A data processing method, apparatus, device, and medium

Abstract

Embodiments of the application disclose a data processing method, device and equipment and medium, the method comprises: acquiring multiple devices collected multi-source work order data; for multi-source work order data, according to the data content of multi-source work order data and target hash algorithm, the first hash value of multi-source work order data is generated;If the second hash value corresponding to the historical work order data is different from the first hash value, then determine that the multi-source work order data is incremental work order data;For incremental work order data, determine the standard field corresponding to each to-be-processed field in the incremental work order data, and cache the data content corresponding to the to-be-processed field to the corresponding standard field, obtain the target work order data of the incremental work order data, so as to avoid full data repeated traversal through incremental identification, reduce the computing power consumption and transmission load;Through automatic field mapping, multi-source heterogeneous data normalization is realized, without manual offline cleaning, the data aggregation efficiency and accuracy are improved, and the response delay is shortened.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of computer technology, and in particular to a data processing method, apparatus, device and medium. Background Technology

[0002] With the deepening of digital transformation in the power industry, the distribution network operation business of power grid companies is becoming increasingly complex, and its core business processes cover various work order types. These work order data are stored in multiple independent business systems. In order to meet the needs of distribution network dispatch management for unified monitoring, comprehensive statistics and full lifecycle tracking of cross-business work orders, it is necessary to aggregate, integrate and normalize the above-mentioned multi-source work order data to support subsequent data analysis and process control.

[0003] Currently, power grid companies generally use a discrete distribution network work order management system to process various work order data. These systems are deployed independently for each business unit, each using its own database and proprietary field specifications, with no unified data exchange gateway between them. At the data aggregation level, existing systems periodically export the entire raw work order dataset from each business system. Maintenance personnel then manually identify and map heterogeneous fields such as field names, time formats, and status codes before importing them into the aggregate database. At the data storage and query level, existing systems store all historical work order data using traditional relational databases. Each time a statistical query is triggered, the entire dataset must be traversed and scanned before results can be returned.

[0004] However, the aforementioned existing technologies have the following drawbacks. On the one hand, the field specifications of various business systems are inconsistent, and there is a lack of automated field matching and normalization mechanisms. Manual offline cleaning and mapping are necessary to complete cross-business data aggregation, which is inefficient and cannot meet real-time aggregation requirements. On the other hand, the existing systems lack a change order identification mechanism. Each statistical query requires traversing all historical data. When the data volume reaches millions, a full scan consumes significant computing and transmission resources, leading to a sharp increase in statistical query latency. This makes it impossible to support efficient, low-latency aggregation and management of multi-source work order data. Summary of the Invention

[0005] This invention provides a data processing method, apparatus, device, and medium that avoids repeated traversal of the entire data through incremental identification, reducing computing power and transmission load; and achieves normalization of multi-source heterogeneous data through automatic field mapping, eliminating the need for manual offline cleaning, improving data aggregation efficiency and accuracy, and shortening response latency.

[0006] In a first aspect, embodiments of the present invention provide a data processing method applied in a distributed cluster, the distributed cluster including multiple devices, the devices being used to acquire corresponding work order data, the method further including: Acquire multi-source work order data collected by the multiple devices, wherein the data content in the multi-source work order data is related to the data generated by power grid business; For the multi-source work order data, a first hash value of the multi-source work order data is generated based on the data content of the multi-source work order data and the target hash algorithm; If the second hash value corresponding to the historical work order data is different from the first hash value, then the multi-source work order data is determined to be incremental work order data; For the incremental work order data, determine the standard field corresponding to each field to be processed in the incremental work order data, and cache the data content corresponding to the field to be processed under the corresponding standard field to obtain the target work order data of the incremental work order data.

[0007] Optionally, the method further includes: substituting the work order number, work order business type code, work order submission timestamp, and current status of the work order in the multi-source work order data into the target hash algorithm to determine the first hash value of the multi-source work order data; wherein, the work order business type code is related to a variety of pre-set work order types, and the current status of the work order includes at least one of the following: pending reporting, under approval, completed, voided, and returned.

[0008] Optionally, the method further includes: for each field to be processed, determining the field similarity between the field to be processed and each standard field; and based on the field similarity, determining the standard field corresponding to the field to be processed.

[0009] Optionally, the method further includes: determining the edit distance between the field to be processed and the current standard field; determining the type similarity between the field to be processed and the current standard field; and determining the field similarity between the field to be processed and the current standard field based on the edit distance, the type similarity, and the corresponding weight information.

[0010] Optionally, the method further includes: displaying a target page including at least one cached data, wherein the at least one cached data is related to the target work order data, and the at least one cached data is presented in the form of a chart on the target page; in response to a click operation at any location on the target page, obtaining pixel coordinate information corresponding to the click operation; and determining and displaying target data based on the pixel coordinate information and the presentation information of the cached data on the target page.

[0011] Optionally, the method further includes: substituting the pixel coordinate information and the chart primitive coordinates of at least one chart in the target page into an Euclidean distance matching retrieval algorithm to obtain a target chart, wherein the target chart is related to the target data; and displaying the target chart.

[0012] Optionally, the method further includes: performing chart processing on all acquired target work order data according to the canvas rendering range, and determining the association between the target work order data and the icon element coordinates, so as to determine the target data based on the icon element coordinates when a click operation is received at any position.

[0013] Secondly, embodiments of the present invention provide a data processing apparatus, comprising: The work order data acquisition module is used to acquire multi-source work order data collected by multiple devices, wherein the data content in the multi-source work order data is related to the data generated by the power grid business; The first hash value generation module is used to generate a first hash value for the multi-source work order data based on the data content of the multi-source work order data and the target hash algorithm. The incremental work order data determination module is used to determine the multi-source work order data as incremental work order data if the second hash value corresponding to the historical work order data is different from the first hash value. The target work order data acquisition module is used to determine the standard field corresponding to each field to be processed in the incremental work order data, and cache the data content corresponding to the field to be processed under the corresponding standard field to obtain the target work order data of the incremental work order data.

