Ship inspection data processing method and device based on edge calculation and medium

Abstract

The invention discloses a ship inspection data processing method and device based on edge computing and a medium, and relates to the technical field of information retrieval, and the method comprises the steps: in an edge computing node, carrying out consistency analysis and standardized verification on low-code configuration data of a current ship inspection task, generating a preliminary data set, converting the preliminary data set into a field configuration set, a view configuration set and a certificate template configuration set through configuration driving structured mapping operation; and mapping the acquired ship multi-modal test data into an original data set corresponding to the field configuration set, adding a time identifier and a space identifier to each acquisition record in the original data set to form a space-time labeling result set, and recombining the space-time labeling result set into structured test data. According to the method, consistency analysis and standardization verification are completed on the low-code configuration data, and the configuration result capable of being directly reused is formed, so that the stability of field data processing, the cross-task reusability and the consistency of subsequent certification can be improved.

Description

Technical Field

[0001] This invention relates to the field of information retrieval technology, and in particular to a method, device and medium for processing ship inspection data based on edge computing. Background Technology

[0002] With the advancement of digitalization in ship inspection operations, edge computing is gradually being used to complete on-site processing and rapid output of inspection data at docks, shipyards, and other locations. By enabling edge computing to perform data acquisition, structured processing, retrieval, presentation, and document generation, it reduces reliance on remote links and central computing power and improves real-time response and on-site collaboration efficiency under weak network conditions. Related applications have expanded from single data entry to the integrated needs of multi-source data aggregation, visual display, and certificate document output.

[0003] Existing methods have shortcomings. The low-code configuration of the inspection task involves multiple sources and iterations, and lacks consistency parsing and normalization verification. Inconsistencies can easily occur between field definitions, view field sequences, and certificate template definitions, leading to unstable field mapping in multimodal inspection data and making structured results unusable across different tasks. In addition, certificate field values ​​are generated by aggregating structured inspection data but lack field-level associations and traceable indexes, making it difficult to trace certificate field values ​​back to specific source data. The lack of request-oriented multidimensional retrieval indexes and inspection view pairing organization results in high cost of edge-side retrieval and inaccurate on-demand output. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a ship inspection data processing method based on edge computing to solve the problems of inconsistent configuration standards and difficulty in tracing results.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a ship inspection data processing method based on edge computing, comprising: performing consistency parsing and normalization verification on the low-code configuration data of the current ship inspection task in an edge computing node to generate a preliminary dataset; converting the preliminary dataset into a field configuration set, a view configuration set, and a certificate template configuration set through configuration-driven structured mapping operations; mapping the collected multimodal ship inspection data to the original dataset corresponding to the field configuration set; attaching time and spatial identifiers to each collection record in the original dataset to form a spatiotemporal annotation result set; reorganizing the spatiotemporal annotation result set into structured inspection data; and processing the structured inspection data according to the aggregation calculation rules in the certificate template configuration set. Grouping statistics and text generation yield certificate field values. Field-level relationships are established between each certificate field value and its corresponding structured inspection data. All field-level relationships are integrated to form a traceable mapping index set. Using view fields as index dimensions, the traceable mapping index set is organized to generate a multidimensional retrieval index. Inspection views are generated based on the multidimensional retrieval index and view configuration set. The multidimensional retrieval index and inspection views are combined to form an edge retrieval view index set. Inspection data processing requests are received, and multidimensional searches are performed on the edge retrieval view index set according to the requests to obtain inspection views and field-level relationships. Ship inspection data processing documents are then generated based on the inspection views and field-level relationships.

[0007] As a preferred embodiment of the edge computing-based ship inspection data processing method described in this invention, the following steps are taken: In the edge computing node, consistency parsing and normalization verification are performed on the low-code configuration data of the current ship inspection task to generate a preliminary dataset. In the edge computing node, low-code configuration data is obtained based on the current ship inspection task identifier. Syntax parsing and structural parsing are performed on the low-code configuration data to obtain the original configuration data set. Perform consistency checks on field identifiers, reference relationships, and constraint information on the original configuration data set. After all checks pass, a set of records that have passed configuration consistency checks is obtained. Configuration consistency is achieved by performing normalization and format unification on the record set, generating a preliminary dataset.

[0008] As a preferred embodiment of the edge computing-based ship inspection data processing method of the present invention, the specific steps for converting the initial dataset into a field configuration set, a view configuration set, and a certificate template configuration set through configuration-driven structured mapping operations are as follows. Extract field attribute information from the initial dataset, organize the field attribute information and group it by field identifier to form a field configuration set; Extract the test view configuration records from the initial dataset, perform field identifier matching on the test view configuration records in combination with field attribute information and retain the matching results, and organize all matching results into a view configuration set; Certificate template configuration records are extracted from the initial dataset. The certificate field set and aggregation calculation rules are determined by combining the field attribute information, and a certificate template configuration set is generated.

