Data processing method and device, electronic equipment and storage medium

Abstract

The invention discloses a data processing method and device, electronic equipment and a storage medium, and the method comprises the steps: responding to a data processing logic configuration operation, and displaying a data structure tree; the data structure tree is composed of a plurality of data items, and each data item is determined based on a data storage center; obtaining a plurality of selected data items and input data processing demand information, and generating data processing logic; the data processing demand information is used for indicating processing rules among the selected data items; in response to a data processing request for first data, acquiring the data processing logic for the first data; the data item corresponding to the first data belongs to the selected data items; and processing the first data according to the data processing logic to obtain a first data processing result, and displaying the first data processing result. According to the scheme, the configuration generation of the data processing logic can be realized, the personalized demand of processing the data in the project engineering can be met, and the actual demand of the service is better adapted.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a data processing method, apparatus, electronic device, and storage medium. Background Technology

[0002] When dealing with complex projects such as power and infrastructure projects, it is often necessary to cover the entire process of the project, from initial planning to final settlement. Therefore, managing and processing the various related data is crucial. While project management systems are currently used in the industry, they generally suffer from low modularity, poor business adaptability, and weak data collaboration capabilities. These systems cannot meet the personalized processing needs of different project types, nor can they support refined management throughout the entire project lifecycle. Summary of the Invention

[0003] This invention provides a data processing method, apparatus, electronic device, and storage medium that can generate configurations for data processing logic, thereby meeting the personalized data processing needs of project engineering and making it more adaptable to the actual needs of business.

[0004] In a first aspect, the present invention provides a data processing method, comprising: In response to data processing logic configuration operations, a data structure tree is displayed; the data structure tree consists of multiple data items, each of which is determined based on the data storage center. The system acquires multiple selected data items and input data processing requirements, and generates data processing logic; the data processing requirements are used to indicate the processing rules between the selected data items. In response to a data processing request for the first data, the data processing logic for the first data is obtained; the data item corresponding to the first data belongs to the selected plurality of data items; The first data is processed according to the data processing logic to obtain the first data processing result and then displayed.

[0005] In a second aspect, the present invention also provides a data processing apparatus, comprising: The first response module is used to respond to data processing logic configuration operations and display a data structure tree; the data structure tree consists of multiple data items, and each data item is determined based on the data storage center. The data processing logic generation module is used to obtain multiple selected data items and input data processing requirement information, and generate data processing logic; the data processing requirement information is used to indicate the processing rules between the selected data items. The second response module is used to respond to a data processing request for the first data and obtain the data processing logic for the first data; the data item corresponding to the first data belongs to the selected plurality of data items; The data processing result generation module is used to process the first data according to the data processing logic, obtain the first data processing result, and display it.

[0006] Thirdly, this invention also provides an electronic device, comprising: One or more processors; Storage device for storing 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.

[0007] Fourthly, embodiments of the present invention also provide a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform the data processing method provided in any embodiment of the present invention.

[0008] The technical solution of this invention, in response to a data processing logic configuration operation, displays a data structure tree, which consists of multiple data items, each determined based on a data storage center. Then, it acquires the selected data items and input data processing requirement information, and generates data processing logic. This data processing requirement information indicates the processing rules between the selected data items. Next, in response to a data processing request for first data, it acquires the data processing logic for the first data. Subsequently, it processes the first data according to the data processing logic, obtains the first data processing result, and displays it. This solution allows for the configuration and generation of data processing logic based on actual processing needs, and the processing of first data based on the configured data processing logic. This satisfies the personalized data processing requirements of project engineering, making it more adaptable to actual business needs. Furthermore, the method of configuring data processing logic in this solution is simpler, faster, and more efficient.

[0009] The above description of the invention is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0010] The above and other features, advantages, and aspects of the various embodiments of the present invention will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0011] Figure 1 This is a flowchart illustrating a data processing method provided in an embodiment of the present invention; Figure 2 This is a flowchart illustrating another data processing method provided in an embodiment of the present invention; Figure 3 A logical framework diagram of a data processing method provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of a data processing device provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of an electronic device for implementing a data processing method, provided in an embodiment of the present invention. Detailed Implementation

[0012] Embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While some embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the invention. It should be understood that the accompanying drawings and embodiments are for illustrative purposes only and are not intended to limit the scope of protection of the invention.

[0013] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0014] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0015] It should be noted that the concepts of "first" and "second" mentioned in this invention are only used to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0016] It should be noted that the terms "a" and "a plurality of" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0017] The names of the messages or information exchanged between the multiple devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0018] Figure 1 This is a flowchart illustrating a data processing method provided in an embodiment of the present invention. This embodiment is applicable to situations involving the processing or management of data related to project engineering. The method can be executed by a data processing device, which can be implemented in software and / or hardware, and is generally integrated into any electronic device with network communication capabilities, such as a mobile terminal, PC, or server. Figure 1 As shown, the data processing method of this embodiment of the invention may include the following steps: S110. In response to the data processing logic configuration operation, display the data structure tree; the data structure tree consists of multiple data items, each of which is determined based on the data storage center.

