A data storage and acquisition method and system based on point location identification

Abstract

The application discloses a kind of data storage and acquisition method and system based on point location identification, it is related to industrial big data processing technical field.The kind of data storage and acquisition method and system based on point location identification, it includes the following steps: S1, acquisition SCADA system and the multi-source point location information and structure path information in business system, constructs point location basic information dataset;S2, based on point location basic information dataset identification point location naming structure classification state;S3, combined with classification state and the change characteristics of path field structure in historical period resolve continuity trend;S4, based on naming attribution state and path structure stability state executes conflict intensity grade assessment.The problem that the same source point of multiple systems lacks unified identification mapping, leading to component path conflict in data call process is solved.

Description

Technical Field

[0001] This invention relates to the field of industrial big data processing technology, specifically to a data storage and retrieval method and system based on location identification. Background Technology

[0002] With the acceleration of industrial digitalization, big data processing, as a core technology supporting the acquisition, cleaning, and retrieval of multi-source heterogeneous data, is widely used in production control, equipment monitoring, and system integration scenarios. In the context of multi-system collaboration, the complexity of data structures increases, placing higher demands on the unification of underlying identifiers, path mapping, and semantic consistency. Point-level data has become a key structural entry point and execution node in the big data processing system.

[0003] For example, invention patent CN116820354B discloses a data storage method, a data storage device, and a data storage system. The data storage method includes: first, in response to acquiring data to be stored, parsing the data to be stored to obtain namespace information corresponding to the data to be stored, directory tree information corresponding to the namespace information, and file data corresponding to the directory tree information; then, storing the namespace information in the data to be stored to a root domain name server group; next, storing the directory tree information corresponding to the namespace information to a target metadata server group, the target metadata server group being associated with the root domain name server group; and finally, storing the file data corresponding to the directory tree information to at least one target file server group, the at least one target file server group being associated with the target metadata server group. In the storage system, a multi-layered storage structure is constructed based on metadata, using a layered approach to manage system metadata.

[0004] For example, invention patent CN112262379B discloses one aspect of writing data items into data blocks of a stream segment. The stream segment includes a stream segment header and multiple data blocks. A first identifier of the data item is written into the stream segment header. A second identifier of the data item is written into the header of the data blocks of the stream segment. In another aspect, a query identifier is used to query the stream segment header of the stream segment. This query operation identifies whether any data item in the data blocks of the stream segment has the query identifier. If any data item in the data blocks of the stream segment has the query identifier, the query identifier is used to query the data blocks of the stream segment to identify which data blocks of the stream segment have the query identifier.

[0005] Current site data management primarily relies on field mapping, path configuration, and interface integration. However, there is a general lack of a unified site identification and path structure organization mechanism across different systems. Due to scattered naming structures, inconsistent source attributes, and inconsistent path hierarchy rules, problems such as field conflicts, path misalignment, and chaotic calls frequently occur, affecting data consistency and configuration reliability.

[0006] To address the above issues, there is an urgent need for a data storage and retrieval method and system based on location identifiers. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a data storage and retrieval method and system based on location identifiers, which solves the problem of path conflicts caused by the lack of unified identifier mapping for common source locations among multiple systems during data retrieval.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a data storage and retrieval method based on location identifiers, comprising the following steps: S1, collecting multi-source location information and structural path information from SCADA and business systems, constructing a location basic information dataset, and preprocessing the location basic information dataset; S2, identifying the location naming structure classification status based on the location basic information dataset, and adjusting the unique identifier generation process, path registration permissions, and component binding levels according to the classification status; S3, analyzing the continuity trend by combining the classification status and the change characteristics of path field structure in historical periods, determining the path structure stability level, and synchronously updating the mapping path configuration strategy and field merging mapping permissions; S4, performing a conflict intensity level assessment based on the naming ownership status and path structure stability status, dividing the path control mode according to the conflict intensity, and outputting the registration permission field, path processing method, and component interaction configuration status.

[0009] Furthermore, the specific steps for collecting multi-source location information and structural path information from the SCADA system and business system to construct a location basic information dataset are as follows: Collect multi-source location information generated by the process execution system, operation record interface, and field structure management unit during system operation. This multi-source location information includes the system's original location name, alias mapping field, and source identifier. Combine this with the structural path information maintained by the path configuration system and task scheduling system. This structural path information includes path number, path level label, and path segment attribution label. Simultaneously extract the classification label and task context information configured in the task scheduling system. Perform preliminary field integration based on time stamps to construct the location basic information dataset.

[0010] Furthermore, the specific steps for preprocessing the location basic information dataset are as follows: Based on the field composition and structural characteristics of the location basic information dataset, a hierarchical consistency detection method based on field tree structure and an edit distance algorithm are used to perform structural comparison of path number, naming field, and source identifier, identifying and removing records with inconsistent field naming and mapping conflicts; combined with regular expression matching algorithm and character segmentation model, structural compliance verification is performed on the naming field, eliminating field samples with non-standard naming formats; an outlier identification algorithm based on density clustering is applied to identify structural position jump samples in the naming field and path field, removing abnormal field distribution data; a kernel density estimation algorithm combined with nearest neighbor interpolation method is used to continuously complete the missing fields in the path number and source identifier; unified processing of field mapping relationships is performed according to the path hierarchical label and field grouping structure, reconstructing the hierarchical index and mapping logic of fields in the path structure; and standardization and normalization processing is performed on the processed location basic information dataset.

[0011] Further, the specific steps for identifying the naming structure classification status of points based on the point basic information dataset are as follows: A naming alias mapping table is generated based on the naming alias mapping field in the point basic information dataset; standardization processing is performed according to the original point names, combined with the naming structure rules and the naming alias mapping table, to extract standard point names; a standard point structure is constructed based on the standard point names, and a feature vector structure is generated based on the standard point structure; a set of naming structures with consistent structural path features is extracted based on path level labels and path segment attribution labels, and attribution aggregation is completed based on naming attribution relationships to form the k-th type of naming structure set; the number of standard point names contained in the current naming group is identified through path number and task context information, and the k-th type of naming structure is statistically obtained. The system first counts the number of names in a list of candidate names. Then, it reads the classification label set configured in the task scheduling system and, combined with task context information, filters all name group numbers that meet the grouping conditions to form a candidate name group number set. Based on the feature vector structure, it performs vector comparison between the current standard point name and each name structure in the candidate name structure set, performs cosine similarity calculation, and obtains the corresponding name structure similarity value. It then calculates the name structure similarity value corresponding to each name structure in the k-th name structure set, sums them, and divides them by the number of name structures in the k-th class to obtain the average similarity value of the k-th name structure set. After calculating the average similarity value for all candidate name structure sets, it selects the name structure set with the largest average similarity value, and uses the corresponding name group number as the point classification mapping value.

