A storage method and system of a hydropower engineering quality acceptance form

Abstract

The application provides a storage method and system for a hydropower engineering quality acceptance form, belongs to the field of engineering data storage, and comprises the following steps: S1, obtaining multi-source data to be accepted for a hydropower engineering, performing standardization processing on the multi-source data to be accepted, and obtaining standardized data to be accepted; S2, generating dynamic acceptance items based on a quality acceptance evaluation standard of the hydropower engineering; S3, converting and performing data processing on the standardized data to be accepted, matching the final processed data with the dynamic acceptance items, and generating a dynamic quality acceptance form; and S4, archiving and storing the dynamic quality acceptance form to form a hydropower engineering quality acceptance form database. The application realizes accurate matching of structured data and acceptance items by performing standardization processing on multi-source data to be accepted and constructing dynamic acceptance items based on engineering characteristics and a quality acceptance evaluation standard, and improves the intelligent level and data management efficiency of hydropower engineering quality acceptance.

Description

Technical Field

[0001] This invention relates to the field of engineering data storage technology, and in particular to a method and system for storing quality acceptance forms for hydropower projects. Background Technology

[0002] With the continuous expansion of hydropower project construction scale and the increasing technical requirements, quality acceptance forms play a crucial role as an important tool for project quality control. Traditional management of hydropower project quality acceptance forms mainly relies on paper documents and manual operations, which have obvious technical limitations in data processing, form generation, and storage management.

[0003] Existing technologies typically employ manual data entry and simple digitization tools for data processing, lacking the ability to standardize and process multi-source heterogeneous data uniformly, making it difficult to effectively integrate acceptance data from different systems and formats. Regarding acceptance item management, traditional methods primarily use fixed form templates, failing to dynamically adjust according to specific project characteristics and real-time conditions, resulting in a lack of targeted acceptance processes. In data matching, existing methods rely mainly on manual experience and simple rules, leading to low matching efficiency and a high risk of errors. Finally, in storage management, the lack of unified storage standards and intelligent management mechanisms results in low efficiency in form retrieval and data utilization.

[0004] Chinese invention patent CN112163796A discloses a management system for digital engineering record forms. This system includes a cloud server and a client. The cloud server has a form storage module for storing pre-compiled digital engineering record forms. Each form includes a form name, form code, form filling instructions, and data groups. Each data group contains multiple fields such as data name, data type, and recording method. The processor generates the corresponding content based on external input data and outputs the form. However, its form structure still uses a pre-compiled, fixed format, lacking dynamic adjustment capabilities and exhibiting technical deficiencies in adaptive matching. Summary of the Invention

[0005] In view of this, the present invention proposes a storage method and system for hydropower project quality acceptance forms, which can solve the problem that forms cannot be dynamically adjusted in the prior art, and improve the intelligence level and data management efficiency of hydropower project quality acceptance.

[0006] The technical solution of this invention is implemented as follows: This invention provides a method for storing quality acceptance forms for hydropower projects, including the following steps:

[0007] S1. Obtain multi-source data of hydropower projects to be accepted, and standardize the multi-source data to be accepted to obtain standardized data to be accepted.

[0008] S2. Generate dynamic acceptance items based on the quality acceptance evaluation standards for hydropower projects;

[0009] S3. Convert and process the standardized data to be accepted to generate the final processed data, and match the final processed data with the dynamic acceptance items to generate a dynamic quality acceptance form.

[0010] S4. Archive and store the dynamic quality acceptance forms to form a database of quality acceptance forms for hydropower projects.

[0011] Based on the above technical solutions, preferably, the multi-source data to be accepted includes structured data, semi-structured data, and unstructured data, wherein,

[0012] Structured data includes inspection reports and test data for hydropower projects;

[0013] The semi-structured data consists of construction record sheets for hydropower projects;

[0014] Unstructured data includes audio and video materials, image materials, and document materials related to hydropower projects.

[0015] Based on the above technical solutions, preferably, step S2 specifically includes:

[0016] S21. Extract features from the standardized data to be accepted to obtain engineering physical features, engineering technical features and engineering time series features;

[0017] S22. Construct an acceptance item structure based on the quality acceptance evaluation standard for hydropower projects, and determine the acceptance items for hydropower projects according to the physical characteristics, technical characteristics, and temporal characteristics of the projects, as well as the acceptance item structure. The acceptance item structure includes project category layer items, sub-project layer items, and inspection index layer items.

[0018] S23. Based on the real-time progress and quality feedback of hydropower projects, the acceptance items of hydropower projects are dynamically adjusted to form dynamic acceptance items.