[0019] In this context, data processing logic can be understood as a series of processing rules / requirements for processing data. It helps determine the specific processing steps required for the data to be processed. Correspondingly, data processing logic configuration refers to the operations of configuring and generating the data processing logic.

[0020] A data structure tree consists of multiple data items arranged in a tree diagram. This structure tree clearly displays the relationships and hierarchical relationships between the data items. Each data item is determined by a data storage center, which can be understood as a repository for storing all data related to a specific project. Each type of data stored in the data storage center corresponds to one data item. For example, "project progress data" stored in the data storage center corresponds to one data item, while "equipment inspection data" corresponds to another. In other words, the number of data items corresponds to the number of different types of data (or business data fields) stored in the data storage center.

[0021] Specifically, users can configure data processing logic based on actual project requirements. For example, users can click on the corresponding functional controls on the interface to trigger the data processing logic configuration. Then, in response to the data processing logic configuration, a data structure tree will be displayed on the screen. When displaying the data structure tree, the entire structure tree can be shown, or only a portion can be shown. Furthermore, as the user drags the displayed data structure tree, the remaining undisplayed parts of the structure tree can be further revealed.

[0022] S120. Obtain the selected multiple data items and the input data processing requirements information, and generate data processing logic; the data processing requirements information is used to indicate the processing rules between the selected data items.

[0023] The data processing requirements can be determined based on the user's needs for the project to which the data belongs. These requirements specify the processing rules to be applied to the selected data items. For example, the input data processing requirements could indicate that the result of calculating data A+B needs to be calculated.

[0024] Specifically, multiple data items can be selected through drag-and-drop or by clicking and checking. After obtaining the selected data items and the input data processing requirements, the corresponding data processing logic can be generated. For example, by having the user drag and drop on the interface, the user can customize business forms (such as project application forms, quality inspection forms, etc.) and select business fields (project type, cost items, etc.), thereby generating a dedicated data processing form (i.e., a data processing logic) adapted to the target project. This approach allows for customized / personalized configuration of the data processing logic, enabling process configuration without code development, making the operation convenient and fast.

[0025] S130. In response to the data processing request for the first data, obtain the data processing logic for the first data; the data item corresponding to the first data belongs to the selected multiple data items.

[0026] The first data can be a single type of data (e.g., inspection personnel certificate data) or multiple types of data with logical relationships (e.g., project progress data and project time data). In this embodiment, the data item corresponding to the first data belongs to multiple selected data items.

[0027] Specifically, users can initiate data processing requests for the first set of data to be processed / calculated. For example, this can be triggered by clicking the corresponding function control, such as triggering the statistics of report data, the processing and entry of scheme design data, etc. Subsequently, the system can respond to the data processing request for the first set of data and obtain the data processing logic for that data.

[0028] S140. Process the first data according to the data processing logic to obtain the first data processing result and display it.

[0029] Specifically, after obtaining the data processing logic, the first data can be processed according to the data processing logic to obtain the first data processing result and display it.

[0030] Optionally, when displaying the initial data processing results, the display method can be determined based on the type of platform being displayed. In other words, different display methods can be determined based on the type of platform. For example, if the platform is a PC (Web system), since this type of platform supports complex data processing operations, the result display method can be a visual chart (such as a bar chart, pie chart, etc.). As another example, if the platform is a mobile app (APP / Mini Program), since this type of platform supports lightweight data processing, the result display method can be a simple list or key indicator cards. Further differentiation settings can be made according to the actual needs of the scenario; these will not be detailed here.

[0031] As an optional but non-limiting implementation, the first data is processed according to the data processing logic to obtain the first data processing result, including: when the first data is progress data, mapping the progress data into a preset progress model and generating a visual display result through a Gantt chart algorithm; the preset progress model is used to indicate the segmentation information of the overall progress of the project to which the progress data belongs; and / or, when the first data is resource consumption data, classifying the resource consumption data to obtain multiple sub-resource consumption data; and calculating the sub-resource consumption deviation rate based on each sub-resource consumption data and a preset consumption threshold; and / or, when the first data is inspection data, identifying and processing the inspection data based on preset safety standard conditions to determine the risk level result corresponding to the inspection data; the preset safety standard conditions are used to indicate the risk level classification information corresponding to the inspection data. Using this optional scheme, different data processing logics can be configured to process the first data based on the different data corresponding to it.