[0012] Further, the specific steps for adjusting the unique identifier generation process, path registration permissions, and component binding levels based on the classification status are as follows: The attribution attribute of the point classification mapping value corresponds to a certain group number in the candidate naming group number set. In the candidate naming group number set, the standard naming group corresponds to number value 0, the mixed naming group corresponds to number value 1, and the abnormal naming group corresponds to number value 2. When the number value corresponding to the point classification mapping value is 0, it corresponds to the standard naming group. A standard point identifier is generated, written to the point identifier main table, the path information is pushed to the component address configuration unit, and the naming trust mark information is registered. When the number value corresponding to the point classification mapping value is 1, it corresponds to the mixed naming group. The naming normalization process is started, the naming alias mapping table is referenced to construct the standard point structure, the identifier information writing is paused, and the synchronous update is performed after normalization is completed. The point field binding permissions are restricted, and only the read-only display attribute is retained. When the number value corresponding to the point classification mapping value is 2, it corresponds to the abnormal naming group. The identifier generation process is stopped, the naming conflict identifier is configured, the binding entry of the point field in the component configuration process is removed, the naming conflict record information is written to the naming audit log, and submitted to the path scheduling control module for path isolation processing.

[0013] Furthermore, the specific steps for analyzing the continuous trend by combining the classification status and path field structure changes within a historical period are as follows: Receive named trusted tag information and the corresponding standard point identifier; bind the interactive components in the system to the standard point identifiers; configure the data access behavior and interaction direction attributes of the components; locate the target path segment endpoint field based on the path segment belonging label; extract the point value at the corresponding time; determine the point value of the j-th path segment by using the reverse index of the path hierarchy label to the endpoint field of the previous path segment sequentially adjacent to the j-th path segment; read the path number and path segment belonging... The system uses the combined information of the attribute tags to call the distance field in the path configuration system to obtain the actual distance length of the j-th path segment; it counts the number of path segments with complete path numbers to obtain the total number of path segments; starting from the second path segment, it performs calculations on each path segment sequentially, subtracting the value of the (j-1)-th path segment from the value of the j-th path point, taking the absolute value of the difference, and dividing it by the corresponding actual distance length of the j-th path segment to obtain the structural jump intensity of the path; it sums the structural jump intensities of all path segments to obtain the total structural path jump value; it subtracts one from the total number of path segments as the normalized segment term; and it divides the total structural path jump value by the normalized segment term to obtain the structural path trend value.

[0014] Furthermore, the specific steps for determining the stability level of the path structure and synchronously updating the mapping path configuration strategy and field merging mapping permissions are as follows: When the single-period change rate of the structure path trend value is greater than the structure path change rate threshold in three consecutive detection periods, and the path jump amplitude in the current period is greater than the path jump amplitude threshold, the path structure splitting program is executed, the path fields are stripped and the field index is reconstructed, the path structure pointer in the field structure mapping table is updated, the normalization registration permission of the path fields is hidden, and the data is written to the structure trend record table; when the change rate of the structure path trend value in the current detection period is less than the structure path change rate threshold, Furthermore, if the path jump amplitude is less than the path jump amplitude threshold in two consecutive cycles, the structural mapping relationship is maintained, and the system is incorporated into the standard normalized mapping process. The path field is registered in the point identifier main table, and the structural stability mark is saved in the field structure cache area. When the rate of change of the structural path trend value in the sliding window crosses the positive and negative direction judgment boundary of the structural path rate of change threshold three times in a row, and the path offset direction of adjacent cycles reverses in two or more cycles, the structural mapping update process is paused, the structural segmentation comparison algorithm is called to perform structural decomposition operation, the automatic merging weight of the oscillation path field is removed, and the oscillation frequency index is saved in the trend identification table.

[0015] Furthermore, the specific steps for evaluating the conflict intensity level based on the naming attribution status and path structure stability status are as follows: Construct a path scheduling control table to record the component access path, bound point identifier, source identifier, calling unit, and calling status; after receiving a path call request, extract the component call context information and determine the primary or backup path as the current call path based on the path priority management mechanism; perform path isolation processing on path fields with attached abnormal naming group markers, closing the field access channel; obtain the point classification mapping value and structure path trend value; square the point classification mapping value and structure path trend value respectively, perform addition on the two squared results to obtain a sum; take the square root of the sum to obtain the root value; multiply the fusion weight coefficient by the root value to obtain the mapping conflict intensity value.

[0016] Further, the specific steps for dividing the path control mode according to the conflict intensity and outputting the registration permission field, path processing method, and component interaction configuration status are as follows: The mapping conflict intensity value is compared with the conflict threshold in real time, where the conflict threshold includes a first-level conflict threshold and a second-level conflict threshold; when the mapping conflict intensity value is greater than or equal to the first-level conflict threshold, path blocking is performed, blocking the component binding registration entry of the point field, configuring a conflict blocking flag, registering the point conflict event to the path conflict log table, and submitting the conflict information to the administrator interface; when the mapping conflict intensity value is greater than the second-level conflict threshold but less than the first-level conflict threshold, standard point identifier generation is paused, the naming structure normalization algorithm and path structure comparison program are called, parallel normalization verification is performed, the point field is set to a read-only path state, path writing and path scheduling calculation processes are disabled, the path field is marked as pending confirmation, and it is moved to the delayed processing queue; when the mapping conflict intensity value is less than or equal to the second-level conflict threshold, standard point identifier generation and structure mapping are performed, the component path binding relationship is registered to the component address configuration unit, a basic path index is generated and saved to the path index table, and the mapping conflict intensity value is saved to the path call log table.

[0017] The second aspect of this invention provides a data storage and retrieval system based on location identifiers, comprising: a data source information acquisition module, a location identifier management module, a component address configuration module, and a path scheduling control module. The system is characterized in that: the data source information acquisition module is used to acquire multi-source location information and structural path information from SCADA systems and business systems, construct a location basic information dataset, and preprocess the location basic information dataset; the location identifier management module is used to identify the location naming structure classification status based on the location basic information dataset, and adjust the unique identifier generation process, path registration permissions, and component binding levels according to the classification status; the component address configuration module is used to analyze the continuity trend by combining the classification status and the change characteristics of path field structures within historical periods, determine the path structure stability level, and synchronously update the mapping path configuration strategy and field merging mapping permissions; the path scheduling control module is used to perform conflict intensity level assessment based on the naming ownership status and path structure stability status, divide the path control mode according to the conflict intensity, and output the registration permission field, path processing method, and component interaction configuration status.

