CN-120780424-B - Task collaborative management method and system based on offline application

CN120780424BCN 120780424 BCN120780424 BCN 120780424BCN-120780424-B

Abstract

The invention discloses a task collaborative management method and a system based on offline application, and relates to the technical field of offline data management, wherein the method comprises the steps of determining a task collaborative type and acquiring a collaborative task resource package; the method comprises the steps of generating a task cooperative system environment based on a cooperative task resource package, receiving cooperative data, predicting modification probability of the cooperative data through a pre-trained prediction model in the cooperative task resource package, pre-warning and checking the cooperative data with high risk of a prediction result, compressing and packaging the cooperative data according to the cooperative task type, and uploading the packaged cooperative data through a priority scheduling mechanism when network connection is restored. Through the fusion technical scheme of intelligently matching the resource package, generating a system environment with edge calculation, pre-training model early warning verification and priority scheduling uploading, the mismatching of the resource package and the parameter error rate are effectively reduced, uploading delay is reduced, and offline cooperative efficiency and data reliability are improved.

Inventors

XU HAIBO
CHEN YUMENG
LIAO YAN
WANG BUTIAN

Assignees

深圳市爱德数智科技股份有限公司

Dates

Publication Date: 20260508
Application Date: 20250624

Claims (8)

1. A method for collaborative management of tasks based on offline applications, the method comprising: The method comprises the steps of determining a task cooperative type according to user information and a task semantic analysis result, and obtaining a corresponding cooperative task resource package, wherein the step of decompressing the cooperative task resource package to obtain a data model preset file, a service model preset file and a cooperative form style preset file, the step of automatically creating a data structure of a task cooperative system in an offline database of equipment according to the data model preset file, wherein the data structure comprises a master-slave table relation and an initialization parameter, the step of automatically generating a service model object entity containing a service rule in the offline application of the equipment according to the service model preset file, wherein the service rule comprises a numerical check logic and a flow control rule, the step of automatically creating a form interface of a cooperative task in the offline application of the equipment according to the cooperative form style preset file, and the step of associating the data structure and the service model object entity with the form interface; initializing and generating a task cooperative system environment based on the cooperative task resource package; The method comprises the steps of extracting historical collaborative data and corresponding modification records from a collaborative task resource package or an offline database, wherein the modification records comprise modification time stamps of the historical collaborative data, parameter modification frequencies and associated parameter modification records, training a single parameter modification frequency prediction model through an LSTM time sequence algorithm, weighting the historical modification frequencies through a time attenuation factor, training the associated parameter modification records through an FP-Growth association rule algorithm, generating a parameter association modification prediction model, mining the association between parameters through confidence and promotion degree quantization indexes, and judging whether strong association exists between the parameters through preset trigger conditions; And compressing and packaging the collaborative data according to the collaborative task type, and uploading the packaged collaborative data through a priority scheduling mechanism when the network connection is restored.
2. The method for task collaborative management based on offline application according to claim 1, wherein determining a task collaborative type according to user information and a task semantic parsing result, and obtaining a corresponding collaborative task resource package comprises: Acquiring task description input by a user, and executing semantic analysis on the task description through a natural language processing technology to extract task keywords; Based on the task keywords and the user authority levels corresponding to the user information, matching a preset task type mapping rule to determine the corresponding task collaboration type; and calling a corresponding collaborative task resource package based on the task collaborative type.
3. The offline application-based task collaborative management method according to claim 2, wherein the collaborative task resource package includes a data model profile, a business model profile, a collaborative form style profile, and a collaborative task flow rule profile.
4. The method for collaborative task management based on offline application according to claim 1, wherein the receiving collaborative data uploaded by a user through the task collaborative system environment, predicting modification probability of the collaborative data through a pre-trained prediction model in the collaborative task resource package, and performing early warning and verification on collaborative data with a high risk prediction result comprises: calculating a modification probability value of the current cooperative data based on the single parameter modification frequency prediction model, and analyzing modification relativity among the cooperative data based on the parameter association modification prediction model; when the modification probability value exceeds a preset threshold value or the modification of the associated parameters is detected, performing visual early warning marking on the corresponding data fields in the collaborative form interface; And calling an edge calculation module in the task cooperative system environment, performing numerical logic verification or image feature recognition verification on the early warning data, generating a verification result and associating the verification result to the cooperative data.
5. The offline application-based task orchestration method according to claim 1, wherein compressing and packaging the orchestration data according to orchestration task types, and uploading the packaged orchestration data through a priority scheduling mechanism when network connection is restored, comprises: Identifying the type of the current cooperative task and acquiring a corresponding compression strategy configuration file; according to the compression strategy configuration file, compression packaging is carried out on the collaborative data, and the collaborative data is packaged; determining a service priority level according to the cooperative task type; acquiring a modification probability value of the current cooperative data from the prediction model, and calculating a prediction risk priority based on the confidence level and the lifting level of the parameter association modification prediction model; Generating a comprehensive priority index based on the service priority level and the predicted risk priority, and arranging the encapsulated cooperative data in descending order according to the comprehensive priority index to form an uploading queue; Monitoring the network quality state in real time, dynamically adjusting an uploading strategy according to the network quality, uploading the cooperative data with high comprehensive priority indexes by adopting an acceleration transmission protocol, caching the cooperative data with medium and low comprehensive priority indexes into a local queue, and uploading the cooperative data according to the queue sequence; and executing block chain hash value record on the uploaded data, and verifying data consistency through a consensus algorithm after networking.
6. A task collaborative management system based on offline applications, comprising: the resource package acquisition module is used for determining a task cooperative type according to user information and a task semantic analysis result and acquiring a corresponding cooperative task resource package, and comprises decompressing the cooperative task resource package to acquire a data model preset file, a service model preset file and a cooperative form style preset file, automatically creating a data structure of a task cooperative system in an offline database of equipment according to the data model preset file, wherein the data structure comprises a master-slave table relation and an initialization parameter, automatically generating a service model object entity containing a service rule in the offline application of the equipment according to the service model preset file, wherein the service rule comprises a numerical check logic and a flow control rule; The environment initialization module is used for initializing and generating a task collaborative system environment based on the collaborative task resource package; The system comprises a task collaborative system environment, a data early warning module, a parameter association modification prediction model, an FP-Growth association rule algorithm, a single parameter modification frequency prediction model, a parameter association prediction model, a single parameter modification frequency prediction model, an LSTM time sequence algorithm, a parameter association prediction model and a local device, wherein the task collaborative system environment is used for receiving collaborative data uploaded by a user, predicting modification probability of the collaborative data through a pre-trained prediction model in the collaborative task resource package, and early warning and checking collaborative data with high risk of a prediction result; and the data uploading module is used for compressing and packaging the collaborative data according to the collaborative task type, and uploading the packaged collaborative data through a priority scheduling mechanism when the network connection is restored.
7. A computer-readable storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method of any of the preceding claims 1-5.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of the preceding claims 1-5 when executing the program.