[0009] As a preferred embodiment of the edge computing-based ship inspection data processing method of the present invention, the specific steps for mapping the collected multimodal ship inspection data to the original dataset corresponding to the field configuration set are as follows: Collect multimodal inspection data of ships and match field identifiers in the field configuration set according to the data source identifier; The data content of the ship multimodal inspection data is filled into the field positions corresponding to the matching field identifiers to form raw data records. All raw data records are collected to generate the raw dataset.

[0010] As a preferred embodiment of the edge computing-based ship inspection data processing method of the present invention, the steps of adding time and spatial identifiers to each collection record in the original dataset to form a spatiotemporal labeled result set, and reorganizing the spatiotemporal labeled result set into structured inspection data are as follows. Each original data record in the original dataset is assigned a collection time point to form a time identifier, and associated with location coordinates to form a spatial identifier. The additional results are then summarized to obtain a spatiotemporal annotation result set. Traverse the spatiotemporal annotation result set, retain only the fields that match the view field sequence in the view configuration set, and arrange them in the order of the view field sequence to generate structured test data.

[0011] As a preferred embodiment of the edge computing-based ship inspection data processing method of the present invention, the specific steps for forming a traceable mapping index set are as follows: Based on the aggregation calculation rules, the grouping criteria are determined, and the structured test data is grouped to form a data group set; For each data group in the data group set, perform statistical calculations and text generation according to the aggregation calculation rules to obtain the certificate field value; Record the certificate field, group identifier, and structured verification data corresponding to each certificate field value. Establish field-level associations between certificate field values ​​and structured verification data through certificate fields and group identifiers. Integrate all field-level associations to form a traceable mapping index set.

[0012] As a preferred embodiment of the ship inspection data processing method based on edge computing described in this invention, the specific steps for forming the edge retrieval view index set are as follows: Extract field-level associations that match the field identifiers of the view field sequence from the traceable mapping index set and integrate them into the view aggregation result set; Iterate through the field-level relationships in the view aggregation result set and read structured test data for each field-level relationship; Extract view field values ​​from structured test data, and perform uniformization and sequential arrangement according to the corresponding view field sequence to obtain the index key feature sequence set; Generate an index key based on the set of index key feature sequences, use the index key and the corresponding field-level association as the same index item, and aggregate all index items to generate a multidimensional retrieval index. The view field values ​​are assembled into view rows, and all view rows are aggregated to form a test view. The multidimensional search index is paired and summarized with the test view to form a marginal search view index set.

[0013] As a preferred embodiment of the edge computing-based ship inspection data processing method of the present invention, the specific steps for generating the ship inspection data processing document based on the inspection view and field-level association relationships are as follows: Receive test data processing requests and perform structured parsing to extract view field sequences, view field search conditions, and output ranges; In the edge retrieval view index set, locate the corresponding multidimensional retrieval index and test view according to the view field sequence to obtain the index view pair; The view field search conditions are standardized and sequentially processed to generate a request search key. A multidimensional search is performed in the multidimensional search index using the request search key to obtain the field-level associations corresponding to the request search key. The corresponding view row is located in the test view to form a search result set. The view rows and field-level associations in the search results set are paired and organized according to the requested search key, and a ship inspection data processing document is generated according to the output range.

[0014] In a second aspect, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein the computer program, when executed by the processor, implements any step of the edge computing-based ship inspection data processing method described in the first aspect of the present invention.

[0015] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the edge computing-based ship inspection data processing method described in the first aspect of the present invention.

[0016] The beneficial effects of this invention are as follows: By completing consistency parsing and standardization verification of low-code configuration data and forming directly reusable configuration results, the drift of field definitions, view field sequence definitions, and certificate definitions between different tasks is avoided, reducing the differences in structured results caused by inconsistent mapping of multimodal inspection data. This is conducive to improving the stability of on-site data processing, cross-task reusability, and consistency of subsequent certificate issuance. By establishing a field-level correspondence between certificate field values ​​and source structured inspection data and forming a searchable indexed view result, certificate field values ​​can be quickly traced to specific data bases and support rapid location and range output upon request. This is conducive to reducing review and accountability costs, improving edge-side retrieval response efficiency, and improving the accuracy and auditability of ship inspection data processing documents. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. 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.

[0018] Figure 1 This is a flowchart of a ship inspection data processing method based on edge computing.

[0019] Figure 2 A flowchart for generating field configuration sets, view configuration sets, and certificate template configuration sets.

[0020] Figure 3 A flowchart for generating structured test data.

[0021] Figure 4 A flowchart for generating ship inspection data processing documents. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0025] Reference Figures 1-4 This is one embodiment of the present invention, which provides a ship inspection data processing method based on edge computing, including the following steps: S1. In the edge computing node, perform consistency parsing and normalization verification on the low-code configuration data of the current ship inspection task to generate a preliminary dataset. Then, convert the preliminary dataset into a field configuration set, a view configuration set, and a certificate template configuration set through configuration-driven structured mapping operations.

[0026] S1.1. Obtain low-code configuration data in the edge computing node based on the current ship inspection task identifier, and perform syntax parsing and structure parsing on the low-code configuration data to obtain the original configuration data set.

[0027] It should be noted that the current ship inspection task identifier is read in the edge computing node, and the current ship inspection task identifier is used as the search key to perform a query to locate the low-code configuration data storage location, so as to obtain the low-code configuration data corresponding to the current ship inspection task identifier.