[0014] Thirdly, embodiments of the present invention provide an electronic device, the electronic device comprising: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the data processing method provided in any embodiment of the present invention.

[0015] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the data processing method provided in any embodiment of the present invention.

[0016] Fifthly, embodiments of the present invention provide a computer program product, including a computer program that, when executed by a processor, implements the data processing method provided in any embodiment of the present invention.

[0017] The technical solution of this invention provides a complete data input foundation for subsequent automated processing by acquiring multi-source work order data collected from multiple devices, enabling cross-system data aggregation to be initiated without manual intervention. By generating a first hash value based on the data content and target hash algorithm, each work order data is assigned a unique digital identifier, providing an efficient and accurate basis for subsequent data change detection. By comparing the second hash value of historical work order data with the first hash value, if they differ, it is determined to be incremental work order data, achieving precise filtering of changed work orders. This allows the system to process only incremental data, fundamentally avoiding repeated traversal scanning of all historical data, and significantly reducing the computational overhead and network transmission load of data synchronization and statistical queries. By determining the standard field corresponding to each field to be processed and caching the data content under the corresponding standard field, automatic normalization processing of multi-source heterogeneous fields is achieved, completely eliminating the manual offline identification, cleaning, and mapping operations, significantly improving the processing efficiency and accuracy of multi-source work order data aggregation, and shortening the response latency of data synchronization and statistical summarization.

[0018] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0020] Figure 1 This is a flowchart of a data processing method provided in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of a gateway connection method according to Embodiment 1 of the present invention; Figure 3 This is a flowchart of a data processing method provided in Embodiment 2 of the present invention; Figure 4 This is a schematic diagram of the structure of a core component of a WebGL rendering engine according to Embodiment 2 of the present invention; Figure 5 This is a schematic diagram of an interface for power outage application completion inspection analysis according to Embodiment 2 of the present invention; Figure 6 This is a schematic diagram of the interface for work order execution rate analysis according to Embodiment 2 of the present invention; Figure 7This is a schematic diagram of an interface for statistical analysis of published documents according to Embodiment 2 of the present invention; Figure 8 This is a schematic diagram of the monthly work order distribution according to Embodiment 2 of the present invention; Figure 9 This is a schematic diagram of an interface for process monitoring and inspection according to Embodiment 2 of the present invention; Figure 10 This is a schematic diagram of the structure of a data processing device provided in Embodiment 3 of the present invention; Figure 11 This is a schematic diagram of the structure of an electronic device that implements the data processing method of the present invention. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0022] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0023] Example 1 Figure 1 This is a flowchart illustrating a data processing method provided in Embodiment 1 of the present invention. This embodiment is applicable to the incremental identification and automatic normalization aggregation of work order data from multi-source heterogeneous power grids. The method can be executed by a data processing device, which can be implemented in hardware and / or software and can be configured in an electronic device. This method is applied in a distributed cluster, which includes multiple devices used to acquire corresponding work order data. Figure 1 As shown, the method includes: S110. Obtain multi-source work order data collected by multiple devices, wherein the data content in the multi-source work order data is related to the data generated by the power grid business.

[0024] In this embodiment, a distributed cluster can refer to a computing cluster composed of multiple server nodes. A distributed cluster can be used to share data processing tasks, improving the system's concurrent processing capabilities and fault tolerance. For example, this distributed cluster is specifically a Spring Cloud microservice server cluster deployed within the power grid. The device can refer to a server, data acquisition terminal, or gateway device deployed on the side of each business system. This device can be used to extract and send raw work order data from the source business system. Work order data can refer to structured or semi-structured data records generated by the power grid company during the distribution network operation and management process to record the entire lifecycle of a specific business (such as power outage application, mode change, reclosing operation, etc.) from initiation, transfer, approval, and execution.

[0025] In this embodiment, multi-source work order data can refer to a collection of work order data originating from multiple different business systems or data sources. For example, multi-source work order data can specifically originate from eight independent business systems: the distribution network application system, monthly plan system, annual plan system, power transfer system, mode system, reclosing system, infrastructure risk system, and startup plan system. The data content in multi-source work order data can refer to the specific business information fields and their corresponding field values ​​carried within the work order data. For example, the data content may include: work order number, business type, submission time, current status, applicant, and organizing unit. Power grid business can refer to a series of production and management activities related to power grid planning, construction, operation, maintenance, and dispatch management. The data generated by power grid business can refer to the record data automatically generated or manually entered by each business system during the execution of the aforementioned power grid business.

[0026] Specifically, the data processing device obtains multi-source work order data collected by multiple devices through RESTful API gateway polling in a loop or through proactive push from the source system. The obtained raw work order data is temporarily stored in a message queue after format validation at the gateway layer for subsequent processing.

[0027] For example, Figure 2 A schematic diagram of one gateway connection method is provided; see [link / reference]. Figure 2 The RESTful API gateway uniformly encapsulates the interface protocols for interfacing with eight major business source systems, supporting HTTP / HTTPS request authentication, parameter conversion, and response parsing. This step can be followed as follows: Figure 2The diagram illustrates the connection method of the RESTful API gateway. The gateway acts as a unified entry point, converting different data formats from heterogeneous source systems into an internally unified message body structure to ensure the consistency of input for subsequent hash calculations and field mappings.

[0028] This step obtains multi-source data through a unified gateway, laying the data foundation for subsequent automated processing and avoiding the tedious operation of manually logging into multiple systems and exporting data one by one.