[0018] The present invention has the following beneficial effects:

[0019] (1) This invention constructs a dataset of basic information of points and cleans, corrects and reconstructs the field naming structure and path hierarchy structure in the preprocessing stage to ensure the consistency of field organization of point identification data in subsequent classification and identification and path judgment, thereby improving the structural stability and field compatibility of component registration, path configuration and data binding.

[0020] (2) This invention identifies the classification status of the point naming structure, combines the naming alias mapping field and the path level label to perform multiple groups of naming structure grouping and vector comparison, generates point classification mapping value, realizes the point field classification judgment and standard identifier generation path dynamic adjustment under the condition of naming structure difference, and enhances the system's adaptability to multi-source naming structures.

[0021] (3) This invention combines the point classification mapping value and the jump characteristics of the path field in the historical period to calculate the structural path trend value, and divides the path structure stability level based on the structural path trend value, so as to realize the dynamic adjustment of the registration permission of the path field and the update and maintenance of the field structure mapping relationship, and improve the automatic adaptation capability and structural synchronization capability of the path configuration rules.

[0022] (4) This invention calculates the mapping conflict intensity value by jointly calculating the point classification mapping value and the structural path trend value, and divides the path control mode and component call state accordingly, thereby completing the registration entry restriction, read-only control or path blocking of component access path in conflict scenario, effectively supporting the access scheduling and configuration state switching of components in multi-path and multi-name environment.

[0023] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0024] Figure 1 This is a flowchart of a data storage and retrieval method based on location identifiers according to the present invention;

[0025] Figure 2 This is a structural diagram of a data storage and retrieval system based on location identifiers according to the present invention;

[0026] Figure 3 This is a distribution diagram of the structural path trend values ​​of the present invention;

[0027] Figure 4 A similarity matrix diagram for the location names in this invention. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Please see Figures 1-4This invention provides a technical solution: a data storage and acquisition method based on location identifiers, comprising the following steps: S1, collecting multi-source location information and structural path information from SCADA and business systems, constructing a location basic information dataset, and preprocessing the location basic information dataset; S2, identifying the location naming structure classification status based on the location basic information dataset, and adjusting the unique identifier generation process, path registration permissions, and component binding levels according to the classification status; S3, analyzing the continuity trend by combining the classification status and the change characteristics of path field structure in historical periods, determining the path structure stability level, and synchronously updating the mapping path configuration strategy and field merging mapping permissions; S4, performing a conflict intensity level assessment based on the naming ownership status and path structure stability status, dividing the path control mode according to the conflict intensity, and outputting the registration permission field, path processing method, and component interaction configuration status. SCADA systems are industrial process monitoring systems used to collect field equipment data in real time and perform monitoring and control operations at the central end.

[0030] Specifically, the steps for collecting multi-source location information and structural path information from the SCADA system and business systems to construct a location basic information dataset are as follows: Collect structural location records generated by the process execution system during system operation, user interaction log fields output by the operation record interface, and field binding configurations maintained by the field structure management unit to form a multi-source location information input set. This multi-source location information includes the system's original location naming information, reflecting the initial identification method of physical locations within each system; a naming alias mapping field, indicating the pointing relationship and unified mapping rules between multiple systems for each naming format; a source identifier, indicating the generating system, data flow direction, and interface call path of each named record; and structural path information maintained by the path configuration system, including information marked by the path number field. The system employs a unique path identification code to support path tracing and mapping positioning. A path hierarchy label field represents the path's hierarchical structure information, recording the field's location within a multi-level path structure. A path segmentation label field indicates the path's block division information, clarifying the functional segment and system boundary location to which the field belongs. Classification label information configured in the task scheduling system is extracted synchronously to label the current task's type structure and field binding range. Task context information, including task number, operation step identifier, process segment number, and scheduling cycle sequence, is extracted to support the mapping between location information and task execution status. All information is merged and structurally compared at the field level according to time stamp order, unifying field structure and naming attributes to construct a basic location information dataset with naming, source, path, and context labels.

[0031] This implementation plan achieves unified integration of point naming structure, path hierarchy and task context among multiple systems, opens up the mapping channel between point source, structural location and task tag, enhances the consistency and structural support capability of basic point information in subsequent normalization processing, path parsing and component configuration, and improves the accuracy of field recognition and the reliability of path binding.

[0032] Specifically, the preprocessing steps for the location basic information dataset are as follows: Based on the field composition and structural characteristics of the location basic information dataset, a hierarchical consistency detection method based on field tree structure and edit distance algorithm are adopted. For the structural arrangement differences between path numbers, naming fields, and source identifiers, node order comparison and character-level similarity calculation of the hierarchical path structure are performed to identify records with naming redundancy, spelling errors, and identifier conflicts within the system, and to remove field entries with structural ambiguity. Combining regular expression matching algorithm and character segmentation model, mapping rules are established between character patterns and naming rule templates to identify illegal characters, abnormal field nesting, and misaligned delimiters in naming fields, eliminating field samples with non-compliant formats. An outlier identification algorithm based on density clustering is applied. This process involves identifying, classifying, and removing abnormal records with structural position jumps in the named and path fields, and addressing abnormal field distributions caused by breaks or node jumps in the path index. A kernel density estimation algorithm is used to evaluate the distribution trend of path numbers and source identifiers in the field space, and nearest neighbor interpolation is combined to continuously complete missing fields, ensuring the consistency of fields between the path structure and source identifiers. The mapping relationship between path fields and named fields is uniformly reconstructed based on path hierarchy labels and field grouping structure, adjusting the hierarchical index and attribution label of fields in the structural path to standardize the field mapping logic. Finally, field-level standardization and normalization are performed on the point-based information dataset after structural cleaning and mapping adjustments to ensure data format uniformity and comparable distribution scales.

[0033] This implementation plan improves the standardization and completeness of the location basic information dataset in terms of naming structure, path hierarchy, and source identifier, eliminates interference factors caused by misaligned path structures and abnormal naming formats, strengthens the mapping consistency and hierarchical stability between fields, and provides a clean, complete, and structurally unified data foundation for subsequent location identifier generation and path scheduling control.