[0028] The low-code configuration data is scanned and segmented character by character according to the configuration syntax rules. Configuration keys, configuration values, hierarchical boundaries, and record boundaries are identified, and a syntax structure sequence is output. Specifically, the low-code configuration data is treated as JSON text and characters are read sequentially. When a double quote is encountered, the data enters string mode and continues to be read until an unescaped double quote is encountered, at which point the string mode is exited. When the string mode is closed, "{} [ ] : ," is identified as a delimiter and segmented into independent syntax structures. All independent syntax structures are integrated into a syntax structure sequence. The syntax structure sequence is merged into a configuration record sequence according to the record boundaries. For each configuration record, structured elements (such as field identifiers, data type identifiers, value constraint descriptions, default value descriptions, view field sequences, aggregation calculation rule information, and reference relationship information) are extracted. The structured elements are written into a unified data record structure according to the configuration record order, and all data records are collected to obtain the original configuration data set.

[0029] It should also be noted that configuration syntax rules refer to the "recognizable syntax" of low-code configuration data at the text level, which is used to break down raw text into a sequence of parsable syntactic structures.

[0030] S1.2 Perform consistency checks on field identifiers, reference relationships, and constraint information on the original configuration data set. After all checks pass, obtain a configuration consistency pass record set. Perform normalization and format unification on the configuration consistency pass record set to generate a preliminary dataset.

[0031] It should be noted that the original configuration data set is summarized into a field definition list by field identifier, and the data type identifier, value constraint description, and default value description corresponding to the field identifier are recorded. Each definition item with the same field identifier in the field definition list is compared to check the consistency of the field identifier. If the field identifier is found to be duplicated and the data type identifier or value constraint description is inconsistent, the inconsistent item is marked as failing the consistency check. After the field identifier consistency check passes, the reference relationship information is extracted from the original configuration data set to form a reference relationship list. For each referenced field identifier in the reference relationship list, an existence check is performed in the field definition list to check the consistency of the reference relationship. The matching relationship between the value constraint description and the data type identifier is checked to check the consistency of the constraint information. After the field identifier consistency check, reference relationship consistency check, and constraint information consistency check all pass, all corresponding data records are integrated into a configuration consistency-passed record set.

[0032] To ensure configuration consistency, normalization and format unification are performed on field identifiers, data type identifiers, value constraint descriptions, default value descriptions, and sequence information in the record set. This includes removing leading and trailing whitespace, unifying character width and case, standardizing delimiters and list expressions into fixed syntax, standardizing Boolean values ​​into a unified value format, and standardizing date and time values ​​into a unified format string. All data records that have undergone normalization and format unification are then organized into a preliminary dataset according to the configuration consistency through the record set order.

[0033] S1.3 Extract field attribute information from the preliminary dataset, organize the field attribute information and group it according to field identifier to form a field configuration set.

[0034] It should be noted that the process involves traversing each data record in the initial dataset in record order, extracting field attribute information (field identifier, data type identifier, value constraint description, default value description, and required attribute identifier) ​​from each data record, and using the field identifier as the grouping key to write the field attribute information into the group entry corresponding to the field identifier. When a field identifier with the same name is encountered, the field attribute information corresponding to the same field identifier is compared item by item. If the field attribute information is completely consistent, the field attribute information already written into the group entry is retained and the current duplicate writing is skipped. If the field attribute information is inconsistent, a field identifier conflict is marked and the generation of the field configuration set is stopped. When there are no conflicts among all field identifiers, blank cleanup and value format unification are performed on the group entry corresponding to each field identifier. The organized field attribute information is then integrated into the field configuration set according to the field identifier order.

[0035] S1.4 Extract the inspection view configuration records from the preliminary dataset, perform field identifier matching on the inspection view configuration records in combination with the field attribute information and retain the matching results, and organize all matching results into a view configuration set.

[0036] It should be noted that the field attribute information is organized into a field lookup table according to the field identifier. The data records in the preliminary dataset are traversed and the test view configuration records are filtered out. Specifically, each data record is checked to see if it contains a "view field sequence" and the "view field sequence" is a non-empty sequence. Data records that meet the conditions are used as test view configuration records, and data records that do not meet the conditions are skipped directly.

[0037] For each inspection view configuration record, extract the field identifiers from the view field sequence one by one and perform field identifier matching verification in the field lookup table. When the field identifier matching verification passes, organize the field attribute information corresponding to the field identifier and the order position of the field identifier in the view field sequence into a matching result entry. When the field identifier matching verification fails, remove the current field identifier from the view field sequence and record it as an unmatched field identifier. Return the matching result entry to the current inspection view configuration record to form a view matching record. Gather and organize all view matching records according to the record order in the preliminary dataset into a view configuration set.

[0038] S1.5 Extract certificate template configuration records from the preliminary dataset, determine the certificate field set and aggregation calculation rules by combining field attribute information, and generate a certificate template configuration set.