[0029] S120. For multi-source work order data, generate the first hash value of the multi-source work order data based on the data content and target hash algorithm.

[0030] In this embodiment of the disclosure, the target hash algorithm can refer to a deterministic, irreversible cryptographic hash function. The target hash algorithm can be used to convert input data of arbitrary length into a hash value of fixed length. For example, the MD5 general encryption algorithm is used as the target hash algorithm. The first hash value can refer to the hash digest value calculated according to the target hash algorithm for the currently acquired multi-source work order data. The first hash value can be used to uniquely represent the content status of the work order data at the current moment.

[0031] Specifically, the data processing device extracts preset key field combinations from the acquired multi-source work order data and concatenates the string values ​​of these fields into a long string in a fixed order. This string is then input into a target hash algorithm for encryption, generating a fixed-length hash value, denoted as the first hash value. This hash value is equivalent to the digital fingerprint of the current work order data; if any key field changes, the recalculated first hash value will be completely different.

[0032] This step generates a unique hash fingerprint for each work order data, enabling a quick and accurate determination of whether the work order content has changed without the need for field-by-field comparison, greatly reducing the complexity of comparison and calculation.

[0033] As an optional implementation of this disclosure, generating a first hash value for the multi-source work order data based on the data content and target hash algorithm may specifically include: substituting the work order number, work order business type code, work order submission timestamp, and current work order status from the multi-source work order data into the target hash algorithm to determine the first hash value of the multi-source work order data; wherein, the work order business type code is related to a variety of pre-defined work order types, and the current work order status includes at least one of the following: pending reporting, under review, completed, voided, and returned.

[0034] In this embodiment, the work order number (OrderNo) can refer to a unique identifier assigned to each work order by each source business system. The work order number can be used to accurately locate a work order record within the system. The work order business type code (BusType) can refer to a coding field used to distinguish different work order categories. For example, various work order types include, but are not limited to, distribution network application, monthly plan, annual plan, power transfer order, mode order, reclosing order, infrastructure risk order, and startup plan order, totaling eight categories. The work order submission timestamp (SubmitTs) can refer to the time record of the initial submission or the latest modified submission of the work order. This timestamp is updated synchronously when the work order content is modified. The work order current status (WorkStatus) can refer to a coding field representing the stage of the work order in its life cycle. For example, the work order current status includes at least the following statuses: pending submission, under review, completed, voided, and returned. Pending submission indicates that the work order has been created but has not yet been submitted for approval. Under review indicates that the work order is in the approval process. Completed indicates that the work order has been fully completed. "Cancelled" means the work order has been revoked. "Returned" means the work order has been returned by the approver for modification.

[0035] Specifically, the data processing device extracts the string values ​​of four fields—work order number, business type code, submission timestamp, and current status—from the currently acquired multi-source work order data. These are then concatenated into a single string in a fixed order: "OrderNo||BusType||SubmitTs||WorkStatus". Here, "||" represents the string concatenation operator. This concatenated string is then used as input to perform an MD5 hash function calculation, outputting a fixed-length hash value as the first hash value.

[0036] This optional implementation limits the specific set of fields involved in the hash calculation, ensuring sufficient differentiation between hash values ​​of different work orders while avoiding increased computational overhead due to the introduction of too many redundant fields. By selecting four key fields—work order number, business type, submission timestamp, and current status—for hash calculation, comprehensive locking of work order identity, work order content, and work order status is achieved. Any substantial changes at the business level can be accurately captured by changes in the hash value.

[0037] S130. If the second hash value corresponding to the historical work order data is different from the first hash value, then the multi-source work order data is determined to be incremental work order data.

[0038] In this embodiment, historical work order data can refer to work order data records belonging to past time nodes that have undergone normalization processing and are stored in a unified data pool. The second hash value can refer to the hash value calculated at a previous synchronization time for the corresponding historical work order data, stored in the cache. For example, the second hash value can be calculated during the previous incremental synchronization and stored in the Redis cache in association with the work order number. Incremental work order data can refer to work order data whose content has undergone substantial changes or is completely new compared to existing historical data. Incremental work order data can be understood as work order data that needs to be synchronized and updated in the unified data pool.

[0039] Specifically, the data processing device uses the work order number of the current multi-source work order data as the key to query whether the cache stores the second hash value of the corresponding historical work order. If the cache does not contain the corresponding second hash value, it indicates that the work order is a brand new work order. Alternatively, if the retrieved second hash value is not equal to the first hash value, it is determined that the current multi-source work order data has changed relative to the historical data and is identified as incremental work order data. If the first hash value is equal to the second hash value, it means that the work order data has not undergone any substantial changes since the last synchronization and requires no further processing; it is directly discarded or skipped.

[0040] This step accurately identifies incremental work orders through hash comparison, so that the system only needs to process the changed data. This completely avoids the waste of computing power in traditional solutions that require traversing millions of historical data for each query, and significantly reduces database scanning load and network transmission overhead.

[0041] S140. For incremental work order data, determine the standard field corresponding to each field to be processed in the incremental work order data, and cache the data content corresponding to the field to be processed under the corresponding standard field to obtain the target work order data of the incremental work order data.

[0042] In this embodiment, the field to be processed can refer to the original field carried in the incremental work order data from the source business system. The field naming, data type, and format specifications may differ between different source systems. For example, System A refers to "applying unit" as "applyOrg," while System B refers to it as "applDept." The standard field can refer to the field name and data type specifications set in the unified data model predefined in this invention. All work order data from all sources must ultimately be mapped to this set of standard fields to achieve consistent expression across business data. The data content corresponding to the field to be processed can refer to the specific field value carried by that field in the incremental work order data. For example, the value corresponding to the field "applyOrg" is "xx power grid dispatch center." The target work order data can refer to the complete work order record conforming to the unified data format specifications obtained after filling the data content of each field to be processed in the incremental work order data into the corresponding standard fields according to the mapping relationship.