[0032] In this context, progress data refers to quantitative data on the implementation status of a project. Correspondingly, a pre-defined progress model can be used to indicate the segmented information of the overall project progress to which the progress data belongs. Understandably, the overall project progress can generally be divided, for example, into the preliminary planning stage, the construction stage, and the completion and acceptance stage. Of course, it can also be divided into multiple levels based on actual needs to collectively reflect the overall project progress. For example, the first level could be "project milestones," the second level could be "project unit works," and the third level could be "project sections," thus obtaining a three-level progress model. Simply put, a pre-defined progress model can be understood as the theoretical result of planning the overall project progress.

[0033] Resource consumption data reflects the consumption of various resources during the project's progress. For example, resource consumption data can be the total resource consumption data for the entire project. Correspondingly, sub-resource consumption data reflects the consumption of specific sub-categories of resources. For instance, sub-resource consumption data could include equipment and material consumption data, outsourced service resource consumption data, and management resource consumption data.

[0034] Inspection data refers to the data obtained from inspecting and testing the on-site conditions of a project, such as inspection records.

[0035] Specifically, when the first data is progress data, it can be mapped into a preset progress model to determine the segment to which it belongs within that model. A Gantt chart algorithm is then used to generate a visual representation. This visualization reflects the actual duration and scope of the progress data within the overall project. The Gantt chart algorithm supports schedule planning, real-time tracking, change approval and early warning (automatic delay risk alerts), and the progress of each subtask can be automatically aggregated to the parent task to ensure data accuracy.

[0036] And / or, when the first data is resource consumption data, the resource consumption data (i.e., the first data) can be classified to obtain multiple sub-resource consumption data; and based on each sub-resource consumption data and a preset consumption threshold, the sub-resource consumption deviation rate can be calculated separately. The preset consumption threshold corresponds one-to-one with each sub-resource, meaning one preset consumption threshold corresponds to one type of sub-resource. The specific value of each preset consumption threshold can be set differently based on actual needs. For example, the acquired project cost data can be classified and collected to obtain data such as equipment and material consumption data, outsourced service resource consumption data, and management resource consumption data. Then, according to the preset consumption threshold corresponding to each sub-resource consumption data, the actual obtained sub-resource consumption data can be dynamically compared and calculated to determine the sub-resource consumption deviation rate. For example, it can be calculated using the following formula: Deviation rate = (Actual obtained sub-resource consumption data - Preset consumption threshold) / Preset consumption threshold × 100%.

[0037] And / or, when the first data is inspection data, the inspection data can be identified and processed based on preset safety standard conditions to determine the risk level result corresponding to the inspection data. The preset safety standard conditions can be used to indicate the risk level classification information corresponding to the inspection data. Optionally, after determining the risk level result corresponding to the inspection data, potential hazards existing in the inspection data can also be marked.

[0038] It should be noted that the data processing described above in this embodiment, when the first data is of different types, can be further expanded and customized based on actual project needs. For example, when the first data is contract data, the configured data processing logic can be used for data entry, review, and archiving. In this embodiment, basic components such as JADP, ATM, and BPMS can be called, and the data processing logic can be customized through configurable interfaces.

[0039] The technical solution of this invention, in response to a data processing logic configuration operation, displays a data structure tree, which consists of multiple data items, each determined based on a data storage center. Then, it acquires the selected data items and input data processing requirement information, and generates data processing logic. This data processing requirement information indicates the processing rules between the selected data items. Next, in response to a data processing request for first data, it acquires the data processing logic for the first data. Subsequently, it processes the first data according to the data processing logic, obtains the first data processing result, and displays it. This solution allows for the configuration and generation of data processing logic based on actual processing needs, and the processing of first data based on the configured data processing logic. This satisfies the personalized data processing requirements of project engineering, making it more adaptable to actual business needs. Furthermore, the method of configuring data processing logic in this solution is simpler, faster, and more efficient.

[0040] Figure 2 This is a flowchart illustrating another data processing method provided by an embodiment of the present invention. The technical solution of this embodiment further optimizes the process of processing the first data according to data processing logic in the aforementioned embodiments based on the technical solutions of the above embodiments. This embodiment can be combined with various optional solutions in one or more of the above embodiments. For example... Figure 2 As shown, the data processing method of this embodiment of the invention may include the following steps: S210. In response to the data processing logic configuration operation, display the data structure tree; the data structure tree consists of multiple data items, each of which is determined based on the data storage center.

[0041] S220. Obtain the selected multiple data items and the input data processing requirement information, and generate data processing logic; the data processing requirement information is used to indicate the processing rules between the selected data items.

[0042] S230. In response to the data processing request for the first data, obtain the data processing logic for the first data; the data item corresponding to the first data belongs to the selected multiple data items.

[0043] As an optional but non-limiting implementation, before responding to a data processing request for the first data, the method further includes: acquiring second data from multiple data sources through a preset data interface; the data sources include at least one of the following: deployed IoT devices, user terminal devices, and third-party data systems; performing preprocessing operations on the acquired second data to obtain third data; the preprocessing operations include at least one of the following: data classification, data encryption, and data anonymization; and storing the third data in a data storage center. This optional solution enables multi-source data sensing and access processing, and allows for secure data processing and standardized storage.