[0034] Specifically, the steps for identifying the naming structure classification status of points based on the point basic information dataset are as follows: A naming alias mapping table is generated based on the naming alias mapping field in the point basic information dataset. This table records the differences in naming methods for the same physical point in different systems and their corresponding relationships, supporting the accurate execution of naming normalization operations. Based on the original point names, standardization processing is performed using the naming structure rules and the naming alias mapping table to extract standard point names with a unified semantic structure and rule format. These standard point names reflect the unified naming results for points originating from the same source across multiple systems. For example, "Temp1", "Node A Temperature", and "Process Temperature" are standardized to "Equipment 01_Temperature_Pipe Segment A". Temp1 represents... The local naming conventions for temperature measurement points typically originate from equipment manufacturers or early system point agreements, serving only as a basic identifier for a specific temperature sensor. A standard point structure is constructed based on these standard point names, organizing the structural hierarchy according to field composition, naming order, and semantic rules. A feature vector structure is then generated based on this structure to support similarity calculations and clustering between naming structures. Before vectorization, the feature vector structure is a list of fields consisting of "equipment number," "monitoring variable," and "spatial location." After vectorization, it is mapped to a sparse vector such as [0.7, 0.1, 0.0, 0.9, ...]. A set of naming structures with consistent structural path features is extracted based on path hierarchy labels and path segment affiliation labels. This is achieved through field hierarchy positions and affiliation segment labels. Combined analysis with path rules is used to screen naming structures with normalization potential; attribution aggregation is completed based on naming attribution relationships to form a set of naming structures of the kth class, where k represents the attribution classification number based on the similarity between naming patterns and path structures, used to express the aggregation features between naming samples in terms of naming patterns and path structures; the number of standard point names contained in the current naming group is identified by path number and task context information, and the number of naming structures of the kth class is statistically obtained, used to normalize subsequent similarity scores; the classification label set configured in the task scheduling system is read, and all naming group numbers that meet the grouping conditions are screened in combination with task context information to form a candidate naming group number set, providing a selection range for naming structure comparison; based on feature orientation... The system performs vector comparisons between the current standard location name and each name structure in the candidate naming structure set, calculates cosine similarity, and quantifies the matching degree between the current name structure and each candidate name structure to obtain the corresponding name structure similarity value. It then calculates the name structure similarity value for each name structure in the k-th class of name structures, sums them, and divides the sum by the number of name structures in the k-th class to obtain the average similarity value of the k-th class of name structures, which is used to characterize the overall structural aggregation degree. After calculating the average similarity value for all candidate name structure sets, it selects the name structure set with the largest average similarity value, and uses the naming group number corresponding to the maximum value as the location classification mapping value, which represents the optimal classification number to which the current location name structure belongs.

[0035] The specific calculation method for the point classification mapping value is as follows:

[0036] ;

[0037] In the formula, This represents the point classification mapping value. This represents the similarity value of the candidate naming structures. Represents the set of naming structures of the kth class. This indicates the number of naming structures of type k. This represents the set of candidate naming group numbers.

[0038] This implementation plan achieves semantic aggregation and structural unification of naming fields across different systems, establishes an association between naming structure and path features, improves the classification accuracy and attribution determination capability of location names in a multi-source environment, and provides a well-defined and clearly structured naming foundation for subsequent location identification generation and path binding rules.

[0039] Specifically, the steps for adjusting the unique identifier generation process, path registration permissions, and component binding level based on the classification status are as follows: The attribution attribute of the point classification mapping value corresponds to a group number in the candidate naming group number set. The candidate naming group number set is aggregated from the naming structures that meet the grouping conditions in the task context. The numbering rules are used to identify the priority of naming consistency and normalization processing. The standard naming group corresponds to a number value of 0, indicating that the naming structure is highly consistent with the standard template; the mixed naming group corresponds to a number value of 1, indicating that the naming fields have certain differences but meet the normalization conditions; the abnormal naming group corresponds to a number value of 2, indicating that the naming structure has serious defects or conflicting information; when the number value corresponding to the point classification mapping value is 0, it belongs to the standard naming group, and a standard point identifier is generated. The standard point identifier is obtained by jointly calculating the standard naming structure and the path structure, and has uniqueness and traceability. It is written to the point identifier master table and a mapping relationship between the identifier and the path is established. At the same time, the path information is pushed to the component address configuration unit to complete the component path binding and access configuration, and the naming trust mark information is registered for use. Subsequent audit calls: When the number corresponding to the point classification mapping value is 1, it belongs to the mixed naming group, and the naming normalization process is initiated. The alias mapping table is used to parse and adjust the naming of the structural fields, and a standard point structure with a unified format is constructed. The current identifier generation process is suspended. After the normalization is completed, the point identifier main table is updated synchronously. During this period, the binding permissions of the point field are restricted, and only the path display function and read-only attribute are retained. Writing and scheduling calls are prohibited. When the number corresponding to the point classification mapping value is 2, it belongs to the abnormal naming group, the identifier generation process is stopped, and a naming conflict identifier is configured to mark the non-normalization problem of missing fields, semantic overlap, and incomplete source information in the naming structure. The binding entry of the point field in the component configuration process is removed synchronously, and the field is prohibited from participating in subsequent path binding and component mapping operations. The naming conflict information is recorded in the naming audit log for system tracing and responsibility analysis. At the same time, it is submitted to the path scheduling control module to trigger the path isolation process to prevent abnormal naming paths from affecting the stability and execution consistency of other structural paths.

[0040] In this implementation plan, the location classification mapping value serves as the basis for determining the naming structure aggregation state, guiding the unique identifier generation process, path registration permissions, and component binding level hierarchical responses, and constructing a multi-layered control path from trusted identifier generation to anomaly isolation handling. Naming structure consistency, alias unification capability, and path configuration stability work together in the mapping value generation process, supporting the switching of strategies for identifier writing, path pushing, and binding permission management, ensuring that the path mapping logic has clear boundaries and hierarchical control capabilities.