[0019] Based on the above technical solutions, preferably, step S22 specifically includes:

[0020] By performing semantic analysis on the quality acceptance evaluation criteria, the clauses of the quality acceptance evaluation criteria are identified;

[0021] Extract the key elements of each quality acceptance evaluation standard clause, and establish a mapping relationship between the quality acceptance items and the physical characteristics, technical characteristics and temporal characteristics of the project based on the key elements, thus forming a hierarchical structure of acceptance items;

[0022] The quality acceptance category is determined according to the type of hydropower project, and the specific sub-projects are determined according to the physical and technical characteristics of the project.

[0023] Cluster analysis was used to determine the project type and acceptance focus of the data to be inspected based on the engineering physical characteristics, engineering technical characteristics and engineering time sequence characteristics.

[0024] The project type and acceptance focus of the data to be inspected are matched with the quality acceptance category and sub-projects and the item template respectively to obtain the acceptance items of the hydropower project.

[0025] Based on the above technical solutions, the preferred, dynamically adjustable logic is as follows:

[0026] ;

[0027] in, This indicates the adjusted weight of the acceptance items. This indicates the original weight of the acceptance item. Indicates the adjustment factor. This represents the risk assessment value. This indicates the threshold standard for risk assessment. This represents the hyperbolic tangent function.

[0028] Based on the above technical solutions, preferably, step S3 specifically includes:

[0029] The standardized data to be accepted is converted in data type and standardized in format to obtain structured data;

[0030] Structured data is identified and grouped to obtain multiple data set sets, and the data in each data set set is merged to obtain fused data;

[0031] Construct the dependencies and constraints between the fused data, and perform cluster analysis on the fused data based on the dependencies and constraints to generate the final processed data;

[0032] The final processed data and dynamic acceptance items are semantically transformed to obtain data semantic vectors and acceptance item semantic vectors. The cosine similarity calculation method is used to calculate the similarity between the data semantic vectors and acceptance item semantic vectors.

[0033] A similarity threshold is set. When the similarity between the semantic vector of the data and the semantic vector of the acceptance item exceeds the threshold, the match is considered successful. Then, based on the successfully matched data-item pair, a complete basic form structure is constructed according to the standard format of the hydropower engineering quality acceptance form, including basic information in the header, acceptance content in the form, and confirmation signature in the footer.

[0034] The successfully matched data is formatted and filled in according to the acceptance item requirements and display rules to form a standardized acceptance form.

[0035] Based on the above technical solutions, preferably, the identification and grouping of structured data specifically includes:

[0036] For each structured data item, feature extraction is performed to obtain data identification features, semantic features, spatiotemporal features, and structural features;

[0037] A multidimensional correlation identification algorithm is used to identify the relationships between structured data based on data identification features, semantic features, spatiotemporal features, and structural features, thereby obtaining the correlation relationships between structured data.

[0038] Based on the correlation, a clustering algorithm is used to group the structured data into multiple data groups, and each data group is then organized in a structured manner to generate a set of data groups.

[0039] Based on the above technical solutions, preferably, the logic for constructing the dependency and constraint relationships between fused data is as follows:

[0040] ;

[0041] ;

[0042] in, This indicates the dependency relationship between data A and data B. This indicates the logical dependency between data A and data B. This indicates the business dependency relationship between data A and data B. Represents a set of constraint relations. and These represent the two data elements involved in the constraint relationship judgment. This indicates whether two data elements satisfy predefined constraints. When it returns True, it means that there is a valid constraint relationship between the two data elements.

[0043] Furthermore, step S4 specifically includes:

[0044] The dynamic quality acceptance form is standardized to form a standardized quality acceptance form.

[0045] Set up a database of quality acceptance forms for hydropower projects. The database includes a data storage layer, a business logic layer, and a presentation layer. The data storage layer is used for persistent data storage, the business logic layer is used for storage management and service control, and the presentation layer is used for user interaction and interface services.

[0046] Standardized quality acceptance forms are sent to the hydropower project quality acceptance form database for hierarchical storage.

[0047] On the other hand, the present invention provides a storage system for hydropower project quality acceptance forms, applied to the storage method for hydropower project quality acceptance forms as described above, including:

[0048] The data processing module is used to acquire multi-source data of hydropower projects to be accepted, and to standardize the multi-source data to be accepted to obtain standardized data to be accepted.

[0049] The acceptance item generation module is used to generate dynamic acceptance items based on the quality acceptance evaluation standards for hydropower projects.

[0050] The quality acceptance form generation module converts standardized data to be accepted into structured data based on mapping rules, and matches the structured data with dynamic acceptance items to generate a dynamic quality acceptance form.