[0043] Specifically, the data processing device iterates through all fields to be processed in the incremental work order data. For each field, it matches the most suitable standard field from the standard field set using a preset field matching strategy. The data content of that field is copied or transformed and written to the storage location corresponding to the matched standard field. After all fields have been processed, a complete target work order data entry conforming to the unified data model is generated. This target work order data is then stored in the unified work order data pool, which can be an RDS relational database. Simultaneously, the first hash value is updated in the cache, replacing the original second hash value, for use in the next incremental identification.

[0044] This step, through automated field mapping and data content caching, completely eliminates the cumbersome steps of manually identifying heterogeneous fields and cleaning data offline before importing, which is a traditional solution. It enables real-time, automatic normalization and aggregation of multi-source work order data, significantly improving the efficiency and accuracy of data summarization.

[0045] As an optional implementation of this disclosure, determining the standard field corresponding to each field to be processed in the incremental work order data may specifically include: for each field to be processed, determining the field similarity between the field to be processed and each standard field; and determining the standard field corresponding to the field to be processed based on the field similarity.

[0046] In this embodiment of the disclosure, field similarity can refer to a numerical indicator used to quantify the degree of matching between a field to be processed and a standard field in terms of semantics, data type, etc. The higher the field similarity, the more likely they are to point to the same business meaning. The standard field corresponding to the field to be processed can refer to the standard field selected from all standard fields that has the highest field similarity to the field to be processed, as the final mapping target.

[0047] Specifically, for a field currently being processed... The data processing device compares it with each standard field in a predefined set of standard fields. Similarity is calculated for each field individually, resulting in a series of field similarity values. Select the maximum similarity value from these values. The standard field corresponding to this maximum value... That is, the field to be processed Corresponding standard fields. The system automatically creates them. arrive The mapping relationship is established, and the subsequent data content of the field to be processed is written into the corresponding standard field. Down.

[0048] This step automates and optimizes field mapping by independently calculating the similarity between each field to be processed and all standard fields, and then selecting the best match. Even if the source system's field naming is irregular or has slight differences, the corresponding standard field can be accurately found.

[0049] As an optional implementation of this disclosure, determining the field similarity between the field to be processed and each standard field may specifically include: determining the edit distance between the field to be processed and the current standard field; determining the type similarity between the field to be processed and the current standard field; and determining the field similarity between the field to be processed and the current standard field based on the edit distance, type similarity, and corresponding weight information.

[0050] In this embodiment of the disclosure, the current standard field can refer to the standard field currently being compared with the field to be processed during similarity calculation. Edit distance can refer to a metric used to measure the degree of difference between two strings. Edit distance can be the minimum number of single-character editing operations (insertion, deletion, replacement) required to transform one string into another. For example, the Levenstein edit distance algorithm is used to calculate text similarity. Output a similarity score between 0 and 1, with values ​​closer to 1 indicating greater text similarity. Type similarity can refer to a Boolean judgment result that measures whether the data type of the field being processed matches the data type of the standard field. For example, through... Function implementation, when (Source field data type) and Output 1 if the source field data type matches the standard field data type, otherwise output 0. For example, the source field data type and the standard field data type can both be date, both be numeric, or both be string. Weighting information can refer to the weighted coefficients assigned to text similarity and type similarity respectively. Weighting information can be used to control the proportion of each contributing to the final field similarity. For example, the text similarity weighting coefficient... The default value is 0.6, which is the data type matching weight coefficient. The default value is 0.4.

[0051] Specifically, the data processing device calculates the fields to be processed according to the following weighted calculation formula. Compared with current standard fields Field similarity : .in, Based on the Levenstein edit distance calculation, the semantic similarity score is output between 0 and 1. This is a Boolean discrimination function; it outputs 1 when the data types match and 0 when they do not. The data processing device will then process the above calculations. As and The similarity of the fields is calculated using a weighted formula. This formula considers both the semantic similarity of the field names and the compatibility of data types, avoiding incorrect mapping of text fields to date fields and making the overall similarity score more reasonable. The data processing device performs the above calculation on each field to be processed and selects the standard field with the highest similarity as the final mapping target for that field.

[0052] This optional implementation significantly improves the accuracy of automatic mapping of heterogeneous fields by introducing a dual judgment of edit distance and data type, and then applying a weighted comprehensive score. Even if the source field names contain abbreviations, spelling discrepancies, or synonym differences, the correct mapping can be found through text similarity. At the same time, the hard constraint of data type matching prevents invalid mappings, ensuring the type safety and usability of the normalized data.

[0053] The technical solution of this invention provides a complete data input foundation for subsequent automated processing by acquiring multi-source work order data collected from multiple devices, enabling cross-system data aggregation to be initiated without manual intervention. By generating a first hash value based on the data content and target hash algorithm, each work order data is assigned a unique digital identifier, providing an efficient and accurate basis for subsequent data change detection. By comparing the second hash value of historical work order data with the first hash value, if they differ, it is identified as incremental work order data, achieving precise filtering of changed work orders. This allows the system to process only incremental data, fundamentally avoiding repeated traversal scanning of all historical data, and significantly reducing the computational overhead and network transmission load of data synchronization and statistical queries. By determining the standard field corresponding to each field to be processed and caching the data content under the corresponding standard field, automatic normalization processing of multi-source heterogeneous fields is achieved, completely eliminating the manual offline identification, cleaning, and mapping operations, significantly improving the processing efficiency and accuracy of multi-source work order data aggregation, and shortening the response latency of data synchronization and statistical summarization.