[0044] The second type of data can be understood as data generated by the data source. This data source includes at least one of the following: deployed IoT devices, user terminal devices, and third-party data systems. For example, deployed IoT devices can be drones, high-definition cameras, sensors, etc.; user terminal devices can be mobile terminals capable of acquiring data related to the project, such as data collected on-site (e.g., construction progress data, quality inspection data, safety hazard data, etc.) that users can upload via an app / mini-program; and third-party data systems can be external financial systems, material systems, smart construction site platforms, etc.

[0045] Specifically, second data from multiple data sources can be obtained through a preset data interface. During the transmission of the second data, a real-time transmission protocol (such as Message Queuing Telemetry Transport) can be used to form a unified data inflow channel, thereby achieving the acquisition of the second data. In this embodiment, the second data can be acquired in real time. If the data acquisition delay exceeds a preset delay time (e.g., 10 seconds), the acquisition of the second data can be restarted. Next, preprocessing operations can be performed on the acquired second data to obtain third data. The preprocessing operations include at least one of the following: data classification, data encryption, and data anonymization. Data classification refers to classifying the acquired second data into categories; data encryption refers to encrypting the acquired second data (e.g., confidential data) using an encryption algorithm for subsequent storage; and data anonymization refers to anonymizing / masking sensitive fields in the acquired second data. Subsequently, the third data obtained after preprocessing can be stored in a data storage center.

[0046] Optionally, when performing data classification operations on the second data, it can be mapped to a preset data set. This preset data set is a standardized data model pre-designed by the data storage center. This standardized data model can be used to indicate the standardized requirements for data storage in terms of data structure, data naming, data type, and data relationships. For example, the data storage center can pre-design the following four preset data sets: project master data set (structured data such as project information and organizational structure), business data set (dynamic business data such as schedule plans and cost details), customer data set (permission-related data such as user accounts and permission configurations), and important data set (confidential data such as government approval documents and unpublished policies). After obtaining the second data, it can be analyzed and mapped to the corresponding preset data set.

[0047] Optionally, in this embodiment, a triple backup mechanism can also be established to back up third-party data locally, remotely, and in the cloud to ensure data integrity. The backup frequency can be real-time incremental backup or daily full backup, and can be customized based on actual needs; no further details are provided here.

[0048] S240. Based on the data processing logic, call the associated first data processing microservice from the microservice library; the microservice library contains multiple data processing microservices.

[0049] In this context, a microservice library can be understood as a library formed by multiple data processing microservices. Each data processing microservice can be used to perform different data processing operations.

[0050] It is understandable that there is a mapping relationship between data processing microservices and data processing logic; generally, one data processing logic corresponds to one data processing microservice. Specifically, based on the data processing logic for the first set of data, the associated first data processing microservice can be called from the microservice library.

[0051] S250. Process the first data based on the first data processing microservice.

[0052] Specifically, after obtaining the first data processing microservice, the first data can be processed based on the first data processing microservice.

[0053] As an optional but non-limiting implementation, the first data processing microservice is also used to: send the first data to a second data processing microservice via a preset network interface; the second data processing microservice is another data processing microservice in the microservice library that has a relationship with the first data. Using this optional solution, the first data can be horizontally collaboratively transferred between related microservices.

[0054] Specifically, the first data processing microservice is also used to: send the first data to a second data processing microservice via a preset network interface; the second data processing microservice is another data processing microservice in the microservice library that has a relationship with the first data. Furthermore, after receiving the first data, the second data processing microservice can automatically trigger data processing based on the first data.

[0055] For example, when the first data is contract signing data, and the first data processing microservice is the contract generation microservice (i.e., the first data processing microservice), the contract generation microservice will also send the contract signing data to the "project cost calculation microservice" (i.e., the second data processing microservice), and subsequently trigger the project cost calculation microservice to perform cost aggregation calculation. As another example, when the first data is progress data (such as project milestone completion data), and the first data processing microservice is the progress data visualization processing microservice (i.e., the first data processing microservice), the progress data visualization processing microservice will also send the progress data to the "project quality acceptance microservice" (i.e., the second data processing microservice), and subsequently trigger the project quality acceptance microservice to enter the first data. Through this scheme, the first data can be horizontally collaboratively transferred between related microservices.

[0056] S260. Obtain the first data processing result and display it.

[0057] Specifically, after processing the first data based on the first data processing microservice, the processing result of the first data can be obtained and displayed.

[0058] As an optional but non-limiting implementation, the data processing method further includes: in response to a project risk assessment request, acquiring multiple risk data related to the project engineering; determining the risk data based on the first data and the risk judgment conditions configured corresponding to the first data; outputting and displaying project risk information by inputting each risk data into the project risk assessment model; and using the project risk assessment model to perform cluster analysis on the multiple risk data related to the project engineering and to predict risks. By adopting this optional solution, the risk situation of the project engineering can be analyzed and risks can be predicted, thereby achieving a comprehensive assessment of the risks existing in the project engineering.

