list, breaking down the work modules and time nodes of the testing items through a task decomposition mechanism; based on the personnel skill map and workload saturation in the project inspection team information, allocating the work modules to the corresponding inspection personnel through personnel-task matching rules to obtain a preliminary task assignment scheme; matching suitable testing equipment through an equipment scheduling path optimization mechanism to formulate an equipment scheduling plan; and dynamically resolving resource conflicts based on the preliminary task assignment scheme and the equipment scheduling plan, adjusting the task execution order and equipment allocation scheme to obtain the testing task assignment scheme and equipment scheduling plan.

[0042] In this embodiment, based on a multi-item optimized configuration list, each inspection item is broken down into several independent work modules through a task decomposition mechanism. The work content, execution steps, quality requirements, and time nodes of each module are clearly defined (e.g., non-destructive testing can be broken down into modules such as equipment debugging, on-site testing, data recording, and preliminary analysis, with the execution order and time limits for each module clearly defined). Based on the personnel skill profile in the project inspection team information (e.g., an inspector is proficient in ultrasonic testing, an inspector has corrosion testing qualifications) and workload saturation (based on system statistics of the current workload undertaken by personnel), each work module is assigned to inspection personnel with matching skills and reasonable workloads through personnel-task matching rules, forming a preliminary task allocation plan.

[0043] Simultaneously, through an equipment scheduling path optimization mechanism, combined with the geographical distribution of testing projects, the technical requirements of testing items for equipment, and the current distribution of equipment, suitable testing equipment is matched, and an equipment scheduling plan is formulated, specifying the equipment's requisition time, usage sequence, transfer route, return requirements, and maintenance arrangements. To address potential resource conflicts in the initial plan (such as multiple tasks simultaneously occupying the same testing equipment or an inspector being assigned tasks exceeding their workload limit), the system dynamically resolves these conflicts by adjusting the task execution order (prioritizing tasks with higher urgency), allocating available equipment, and optimizing personnel division of labor. Ultimately, a conflict-free, efficient, and reasonable testing task allocation scheme and equipment scheduling plan are determined.

[0044] Specifically, the process of generating the targeted supplementary sampling task instruction includes: locating the anomalies in the verification results through anomaly tracing, locking down the corresponding data information of the anomaly data; and using the type characteristics of the anomalies through the supplementary sampling scheme generation mechanism to obtain the corresponding indicators of the supplementary sampling data and generate the targeted supplementary sampling task instruction.

[0045] In this embodiment, for anomalies in the verification results, the system uses anomaly tracing and location technology, combined with tracing markers from data acquisition, to pinpoint key information such as the acquisition time, location, equipment, and personnel involved in the acquisition, thus clarifying the context in which the anomalies occurred. Based on the characteristics of the anomalies (missing data, numerical deviations, logical contradictions), a supplementary data acquisition mechanism is used to determine the indicators for supplementary data acquisition (supplementing missing pipe wall thickness data, re-acquiring soil moisture data with significant deviations), acquisition methods (on-site re-acquisition, cross-validation through other testing methods), acquisition time limits, and responsible personnel. Standardized targeted supplementary data acquisition task instructions are generated and pushed to the mobile devices of the corresponding acquisition personnel, clearly informing them of the specific content, execution requirements, and completion time of the supplementary data acquisition task. This ensures that the supplementary data accurately fills the anomaly gaps, guaranteeing the integrity and accuracy of the detection data.

[0046] Specifically, the process of automatically filling in and standardizing the report content includes: extracting the corresponding report content through information extraction; filling in the report content through a report filling mechanism based on the field mapping rules of the preset report template; and checking the completeness and logical consistency of the filled content using a report integrity verification matrix to obtain a standardized test report.

[0047] In this embodiment, the matching between structured inspection data and inspection tasks is verified on the one hand, such as whether the inspection items corresponding to the inspection data are consistent with the assigned inspection tasks, whether the data collection time is within the task execution cycle, and whether the accuracy of the inspection data meets the task requirements. On the other hand, the consistency and rationality of the data are verified internally, such as whether the same pipeline parameters (pipeline diameter, wall thickness) collected from different inspection items are consistent, whether the changing trends of the inspection data and historical pipeline data are reasonable, and whether the data calculation results conform to physical laws. Through multi-dimensional cross-verification, contradictory data is eliminated and biased data is corrected to obtain comprehensive and accurate data verification results. Using the data verification results and preset report templates, the core content required for the report is extracted from the structured inspection dataset, data verification results, and task assignment scheme through information extraction technology. This includes basic project information, inspection item configuration, inspection data statistics, abnormal data processing results, equipment operating status, personnel execution status, and inspection conclusions. According to the field mapping rules of the preset report template, the extracted content is automatically filled into the corresponding positions in the report through the report filling mechanism. For example, the inspection data statistics results are filled into the inspection results section of the report, and the abnormal data processing results are filled into the quality control section of the report. Then, using the report integrity verification matrix, the system checks the completeness of the report content (whether any test results are missing or whether any exception handling instructions are missing) and logical consistency (whether the data before and after the report is contradictory and whether the conclusions match the test results). It automatically corrects problems such as missing fields and logical contradictions, and generates a standardized test report with a unified format, complete content, and rigorous logic.