Description

Task collaborative management method and system based on offline application Technical Field The invention relates to the technical field of offline data management, in particular to a task collaborative management method and system based on offline application. Background In the field of project task collaborative management, a traditional task collaborative system depends on a network environment to realize task initiation and data processing, and multiple technical bottlenecks are exposed in non-network or weak network scenes such as construction sites. In the existing scheme, users need to manually match task types with resource packages, when task descriptions including professional terms such as 'bridge pile foundation concrete pouring quality inspection' are faced, data models and business rules are not matched easily due to manual judgment deviation, for example, quality inspection standards and bridge engineering requirements are disjointed when road construction resource packages are mistakenly selected. Meanwhile, the traditional offline system can only passively record data, lacks the ability of prejudging parameter modification risks, and is especially prominent in multi-person collaborative scenes because constructors often forget key parameter modification or do not perceive parameter association influence to cause data reworking, and industry statistics shows that the occurrence rate of the problems is about 19%. In addition, the traditional system adopts a direct uploading mode when the network is recovered, a large amount of offline data is transmitted simultaneously, so that network congestion is easily caused, the uploading of key data such as quality correction list is delayed, the compression strategy is not optimized for the data type, and the network burden is further increased. In addition, the system environment generation depends on static template loading, cannot be dynamically adjusted according to task semantics and equipment types, lacks edge computing capability to verify collected data in real time, needs to be manually checked after networking, and severely restricts offline collaborative efficiency. Disclosure of Invention In view of the above, the invention provides a task collaborative management method and a system based on offline application, which can effectively solve the problems of low resource matching efficiency and data error caused by no verification of data uploading in an offline scene. The invention provides the following technical scheme: A task collaborative management method based on offline application, the method comprising: Determining a task cooperative type according to user information and a task semantic analysis result, and acquiring a corresponding cooperative task resource package; Receiving collaborative data uploaded by a user through the task collaborative system environment, predicting modification probability of the collaborative data through a pre-trained prediction model in the collaborative task resource package, and pre-warning and checking collaborative data with high risk prediction results; And compressing and packaging the collaborative data according to the collaborative task type, and uploading the packaged collaborative data through a priority scheduling mechanism when the network connection is restored. Optionally, determining the task collaboration type according to the user information and the task semantic analysis result, and obtaining the corresponding collaborative task resource package includes: Acquiring task description input by a user, and executing semantic analysis on the task description through a natural language processing technology to extract task keywords; Based on the task keywords and the user authority levels corresponding to the user information, matching a preset task type mapping rule to determine the corresponding task collaboration type; and calling a corresponding collaborative task resource package based on the task collaborative type. Optionally, the collaborative task resource package includes a data model preset file, a business model preset file, a collaborative form style preset file, and a collaborative task flow rule file. Optionally, the generating the task collaborative system environment based on the collaborative task resource package initialization includes: decompressing the collaborative task resource package to obtain a data model preset file, a service model preset file and a collaborative form style preset file; Automatically creating a data structure of a task cooperative system in an offline database of the equipment according to the data model preset file, wherein the data structure comprises a master-slave table relation and initialization parameters; automatically generating a business model object entity containing business rules in offline application of equipment according to the business model preset file, wherein the business rules comprise numerical check logic and flow co