[0054] Example 2 Figure 3 This is a flowchart of a data processing method provided in Embodiment 2 of the present invention. Based on the above embodiments, this embodiment describes in detail the process of graphical display, viewport clipping rendering, and pixel coordinate drill-down interaction of target work order data. Explanations of terms that are the same as or corresponding to those in the above embodiments are not repeated here. Figure 3 As shown, the method includes: S310. Obtain multi-source work order data collected by multiple devices, wherein the data content in the multi-source work order data is related to the data generated by the power grid business.

[0055] S320. For multi-source work order data, generate the first hash value of the multi-source work order data based on the data content and target hash algorithm.

[0056] S330. If the second hash value corresponding to the historical work order data is different from the first hash value, then the multi-source work order data is determined to be incremental work order data.

[0057] S340. For incremental work order data, determine the standard field corresponding to each field to be processed in the incremental work order data, and cache the data content corresponding to the field to be processed under the corresponding standard field to obtain the target work order data of the incremental work order data.

[0058] S350. Display a target page including at least one cached data, wherein at least one cached data is related to target work order data, and at least one cached data is presented in the form of a chart on the target page.

[0059] In this embodiment of the disclosure, cached data can refer to target work order data or its statistical summary results that have been stored in a unified data pool or cache (such as Redis), undergone normalization, and are ready for display. The target page can refer to the display area in the front-end interactive interface used to present visual charts and work order information. For example, the target page can be the main operation panel or data analysis dashboard of the network distribution workbench.

[0060] Specifically, the data processing device or front-end rendering engine extracts target work order data that meets the current filtering criteria from the unified work order data pool. This data is then sent to the process visualization engine module. This module internally connects a basic statistics submodule and a WebGL layered vector rendering submodule. In the basic statistics submodule, only single-dimensional basic counting statistics such as total number of work orders, number of completed work orders, and number of pending work orders are performed, generating a statistical array. This statistical array is then sent to the WebGL layered vector rendering submodule. This submodule has a built-in viewport clipping acceleration algorithm, and its visual data determination criteria are: .in, , , , This provides the horizontal and vertical boundary values ​​of the current viewport of the front-end canvas in the data coordinate system. The rendering submodule receives these viewport boundary values ​​synchronized in real time from the front-end, iterates through all work order statistical coordinate points, and only includes data whose horizontal and vertical coordinates both fall within the viewport interval into the visible set. Layered vector rendering is performed only for this visual set. That is, the chart is first split into three layers: a static coordinate axis layer, a dynamic data layer, and a floating interaction layer. Then, the rendering computing power is dynamically allocated by the GPU to render only the data points within the visible range of the canvas, discarding all redundant data outside the screen.

[0061] This step, through viewport clipping and layered rendering, renders only data points within the visible range, significantly reducing GPU graphics processing load and lowering the rendering latency for tens of thousands of work order data. Vector charts maintain their sharpness during scaling and panning, greatly improving interactive smoothness. Furthermore, the integrated display of multiple charts provides dispatchers with a comprehensive business monitoring perspective.

[0062] For example, Figure 4 A schematic diagram of the core components of a WebGL rendering engine is provided. See also... Figure 4The core components of this engine include: a viewport management unit, which receives and maintains the horizontal and vertical value domain boundaries synchronized in real time on the front-end canvas; a clipping calculation unit, which traverses all data points based on the visual data judgment conditions, filters redundant data outside the screen, and outputs the visual set; a layer management unit, which allocates the data to be rendered to three layers: the static coordinate axis layer, the dynamic data layer, and the floating interaction layer; and a GPU rendering scheduling unit, which dynamically allocates GPU computing resources according to the rendering requirements of each layer, performs layered vector rendering only for the visual set, and triggers re-filtering and refreshing when the viewport boundary changes.

[0063] For example, Figure 5 A schematic diagram of the interface for power outage application completion and inspection analysis is provided. See also... Figure 5 This interface displays the execution status of power outage application forms for each organization using a combination of bar charts and line charts. The bar chart represents the number of reports and completed applications for each organization (corresponding to the left vertical axis), while the line chart represents the completion rate for each organization (corresponding to the right vertical axis, displayed as a percentage). The horizontal axis represents the name of each organization. Users can visually compare the work order completion efficiency of different organizations using these charts.

[0064] For example, Figure 6 A schematic diagram of an interface for work order execution rate analysis is provided. See [link / reference] Figure 6 The interface displays the execution rate distribution of various organizational units or work orders using a combination of bar charts and line charts. The bar charts show the number of work orders executed under each dimension, while the line charts show the corresponding execution rate percentages. The horizontal axis represents the organizational unit or work order type dimension, making it easier for operations and maintenance personnel to identify key links and units with low execution rates.

[0065] For example, Figure 7 A schematic diagram of an interface for statistical analysis of published documents is provided. See also... Figure 7 This interface displays the distribution and trend of published work orders in the form of a combination of bar charts or pie charts and line charts. The bar chart shows the number of published work orders for each organizational unit or time period, while the line chart shows the month-on-month growth rate or percentage trend of published work orders. The horizontal axis represents the organizational unit or time period (such as month), assisting dispatchers in gaining a macro-level understanding of published work orders.

[0066] For example, Figure 8 A schematic diagram illustrating the monthly work order distribution is provided. See [link / reference] Figure 8 The interface displays the monthly number and distribution trend of work orders over the past 12 months (or a specified time span) using a combination of line charts and bar charts. The bar chart represents the number of work orders issued each month, and the line chart represents the trend line of the monthly number of work orders. The horizontal axis represents the months, and the vertical axis represents the number of work orders, which helps dispatchers identify the seasonal fluctuations in the number of work orders and the months with abnormal peaks.