[0051] The storage module is used to archive and store dynamic quality acceptance forms, forming a database of quality acceptance forms for hydropower projects.

[0052] The storage method and system for quality acceptance forms of hydropower projects of the present invention have the following advantages over the prior art:

[0053] (1) By adaptively standardizing the structured, semi-structured and unstructured multi-source data to be accepted, standardized data to be accepted is generated, and dynamic acceptance items are constructed based on engineering characteristics and quality acceptance evaluation standards, so as to achieve accurate matching between structured data and acceptance items, thereby improving the intelligent level of hydropower engineering quality acceptance and data management efficiency.

[0054] (2) By establishing a dynamic adjustment mechanism for the weight of acceptance items based on risk assessment, the importance weight of acceptance items is automatically adjusted, and the hyperbolic tangent function is used to avoid data instability, thus realizing the transformation from passive quality inspection to proactive quality prevention and improving the pertinence and effectiveness of quality acceptance.

[0055] (3) By using a multi-dimensional matching algorithm based on semantic similarity, structural similarity and contextual similarity, a comprehensive evaluation of the matching relationship between data and acceptance items is achieved, accurately identifying the correspondence between data and acceptance items, and improving the accuracy and completeness of form generation. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 A flowchart illustrating a method for storing a quality acceptance form for hydropower projects according to the present invention;

[0058] Figure 2 This is a flowchart illustrating the generation of dynamic acceptance entries in a method for storing quality acceptance forms for hydropower projects according to the present invention. Detailed Implementation

[0059] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0060] like Figure 1 As shown, the present invention provides a method for storing a quality acceptance form for hydropower projects, including the following steps:

[0061] S1. Obtain multi-source data for the hydropower project to be inspected and accepted, and standardize the multi-source data to be inspected and accepted to obtain standardized data for inspection and acceptance; the multi-source data to be inspected and accepted includes structured data, semi-structured data, and unstructured data.

[0062] Structured data includes inspection reports and test data for hydropower projects;

[0063] The semi-structured data consists of construction record sheets for hydropower projects;

[0064] Unstructured data includes audio and video materials, image materials, and document materials related to hydropower projects.

[0065] In one embodiment of the present invention, the standardization process includes data denoising and outlier processing, wherein the data denoising employs a combination of median filtering and mean filtering. Let the original multi-source data sequence to be accepted be... ,in, This represents the original multi-source data sequence awaiting acceptance. This represents the first data point. This represents the second data point. This represents the nth data point, after noise reduction:

[0066] ;

[0067] ;

[0068] ;

[0069] ;

[0070] in, This is the result of median filtering. This is the result of mean filtering. This is the weighting coefficient (usually taken as 0.6). This represents the standard deviation of the original multi-source data sequence awaiting acceptance. This represents the total number of data points in the original multi-source data sequence awaiting acceptance. This represents the index of the original multi-source data sequence to be accepted. This represents the i-th data point in the original multi-source data sequence to be accepted. This represents the mean of the original multi-source data sequence to be accepted. This indicates the data distribution skewness of the original multi-source data sequence to be accepted.

[0071] Outlier handling employs the 3σ criterion for outlier identification, marking data points exceeding the mean ± 3 standard deviations as outliers. A tiered processing strategy is used for identified outliers: minor outliers are corrected using interpolation to maintain data continuity; severe outliers are marked as missing values ​​and handled manually.

[0072] By adaptively adjusting the noise reduction coefficient, we can effectively cope with different distribution characteristics of the data to be accepted and improve the noise reduction effect. At the same time, we adopt a hierarchical processing strategy for outliers to ensure the reliability of the data to be accepted.

[0073] like Figure 2 As shown, S2 generates dynamic acceptance items based on the quality acceptance evaluation standards for hydropower projects;

[0074] Specifically, step S2 includes:

[0075] S21. Extract features from the standardized data to be accepted to obtain engineering physical features, engineering technical features and engineering time series features;

[0076] S22. Construct an acceptance item structure based on the quality acceptance evaluation standard for hydropower projects, and determine the acceptance items for hydropower projects according to the physical characteristics, technical characteristics, and temporal characteristics of the projects, as well as the acceptance item structure. The acceptance item structure includes project category layer items, sub-project layer items, and inspection index layer items.

[0077] S23. Based on the real-time progress and quality feedback of hydropower projects, the acceptance items of hydropower projects are dynamically adjusted to form dynamic acceptance items.