[0041] Specifically, the steps for analyzing the continuous trend by combining the changes in classification status and path field structure over historical periods are as follows: Receive named trusted tag information and corresponding standard point identifiers; bind interactive components in the system to the standard point identifiers; register the interaction direction attribute in the component metadata to define the calling permissions of data access actions during path mapping; locate the target path segment endpoint field based on the path segment ownership tag, extract the point value at the corresponding time, and obtain the point value of the j-th path segment, which serves as the state characterization of the path segment tail field at the target time; use the path hierarchy tag to reverse index the endpoint field of the previous path segment sequentially adjacent to the j-th path segment, where j represents the segment sequence number in the overall structure, extract the point value at the corresponding time of the previous segment, and determine the point value of the (j-1)-th path segment, which reflects the differences in continuous states between path segments; read the combination information of path number and path segment ownership tag, call the distance field in the path configuration system, and obtain the actual distance length of the j-th path segment to ensure that the jump intensity calculation has a physical span benchmark; statistically mark the complete path code. The number of path segments is used to obtain the total number of path segments, which serves as the global structural parameter for trend recognition calculation. Starting from the second path segment, calculations are performed sequentially for each path segment. The value of the j-th path point is subtracted from the value of the (j-1)-th path point, and the absolute value of the difference is divided by the actual distance length of the corresponding j-th path segment to construct an expression reflecting the fluctuation intensity of the path field in the local structure, avoiding deviations introduced by uneven path lengths. The structural jump intensities of all path segments are summed to obtain the total structural path jump value, which serves as the cumulative fluctuation intensity of the entire path within a specified period. The total number of path segments is subtracted by one to obtain a normalized segmentation term, used to calibrate the impact of the number of path segments on the average level of jump intensity. The total structural path jump value is divided by the normalized segmentation term to obtain the structural path trend value. The structural path trend value serves as a trend indicator of structural continuity in the classification scenario, reflecting not only the overall stability of the cascading changes of path segments but also forming a two-dimensional indicator system with the naming deviation. Subsequently, the Euclidean norm is used to fuse and form a mapping conflict intensity, providing a unified intensity expression and control trigger basis for path structure jitter recognition and normalization judgment.

[0042] The specific calculation method for the structural path trend value is as follows:

[0043] ;

[0044] In the formula, Indicates the structural path trend value. This represents the value of the point in the j-th path segment. This represents the value of the point in the (j-1)th path segment. This represents the actual distance length of the j-th path segment. This indicates the total number of path segments.

[0045] In this implementation plan, the structural path trend value is constructed by normalizing the difference in the status of standard points and the path distance to express the jump intensity. Combined with the classification status, the continuity trend between path segments is quantitatively analyzed. The trend results are not only used for structural stability assessment, but also serve as a core component for mapping conflict intensity. They are integrated with naming bias to form a unified path identification control quantity, supporting the dynamic adjustment of path binding strategies and the proactive avoidance of misclassification risks.

[0046] Specifically, the steps for determining the stability level of the path structure and synchronously updating the mapping path configuration strategy and field merging mapping permissions are as follows: Extract the single-cycle change rate sequence of the structural path trend value within a continuous control period; the upper quartile value corresponds to the structural path change rate threshold; construct a jump amplitude sequence based on the path position number offset value of the structural field in adjacent periods; extract the upper limit of the interval between the mean and the maximum value in the jump amplitude sequence as the path jump amplitude threshold; when the single-cycle change rate of the structural path trend value in three consecutive detection periods is greater than the structural path change rate threshold, and the path jump amplitude in the current period is greater than the path jump amplitude threshold, it indicates that the path structure is in a high-frequency unstable state. Execute the path structure splitting program to strip abnormal path fields in the continuous structure, reconstruct the field index system, reset the path structure pointer of the field in the structure mapping table, shield the unified registration permission, prevent jump path fields from being incorrectly included in the merging process, strengthen the structural boundary control capability, and write the processing results into the structural trend record table, retaining the entire change information for trend tracking; when the change rate of the structural path trend value in the current detection period is less than the structural path change rate threshold, and the change amplitude in two consecutive periods is less than the structural path change rate threshold... If the path jump amplitude is less than the path jump amplitude threshold, it indicates that the path structure is stable and has good continuity. The original structural mapping relationship is maintained, and the path is directly incorporated into the standard normalized mapping process. The path field is registered in the point identifier main table to form a formal normalized index, and a structural stability marker is generated and written to the field structure cache to provide a status basis for subsequent path priority matching and component calls. When the rate of change of the structural path trend value within the sliding window crosses the positive and negative direction judgment boundary of the structural path change rate threshold three times consecutively, and the path offset direction of adjacent periods reverses within two or more periods, the path fluctuation mode exhibits unstable oscillation. The current structural mapping update process is paused, and the structural segmentation comparison algorithm is called to decompose the relevant fields, clarify the structural boundaries, and remove the automatic merging weight of the oscillating path field to avoid classification errors caused by path reversal. Finally, the oscillation frequency index and segmentation processing information are written into the trend identification table. The above multi-level judgment logic uses the structural path trend value as the core variable, comprehensively considering jump amplitude and direction reversal information to accurately identify the path stability level, effectively controlling the risk of mis-merging of structures caused by short-period path jitter, and improving the credibility of path identification decisions and the execution accuracy of mapping strategies.

[0047] In this implementation plan, the structural path trend value drives the dynamic adjustment of path stability level classification and configuration strategy. The structural continuity status is judged by the linkage of multi-period change rate and jump amplitude. The unstable path identification mechanism is strengthened by combining direction reversal and oscillation frequency. A hierarchical processing system of path splitting, normalization registration and weight removal is constructed to effectively suppress merging misjudgment caused by path jitter and improve the boundary clarity of field structure mapping and the stable decision-making ability of path configuration.

[0048] Specifically, the steps for evaluating the conflict intensity level based on naming attribution status and path structure stability are as follows: Construct a path scheduling control table to record component access paths, binding point identifiers, source identifiers, calling units, and calling status. This table is used to uniformly manage path configuration and component calling behavior, supporting path priority selection and conflict status determination. Upon receiving a path call request, extract the component call context information, including task type, system affiliation, and interaction strategy. Determine the primary or backup path as the current call path based on the path priority management mechanism and write it to the path call log table for subsequent strategy tracing. Perform path isolation processing on path fields with abnormal naming group markers, closing field access channels and blocking non-standard paths from participating in component configuration and path normalization processes. Obtain point classification mapping values ​​and structural path trend values, representing the degree of deviation of the naming structure under the attribution classification and the fluctuation state of the path field in the structural hierarchy, respectively. Map the point classification mapping values... The projected value and the structural path trend value are squared respectively, and the two squared results are added to form the total combined bias. The square root of the sum is then taken to obtain the fusion root value, which is used to construct a unified conflict expression benchmark. The fusion weight coefficient is obtained by extracting the output of the fusion evaluation function in the multivariate association analysis of path change features and group similarity features. The value range is 0.0-1.0. The fusion weight coefficient is multiplied by the fusion root value to obtain the mapping conflict intensity value, which is used to represent the comprehensive conflict intensity of the current path field under the dual dimensions of category bias and structural fluctuation. The above conflict intensity value is based on Euclidean norm as a mathematical basis. The fusion naming bias and structural fluctuation are used as single path control variables to uniformly drive path isolation, priority judgment and component binding permission adjustment. This constitutes the core mechanism of the path control strategy based on the attribution-trend fusion expression. It belongs to the algorithm innovation principle that can be clearly stated in path mapping control, which significantly improves the stability of path identification results and the consistency of classification decisions.