[0067] For example, Figure 9 A schematic diagram of a process monitoring and inspection interface is provided. See also... Figure 9 This interface displays a work order workflow topology diagram or workflow status distribution panel, including statistics on the number of work orders currently at each workflow node (pending reporting, under approval, completed, voided, returned), as well as the workflow path and dwell time indicators between nodes. Operations personnel can use this interface to monitor the workflow health of the entire work order lifecycle in real time and promptly identify workflow bottlenecks or abnormal backlog nodes.

[0068] S360: In response to a click operation at any location on the target page, obtain the pixel coordinate information corresponding to the click operation.

[0069] In this embodiment of the disclosure, any location can refer to any pixel location within any chart area on the target page. Any location may include the bar area of ​​a bar chart, a data point of a line chart, or a legend, etc. A click operation can refer to a single-click or double-click interaction performed by a user on the target page using a mouse, touchpad, or touchscreen. Pixel coordinate information can refer to the precise two-dimensional coordinate values ​​of the click location in the screen or canvas coordinate system, denoted as... .in The pixel values ​​are on the horizontal axis. The vertical axis represents pixel values.

[0070] Specifically, after loading the WebGL vector visualization layer, the front-end page uses a JavaScript event listener mechanism to capture mouse click events at any location on the target page. When a click event is triggered, the front-end obtains the precise pixel coordinates of the clicked location relative to the canvas container by calling built-in browser APIs (such as event.clientX, event.offsetX, etc.), and uploads this pixel coordinate information to the back-end server via an HTTP request or WebSocket message for processing by the interaction control module.

[0071] This step accurately captures the pixel coordinates of the user's click location, providing high-precision input parameters for subsequent precise drill-down and backtracking, thus avoiding the problems of blurry click areas and inability to accurately locate user intent in traditional solutions.

[0072] S370. Based on pixel coordinate information and the rendering information of cached data on the target page, determine the target data and display it.

[0073] In this embodiment of the disclosure, presentation information may refer to the spatial layout information when cached data is visualized on the target page. Presentation information may include the reference pixel coordinates of each chart element in the canvas coordinate system, denoted as... This includes the corresponding business dimension information. For example, chart elements can be each bar in a bar chart or each data point marker in a line chart. Business dimension information can be the specific organizational unit corresponding to a bar in a bar chart. Target data can refer to the work order details most relevant to the user's click location, i.e., the underlying work order details list that the user wants to view.

[0074] Specifically, after receiving the click pixel coordinates uploaded by the frontend, the backend interaction control module reads the pre-stored baseline pixel coordinates of all chart elements and their corresponding business dimension mapping relationships from the cache. The interaction control module then calls the pixel coordinate drill-down backtracking submodule. This submodule incorporates a Euclidean distance matching retrieval algorithm to calculate the Euclidean distance between the click coordinates and the coordinates of each element. The calculation formula is as follows: .

[0075] The data processing unit iteratively inputs the coordinates of all pre-cached elements and calculates the Euclidean distance to the clicked coordinates for each. Then, it selects the element with the smallest distance value from all the calculated results. This element is the target element that the user's click intent is directed to. Next, based on the business dimension (such as an organizational unit or a work order status type) corresponding to this target element, the system quickly retrieves all work order details under that dimension using a database composite index and displays this detailed data to the user in the form of a list or pop-up window on the front-end page.

[0076] This step utilizes a pixel coordinate matching algorithm based on Euclidean distance to achieve high-precision, low-latency matching between click locations and graphic elements, significantly improving work order retrieval speed compared to traditional full-database fuzzy searches. Simultaneously, it enables real-time sequential feedback of filtering parameters, indicator statistics, visualization layers, and work order detail queries, achieving the goal of dynamic control of the basic process and greatly enhancing the real-time management efficiency of the work order process by the power distribution network dispatching terminal.

[0077] As an optional implementation of this disclosure, determining and displaying target data based on pixel coordinate information and the presentation information of cached data on the target page may specifically include: substituting the pixel coordinate information and the coordinates of the graphic elements of at least one chart on the target page into an Euclidean distance matching retrieval algorithm to obtain a target chart, wherein the target chart is related to the target data; and displaying the target chart.

[0078] In this embodiment of the disclosure, the coordinates of chart elements can refer to the baseline positioning coordinates of basic graphic elements with interactive responsiveness in the chart, which are pre-set or calculated in real time in the canvas coordinate system. Basic graphic elements can be a single bar in a bar chart, a sector in a pie chart, or a data point marker in a line chart. The Euclidean distance matching retrieval algorithm can be a retrieval algorithm that performs nearest neighbor matching based on the straight-line distance between two points in a two-dimensional plane. The target chart can refer to the data group or category represented by the chart element hit by the user through a click operation. A chart element can be a bar or a point.

[0079] Specifically, after obtaining the click coordinates, the backend no longer relies solely on pre-cached element coordinates for traversal, but directly constructs the calculation process of the Euclidean distance matching retrieval algorithm. It substitutes the pixel coordinate information and the coordinates of all elements in all charts on the target page into the Euclidean distance formula to calculate the distance value corresponding to each element. The system automatically finds the element with the smallest distance, and the chart to which that element belongs (e.g., ...) Figure 4 A specific column in the chart is then identified as the target chart. The system then displays the data details or drill-down page corresponding to that target chart.

[0080] This optional approach further clarifies that the entire matching and retrieval process uses chart elements as the basic matching unit, which better aligns with the need for drill-down on specific elements in actual interaction scenarios. Through a standardized Euclidean distance matching process, the algorithmic logic for click interaction and element matching is unified, adapting to coordinate transformations after chart scaling and translation, and resolving the issue of ambiguous click positioning caused by chart deformation or coordinate offset in traditional systems.