[0059] Specifically, the risk data is determined based on the first data and the corresponding risk assessment conditions configured for the first data. Specifically, if the first data meets the risk assessment conditions, then the first data can be considered risk data. For example, if the first data is progress data 'a', and the corresponding configured risk assessment condition is "the delay time of the progress data cannot exceed 3 days," if it is determined that the delay time of progress data 'a' is greater than 3 days, then it means that progress data 'a' meets the risk assessment conditions, which is equivalent to a high probability of risk, and therefore progress data 'a' can be considered risk data. Optionally, in this embodiment, the risk assessment conditions can be represented in the form of a set data threshold, or in the form of rules / logic, and can be determined based on the needs of the actual scenario; further details are not provided here.

[0060] Understandably, the data associated with a project is diverse, and therefore, there may be multiple identified risk data points. For example, progress data that has exceeded the delay time can be considered one risk data point; resource consumption data that has exceeded the preset consumption threshold can also be considered another risk data point; and inspection data identified as posing safety hazards can further be considered yet another risk data point. In other words, risk data can be understood as data that has been determined to pose a risk, and this risk data is used to conduct an overall risk assessment of the project in subsequent planning stages.

[0061] Specifically, users can click on relevant function controls to trigger a project risk assessment request. The system will then respond to the request by obtaining multiple risk data related to the project. Next, by inputting these risk data into the project risk assessment model, the system outputs and displays project risk information, thereby achieving a comprehensive assessment of the risks present in the project. This project risk assessment model can be used to perform cluster analysis on multiple risk data related to the project and to predict risks. The output project risk information includes the assessment results for each risk data point and the overall risk level of the project. For example, the assessment results for each risk data point can be schedule analysis results, resource consumption analysis results, and safety and quality analysis results. The overall risk level of the project can include: general risk level, severe risk level, and emergency risk level.

[0062] When displaying the output project risk information, a dashboard can be used to visually represent the assessment results of each specific risk data point, the overall risk level of the project, and so on. Furthermore, this data can be updated at fixed time intervals (e.g., every hour).

[0063] Furthermore, after outputting project risk information, early warnings can be issued for this information. Warning methods include, but are not limited to, email notifications, SMS notifications, and system message warnings. Alternatively, matching corrective action data can be retrieved from the risk response solution library based on actual needs and sent along with the warning information.

[0064] As an optional but non-limiting implementation, this data processing method further includes: determining the data information set associated with the entire lifecycle of the project to which the first data belongs; the data information set containing at least the first data and / or the processing result of the first data; and classifying and archiving the data in the data information set based on the phase progress information of the project to obtain the project's periodic archive information. By adopting this optional solution, closed-loop management of project-related data throughout its entire lifecycle can be achieved, thereby establishing a traceable data link and facilitating data traceability in the future.

[0065] The phase progress information can be pre-divided and set based on actual needs. For example, the phase progress information can be set as: preliminary phase, construction phase, and completion phase.

[0066] Specifically, the system can also identify the data set associated with the entire lifecycle of the project to which the first data belongs. This data set must contain at least the first data and / or its processing results. Next, based on the project's phase progress information, the data in the data set is categorized and archived to determine which phase of the project each piece of data belongs to, thus obtaining the project's lifecycle archive information. This achieves closed-loop management of the entire data lifecycle, establishes a traceable data link, and enables data traceability and auditability. Of course, during the formation of the lifecycle archive information, key core business data (such as quality issue data) can also be associated with other information such as its source, processing node, operator, and timestamp.

[0067] The technical solution of this invention, in response to a data processing logic configuration operation, displays a data structure tree, which consists of multiple data items, each determined based on a data storage center. Then, it acquires the selected data items and input data processing requirement information, and generates data processing logic, whereby the data processing requirement information indicates the processing rules between the selected data items. Next, in response to a data processing request for first data, it acquires the data processing logic for the first data. Subsequently, based on the data processing logic, it calls the associated first data processing microservice from a microservice library; the microservice library contains multiple data processing microservices. The first data is processed based on the first data processing microservice to obtain and display the first data processing result. This solution allows for the configuration and generation of data processing logic based on actual processing needs, and the processing of first data based on the configured data processing logic. This satisfies the personalized data processing requirements of project engineering, making it more adaptable to actual business needs. Furthermore, the method of configuring data processing logic in this solution is simpler, faster, and more efficient.

[0068] Figure 3 This is a logical framework diagram of a data processing method provided in an embodiment of the present invention, as shown below. Figure 3 As shown, the data processing method of this invention can be implemented according to the following steps: Step 1: Multi-source data perception and access processing (core process of perception layer), thereby realizing real-time data access and cross-domain connection.