[0039] It should be noted that the data records in the preliminary dataset are traversed in record order and the certificate template configuration records are filtered out. Specifically, for each data record, it is checked whether it contains "aggregate calculation rule information" and whether the "aggregate calculation rule information" is a non-empty sequence. Data records that meet the conditions are used as certificate template configuration records, and data records that do not meet the conditions are skipped. For each certificate template configuration record, the aggregate calculation rule information is read and the certificate field identifier and the corresponding aggregate calculation rule information are extracted one by one. The certificate field identifier is checked for field identifier matching in the field lookup table. When the field identifier matching verification is successful, the field attribute information corresponding to the certificate field identifier is bound to the aggregate calculation rule information and organized into a certificate field entry. When the field identifier matching verification fails, the current certificate field identifier is removed and recorded as an unmatched certificate field identifier. All certificate field entries are organized into certificate template matching records. All certificate template matching records are collected and organized into a certificate template configuration set in the order of the records in the preliminary dataset.

[0040] It should also be noted that existing technologies, which rely on manual maintenance of fixed field tables and the assembly of fields and certificate fields based on experience, are prone to issues such as duplicate field identifiers with inconsistent meanings, missing field references, and mismatches between value constraints and data types. This leads to unstable data mapping and certificate value interpretation, making traceability difficult. This solution avoids configuration conflicts and interpretation drift by performing consistency checks on low-code configuration data in edge computing nodes and standardizing and unifying the format of records. This improves configuration consistency and reusability. By grouping the initial dataset by field identifier to form field configuration sets, view configuration sets, and certificate template configuration sets, it ensures that both view fields and certificate fields have clear field definitions, thereby improving the stability and traceability of data mapping, view generation, and certificate generation.

[0041] S2. Map the collected ship multimodal inspection data to the original dataset corresponding to the field configuration set. Add time and space identifiers to each collected record in the original dataset to form a spatiotemporal labeled result set. Reorganize the spatiotemporal labeled result set into structured inspection data.

[0042] S2.1 Collect ship multimodal inspection data, and fill the matching fields with field identifiers in the data source identifier matching field configuration set according to the data source identifier. Fill the data content of ship multimodal inspection data into the matching fields to form raw data records. Collect all raw data records to generate raw dataset.

[0043] It should be noted that on the edge computing node, a high-definition industrial camera is connected and enabled to acquire image data, an infrared thermal imager is used to acquire temperature distribution maps, an ultrasonic thickness sensor is used to acquire thickness values, and a GNSS positioning receiver is used to acquire position coordinates.

[0044] For each piece of collected data, a data source identifier is generated according to the collection order, and the corresponding data content is obtained. The data source identifier is obtained by combining the sensor type identifier and the collection item name. The field identifiers in the field configuration set are used to build a corresponding matching table according to the data source identifier. The data source identifier of each piece of collected data is used as the search key of the matching table to find the matching field identifier. The data content is written into the field position corresponding to the matching field identifier to form the original data record. When the data source identifier does not have a matching field identifier in the matching table, the current collected data is recorded as an unmatched item and skipped. After the field filling of all collected data is completed, all original data records are collected in the collection order to generate the original dataset.

[0045] S2.2. Add a collection time point to each original data record in the original dataset to form a time identifier, associate the location coordinates to form a spatial identifier, and summarize the additional results to obtain a spatiotemporal annotation result set.

[0046] It should be noted that each original data record in the original dataset is traversed, the collection time point is read from the original data record and used as the time identifier field, the location coordinates collected by the GNSS positioning receiver already contained in the original data record are used as spatial identifiers, and all original data records with added time and spatial identifiers are integrated into a spatiotemporal annotation result set, while keeping the original data record order unchanged.

[0047] S2.3. Traverse the spatiotemporal annotation result set, retain only the fields that match the view field sequence in the view configuration set, and arrange them in the order of the view field sequence to generate structured test data.

[0048] It should be noted that the view field sequence is extracted from the view configuration set and organized into an ordered field list. Each original data record in the spatiotemporal annotation result set is traversed, and a structured test data record is created according to the ordered field list. The value of each field in the ordered field list is initialized to a null placeholder. The current original data record is checked one by one according to the field order of the ordered field list to see if it contains the corresponding field. If it does, the field value is extracted and filled into the field position with the same name in the structured test data record. If it does not, the null placeholder is retained. At the same time, fields in the current original data record that do not belong to the ordered field list are ignored. The resulting structured test data record is consistent with the view field sequence. All structured test data records are summarized and integrated into structured test data.

[0049] S3. Based on the aggregation calculation rule information in the certificate template configuration set, perform group statistics and text generation on the structured inspection data to obtain certificate field values. Establish field-level associations between each certificate field value and the corresponding structured inspection data. Integrate all field-level associations to form a traceable mapping index set.

[0050] S3.1 Determine the grouping criteria based on the aggregation calculation rules, group the structured test data, and form a data group set.