[0081] As an optional implementation of this disclosure, the method further includes: performing chart processing on all acquired target work order data according to the canvas rendering range, and determining the association between the target work order data and the icon element coordinates, so as to determine the target data based on the icon element coordinates when a click operation is received at any location.

[0082] In this embodiment of the disclosure, the canvas rendering range may refer to the numerical boundary of the visible canvas area used to display the chart in the current front-end page in the data coordinate system, i.e., the aforementioned viewport boundary. , , , This range is determined by the current canvas size, scaling factor, and translation offset.

[0083] Specifically, during the chart rendering process in step S350, after completing viewport clipping, the WebGL layered vector rendering submodule determines the actual subset of target work order data to be drawn based on the current canvas rendering range (i.e., viewport boundary). When drawing each graphic element (such as each bar in a bar chart), the rendering submodule not only records the pixel coordinates of the graphic element on the canvas, but also records the corresponding business dimension value (such as the name of an organization or the code of a work order type), thereby establishing a one-to-one mapping relationship between the target work order data and the graphic element coordinates. This relationship is cached in the background (such as in Redis) in the form of key-value pairs or index tables. When the user initiates a click operation in subsequent steps S360 or S370, the system can directly use this relationship to quickly locate the corresponding graphic element coordinates through the click coordinates, thereby locating and retrieving the target work order data associated with those coordinates, without having to temporarily recalculate or scan the entire dataset.

[0084] This optional implementation pre-establishes a static mapping cache between data and element coordinates, eliminating the need for real-time database queries and re-rendering in click responses, thus achieving millisecond-level response times for click events. Simultaneously, it provides a stable coordinate index foundation for interactive scenarios such as chart zooming and panning, ensuring adaptive capabilities at interaction boundaries.

[0085] The technical solution of this invention, by displaying a target page including at least one cached data, with the cached data presented in chart form, allows normalized multi-source work order data to be presented to dispatchers in an intuitive and visual way. Simultaneously, viewport clipping and layered rendering technology enables smooth interaction under large data volumes. By responding to clicks on any location on the target page and obtaining the corresponding pixel coordinate information, a flexible and precise interaction entry point is provided to the user, transforming the user's operational intent into calculable coordinate parameters. By determining and displaying target data based on pixel coordinate information and the presentation information of cached data, and utilizing Euclidean distance matching algorithms and pre-cached primitive coordinate indexes, seamless drill-down and backtracking from macro-level charts to micro-level work order details are achieved. This enables real-time serial linkage and feedback of filtering parameters, indicator statistics, visualization layers, and work order process status, achieving high-performance dynamic control of the entire distribution network work order chain from data aggregation to visual monitoring and detailed traceability.

[0086] The data processing apparatus provided in the embodiments of the present invention can execute the data processing method provided in any embodiment of the present invention, and has the corresponding beneficial effects of executing the data processing method.

[0087] The following are embodiments of the data processing apparatus provided in this invention. This apparatus and the data processing methods in the above embodiments belong to the same inventive concept. For details not described in detail in the embodiments of the data processing apparatus, please refer to the embodiments of the above data processing methods.

[0088] Example 3 Figure 10 This is a schematic diagram of the structure of a data processing device provided in Embodiment 3 of the present invention. Figure 10 As shown, the device includes: a work order data acquisition module 1010, a first hash value generation module 1020, an incremental work order data determination module 1030, and a target work order data acquisition module 1040.

[0089] The system includes: a work order data acquisition module 1010, used to acquire multi-source work order data collected by multiple devices, wherein the data content of the multi-source work order data is related to the data generated by the power grid business; a first hash value generation module 1020, used to generate a first hash value for the multi-source work order data based on the data content of the multi-source work order data and the target hash algorithm; an incremental work order data determination module 1030, used to determine that the multi-source work order data is incremental work order data if the second hash value corresponding to the historical work order data is different from the first hash value; and a target work order data acquisition module 1040, used to determine the standard field corresponding to each field to be processed in the incremental work order data, and cache the data content corresponding to the field to be processed under the corresponding standard field to obtain the target work order data of the incremental work order data.

[0090] The technical solution of this invention provides a complete data input foundation for subsequent automated processing by acquiring multi-source work order data collected from multiple devices, enabling cross-system data aggregation to be initiated without manual intervention. By generating a first hash value based on the data content and target hash algorithm, each work order data is assigned a unique digital identifier, providing an efficient and accurate basis for subsequent data change detection. By comparing the second hash value of historical work order data with the first hash value, if they differ, it is identified as incremental work order data, achieving precise filtering of changed work orders. This allows the system to process only incremental data, fundamentally avoiding repeated traversal scanning of all historical data, and significantly reducing the computational overhead and network transmission load of data synchronization and statistical queries. By determining the standard field corresponding to each field to be processed and caching the data content under the corresponding standard field, automatic normalization processing of multi-source heterogeneous fields is achieved, completely eliminating the manual offline identification, cleaning, and mapping operations, significantly improving the processing efficiency and accuracy of multi-source work order data aggregation, and shortening the response latency of data synchronization and statistical summarization.

[0091] Based on the above technical solution, the first hash value generation module 1020 is specifically used to: substitute the work order number, work order business type code, work order submission timestamp, and work order current status from the multi-source work order data into the target hash algorithm to determine the first hash value of the multi-source work order data; wherein, the work order business type code is related to a variety of pre-set work order types, and the current status of the work order includes at least one of the following: pending report, under approval, completed, voided, and returned.

[0092] Based on the above technical solution, the target work order data acquisition module 1040 may include: The field similarity determination submodule is used to determine the field similarity between each field to be processed and each standard field. The Standard Field Determination Submodule is used to determine the standard field corresponding to the field to be processed based on field similarity.