[0048] Specifically, the process of obtaining the compliance report document includes: pushing the standardized testing report through report circulation and conducting hierarchical review; obtaining an approval report through intelligent revision of the report based on the review comments; a collaborative process of electronic signature based on the approval report; and obtaining the compliance report document by performing consistency verification on the electronic signature through compliance verification.

[0049] In this embodiment, after the standardized test report is generated, it is pushed to the corresponding approval node through the system's report flow function according to the preset approval process (test team leader review → technical manager approval → quality department review). After logging into the system, the reviewers can view the full text of the report and the associated test data and verification records, and provide review comments (supplementing detailed explanations of a certain test item, correcting errors in the report). Based on the review comments, the system activates the intelligent report revision function, automatically locating the content that needs to be revised and providing revision suggestions. After confirmation by relevant personnel, the report is optimized and an approved report is generated.

[0050] The electronic signature collaborative process is initiated, with testing personnel, reviewers, approvers, and company seal affixers sequentially completing the electronic signature operation through the system. The system records the signing time, identity information, and signature location of each signer in real time. Finally, compliance verification verifies the legality of the electronic signatures (e.g., whether the signer has the corresponding permissions) and consistency (whether the signature matches the report content and shows no signs of tampering), ensuring the integrity and validity of the signature information, and ultimately generating a compliant report document. Based on project archiving rules, a multi-level archiving directory is established according to project name, testing type, testing time, client name, etc., clearly defining the archiving path and storage period for various documents. The compliant report document is linked and stored with all relevant materials from the entire testing process, including raw data, structured datasets, data verification results, testing item configuration lists, task assignment plans, equipment scheduling records, targeted supplementary sampling task instructions, approval opinion records, electronic signature archives, etc., forming a complete project management file. Establish an archive retrieval mechanism that supports quick location of required archives by multiple search criteria such as project name, inspection time, inspection type, and report number. At the same time, set up hierarchical access control for archives (such as administrators having full access while ordinary employees can only view archives related to the project) to ensure the security of archive information and provide comprehensive support for pipeline maintenance, re-inspection, and historical data traceability.

[0051] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A pipeline integrity management system based on multi-detection item configuration and data verification, characterized in that, include: Data acquisition unit, data processing unit, detection item configuration and task assignment unit, data verification and report generation unit; The data acquisition unit is used to acquire multi-dimensional raw data based on pipeline inspection business needs; The data processing unit performs classification statistics and format standardization processing on the multi-dimensional raw data to obtain a structured testing dataset; it then performs permission matching by combining personnel qualification data with testing item requirements to obtain project testing team information. The device operation status data is tracked to generate a device availability status identifier. The structured detection dataset is compared and verified with preset verification rules to obtain the verification result. The detection item configuration and task assignment unit filters valid detection data based on the verification results and identifies the corresponding detection items to obtain a detection item dataset with identification features; it performs dynamic adaptation configuration of detection items based on the detection item dataset and corresponding pipeline information to obtain a multi-detection item optimized configuration list; it obtains a detection task assignment scheme and equipment scheduling plan by allocating corresponding tasks and resources; and it re-collects abnormal items in the detection item dataset to generate a targeted re-collection task instruction. The data verification and report generation unit performs cross-validation on the structured detection dataset and the detection task assignment scheme to obtain data verification results; using the data verification results and a preset report template, it automatically fills in the report content and standardizes the format to obtain a standardized detection report; based on the approval process, it performs corresponding operations on the standardized detection report to obtain a compliant report file; and uses project archiving rules to classify and archive the compliant report file to obtain a complete management archive.

2. The system according to claim 1, characterized in that, The specific process of obtaining multi-dimensional raw data based on pipeline inspection business needs includes: entering basic pipeline parameters through a PC and collecting on-site environmental data through a mobile device based on the collection category to obtain an initial raw data set; performing integrity verification on the initial raw data set, supplementing missing data, and using data collection timestamps and version identifiers to trace the source of the initial raw data to obtain compliant multi-dimensional raw data.

