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CN-121996395-A - Method for intelligent task batch processing and computer equipment

CN121996395ACN 121996395 ACN121996395 ACN 121996395ACN-121996395-A

Abstract

The application provides an intelligent task batch processing method and computer equipment. The method comprises the steps of obtaining data to be processed, defining processing tasks corresponding to the data to be processed, wherein the processing tasks comprise task targets and constraint conditions, converting the data to be processed and the processing tasks into batch processing tasks according to a preset batch processing format, uploading the batch processing tasks to a server, creating batch processing jobs so that a generated artificial intelligent model sequentially processes the batch processing jobs in a batch processing mode, analyzing results returned by batch processing, and extracting processing results of each task. According to the application, the generated artificial intelligent model adopts a batch operation processing mode, so that the efficient and intelligent processing of large-scale data is realized, and the method can be widely applied to a plurality of application scenes such as text comparison analysis, technical requirement matching and the like.

Inventors

  • WEI YUXING
  • ZHANG YANG

Assignees

  • 深圳市智行睿远科技有限公司

Dates

Publication Date
20260508
Application Date
20241101

Claims (12)

  1. 1. A method for intelligent task batch processing, the method comprising: Acquiring data to be processed; defining a processing task corresponding to the data to be processed, wherein the processing task comprises a task target and constraint conditions; Converting the data to be processed and the processing task into batch processing tasks according to a preset batch processing format; uploading the batch task to a server and creating a batch job, so that the generated artificial intelligent model adopts a batch job processing mode to sequentially process the batch job; Analyzing the returned result of batch processing, and extracting the processing result of each task.
  2. 2. The method of claim 1, wherein converting the data to be processed and the processing task into batch processing tasks in a predetermined batch format comprises: generating a unique identifier and a processing parameter for the processing task; And converting the data to be processed and the processing parameters into preset format files, wherein each preset format file record comprises a task identifier, the processing parameters, the data to be processed and the processing tasks, and the preset format is JSON, CSV, XML, DOC, DOCX, TXT, PPT, PPTX, XLS or XLSX.
  3. 3. The method of claim 1, wherein the generating an artificial intelligence model to sequentially process the batch job using a batch job processing mode comprises: selecting a corresponding pre-training model and parameter configuration according to the task type; Acquiring a task target and a prompting word of a constraint condition contained in each task, or generating the task target and the prompting word of the constraint condition for each task; And processing the data to be processed in a sequential batch mode based on the prompt words, and outputting corresponding processing results in a sequential batch mode.
  4. 4. A method according to claim 3, characterized in that the method further comprises: based on an asynchronous processing mechanism, when the server resource is idle, the corresponding processing results are output in batches; when the resource is not idle, the batch output operation is suspended.
  5. 5. The method of claim 1, wherein uploading the batch job to a server and creating a batch job comprises: verifying the integrity and the validity of the batch processing task; YY+242322P-DJ setting execution parameters of batch processing jobs, including an expected completion time window, task priority and/or upper limit of concurrent task number; Initializing job monitoring indexes including start time, processing progress and/or resource use condition; computing resources are dynamically allocated based on system load.
  6. 6. The method according to any one of claims 1-5, wherein the processing task is configured to retrieve and analyze a batch differentiation comparison of comparison files, the data to be processed includes a plurality of comparison files, and the generating artificial intelligence model sequentially processes the batch processing job using a batch job processing mode, and the method comprises: Acquiring a target technical scheme and the plurality of comparison files, wherein the target scheme comprises one or more key technical features; Sequentially analyzing the technical feature coincidence degree or similarity of the plurality of comparison files and the target file in batches by using the generated artificial intelligent model; And generating a differential analysis conclusion, wherein the qualitative conclusion comprises a similar or dissimilar conclusion and/or a quantitative conclusion comprises a similarity score, a similar technical feature and/or a key differential point.
  7. 7. A method for intelligent task batch processing, the method comprising: Acquiring data to be processed; Defining a processing task corresponding to the data to be processed, wherein the processing task is used for technical classification of the data to be processed; Converting the data to be processed and the processing task into batch processing tasks according to a preset batch processing format; uploading the batch task to a server and creating a batch job so that the generated artificial intelligent model sequentially processes the batch job by adopting a batch job processing mode, comprising: Acquiring a preset classification system in the processing task, or automatically inducing the classification system by the generated artificial intelligent model according to the data to be processed, wherein the classification system comprises one-level or multi-level classification rules; Based on the classification system, sequentially and orderly classifying and marking a plurality of data to be processed; Analyzing the returned result of batch processing, and extracting the processing result of each task.
  8. 8. The method according to claim 7, wherein sequentially sorting the plurality of data to be processed based on the sorting hierarchy comprises: sequentially extracting key features of the data to be processed: Calculating the matching degree of the key features and each category in the classification system; YY+242322P-DJ Determining an optimal classification of the key features, wherein the optimal classification includes one or more classification labels; And outputting a classification result and a classification basis description.
  9. 9. A method for intelligent task batch processing, the method comprising: acquiring a plurality of texts to be analyzed to be processed, wherein the texts to be analyzed are patent application files; defining processing tasks corresponding to a plurality of texts to be analyzed, wherein the processing tasks are used for patent risk batch comparison and investigation; converting a plurality of texts to be analyzed and the processing tasks into batch processing tasks according to a preset batch processing format; uploading the batch task to a server and creating a batch job so that the generated artificial intelligent model sequentially processes the batch job by adopting a batch job processing mode, comprising: obtaining a technical scheme of a target product, and extracting a technical feature set of the target product; analyzing the corresponding relation between the technical characteristics of the target product and each patent application file in a batched manner; Analyzing the returned result of batch processing, and extracting the processing result of each task.
  10. 10. A method for intelligent task batch processing, the method comprising: acquiring a plurality of texts to be analyzed to be processed, wherein the texts to be analyzed comprise technical documents of a document library; Defining processing tasks corresponding to a plurality of texts to be analyzed, wherein the processing tasks are used for correlation comparison of technical requirements and a plurality of technical documents in a document library; converting a plurality of texts to be analyzed and the processing tasks into batch processing tasks according to a preset batch processing format; uploading the batch task to a server and creating a batch job so that the generated artificial intelligent model sequentially processes the batch job by adopting a batch job processing mode, comprising: Extracting key technical indexes and parameter requirements in a technical requirement document, and constructing requirement characteristics; calculating the technical matching degree of the demand characteristics and each technical document in the document library in batches; And generating a matching result, wherein the matching result comprises a matching degree score and/or a matching basis.
  11. 11. A method for intelligent task batch processing, the method comprising: Acquiring a plurality of texts to be analyzed to be processed, wherein the texts to be analyzed comprise a plurality of technical documents; Defining processing tasks corresponding to a plurality of texts to be analyzed, wherein the processing tasks are used for comparing the association degree of product information and technical documents; converting a plurality of texts to be analyzed and the processing task into YY+242322P-DJ at batch according to a preset batch format Managing tasks; Uploading the batch task to a server and creating a batch job, so that the generated artificial intelligent model sequentially processes the batch job by adopting a batch job processing mode according to the batch job, and the method comprises the following steps of: extracting key technical characteristics or product characteristics of the product information; acquiring a technical feature set of each technical document; analyzing the key technical characteristics of the product information or the matching degree of the product characteristics and the technical characteristic set of each technical document in batches; And generating a matching result, wherein the matching result comprises a matching degree score and/or a matching basis.
  12. 12. A computer device, comprising: A processor and a memory, the memory storing a computer program, which when executed by the processor implements the steps of the method according to any one of claims 1 to 11.