[0051] It should be noted that the field identifiers used for grouping statistics in the aggregation calculation rule information are used as the grouping basis. Specifically, each certificate field entry in the certificate template configuration set is traversed, the aggregation calculation rule information bound to the certificate field entry is read, the field identifiers used for grouping statistics are extracted from the aggregation calculation rule information and summarized into a grouping basis list. When the field identifiers extracted from multiple certificate field entries are duplicated, only one copy is retained and the order of first appearance is maintained. When no field identifiers used for grouping statistics are extracted, the grouping basis is set to empty, and all structured test data are grouped into the same data group.

[0052] The process iterates through each structured test data record in the record order. Based on the grouping criteria, it reads the values ​​of the corresponding field identifiers one by one and concatenates them according to the order of the field identifiers to form a group identifier. Structured test data records with the same group identifier are grouped together into the same data group, and this process is continued until all structured test data records have been traversed. Finally, all data groups are integrated into a data group set, and the expression for the group identifier is: ; ; in, Indicates group identifier; This indicates the number of field identifiers in the grouping criteria list; This indicates that the index is identified by the field in the grouping criteria list, and the value range is 1 to... ; Indicates the first Each field identifies the field value in the current structured test data record; This represents an encoding function that transforms string values ​​into integers. Indicates the position-weighted base of Power of; This represents the modulo operator; Represents a fixed positive integer constant used for modulo control. The range of values ​​for ; This represents the independent variable, used to represent the input string; Indicates the character index of the input string; Indicates the length of the input string; This represents the first character of the input string. One character; A function that represents the mapping of character to integer encoded values.

[0053] It should also be noted that the aggregate calculation rule information refers to a set of executable conventions pre-defined for each certificate field in the certificate template configuration set, used to transform structured verification data into certificate field values.

[0054] S3.2 Perform statistical calculations and text generation on each data group in the data group set according to the aggregation calculation rules to obtain the certificate field value.

[0055] It should be noted that the certificate field entries are extracted one by one from the certificate template configuration set, and the aggregation calculation rule information bound to the certificate field entries is read. Each data group in the data group set is processed sequentially. Within the current data group, the field values ​​of the structured test data records are read one by one according to the field identifiers listed in the aggregation calculation rule information, and the value set is summarized. Statistical calculations are performed on the computable field value set. Null values ​​and non-numerical values ​​are not included in the statistical calculations. The statistical calculations are performed to obtain the statistical results (the statistical results are the results corresponding to the statistical calculation types listed in the aggregation calculation rule information, such as average, maximum, minimum, range, variance, and standard deviation).

[0056] The statistical results are converted into text and combined into certificate field values ​​according to the output format given by the aggregation calculation rules. Specifically, the statistical results are processed according to the number of digits to retain in the output format, the processed values ​​are converted into numeric strings, and the units, prefixes, suffixes, and delimiters are concatenated into certificate field values ​​according to the output format. If a certificate field entry corresponds to multiple statistical results, each statistical result is converted into a numeric string according to the output order given in the output format, and then combined into a certificate field value using delimiters. After processing all certificate field entries in the current data group, the certificate field values ​​corresponding to each certificate field identifier in the current data group are obtained, until all data groups are processed and all certificate field values ​​are obtained.

[0057] S3.3 Record the certificate field, group identifier, and structured verification data corresponding to each certificate field value. Establish field-level associations between certificate field values ​​and structured verification data through certificate fields and group identifiers. Integrate all field-level associations to form a traceable mapping index set.

[0058] It should be noted that, in the order of the data group set, the group identifier corresponding to each data group and the structured inspection data record in the data group are extracted sequentially. The certificate field identifier is extracted one by one in the order of the certificate field entries in the certificate template configuration set. The certificate field value corresponding to the certificate field identifier is extracted from all the certificate field values. The combination of each certificate field identifier and the group identifier is used as the association key to form a field-level association relationship. The field-level association relationship includes the certificate field identifier, the group identifier, the certificate field value, and the structured inspection data record in the data group corresponding to the group identifier. After the field-level association relationship between all data groups and all certificate field entries is generated, all field-level association relationships are collected and integrated to form a traceable mapping index set.

[0059] It should also be noted that existing technologies involve manually grouping and statistically analyzing the inspection data and then manually piecing together the certificate field text. While this is simple and facilitates rapid certificate issuance, it is prone to inconsistencies in grouping criteria and unclear correspondence between statistical results and certificate fields. This leads to difficulties in tracing the source of certificate field values ​​and high verification costs. This solution, on the other hand, forms a data group set from the structured inspection data and performs statistical calculations and text generation within each data group to obtain certificate field values. This ensures that the calculation criteria for certificate field values ​​are consistent and the output format is uniform. By generating field-level relationships and compiling them into a traceable mapping index set, a clear correspondence is established between certificate field values ​​and structured inspection data. This improves the traceability and consistency of certificate generation and solves the problems of drifting criteria and unclear sources of certificate field values.

[0060] S4. Using the view fields as the index dimension, organize the traceable mapping index set to generate a multidimensional retrieval index. Generate a verification view based on the multidimensional retrieval index and the view configuration set, and combine the multidimensional retrieval index and the verification view to form an edge retrieval view index set.

[0061] S4.1 Extract field-level associations that match the field identifiers of the view field sequence from the traceable mapping index set and integrate them into the view aggregation result set.