[0093] Based on the above technical solution, the field similarity determination submodule is specifically used to: determine the edit distance between the field to be processed and the current standard field; determine the type similarity between the field to be processed and the current standard field; and determine the field similarity between the field to be processed and the current standard field based on the edit distance, type similarity, and corresponding weight information.

[0094] Based on the above technical solution, the device also includes: The target page display module is used to display a target page that includes at least one cached data, wherein at least one cached data is related to the target work order data, and at least one cached data is presented in the form of a chart on the target page; The pixel coordinate information acquisition module is used to acquire pixel coordinate information corresponding to the click operation in response to a click operation at any position on the target page. The first target data determination module is used to determine and display target data based on pixel coordinate information and the presentation information of cached data on the target page.

[0095] Based on the above technical solution, the first target data determination module is specifically used to: substitute the pixel coordinate information and the coordinates of the graphic elements of at least one chart in the target page into the Euclidean distance matching retrieval algorithm to obtain the target chart, wherein the target chart is related to the target data; and display the target chart.

[0096] Based on the above technical solution, the device also includes: The second target data determination module is used to perform chart processing on all acquired target work order data according to the canvas rendering range, and to determine the association between the target work order data and the icon element coordinates, so as to determine the target data based on the icon element coordinates when a click operation is received at any position.

[0097] The data processing apparatus provided in the embodiments of the present invention can execute the data processing method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the data processing method.

[0098] It is worth noting that in the above data processing embodiments, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0099] Example 4 Figure 11 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0100] like Figure 11 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0101] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0102] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as data processing methods.

[0103] In some embodiments, the data processing method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or mounted on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the data processing method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the data processing method by any other suitable means (e.g., by means of firmware).

[0104] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0105] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0106] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0107] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0108] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0109] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0110] This application also discloses a computer program product, which includes a computer program that, when executed by a processor, implements the data processing method provided in any embodiment of this application. This program product and the data processing methods disclosed in the embodiments of this application belong to the same inventive concept, and therefore will not be described in detail here.

[0111] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0112] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A data processing method, characterized in that, Applied in a distributed cluster, the distributed cluster includes multiple devices, the devices are used to acquire corresponding work order data, and the method further includes: Acquire multi-source work order data collected by the multiple devices, wherein the data content in the multi-source work order data is related to the data generated by power grid business; For the multi-source work order data, a first hash value of the multi-source work order data is generated based on the data content of the multi-source work order data and the target hash algorithm; If the second hash value corresponding to the historical work order data is different from the first hash value, then the multi-source work order data is determined to be incremental work order data; For the incremental work order data, determine the standard field corresponding to each field to be processed in the incremental work order data, and cache the data content corresponding to the field to be processed under the corresponding standard field to obtain the target work order data of the incremental work order data.

2. The method according to claim 1, characterized in that, The step of generating a first hash value for the multi-source work order data based on the data content and target hash algorithm of the multi-source work order data includes: Substitute the work order number, work order business type code, work order submission timestamp, and work order current status from the multi-source work order data into the target hash algorithm to determine the first hash value of the multi-source work order data. The work order business type code is related to a variety of pre-defined work order types, and the current status of the work order includes at least one of the following: pending, under review, completed, voided, or returned.

3. The method according to claim 1, characterized in that, The step of determining the standard field corresponding to each field to be processed in the incremental work order data includes: For each field to be processed, determine the field similarity between the field to be processed and the corresponding standard field; Based on the field similarity, a standard field corresponding to the field to be processed is determined.

4. The method according to claim 3, characterized in that, The step of determining the field similarity between the field to be processed and the corresponding field of each standard field includes: Determine the edit distance between the field to be processed and the current standard field; Determine the type similarity between the field to be processed and the current standard field; Based on the edit distance, the type similarity, and the corresponding weight information, the field similarity between the field to be processed and the current standard field is determined.

5. The method according to claim 1, characterized in that, Also includes: The target page is displayed, which includes at least one cached data, wherein the at least one cached data is related to the target work order data, and the at least one cached data is presented in the form of a chart on the target page; In response to a click operation at any location on the target page, obtain pixel coordinate information corresponding to the click operation; Based on the pixel coordinate information and the presentation information of the cached data on the target page, the target data is determined and displayed.

6. The method according to claim 5, characterized in that, The process of determining and displaying target data based on the pixel coordinate information and the rendering information of the cached data on the target page includes: The pixel coordinate information and the coordinates of the graphic elements of at least one chart in the target page are substituted into the Euclidean distance matching retrieval algorithm to obtain the target chart, wherein the target chart is related to the target data; Display the target chart.

7. The method according to claim 5, characterized in that, The method further includes: Based on the canvas rendering range, all acquired target work order data are processed into charts, and the target work order data and icon element coordinates are associated to determine the target data based on the icon element coordinates when a click operation is received at any location.

8. A data processing apparatus, characterized in that, The device includes: The work order data acquisition module is used to acquire multi-source work order data collected by multiple devices, wherein the data content in the multi-source work order data is related to the data generated by the power grid business; The first hash value generation module is used to generate a first hash value for the multi-source work order data based on the data content of the multi-source work order data and the target hash algorithm. The incremental work order data determination module is used to determine the multi-source work order data as incremental work order data if the second hash value corresponding to the historical work order data is different from the first hash value. The target work order data acquisition module is used to determine the standard field corresponding to each field to be processed in the incremental work order data, and cache the data content corresponding to the field to be processed under the corresponding standard field to obtain the target work order data of the incremental work order data.

9. An electronic device, characterized in that, The electronic device includes: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the data processing method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the data processing method as described in any one of claims 1-7.

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