[0078] Understandably, the physical characteristics of an engineering project include quantitative parameters related to the project scale, such as installed capacity, dam height, and reservoir capacity. The technical characteristics of an engineering project mainly reflect the technical implementation methods and complexity of the project, including key technical parameters such as construction process characteristics, equipment type configuration, and technical complexity assessment. The temporal characteristics of an engineering project focus on the time dimension of the project construction, including time-related information such as construction cycle planning, construction phase division scheme, and key node time arrangement.

[0079] Furthermore, the logic for dynamic adjustment is as follows:

[0080] ;

[0081] in, This indicates the adjusted weight of the acceptance items. This indicates the original weight of the acceptance item. Indicates the adjustment factor. This represents the risk assessment value. This indicates the threshold standard for risk assessment. This represents the hyperbolic tangent function.

[0082] Understandably, the range of the hyperbolic tangent function is limited to the interval (-1,1), which means that no matter how the risk assessment value changes, the adjustment range is strictly controlled within a bounded range, avoiding instability caused by excessive weight adjustment. This indicates the deviation between the current risk assessment value and the preset threshold. When the deviation is positive, it indicates that the current risk level of the hydropower project has exceeded the safety threshold, and the weight of the acceptance items needs to be increased to strengthen quality control; conversely, when... When the deviation is negative, it indicates that the current risk level is within an acceptable range, and the weight of the acceptance items can be appropriately reduced to optimize resource allocation. The absolute value of the deviation reflects the severity of the risk deviation; a larger deviation means a more significant weight adjustment is needed. Adjustment coefficient Multiple factors need to be considered comprehensively, including project type, historical experience, and risk characteristics. A suitable numerical range is typically determined through a combination of historical data analysis and expert experience. Threshold standards for risk assessment. The determination is usually made through statistical analysis of historical engineering data, combined with expert evaluation and industry standards.

[0083] By establishing a dynamic adjustment mechanism for the weight of acceptance items based on risk assessment, the importance weight of acceptance items is automatically adjusted, and the hyperbolic tangent function is used to avoid data instability. This realizes the transformation from passive quality inspection to proactive quality prevention and improves the efficiency of quality acceptance.

[0084] Furthermore, step S22 specifically includes:

[0085] By performing semantic analysis on the quality acceptance evaluation criteria, the clauses of the quality acceptance evaluation criteria are identified;

[0086] Key elements of each quality acceptance evaluation standard clause are extracted. Based on these key elements, a mapping relationship is established between the quality acceptance items and the physical, technical, and temporal characteristics of the project, forming a hierarchical structure of acceptance items. The logic is as follows:

[0087] ;

[0088] ;

[0089] in, This indicates a clause identifier identified from the quality acceptance and evaluation standard document. This indicates that the text fragments following a predefined regular expression pattern will be searched within the complete content of the quality acceptance evaluation standard document. This indicates a predefined regular expression pattern. This indicates the complete content of the quality acceptance and evaluation standard document. This indicates the key elements of the acceptance items extracted from the terms and conditions. This indicates the use of standard templates to extract key elements from the clause content. This indicates the specific terms and conditions. This represents a standard template;

[0090] The quality acceptance category is determined based on the type of hydropower project, and the specific sub-projects are determined based on the physical and technical characteristics of the project. The logic is as follows:

[0091] ;

[0092] ;

[0093] in, The weighted score represents the engineering category. This indicates the investment amount for this type of project. This indicates the total investment amount for the hydropower project. This represents the weighting coefficient for the proportion of investment. Indicates the technical complexity score. The maximum benchmark value representing technical complexity, Weighting coefficients representing technical complexity This indicates the risk assessment score. The weighting coefficients representing risk. Indicates a specific category of sub-project. This represents a classification model function based on machine learning. Represents the engineering physical characteristic vector. Represents the feature vector of engineering technology. This represents a set of historical engineering experience data;

[0094] Based on engineering physical characteristics, engineering technical characteristics, and engineering time-series characteristics, the K-means method is used to determine the engineering type and acceptance focus of the data to be accepted for quality inspection.

[0095] The project type and acceptance focus of the data to be inspected are matched with the quality acceptance category and sub-projects, respectively, against the item template to obtain the acceptance items for hydropower projects. The item template is as follows:

[0096] ;

[0097] The matching logic is as follows:

[0098] ;

[0099] in, This indicates the selected best-matching entry template. This represents the similarity calculation function. This represents the feature vector of the current project. The feature vector representing the entry template. This represents the feature dimension index in the feature vector. This represents the total number of dimensions of the feature vector. This represents the weight coefficient of the j-th feature dimension. This represents the feature value of the current hydropower project in the j-th feature dimension. This represents the feature value of the entry template in the j-th feature dimension.