[0062] It should be noted that the view field sequence information is extracted from the view configuration set, and the field identifiers in the view field sequence information are extracted one by one. The field-level associations in the traceable mapping index set are traversed, and the certificate field identifier and group identifier in the field-level associations are read. When the certificate field identifier is consistent with the field identifier in the view field sequence information, the current field-level association is retained and aggregated together with the corresponding view field sequence information. When the certificate field identifier is inconsistent with the field identifier in the view field sequence information, the current field-level association is skipped and not aggregated. The traversal and aggregation are performed separately for each view field sequence information in the view configuration set. After the traversal is completed, the retained field-level associations are integrated according to the view field sequence information and group identifier to form the view aggregation result set.

[0063] S4.2. Traverse the field-level relationships in the view aggregation result set, read the structured test data for each field-level relationship, extract the view field values ​​from the structured test data, and perform uniformization and sequential arrangement according to the corresponding view field sequence to obtain the index key feature sequence set.

[0064] It should be noted that the process involves traversing each aggregation result in the view aggregation result set and extracting the corresponding view field sequence information, grouping identifier, and field-level association relationship. The structured test data records contained in the field-level association relationship are read. Using the field identifier in the view field sequence information as the extraction order, the corresponding field values ​​are extracted one by one from the structured test data records. If a structured test data record does not contain a corresponding field identifier, a null value is retained as a placeholder. The extracted field values ​​are then cleaned of blanks, and character widths, capitalization, and delimiters are standardized to obtain unified view field values. These standardized view field values ​​are then sequentially arranged according to the field identifier order in the view field sequence information to form an index key element sequence. After completing the standardization and sequencing of all aggregation results, all index key element sequences are integrated to obtain an index key element sequence set.

[0065] S4.3 Generate index keys based on the set of index key feature sequences, treat the index keys and their corresponding field-level associations as the same index item, and aggregate all index items to generate a multidimensional retrieval index.

[0066] It should be noted that the index key feature sequences are extracted one by one in the order of the index key feature sequence set, and then concatenated using a predetermined connector to form the index key. The expression for forming the index key is as follows: ; ; in, Indicates the index key; Indicates concatenating strings, putting arrive Use the predefined connector " The concatenated string; arrive This represents a standardized string of view field values ​​in the index key feature sequence, arranged in the order of field identifiers in the view field sequence information. This indicates the total number of string values ​​for the view fields that are being concatenated; The string concatenation symbol is used to link the left and right strings together. Indicates a predefined connector.

[0067] Retrieve field-level associations corresponding to the current index key feature sequence from the view aggregation result set. The correspondence is based on the aggregation result that generated the current index key feature sequence. Field-level associations are retrieved from the same aggregation result for pairing. The corresponding field-level associations are paired with the index key to form index items. The index item contains the index key and the field-level association. When multiple index key feature sequences are concatenated to obtain the same index key, the field-level associations corresponding to the same index key are merged into the same index item and all field-level associations are retained. After generating index items for all index key feature sequences, all index items are aggregated and organized according to the index key to form a multidimensional retrieval index.

[0068] It should also be noted that the established connector refers to the fixed delimiter used in the edge computing node to connect the values ​​of each view field in the index key feature sequence into an index key. For example, the vertical bar "|" is used as the connector to connect the index key feature sequence in sequence with "|" to form the index key.

[0069] S4.4 Assemble the view field values ​​into view rows, and gather all view rows to form a test view. Pair and summarize the multidimensional search index with the test view to form a marginal search view index set.

[0070] It should be noted that the view field sequence information and the field identifier order in the view field sequence information are extracted from the view aggregation result set. The index key element sequence is extracted one by one according to the order of the index key element sequence set. First, an empty view row is created according to the field identifier order, and null values ​​are set for each field in the field identifier order. The unified view field values ​​in the index key element sequence are filled into the corresponding field positions of the empty view row one by one according to the field identifier order. The index key corresponding to the current index key element sequence is used as the index key field value of the view row. After the filling is completed, a view row is obtained. Until all index key element sequences are processed, all view rows are aggregated in the order of the index key element sequence set to form a test view.

[0071] After the test view is formed, the index items are retrieved one by one in the order of the index keys in the multidimensional retrieval index, and the view row corresponding to the same index key of the index item is located in the test view. The index items and view rows are paired and summarized to form index view pairs. All index view pairs are gathered and integrated to form the edge retrieval view index set.

[0072] S5. Receive the inspection data processing request, perform a multi-dimensional search in the edge search view index set according to the inspection data processing request, obtain the inspection view and field-level association, and generate the ship inspection data processing document based on the inspection view and field-level association.

[0073] S5.1 Receive the test data processing request and perform structured parsing to extract the view field sequence, view field retrieval conditions and output range. In the marginal retrieval view index set, locate the corresponding multidimensional retrieval index and test view according to the view field sequence to obtain the index view pair.