[0069] Step 2: Standardized data storage and secure processing (core data layer process), which enables the classification, encryption / de-identification, storage and backup of the acquired data.

[0070] Step 3: Modular and configurable data processing (core functional layer, core process), which allows for the configuration of different data processing logic for different processing modules. For example: Preliminary Management Module: This module can develop sub-modules for project initiation management, scientific research management, and preliminary procedures application. It supports online maintenance of project initiation information (including project background, objectives, risk assessment, etc.), online review and electronic signature of scientific research reports, and customization of procedures application templates, thereby achieving standardized management and control during the project initiation phase.

[0071] Technical Management Module: It can establish a standard design library and a technical solution review mechanism, support bidding design management and online review of solutions, and reduce resource consumption and project investment by unifying construction standards and equipment specifications.

[0072] The progress management module allows for the design of multi-level progress management models (first-level milestones, second-level unit projects, and third-level section progress). It employs Gantt chart visualization technology and supports progress planning, real-time tracking, change approval, and early warning (automatic prompts for delay risks). Subtask progress is automatically aggregated to the parent task to ensure the accuracy of progress data.

[0073] Cost Management Module: It can build a full-process cost control model of "budget-cost-settlement-final account", realize budget preparation and review, real-time cost collection (classified statistics of equipment materials, outsourced services and management fees), online approval of settlement sheets, and automatic generation of final account reports. By dynamically comparing budget and actual costs, it optimizes the efficiency of fund use.

[0074] The procurement management module integrates bidding and procurement, contract management, and material tracking sub-modules. It supports initiating bidding requests (design / supervision / construction bidding), full lifecycle management of contracts (signing-performance-payment-settlement), material arrival acceptance and supervision management, and data collaboration with the material system.

[0075] Safety Management Module: Based on the concept of risk-based hierarchical control, sub-modules for safety inspection, hazard rectification, and emergency management can be designed. It supports uploading inspection records from PC / mobile devices, automatic early warning of overdue hazards (reminders for overdue personnel certificates / equipment testing), online storage of emergency plans, and management of drill records, forming a closed loop for safety management.

[0076] Quality Management Module: It can adopt digital testing technology, support online filling of quality inspection records (with built-in quality standard guidance), entry of actual measurement data for stage acceptance, and approval of project completion acceptance process. Through automatic archiving of quality ledgers and weekly reports, it can achieve traceability of quality issues.

[0077] Construction Management Module: This module can cover start-up, stop-work, and resumption management, project measurement, construction logs, and work contact forms. It supports real-time recording of key information during the construction process (weather, construction content, problem feedback), online review of project quantities, and tracking of contact form circulation to ensure the standardization of the construction process.

[0078] The comprehensive management module can develop project organization management (maintaining information on participating units / personnel), change management (automatic reminders for exceeding the budget), information query and statistics (multi-dimensional filtering and visual chart display), document management (classification and archiving of archives throughout the entire lifecycle), and project panorama (centralized display of core indicators), to achieve overall collaborative control of the project.

[0079] Step 4: Distributed collaborative data flow processing (core process of collaborative mechanism) enables the horizontal flow of related data.

[0080] Step 5: Multi-terminal data adaptation and interaction processing (core application layer process), which can display differentiated results for different application terminals.

[0081] Step 6: Intelligent decision-making and risk warning data processing (core process of decision-making level) can determine the overall risk of the project and generate decision reports and risk warnings.

[0082] Step 7: Closed-loop management of the entire data lifecycle enables the archiving and processing of all related data in the project, facilitating subsequent data traceability.

[0083] The technical solution of this invention can configure and generate data processing logic based on actual processing needs, and process the first data based on the configured data processing logic, thereby meeting the personalized data processing needs of the project and making it more suitable for the actual needs of the business; moreover, the way this solution configures data processing logic is simpler, faster and more efficient.

[0084] Figure 4 This is a schematic diagram of a data processing device provided in an embodiment of the present invention. This embodiment is applicable to situations involving the processing or management of data related to project engineering. The data processing device can be implemented in software and / or hardware and is generally integrated into any electronic device with network communication capabilities, such as a mobile terminal, PC, or server. Figure 4 As shown, the data processing apparatus of this embodiment of the invention may include a first response module 410, a data processing logic generation module 420, a second response module 430, and a data processing result generation module 440. Wherein: The first response module 410 is used to respond to data processing logic configuration operations and display a data structure tree; the data structure tree consists of multiple data items, and each data item is determined based on the data storage center. The data processing logic generation module 420 is used to obtain multiple selected data items and input data processing requirement information, and generate data processing logic; the data processing requirement information is used to indicate the processing rules between the selected data items. The second response module 430 is used to respond to a data processing request for the first data and obtain the data processing logic for the first data; the data item corresponding to the first data belongs to the selected plurality of data items; The data processing result generation module 440 is used to process the first data according to the data processing logic, obtain the first data processing result, and display it.