[0100] Understandably, the project type classification includes information such as pumped storage, conventional hydropower, and small hydropower, as well as geographical location characteristics such as geological conditions, climate features, and transportation convenience. The item templates are a standardized acceptance item library built upon historical engineering experience and quality acceptance evaluation standards. Each acceptance item template contains typical acceptance requirements, inspection methods, and qualification standards for a specific project type and specific sub-project. The determination of technical complexity employs a multi-dimensional evaluation method, comprehensively considering multiple dimensions such as the degree of technological innovation, construction difficulty level, equipment precision requirements, process control difficulty, and quality control complexity. Each dimension is scored on a scale of 1-10, and a weighted average is used to obtain the overall complexity score. The scoring process combines expert review, historical data analysis, and industry benchmark comparisons.

[0101] The machine learning-based classification model uses supervised learning, taking engineering experience datasets as input and corresponding sub-project classifications as labels. Through training, the mapping relationship between features and classifications is obtained. It is based on the engineering physical characteristics, engineering technical characteristics, and historical engineering experience data sets to determine the applicable sub-project categories for the current project.

[0102] Specifically, the calculation formula for the K-means method is as follows:

[0103] ;

[0104] in, This represents the results of the cluster analysis. Represents the cluster index. This indicates the preset number of clusters. Sample data representing clusters, Let q represent the sample set of the q-th cluster. This represents the position vector of the q-th cluster center.

[0105] S3. Convert and process the standardized data to be accepted to generate the final processed data, and match the final processed data with the dynamic acceptance items to generate a dynamic quality acceptance form.

[0106] Specifically, step S3 includes:

[0107] The standardized data to be accepted undergoes data type conversion and format standardization to obtain structured data; the data type conversion includes unified conversion of numeric, text, date, etc.

[0108] Structured data is identified and grouped to obtain multiple data set sets. The data from each data set is then merged to obtain fused data. The fusion logic is as follows:

[0109] ;

[0110] in, Indicates data fusion. Indicates the index of the data source. This indicates the total number of data sources participating in the fusion. This represents the weight coefficient of the y-th data source. This represents the original data value of the y-th data source;

[0111] Construct the dependencies and constraints between the fused data, and perform cluster analysis on the fused data based on the dependencies and constraints to generate the final processed data;

[0112] The final processed data and dynamic acceptance items are semantically transformed separately to obtain data semantic vectors. and acceptance item semantic vector The similarity between the data semantic vector and the acceptance item semantic vector is calculated using the cosine similarity method; among them, Represents a data semantic vector. This represents a pre-trained semantic embedding model function. This represents a string concatenation function. Indicates the data name, Represents data type, Indicates the unit of measurement. Represents the numerical value of the data. Indicates contextual information, Represents the semantic vector of the acceptance item. Indicates the title of the entry. This indicates the entry description. Indicates acceptance requirements. The judgment criteria are expressed, and the calculation formula is as follows:

[0113] ;

[0114] in, This represents the similarity between the data semantic vector and the acceptance item semantic vector. Represents a data semantic vector. Represents the semantic vector of the acceptance item. The Euclidean norm of a vector;

[0115] A similarity threshold is set. When the similarity between the semantic vector of the data and the semantic vector of the acceptance item exceeds the threshold, a successful match is considered. Then, based on the successfully matched data-item pairs, a complete basic form structure is constructed according to the standard format of the hydropower engineering quality acceptance form. ,in, This represents the basic structure of the form. This indicates the basic information in the table header. This indicates the content received by the table. This indicates confirmation and signature at the end of the form;

[0116] The successfully matched data is formatted and populated according to the acceptance criteria and display rules to form a standardized acceptance form. , This indicates the contents of the acceptance form. This represents the formatting processing function. This indicates data that was successfully matched. Indicates an entry template. This indicates the display rules.

[0117] Furthermore, the identification and grouping of structured data specifically includes:

[0118] For each structured data set, feature extraction is performed to obtain data identification features, semantic features, spatiotemporal features, and structural features;

[0119] A multidimensional correlation identification algorithm is used to identify the relationships between structured data based on data identification features, semantic features, spatiotemporal features, and structural features, thereby obtaining the correlation relationships between structural data. Among them, data identification features include engineering location identifiers, equipment numbers, component codes, station information, and coordinate information; semantic features include data names, descriptions, unit information, and keyword information; spatiotemporal features include data acquisition time, effective time range, spatial location coordinates, and engineering area identifiers; structural features include data type, numerical range, precision level, data format, and source identifier.