[0074] It should be noted that after receiving the inspection data processing request in the edge computing node, the inspection data processing request is subjected to structured parsing. The inspection data processing request is split according to field identifier and field value, and the view field sequence, view field retrieval conditions and output range are identified item by item. If the view field sequence is missing or empty, the inspection data processing request is determined to be unavailable and the current positioning operation is terminated. If the view field sequence is available, the index view pairs are traversed sequentially in the edge retrieval view index set, and the view field sequence information corresponding to the index view pairs is read.

[0075] The view field sequence of the test data processing request is compared item by item with the view field sequence information. If all items match, the current index view pair is selected and the multidimensional retrieval index and test view are retrieved as the positioning result. If there is an inconsistency in the item-by-item comparison, the current index view pair is skipped and the traversal continues. If the positioning result is still not obtained after the traversal ends, it is determined that there is no multidimensional retrieval index and test view in the edge retrieval view index set that corresponds to the view field sequence of the test data processing request, and the current positioning operation ends.

[0076] S5.2. Perform unified and sequential processing on the view field search conditions to generate the request search key. Perform multidimensional search in the multidimensional search index with the request search key to obtain the field-level association relationship corresponding to the request search key, and locate the corresponding view row in the test view to form a search result set.

[0077] It should be noted that the view field search conditions are uniformized item by item. The field values ​​corresponding to each field identifier are cleaned of leading and trailing whitespace, and the character width, capitalization, and delimiters are unified to obtain uniform field values. The uniform field values ​​are then arranged sequentially according to the field identifier order in the view field sequence. If a field identifier is missing in the view field search conditions, an empty value is used to fill in the missing position. After the sequential arrangement is completed, the uniform field values ​​are concatenated in sequence using a predetermined connector to form the request search key.

[0078] A multidimensional search is performed in the multidimensional search index using the requested search key. Index entries whose index keys equal the requested search key are found, and the field-level relationships contained in these entries are obtained. If no index entry is found, an empty field-level relationship is obtained. In the validation view, the view rows are traversed one by one according to the field identifier order of the view field sequence, and the view field values ​​are extracted. The view field values ​​are then processed for unification and sequencing, and concatenated using predetermined connectors to form an in-row search key. View rows whose in-row search keys equal the requested search key are used as the corresponding view rows. The field-level relationships and the corresponding view rows are then summarized and integrated to form the search result set.

[0079] S5.3 Pair and organize the view rows and field-level associations in the search results set according to the requested search key, and generate a ship inspection data processing document according to the output range.

[0080] It should be noted that the view rows and field-level associations in the search result set are paired and organized according to the requested retrieval key. Specifically, the retrieval key within each view row in the search result set is extracted, and it is verified whether the retrieval key within the row is equal to the requested retrieval key. The index key of the field-level association in the search result set is read, and it is verified whether the index key is equal to the requested retrieval key. The view rows and field-level associations that pass the verification are paired and aggregated according to the rule of consistent group identifiers. The view rows and field-level associations corresponding to the same group identifier are aggregated into the same pairing result and form a pairing result set.

[0081] The paired result set is cropped and filtered according to the output range. When the output range is limited to view fields, only the corresponding field values ​​in the view row are retained. When the output range is limited to certificate fields, only the certificate field identifier and certificate field value in the field-level association are retained. When the output range is limited to simultaneous output, both the view row and the field-level association are retained. After the output range is cropped, the retained content is summarized and arranged in the order of the paired result set to form the ship inspection data processing document.

[0082] This embodiment also provides a computer device applicable to the edge computing-based ship inspection data processing method, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the edge computing-based ship inspection data processing method proposed in the above embodiment.

[0083] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0084] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the edge computing-based ship inspection data processing method proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0085] In summary, this invention achieves consistency parsing and standardization verification of low-code configuration data, generating directly reusable configuration results. This avoids the drift of field definitions, view field sequence definitions, and certificate definitions between different tasks, reducing the differences in structured results caused by inconsistent mapping of multimodal inspection data. This improves the stability of on-site data processing, cross-task reusability, and consistency of subsequent certification. By establishing field-level correspondences between certificate field values ​​and source structured inspection data and generating corresponding searchable indexed view results, certificate field values ​​can be quickly traced to specific data bases and support rapid location and range-based output upon request. This reduces review and accountability costs, improves edge-side retrieval response efficiency, and enhances the accuracy and auditability of ship inspection data processing documents.

[0086] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for processing ship inspection data based on edge computing, characterized in that: include, In the edge computing node, consistency parsing and normalization verification are performed on the low-code configuration data of the current ship inspection task to generate a preliminary dataset. The preliminary dataset is then converted into a field configuration set, a view configuration set, and a certificate template configuration set through configuration-driven structured mapping operations. The collected ship multimodal inspection data is mapped to the original dataset corresponding to the field configuration set. Time and space identifiers are added to each collection record in the original dataset to form a spatiotemporal labeled result set. The spatiotemporal labeled result set is then reorganized into structured inspection data. Based on the aggregation calculation rules in the certificate template configuration set, the structured inspection data is grouped and statistically analyzed and text generated to obtain the certificate field values. A field-level association relationship is established between each certificate field value and the corresponding structured inspection data. All field-level association relationships are integrated to form a traceable mapping index set. Using view fields as index dimensions, the traceable mapping index set is organized to generate a multidimensional retrieval index. Based on the multidimensional retrieval index and the view configuration set, a verification view is generated. The multidimensional retrieval index and the verification view are then combined to form an edge retrieval view index set. Upon receiving a request for inspection data processing, the system performs a multidimensional search within the edge search view index set based on the request to obtain the inspection view and field-level relationships. Finally, it generates a ship inspection data processing document based on the inspection view and field-level relationships.