[0049] The specific calculation method for the mapping conflict intensity value is as follows:

[0050] ;

[0051] In the formula, This represents the mapping conflict intensity value. Indicates the fusion weighting coefficient. This represents the point classification mapping value. This indicates the trend value of the structural path.

[0052] Table 1 shows the mapping conflict intensity value data table provided in the embodiments of this application. The fusion weight coefficient of conflict 1 is set to 0.80, the point classification mapping value is set to 0.65, and the structural path trend value is set to 0.45; the fusion weight coefficient of conflict 2 is set to 0.75, the point classification mapping value is set to 0.60, and the structural path trend value is set to 0.50; the fusion weight coefficient of conflict 3 is set to 0.65, the point classification mapping value is set to 0.40, and the structural path trend value is set to 0.20; the fusion weight coefficient of conflict 4 is set to 0.90, the point classification mapping value is set to 0.80, and the structural path trend value is set to 0.60; and the fusion weight coefficient of conflict 5 is set to 0.60, the point classification mapping value is set to 0.50, and the structural path trend value is set to 0.35.

[0053] Table 1. Mapping Conflict Intensity Values ​​Data Table

[0054]

[0055] like Figure 3 The figure shows the distribution of mapping conflict intensity values ​​provided in this application embodiment. According to the data in the image and table, the first-level conflict threshold and the second-level conflict threshold are marked with dashed lines. The mapping conflict intensity values ​​corresponding to the five sets of conflict data fluctuate between 0.29 and 0.90, with significant overall differences. Conflict 4 has a mapping conflict intensity value of 0.90, exceeding the first-level conflict threshold, indicating a significant superposition of naming deviation and structural fluctuation, requiring immediate blocking of the path mapping. Conflicts 1 and 2 have intensity values ​​of 0.63 and 0.59 respectively, between the second-level and first-level conflict thresholds, indicating a moderate conflict risk, requiring path delay processing and temporary suspension of normalization. Conflicts 3 and 5 have intensity values ​​of 0.29 and 0.37 respectively, below the second-level conflict threshold, indicating good path structure continuity and low naming deviation, allowing direct execution of component binding and path registration. This figure can be used to intuitively identify the path mapping stability level, providing a strategic basis for subsequent path isolation, normalization push, and scheduling optimization.

[0056] In this implementation scheme, the mapping conflict intensity value is fused with the Euclidean norm of the naming attribution state and the path structure stability state to construct a path conflict expression index with unified dimensions and continuous measurement capabilities. This index serves as the core judgment basis for the path scheduling control table and component call context, driving path priority judgment, anomaly isolation and access channel control, strengthening the mathematical support and strategy consistency of path management logic, and improving the accuracy of multi-source path conflict identification and the stability of component binding strategy.

[0057] Specifically, the steps for classifying path control modes based on conflict intensity and outputting registration permission fields, path processing methods, and component interaction configuration states are as follows: The mapped conflict intensity value is compared in real-time with the conflict threshold, which includes a first-level conflict threshold and a second-level conflict threshold. These thresholds correspond to the identification boundaries of high-risk and medium-risk path structures, respectively, and are used to finely distinguish the permission levels and processing strategies of path fields during component binding. When the mapped conflict intensity value is greater than or equal to the first-level conflict threshold, it is determined that the path field has a high-intensity naming deviation and structural instability. A path blocking operation is performed, blocking the component binding registration entry point for the point field. A conflict blocking flag is configured to identify the unregisterable state. Simultaneously, the point conflict event is registered in the path conflict log table, recording the conflict source information and triggering context. The conflict information is submitted to the administrator interface as a basis for policy intervention. When the mapped conflict intensity value is greater than the second-level conflict threshold but less than the first-level conflict threshold, it is determined that the path field has a medium-risk conflict. The standard point identifier generation process is paused, and the naming structure normalization algorithm and path structure comparison program are called to address the naming... The process involves mapping aliases to the path field, comparing its hierarchical position, performing parallel normalization checks, constructing a multi-dimensional normalization judgment structure, setting the point field to a read-only path state, disabling path writing and path scheduling calculation processes to prevent conflicting fields from participating in dynamic path scheduling logic, marking the path field as pending confirmation, and moving it to a delayed processing queue for subsequent judgment and updates. When the mapping conflict intensity value is less than or equal to the second-level conflict threshold, it indicates that the path field has clear classification and structural continuity. The standard point identifier generation process is then executed to construct a one-to-one mapping relationship between the path field and the naming structure, while simultaneously completing the structural mapping logic. The component path binding relationship is registered to the component address configuration unit, a basic path index is generated and written to the path index table, forming a reusable path configuration template. The current mapping conflict intensity value is written to the path call log table for subsequent path status monitoring and normalization strategy updates. The entire path control mechanism dynamically adjusts path registration permissions, component interaction configuration status, and path processing methods in a hierarchical response manner to ensure precise control of path binding based on naming consistency and structural stability.

[0058] In this implementation scheme, the mapping conflict intensity value is used as the core control variable for path status identification. A dynamic switching mechanism between path blocking, delay normalization and normal registration is driven by a hierarchical threshold structure. A linkage update strategy is constructed for path control mode, registration permission field and component interaction configuration status. This realizes real-time coupling between path identification results and component configuration behavior, and improves the identification accuracy, strategy response efficiency and structural robustness in the path binding process.