[0120] Based on the correlation, a clustering algorithm is used to group the structured data into multiple data groups, and each data group is then organized in a structured manner to generate a set of data groups.

[0121] In one embodiment of the present invention, the multidimensional relevance recognition algorithm specifically includes identifier matching recognition, semantic similarity recognition, spatiotemporal correlation recognition, and data structure similarity recognition.

[0122] Specifically, identifier matching identification uses the project location identifiers, equipment numbers, component codes, and station numbers of two structured data sets. When the identifiers match completely or an inclusion relationship exists, the two data items are determined to have an object association. Semantic similarity identification converts the names, descriptions, and keywords of structured data into semantic vectors and calculates the cosine similarity between the semantic vectors. When the similarity is greater than a preset threshold, the two data items are determined to have a semantic association. Spatiotemporal association identification compares the timestamps and spatial coordinates of two structured data sets. When the time interval is within a preset time window and the spatial distance is within a preset range, the two structured data sets are determined to have a spatiotemporal association. The time window is determined based on the project construction cycle, and the spatial range is determined based on the actual hydropower project. Data structure similarity identification compares the data types, units, numerical ranges, and precision levels of two structured data sets. When the data types are the same, the units are the same or convertible, the numerical ranges overlap, and the precision levels are comparable, the two data items are determined to have a structural association.

[0123] In one embodiment of the present invention, based on correlation relationships, a clustering algorithm is used to group structured data to obtain multiple data groups, and each data group is then structured to generate a data group set, specifically including:

[0124] All structured data items are treated as nodes, and edges are established between related data items to form a data correlation graph;

[0125] The density-based clustering algorithm DBSCAN is used to cluster the data correlation map, and a neighborhood radius and a minimum number of points threshold are set; the neighborhood radius is determined according to the structural correlation, and the minimum number of points threshold is not less than 2.

[0126] Clustering algorithms divide a data correlation graph into multiple connected subgraphs. Each connected subgraph corresponds to a data group, which contains multiple structured data describing the same engineering object or the same quality characteristic.

[0127] Assign a unique group number to each data group, extract the common features of the structured data within the data group to generate group identification information, including engineering object identification, quality feature type, time range, and spatial range;

[0128] Analyze the source, accuracy, timeliness, and other attributes of each data item within the data group, and establish the primary and secondary relationships, temporal relationships, and accuracy level relationships among the data items;

[0129] Each data group consists of three parts: group identifier information, a list of data items within the group, and a description of the relationships between the data items, forming a data group set. The data items within the group retain their original complete information, such as values, units, sources, and timestamps.

[0130] In one embodiment of the present invention, the logic for constructing the dependency and constraint relationships between fused data is as follows:

[0131] ;

[0132] ;

[0133] in, This indicates the dependency relationship between data A and data B. This indicates the logical dependency between data A and data B. This indicates the business dependency relationship between data A and data B. Represents a set of constraint relations. and These represent the two data elements involved in the constraint relationship judgment. This indicates whether two data elements satisfy predefined constraints. When it returns True, it means that there is a valid constraint relationship between the two data elements.

[0134] Understandably, data dependencies return true when there is any form of dependency between two pieces of data, and false otherwise. Logical dependencies mainly refer to dependencies based on mathematical logic, causal relationships, or inference rules, such as a test result that can only be obtained after the relevant equipment is installed. Business dependencies refer to dependencies based on engineering practices, industry standards, or management processes, such as an acceptance test that can only be conducted after the preceding acceptance test has passed.

[0135] Logical dependencies can usually be automatically identified and established through mathematical models or expert systems, while business dependencies need to be defined in conjunction with specific engineering backgrounds and management systems.

[0136] S4. Archive and store the dynamic quality acceptance forms to form a database of quality acceptance forms for hydropower projects.

[0137] Specifically, step S4 includes:

[0138] The dynamic quality acceptance form is standardized to form a standardized quality acceptance form.

[0139] Set up a database of quality acceptance forms for hydropower projects. The database includes a data storage layer, a business logic layer, and a presentation layer. The data storage layer is used for persistent data storage, the business logic layer is used for storage management and service control, and the presentation layer is used for user interaction and interface services.

[0140] Standardized quality acceptance forms are sent to the hydropower project quality acceptance form database for hierarchical storage.

[0141] Understandably, form standardization includes form format standardization, data field standardization, and metadata improvement. Form format standardization ensures that all forms conform to a unified format standard, including the uniform processing of visual presentation elements such as page layout, font specifications, and table structure. Data field standardization applies unified naming conventions and data type conversions to various data fields in the form, ensuring consistency in data structure for forms generated from different sources and at different times. The metadata improvement process supplements each form with complete metadata information, including key attribute information such as creation time, creator, project information, version number, and approval status.