2. The ship inspection data processing method based on edge computing as described in claim 1, characterized in that: In the edge computing node, consistency parsing and normalization verification are performed on the low-code configuration data of the current ship inspection task to generate a preliminary dataset. The specific steps are as follows. In the edge computing node, low-code configuration data is obtained based on the current ship inspection task identifier. Syntax parsing and structural parsing are performed on the low-code configuration data to obtain the original configuration data set. Perform consistency checks on field identifiers, reference relationships, and constraint information on the original configuration data set. After all checks pass, a set of records that have passed configuration consistency checks is obtained. Configuration consistency is achieved by performing normalization and format unification on the record set, generating a preliminary dataset.

3. The ship inspection data processing method based on edge computing as described in claim 2, characterized in that: The process of converting the initial dataset into a field configuration set, a view configuration set, and a certificate template configuration set through configuration-driven structured mapping operations is described in the following steps. Extract field attribute information from the initial dataset, organize the field attribute information and group it by field identifier to form a field configuration set; Extract the test view configuration records from the initial dataset, perform field identifier matching on the test view configuration records in combination with field attribute information and retain the matching results, and organize all matching results into a view configuration set; Certificate template configuration records are extracted from the initial dataset. The certificate field set and aggregation calculation rules are determined by combining the field attribute information, and a certificate template configuration set is generated.

4. The ship inspection data processing method based on edge computing as described in claim 3, characterized in that: The specific steps for mapping the collected ship multimodal inspection data to the original dataset corresponding to the field configuration set are as follows. Collect multimodal inspection data of ships and match field identifiers in the field configuration set according to the data source identifier; The data content of the ship multimodal inspection data is filled into the field positions corresponding to the matching field identifiers to form raw data records. All raw data records are collected to generate the raw dataset.

5. The ship inspection data processing method based on edge computing as described in claim 4, characterized in that: The process involves adding time and spatial identifiers to each collected record in the original dataset to form a spatiotemporal labeled result set. This spatiotemporal labeled result set is then reorganized into structured test data. The specific steps are as follows: Each original data record in the original dataset is assigned a collection time point to form a time identifier, and associated with location coordinates to form a spatial identifier. The additional results are then summarized to obtain a spatiotemporal annotation result set. Traverse the spatiotemporal annotation result set, retain only the fields that match the view field sequence in the view configuration set, and arrange them in the order of the view field sequence to generate structured test data.

6. The ship inspection data processing method based on edge computing as described in claim 5, characterized in that: The specific steps for forming a traceable mapping index set are as follows. Based on the aggregation calculation rules, the grouping criteria are determined, and the structured test data is grouped to form a data group set; For each data group in the data group set, perform statistical calculations and text generation according to the aggregation calculation rules to obtain the certificate field value; Record the certificate field, group identifier, and structured verification data corresponding to each certificate field value. Establish field-level associations between certificate field values ​​and structured verification data through certificate fields and group identifiers. Integrate all field-level associations to form a traceable mapping index set.

7. The ship inspection data processing method based on edge computing as described in claim 6, characterized in that: The specific steps for forming the edge retrieval view index set are as follows: Extract field-level associations that match the field identifiers of the view field sequence from the traceable mapping index set and integrate them into the view aggregation result set; Iterate through the field-level relationships in the view aggregation result set and read structured test data for each field-level relationship; Extract view field values ​​from structured test data, and perform uniformization and sequential arrangement according to the corresponding view field sequence to obtain the index key feature sequence set; Generate an index key based on the set of index key feature sequences, use the index key and the corresponding field-level association as the same index item, and aggregate all index items to generate a multidimensional retrieval index. The view field values ​​are assembled into view rows, and all view rows are aggregated to form a test view. The multidimensional search index is paired and summarized with the test view to form a marginal search view index set.

8. The ship inspection data processing method based on edge computing as described in claim 7, characterized in that: The specific steps for generating ship inspection data processing documents based on inspection views and field-level relationships are as follows. Receive test data processing requests and perform structured parsing to extract view field sequences, view field search conditions, and output ranges; In the edge retrieval view index set, locate the corresponding multidimensional retrieval index and test view according to the view field sequence to obtain the index view pair; The view field search conditions are standardized and sequentially processed to generate a request search key. A multidimensional search is performed in the multidimensional search index using the request search key to obtain the field-level associations corresponding to the request search key. The corresponding view row is located in the test view to form a search result set. The view rows and field-level associations in the search results set are paired and organized according to the requested search key, and a ship inspection data processing document is generated according to the output range.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the edge computing-based ship inspection data processing method according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the edge computing-based ship inspection data processing method according to any one of claims 1 to 8.

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