[0059] like Figure 2The diagram shown is a structural schematic of a data storage and retrieval system based on location identifiers provided in this application embodiment. This system applies a data storage and retrieval method based on location identifiers, including: a data source information acquisition module, a location identifier management module, a component address configuration module, and a path scheduling control module. The data source information acquisition module is used to collect multi-source location information and structural path information from the SCADA system and business systems, extract raw data containing naming fields, source identifiers, path numbers, and context labels, construct a location basic information dataset with time sequence and structural hierarchical characteristics, and perform preprocessing operations on the location basic information dataset including field consistency verification, naming compliance cleaning, and structural location completion. The location identifier management module is used to identify the location naming structure classification status based on the location basic information dataset, generate classification labels through naming alias mapping and feature vector comparison, adjust the unique identifier generation process according to the classification status, and set path registration permission boundaries. The system assigns binding level constraints to components, enabling component binding operations to verify the accuracy of naming and the integrity of normalization. The component address configuration module analyzes the continuity trend by combining the changes in classification status and path field structure over historical periods, constructs an expression of the jump intensity between path fields, generates structural path trend values, determines the stability level of the path structure, and performs structural stripping, normalization retention, or state suspension based on the identification of oscillation frequency and direction reversal. It also synchronously updates the mapping path configuration strategy and field merging mapping permissions to ensure the temporal consistency and clarity of classification boundaries in structural mapping decisions. The path scheduling control module performs conflict intensity level assessment based on the naming attribution status and path structure stability status. It uses Euclidean norm to fuse naming deviations and structural fluctuations, generates mapping conflict intensity values, uniformly expresses path classification anomalies and structural jump risks, divides path control modes according to conflict intensity, dynamically switches path registration permissions, path processing methods, and component interaction configuration states, and drives the coordinated response of path invocation strategies, component access behavior, and structural mapping logic.

[0060] like Figure 4The figure shows the point naming similarity matrix provided in this application embodiment. The figure displays the pairwise similarity distribution between five typical point names, with the similarity values ​​of each combination directly labeled using a matrix table structure. The similarity values ​​are set from 0.40 to 1.00. Temp1 and Temp_01 have a similarity of 0.91, and Temp1 and process temperature have a similarity of 0.84, both showing high consistency, indicating a strong normalization basis in terms of character structure and semantic orientation. However, Temp1 and the set temperature have a similarity of only 0.40, and Temp_01 and the set temperature have a similarity of 0.45, showing significant naming differences and a high risk of classification processing. The overall similarity matrix shows a high similarity clustering trend centered on Temp1, which can provide a basis for naming structure normalization processing strategies and assist in determining naming attribution relationships and structural uniformity levels.

[0061] In this implementation plan, four modules construct a linked control structure for data acquisition, naming and classification, path stability judgment and conflict intensity assessment. This connects the logical links between point information sources, naming structure recognition, path status parsing and component binding strategies, forming a closed loop of path control with naming attribution and structural trends as the dual cores. This enables fine-grained dynamic adjustment of path registration permissions, merging strategies and component configurations, improving the stability of path recognition and the accuracy of interactive control across multiple systems.

[0062] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0063] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A data storage and acquisition method based on point location identification, characterized in that, Includes the following steps: S1: Collect multi-source location information and structural path information from the SCADA system and business system, construct a location basic information dataset, and preprocess the location basic information dataset; S2, based on the location basic information dataset, identifies the location naming structure classification status, and adjusts the unique identifier generation process, path registration permissions and component binding level according to the classification status; S3 combines the changes in classification status and path field structure over historical periods to analyze the continuous trend, determine the stability level of the path structure, and synchronously update the mapping path configuration strategy and field merging mapping permissions. The specific steps for determining the stability level of the path structure and synchronously updating the mapping path configuration strategy and field merging mapping permissions are as follows: When the single-cycle change rate of the structural path trend value is greater than the structural path change rate threshold in three consecutive detection cycles, and the path jump amplitude in the current cycle is greater than the path jump amplitude threshold, the path structure splitting program is executed to strip the path field and reconstruct the field index, update the path structure pointer in the field structure mapping table, block the normalization registration permission of the path field, and write it into the structural trend record table. When the rate of change of the structural path trend value in the current detection period is less than the structural path change rate threshold, and the path jump amplitude in two consecutive periods is less than the path jump amplitude threshold, the structural mapping relationship is maintained, it is included in the standard normalized mapping process, the path field is registered to the point identifier main table, and the structural stability mark is saved to the field structure cache area. When the rate of change of the structural path trend value within the sliding window crosses the positive and negative boundary of the structural path rate of change threshold three times consecutively, and the path offset direction of adjacent periods reverses within two or more periods, the structural mapping update process is paused, the structural segmentation comparison algorithm is called to perform structural decomposition, the automatic merging weight of the oscillation path field is removed, and the oscillation frequency index is saved to the trend recognition table. S4 performs a conflict intensity level assessment based on the naming ownership status and path structure stability status, divides the path control mode according to the conflict intensity, and outputs the registration permission field, path processing method, and component interaction configuration status.

2. The data storage and acquisition method based on point identification according to claim 1, characterized in that: The specific steps for collecting multi-source location information and structural path information from the SCADA system and business systems to construct a location basic information dataset are as follows: The system collects multi-source location information generated by the process execution system, operation record interface, and field structure management unit during system operation. This multi-source location information includes the system's original location name, name alias mapping field, and source identifier. It also combines the structured path information maintained by the path configuration system and task scheduling system, which includes path number, path level label, and path segment attribution label. The system simultaneously extracts the classification label and task context information configured in the task scheduling system. The above information is then preliminarily integrated based on time stamps to construct a basic location information dataset.

3. The data storage and acquisition method based on point identification according to claim 1, characterized in that: The specific steps for preprocessing the basic information dataset of the locations are as follows: Based on the field composition and structural characteristics of the location basic information dataset, a hierarchical consistency detection method based on field tree structure and edit distance algorithm are used to perform structural comparison of path number, naming field, and source identifier, identifying and removing records with inconsistent field naming and mapping conflicts. A regular expression matching algorithm and character segmentation model are combined to verify the structural compliance of naming fields, eliminating field samples with non-standard naming formats. A density clustering-based outlier identification algorithm is applied to identify samples with abrupt structural position changes in naming and path fields, removing abnormal field distribution data. A kernel density estimation algorithm combined with nearest neighbor interpolation is used to continuously complete missing fields in path number and source identifier. Based on the path hierarchical label and field grouping structure, a unified processing of field mapping relationships is performed, reconstructing the hierarchical index and mapping logic of fields in the path structure. Finally, the processed location basic information dataset is standardized and normalized.