[0085] The technical solution of this invention involves a first response module responding to a data processing logic configuration operation, displaying a data structure tree composed of multiple data items, each determined based on a data storage center. Then, a data processing logic generation module acquires the selected data items and input data processing requirements, generating data processing logic. This data processing requirements information indicates the processing rules between the selected data items. Next, a second response module responds to a data processing request for the first data, acquiring the data processing logic for that first data. Subsequently, a data processing result generation module processes the first data according to the data processing logic, obtaining and displaying the first data processing result. This solution allows for the configuration and generation of data processing logic based on actual processing needs, and the processing of the first data based on the configured logic. This satisfies the personalized data processing requirements of project engineering, making it more adaptable to actual business needs. Furthermore, the data processing logic configuration method of this solution is simpler, faster, and more efficient.

[0086] As an optional but non-limiting implementation, the data processing device further includes: a second data acquisition module, a third data determination module, and a third data storage module. Wherein: The second data acquisition module is used to acquire second data from multiple data sources through a preset data interface; the data sources include at least one of the following: deployed IoT devices, user terminal devices, and third-party data systems; The third data determination module is used to perform preprocessing operations on the acquired second data to obtain third data; the preprocessing operations include at least one of the following: data classification operation, data encryption operation, and data desensitization operation; The third data storage module is used to store the third data in the data storage center.

[0087] As an optional but non-limiting implementation, the data processing result generation module 440 includes: a first data processing microservice invocation unit and a data processing unit. Wherein: The first data processing microservice invocation unit is used to invoke the associated first data processing microservice from the microservice library based on the data processing logic; the microservice library contains multiple data processing microservices. A data processing unit is used to process the first data based on the first data processing microservice.

[0088] As an optional but non-limiting implementation, the first data processing microservice is further used to: send the first data to a second data processing microservice through a preset network interface; the second data processing microservice is another data processing microservice in the microservice library that has an association with the first data.

[0089] As an optional but non-limiting implementation, the data processing result generation module 440 is specifically used for: when the first data is progress data, mapping the progress data into a preset progress model, and generating a visual display result through a Gantt chart algorithm; the preset progress model is used to indicate the segmentation information of the overall progress of the project to which the progress data belongs; and / or, when the first data is resource consumption data, classifying the resource consumption data to obtain multiple sub-resource consumption data; and calculating the sub-resource consumption deviation rate based on each sub-resource consumption data and a preset consumption threshold; and / or, when the first data is inspection data, identifying the inspection data based on preset safety standard conditions to determine the risk level result corresponding to the inspection data; the preset safety standard conditions are used to indicate the risk level classification information corresponding to the inspection data.

[0090] As an optional but non-limiting implementation, the data processing device further includes: a third response module and a project risk information determination module. Wherein: The third response module is used to respond to a project risk assessment request and obtain multiple risk data related to the project engineering; the risk data is determined based on the first data and the risk judgment conditions configured for the first data; The project risk information determination module is used to input the various risk data into the project risk assessment model, output project risk information and display it; the project risk assessment model is used to perform cluster analysis on multiple risk data related to the project and make risk predictions.

[0091] As an optional but non-limiting implementation, the data processing device further includes: a data information set determination module and a periodic archive information determination module. Wherein: A data information set determination module is used to determine the data information set associated with the entire life cycle of the project to which the first data belongs; the data information set includes at least the first data and / or the processing result of the first data. The periodic archive information determination module is used to classify and archive the data in the data information set based on the phase progress information of the project, so as to obtain the periodic archive information of the project.

[0092] The data processing apparatus provided in this embodiment of the invention can be used to execute data processing methods, and has corresponding functional modules and beneficial effects for executing data processing methods.

[0093] It is worth noting that the various units and modules included in the above-mentioned device are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the protection scope of the embodiments of the present invention.

[0094] Figure 5 This is a schematic diagram of the structure of an electronic device for implementing a data processing method according to an embodiment of the present invention. Refer to the following... Figure 5 The diagram illustrates a structural schematic of an electronic device 510 suitable for implementing embodiments of the present invention. The terminal devices in these embodiments may include, but are not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.

[0095] like Figure 5 As shown, the electronic device 510 includes at least one processor 511 and a memory, such as a read-only memory (ROM) 512 and a random access memory (RAM) 513, communicatively connected to the at least one processor 511. The memory stores computer programs executable by the at least one processor. The processor 511 can perform various appropriate actions and processes based on the computer program stored in the ROM 512 or loaded from storage unit 518 into the RAM 513. The RAM 513 may also store various programs and data required for the operation of the electronic device 510. The processor 511, ROM 512, and RAM 513 are interconnected via a bus 514. An input / output (I / O) interface 515 is also connected to the bus 514.