[0142] The data storage layer includes a form data table to store the main content of the form, a metadata table to store the attribute information of the form, an index table to provide fast retrieval capabilities, and a log table to record the system operation history; the business logic layer includes basic operations such as form insertion, updating, and deletion, a retrieval service module to provide multi-dimensional query and retrieval functions, an access control module to ensure data access security, and a version management module to handle form version control and historical tracing; the presentation layer is the user interface, including a user interface module to provide an intuitive operation interface, an API interface module to provide data exchange interfaces for external systems, and a report generation module to support the automatic generation of various statistical reports.

[0143] This invention generates standardized acceptance data by adaptively standardizing multi-source data of structured, semi-structured and unstructured data to be accepted, and constructs dynamic acceptance items based on engineering characteristics and quality acceptance evaluation standards, thereby achieving accurate matching between structured data and acceptance items, and improving the intelligence level and data management efficiency of hydropower project quality acceptance.

[0144] In one embodiment of the present invention, the tiered storage adopts a three-tiered storage strategy of hot storage, warm storage, and cold storage. The hot storage layer is used to store forms generated within the last three months and frequently accessed form data, employing high-performance solid-state storage devices. The warm storage layer is used to store forms within the medium term (three months to two years) and medium-frequency accessed form data. The cold storage layer is used to store forms older than two years and infrequently accessed form data, employing large-capacity, low-cost storage devices. Through access pattern analysis and lifecycle management, the tiered storage strategy achieves optimized allocation of storage resources and effective cost control.

[0145] In one embodiment of the present invention, the storage method for the hydropower project quality acceptance form further includes a multi-replica storage mechanism. The primary replica is stored in a local high-performance storage system, handling daily read and write operations. The backup replica is stored off-site, periodically synchronizing the primary replica data to a remote storage database. The historical replica employs an incremental backup strategy, periodically saving historical versions of the data to support point-in-time recovery and historical query needs. The multi-replica storage mechanism, through data consistency checks and automatic failover, ensures rapid switching to the backup replica in the event of a primary replica failure, guaranteeing the continuous availability of the storage database while ensuring the security and traceability of hydropower project data.

[0146] This invention also provides a storage system for hydropower project quality acceptance forms, which has been applied to the storage method for hydropower project quality acceptance forms as described above, including:

[0147] The data processing module is used to acquire multi-source data of hydropower projects to be accepted, and to standardize the multi-source data to be accepted to obtain standardized data to be accepted.

[0148] The acceptance item generation module is used to generate dynamic acceptance items based on the quality acceptance evaluation standards for hydropower projects.

[0149] The quality acceptance form generation module converts standardized data to be accepted into structured data based on mapping rules, and matches the structured data with dynamic acceptance items to generate a dynamic quality acceptance form.

[0150] The storage module is used to archive and store dynamic quality acceptance forms, forming a database of quality acceptance forms for hydropower projects.

[0151] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for storing quality acceptance forms for hydropower projects, characterized in that: Includes the following steps: S1. Obtain multi-source data of hydropower projects to be accepted, and standardize the multi-source data to be accepted to obtain standardized data to be accepted. S2. Generate dynamic acceptance items based on the quality acceptance evaluation standards for hydropower projects; Step S2 specifically includes: S21. Extract features from the standardized data to be accepted to obtain engineering physical features, engineering technical features and engineering time series features; S22. Construct an acceptance item structure based on the quality acceptance evaluation standard for hydropower projects, and determine the acceptance items for hydropower projects according to the physical characteristics, technical characteristics, and temporal characteristics of the projects, as well as the acceptance item structure. The acceptance item structure includes project category layer items, sub-project layer items, and inspection index layer items. S23. Based on the real-time progress and quality feedback of hydropower projects, the acceptance items of hydropower projects are dynamically adjusted to form dynamic acceptance items. The logic for dynamic adjustment is as follows: ； in, This indicates the adjusted weight of the acceptance items. This indicates the original weight of the acceptance item. Indicates the adjustment factor. This represents the risk assessment value. This indicates the threshold standard for risk assessment. Represents the hyperbolic tangent function; S3. Convert and process the standardized data to be accepted to generate the final processed data, and match the final processed data with the dynamic acceptance items to generate a dynamic quality acceptance form. S4. Archive and store the dynamic quality acceptance forms to form a database of quality acceptance forms for hydropower projects.