4. The data storage and acquisition method based on point identification according to claim 1, characterized in that: The specific steps for identifying the naming structure and classification status of points based on the point basic information dataset are as follows: A naming alias mapping table is generated based on the naming alias mapping field in the basic information dataset of the points; standardization processing is performed based on the original point names, combined with the naming structure rules and the naming alias mapping table, to extract standard point names; a standard point structure is constructed based on the standard point names, and a feature vector structure is generated based on the standard point structure. Based on path level labels and path segment attribution labels, a set of naming structures with consistent path features is extracted. Attribution is then performed by combining naming attribution relationships to form the k-th type of naming structure set. The number of standard point names contained in the current naming group is identified by path number and task context information, and the number of the k-th type of naming structure is counted. The set of classification labels configured in the task scheduling system is read, and all naming group numbers that meet the grouping conditions are filtered by combining task context information to form a candidate naming group number set. Based on the feature vector structure, the current standard point name is compared with each naming structure in the candidate naming structure set one by one, and the cosine similarity is calculated to obtain the corresponding naming structure similarity value. Calculate the name structure similarity value for each name structure in the k-th name structure set, sum them, and divide by the number of name structures in the k-th class to obtain the average similarity value of the k-th name structure set. After calculating the average similarity value for all candidate name structure sets, select the name structure set with the largest average similarity value, and use the corresponding name group number as the point classification mapping value.

5. The data storage and acquisition method based on point identification according to claim 1, characterized in that: The specific steps for adjusting the unique identifier generation process, path registration permissions, and component binding levels based on the classification status are as follows: The attribution attribute of the point classification mapping value corresponds to a group number in the candidate naming group number set. In the candidate naming group number set, the standard naming group corresponds to number value 0, the mixed naming group corresponds to number value 1, and the abnormal naming group corresponds to number value 2. When the number value corresponding to the point classification mapping value is 0, it corresponds to the standard naming group. A standard point identifier is generated, written to the point identifier master table, the path information is pushed to the component address configuration unit, and the naming trust mark information is registered. When the number value corresponding to the point classification mapping value is 1, it corresponds to the mixed naming group. The naming normalization process is started. The naming alias mapping table is referenced to build the standard point structure. The writing of identifier information is paused. After normalization is completed, it is updated synchronously. The binding permissions of the point field are restricted, and only the read-only display attribute is retained. When the number value corresponding to the point classification mapping value is 2, it corresponds to the abnormal naming group. The identifier generation process is stopped, the naming conflict identifier is configured, the binding entry of the point field in the component configuration process is removed, the naming conflict record information is written to the naming audit log, and it is submitted to the path scheduling control module for path isolation processing.

6. The data storage and acquisition method based on point identification according to claim 1, characterized in that: The specific steps for analyzing the continuous trend by combining the changes in classification status and path field structure within historical periods are as follows: Receive the named trusted tag information and the corresponding standard point identifier, bind the interactive components in the system to the standard point identifier, and configure the data access behavior and interaction direction attributes of the components; locate the endpoint field of the target path segment according to the path segment ownership tag, extract the point value at the corresponding time, and obtain the point value of the j-th path segment; extract the point value at the corresponding time of the previous path segment by using the reverse index of the path level tag and the endpoint field of the path segment sequentially adjacent to the j-th path segment, and determine the point value of the (j-1)-th path segment; read the combination information of the path number and the path segment ownership tag, call the distance field in the path configuration system, and obtain the actual distance length of the j-th path segment; The total number of path segments is obtained by counting the number of path segments with complete path numbers. Starting from the second path segment, perform calculations for each path segment in sequence. Subtract the value of the point in the (j-1)th path segment from the value of the point in the jth path segment, take the absolute value of the difference, and divide it by the actual distance length of the corresponding jth path segment to obtain the structural jump intensity of the path. Sum the structural jump intensities of all path segments to obtain the total structural path jump value. Subtract one from the total number of path segments as the normalized segmentation term; The structural path trend value is obtained by dividing the sum of the structural path jump values ​​by the normalized segmentation term.

7. The data storage and acquisition method based on point identification according to claim 1, characterized in that: The specific steps for performing conflict intensity level assessment based on naming attribution status and path structure stability status are as follows: Construct a path scheduling control table to record component access paths, binding point identifiers, source identifiers, calling units, and calling status; after receiving a path call request, extract the component call context information and determine the primary path or backup path as the current call path based on the path priority management mechanism; Perform path isolation processing on path fields with attached exception naming group tags, and close the field access channel; Obtain point classification mapping values ​​and structural path trend values; The point classification mapping value and the structural path trend value are squared respectively. The two squared results are added together to obtain a sum. The square root of the sum is obtained. The fusion weight coefficient is multiplied by the root value to obtain the mapping conflict intensity value.

8. The data storage and acquisition method based on point identification according to claim 1, characterized in that: The specific steps for dividing the path control mode according to the conflict intensity and outputting the registration permission field, path processing method, and component interaction configuration status are as follows: The mapping conflict intensity value is compared with the conflict threshold in real time, and the conflict threshold includes a first-level conflict threshold and a second-level conflict threshold; When the mapping conflict intensity value is greater than or equal to the first-level conflict threshold, path blocking is executed, the component binding registration entry of the blocking point field is bound, the conflict blocking flag is configured, the point conflict event is registered to the path conflict log table, and the conflict information is submitted to the administrator interface. When the mapping conflict intensity value is greater than the second-level conflict threshold and less than the first-level conflict threshold, the standard point identifier generation is paused, the naming structure normalization algorithm and the path structure comparison program are called, parallel normalization verification is performed, the point field is set to read-only path status, path writing and path scheduling calculation processes are disabled, the path field is marked as pending confirmation status, and it is moved into the delayed processing queue. When the mapping conflict intensity value is less than or equal to the secondary conflict threshold, standard point identifier generation and structure mapping are performed, the component path binding relationship is registered to the component address configuration unit, a basic path index is generated and saved to the path index table, and the mapping conflict intensity value is saved to the path call log table.

9. A point location identification based data storage and retrieval system, applying the point location identification based data storage and retrieval method of any one of claims 1-8, comprising: The data source information acquisition module, the point identification management module, the component address configuration module, and the path scheduling control module are characterized by: The data source information acquisition module is used to collect multi-source location information and structural path information from the SCADA system and business system, construct a location basic information dataset, and preprocess the location basic information dataset. The location identification management module is used to identify the location naming structure classification status based on the location basic information dataset, and adjust the unique identifier generation process, path registration permissions and component binding level according to the classification status. The component address configuration module is used to analyze the continuous trend by combining the classification status and the change characteristics of the path field structure in the historical period, determine the stability level of the path structure, and synchronously update the mapping path configuration strategy and field merging mapping permissions. The path scheduling control module is used to perform conflict intensity level assessment based on naming ownership status and path structure stability status, divide path control mode according to conflict intensity, and output registration permission field, path processing method and component interaction configuration status.

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