[0096] Multiple components in electronic device 510 are connected to input / output (I / O) interface 515, including: input unit 516, such as keyboard, mouse, etc.; output unit 517, such as various types of monitors, speakers, etc.; storage unit 518, such as disk, optical disk, etc.; and communication unit 519, such as network card, modem, wireless transceiver, etc. Communication unit 519 allows electronic device 510 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0097] Processor 511 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 511 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 511 performs the data processing methods provided in any embodiment of the present invention.

[0098] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the data processing method shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication unit 519, or installed from storage unit 518, or installed from read-only memory (ROM) 512. When the computer program is executed by processor 511, it performs the functions defined in the data processing method of the embodiments of the present invention.

[0099] The names of the messages or information exchanged between the multiple devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0100] The electronic device provided in this embodiment of the invention and the data processing method provided in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0101] This invention provides a computer storage medium storing a computer program that, when executed by a processor, implements the data processing method provided in the above embodiments.

[0102] It should be noted that the computer-readable medium described above in this invention can be a computer-readable signal medium, a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0103] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0104] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0105] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0106] The units described in the embodiments of the present invention can be implemented in software or in hardware. The names of the units are not, in some cases, intended to limit the specific unit.

[0107] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0108] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. 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 of the foregoing.

[0109] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

[0110] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in sequential order. In certain circumstances, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the invention. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0111] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A data processing method, characterized in that, The method includes: In response to data processing logic configuration operations, a data structure tree is displayed; the data structure tree consists of multiple data items, each of which is determined based on the data storage center. The system acquires multiple selected data items and input data processing requirements, and generates data processing logic; the data processing requirements are used to indicate the processing rules between the selected data items. In response to a data processing request for the first data, the data processing logic for the first data is obtained; the data item corresponding to the first data belongs to the selected plurality of data items; The first data is processed according to the data processing logic to obtain the first data processing result and then displayed.

2. The method according to claim 1, characterized in that, Prior to responding to the data processing request for the first data, it also includes: Secondary data is obtained from multiple data sources through a preset data interface; the data sources include at least one of the following: deployed IoT devices, user terminal devices, and third-party data systems; The acquired second data is preprocessed to obtain the third data; the preprocessing operation includes at least one of the following: data classification operation, data encryption operation, and data desensitization operation; The third data is stored in the data storage center.

3. The method according to claim 1, characterized in that, The step of processing the first data according to the data processing logic includes: Based on the data processing logic, the associated first data processing microservice is called from the microservice library; the microservice library contains multiple data processing microservices. The first data is processed based on the first data processing microservice.

4. The method according to claim 3, characterized in that, The first data processing microservice is also used for: The first data is sent to the second data processing microservice through a preset network interface; the second data processing microservice is another data processing microservice in the microservice library that has a relationship with the first data.

5. The method according to claim 1, characterized in that, The step of processing the first data according to the data processing logic to obtain the first data processing result includes: When the first data is progress data, the progress data is mapped into a preset progress model, and a visual display result is generated using a Gantt chart algorithm; the preset progress model is used to indicate the segmentation information of the overall progress of the project to which the progress data belongs; And / or, When the first data is resource consumption data, the resource consumption data is classified and processed to obtain multiple sub-resource consumption data; and the sub-resource consumption deviation rate is calculated based on each of the sub-resource consumption data and a preset consumption threshold. And / or, When the first data is inspection data, the inspection data is identified and processed based on preset safety standard conditions to determine the risk level result corresponding to the inspection data; the preset safety standard conditions are used to indicate the risk level classification information corresponding to the inspection data.

6. The method according to claim 1, characterized in that, The method further includes: In response to a project risk assessment request, multiple risk data related to the project are obtained; the risk data is determined based on the first data and the risk judgment conditions configured for the first data. By inputting the various risk data into the project risk assessment model, the project risk information is output and displayed; the project risk assessment model is used to perform cluster analysis on multiple risk data related to the project and to make risk predictions.

7. The method according to claim 1, characterized in that, The method further includes: Determine the data information set associated with the entire life cycle of the project to which the first data belongs; the data information set shall at least contain the first data and / or the processing result of the first data; Based on the phased progress information of the project, the data in the data information set is classified and archived to obtain the periodic archive information of the project.

8. A data processing apparatus, characterized in that, The device includes: The first response module is used to respond to data processing logic configuration operations and display a data structure tree; the data structure tree consists of multiple data items, and each data item is determined based on the data storage center. The data processing logic generation module is used to obtain multiple selected data items and input data processing requirement information, and generate data processing logic; the data processing requirement information is used to indicate the processing rules between the selected data items. The second response module is used to respond to a data processing request for the first data and obtain the data processing logic for the first data; the data item corresponding to the first data belongs to the selected plurality of data items; The data processing result generation module is used to process the first data according to the data processing logic, obtain the first data processing result, and display it.

9. An electronic device, characterized in that, The electronic device includes: One or more processors; Storage device for storing 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 storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform the data processing method as described in any one of claims 1-7.

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