3. The system according to claim 1, characterized in that, The process of obtaining the structured detection dataset includes: dividing the type features of the multi-dimensional original data, performing format standardization processing based on the division results, and generating standardized subsets; establishing a mapping relationship for the standardized subsets based on preset data association rules; and encapsulating the standardized subsets based on the mapping relationship to obtain the structured detection dataset.

4. The system according to claim 1, characterized in that, The specific process for obtaining project inspection team information includes: extracting the required professional qualification types and skill level requirements based on the testing item requirements to obtain personnel qualification data; screening candidate inspection personnel who meet the qualification requirements based on the personnel qualification data to obtain a candidate pool; using the division of labor data and project priority of the candidate pool to perform personnel suitability scoring; and selecting the best combination based on the suitability scoring results to obtain project inspection team information.

5. The system according to claim 1, characterized in that, The specific process of tracking the equipment operating status data includes: obtaining equipment status data based on equipment requisition records and real-time operation monitoring data; comparing the status data with the equipment's rated parameter thresholds to determine the equipment's operating status; generating a tracking curve for the equipment's operating status based on the time-series data of equipment status changes; and generating an equipment availability status identifier based on the tracking curve for the equipment's operating status and the equipment maintenance cycle.

6. The system according to claim 1, characterized in that, The process of obtaining the verification results includes: constructing a three-dimensional verification matrix based on preset verification rules; performing point-by-point penetration verification on the structured detection dataset and marking the data of abnormal rules using an abnormal data anchoring algorithm; calculating the data anomaly density value based on the type characteristics and frequency of occurrence of the abnormal data; and generating a data verification result with an anomaly level identifier by comparing the data anomaly density value with a preset threshold and combining it with anomaly tracing data.

7. The system according to claim 1, characterized in that, The specific process of obtaining the dataset of detection items with identification features includes: based on the data verification results, filtering detection data that conforms to the verification rules through a data purification mechanism; using the detection item identifiers of the detection data to identify the technical parameters and execution standards of the detection items accordingly, generating a binding relationship between the detection items and the data; and using the binding relationship to add data fingerprints and traceability codes to obtain a traceable identification chain.

8. The system according to claim 1, characterized in that, The specific process of dynamically adapting and configuring the test items includes: constructing a test item adaptation decision tree based on the pipeline project type and test standards, and obtaining the test item priority and combination rules; dynamically filtering the test items to obtain an initial configuration list; and performing a closed-loop verification of the adaptability based on the initial configuration list and the actual test capabilities of the project to obtain an optimized configuration list for multiple test items.

9. The system according to claim 1, characterized in that, The specific process of obtaining the testing task assignment scheme and equipment scheduling plan includes: based on the optimized configuration list of multiple testing items, the work modules and time nodes of the testing items are broken down through a task decomposition mechanism; based on the personnel skill map and workload saturation in the project inspection team information, the work modules are assigned to the corresponding inspection personnel through personnel-task matching rules to obtain a preliminary task assignment scheme; suitable testing equipment is matched through an equipment scheduling path optimization mechanism to formulate an equipment scheduling plan; based on the preliminary task assignment scheme and the equipment scheduling plan, resource conflicts are dynamically resolved, and the task execution order and equipment allocation scheme are adjusted to obtain the testing task assignment scheme and equipment scheduling plan.

10. The system according to claim 1, characterized in that, The specific process of generating the targeted supplementary sampling task instruction includes: locating the anomalies in the verification results through anomaly tracing, locking down the corresponding data information of the anomaly data; and using the type characteristics of the anomalies through the supplementary sampling scheme generation mechanism to obtain the corresponding indicators of the supplementary sampling data and generate the targeted supplementary sampling task instruction.

11. The system according to claim 1, characterized in that, The specific process of automatically filling in and standardizing the report content includes: extracting the corresponding report content through information extraction; filling in the report content through a report filling mechanism based on the field mapping rules of the preset report template; and checking the completeness and logical consistency of the filled content using a report integrity verification matrix to obtain a standardized test report.

12. The system according to claim 1, characterized in that, The specific process for obtaining the compliance report includes: pushing the standardized testing report through report circulation and conducting hierarchical review; obtaining an approved report through intelligent revision of the report based on the review comments; a collaborative process of electronic signature based on the approved report; and obtaining the compliance report by performing consistency verification on the electronic signature through compliance verification.

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