2. The method for storing a hydropower project quality acceptance form as described in claim 1, characterized in that: The multi-source data to be accepted includes structured data, semi-structured data, and unstructured data, among which... Structured data includes inspection reports and test data for hydropower projects; The semi-structured data consists of construction record sheets for hydropower projects; Unstructured data includes audio and video materials, image materials, and document materials related to hydropower projects.

3. The method for storing a hydropower project quality acceptance form as described in claim 1, characterized in that: Step S22 specifically includes: By performing semantic analysis on the quality acceptance evaluation criteria, the clauses of the quality acceptance evaluation criteria are identified; Extract the key elements of each quality acceptance evaluation standard clause, and establish a mapping relationship between the quality acceptance items and the physical characteristics, technical characteristics and temporal characteristics of the project based on the key elements, thus forming a hierarchical structure of acceptance items; The quality acceptance category is determined according to the type of hydropower project, and the specific sub-projects are determined according to the physical and technical characteristics of the project. Cluster analysis was used to determine the project type and acceptance focus of the data to be inspected based on the engineering physical characteristics, engineering technical characteristics and engineering time sequence characteristics. The project type and acceptance focus of the data to be inspected are combined with the quality acceptance category and sub-projects, and matched with the item template to obtain the acceptance items for hydropower projects.

4. The method for storing a hydropower project quality acceptance form as described in claim 1, characterized in that: Step S3 specifically includes: The standardized data to be accepted is converted in data type and standardized in format to obtain structured data; Structured data is identified and grouped to obtain multiple data set sets, and the data in each data set set is merged to obtain fused data; Construct the dependencies and constraints between the fused data, and perform cluster analysis on the fused data based on the dependencies and constraints to generate the final processed data; The final processed data and dynamic acceptance items are semantically transformed to obtain data semantic vectors and acceptance item semantic vectors. The cosine similarity calculation method is used to calculate the similarity between the data semantic vectors and acceptance item semantic vectors. A similarity threshold is set. When the similarity between the semantic vector of the data and the semantic vector of the acceptance item exceeds the threshold, the match is considered successful. Then, based on the successfully matched data-item pair, a complete basic form structure is constructed according to the standard format of the hydropower engineering quality acceptance form, including basic information in the header, acceptance content in the form, and confirmation signature in the footer. The successfully matched data is formatted and filled in according to the acceptance item requirements and display rules to form a standardized acceptance form.

5. The method for storing a hydropower project quality acceptance form as described in claim 4, characterized in that: The identification and grouping of structured data specifically includes: For each structured data item, feature extraction is performed to obtain data identification features, semantic features, spatiotemporal features, and structural features; A multidimensional correlation identification algorithm is used to identify the relationships between structured data based on data identification features, semantic features, spatiotemporal features, and structural features, thereby obtaining the correlation relationships between structured data. Based on the correlation, a clustering algorithm is used to group the structured data into multiple data groups, and each data group is then organized in a structured manner to generate a set of data groups.

6. The method for storing a hydropower project quality acceptance form as described in claim 5, characterized in that: The logic for constructing the dependencies and constraints between the fused data is as follows: ； ； in, This indicates the dependency relationship between data A and data B. This indicates the logical dependency between data A and data B. This indicates the business dependency relationship between data A and data B. Represents a set of constraint relations. and These represent the two data elements involved in the constraint relationship judgment. This indicates whether two data elements satisfy predefined constraints. When it returns True, it means that there is a valid constraint relationship between the two data elements.

7. The method for storing a hydropower project quality acceptance form as described in claim 1, characterized in that: Step S4 specifically includes: The dynamic quality acceptance form is standardized to form a standardized quality acceptance form. Set up a database of quality acceptance forms for hydropower projects. The database includes a data storage layer, a business logic layer, and a presentation layer. The data storage layer is used for persistent data storage, the business logic layer is used for storage management and service control, and the presentation layer is used for user interaction and interface services. Standardized quality acceptance forms are sent to the hydropower project quality acceptance form database for hierarchical storage.

8. A storage system for quality acceptance forms for hydropower projects, characterized in that, A method for storing the quality acceptance form for hydropower projects as described in any one of claims 1-7, comprising: The data processing module is used to acquire multi-source data of hydropower projects to be accepted, and to standardize the multi-source data to be accepted to obtain standardized data to be accepted. The acceptance item generation module is used to generate dynamic acceptance items based on the quality acceptance evaluation standards for hydropower projects. The quality acceptance form generation module converts standardized data to be accepted into structured data based on mapping rules, and matches the structured data with dynamic acceptance items to generate a dynamic quality acceptance form. The storage module is used to archive and store dynamic quality acceptance forms, forming a database of quality acceptance forms for hydropower projects.

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