Description

Method for intelligent task batch processing and computer equipment Technical Field The application relates to the technical field of artificial intelligence, in particular to a method for processing intelligent tasks in batches and computer equipment. Background With the development of digitization technology, the amount of text data that needs to be processed has shown explosive growth. In the fields of technical analysis, technical literature retrieval, etc., it is often necessary to process and analyze a large amount of text data. At present, a simple batch processing mode is mainly adopted to combine a plurality of documents into batches for processing. This approach suffers from a lack of intelligence and is not able to understand the deep semantics of the text. The method has the advantages that the accuracy of the processing result is low due to the fact that the deep semantic understanding capability of the text is lacked, the method is difficult to flexibly adapt to different types of text processing tasks, such as complex scenes of technical analysis, technical matching and the like, and the processing efficiency is difficult to meet the requirement of large-scale data analysis. Disclosure of Invention The application aims to provide a method and equipment for intelligent task batch processing, which are used for solving the technical problems of low processing efficiency, insufficient intelligent degree and the like in the prior art. In a first aspect, the present application provides a method for intelligent task batch processing, including: The method comprises the steps of obtaining data to be processed, defining processing tasks corresponding to the data to be processed, wherein the processing tasks comprise task targets and constraint conditions, converting the data to be processed and the processing tasks into batch processing tasks according to a preset batch processing format, uploading the batch processing tasks to a server and creating batch processing jobs so that a generated artificial intelligent model sequentially processes the batch processing jobs in a batch processing mode, analyzing results returned by batch processing, and extracting processing results of each task. The method comprises the steps of generating unique identifiers and processing parameters for the processing tasks, converting the data to be processed and the processing tasks into preset format files, wherein each preset format file record comprises a task identifier, the processing parameters, the data to be processed and the processing tasks, and the preset format is JSON, CSV, XML, DOC, DOCX, TXT, PPT, PPTX, XLS or XLSX. The generated artificial intelligence model adopts a batch job processing mode to sequentially process the batch jobs, YY+242322P-DJ The method comprises the steps of selecting a corresponding pre-training model and parameter configuration according to task types, obtaining prompt words of task targets and constraint conditions contained in each task or generating the prompt words of the task targets and the constraint conditions in each task, sequentially and batched processing the data to be processed based on the prompt words, and sequentially and batched outputting corresponding processing results. The method further comprises the steps of outputting the corresponding processing results in batches based on an asynchronous processing mechanism when the server resources are idle, and suspending batch output operation when the resources are not idle. Uploading the batch task to a server and creating a batch job, wherein the batch job comprises the steps of verifying the integrity and the effectiveness of the batch task, setting execution parameters of the batch job, including an expected completion time window, a task priority and/or an upper limit of the number of concurrent tasks, initializing job monitoring indexes, including start time, processing progress and/or resource use conditions, and dynamically distributing computing resources based on system loads. The application provides a batched processing method for search analysis, wherein a processing task is used for batched difference comparison of comparison files, data to be processed comprises a plurality of comparison files, the generated artificial intelligent model sequentially processes batch processing operation by adopting a batched operation processing mode, the batched processing method comprises the steps of obtaining a target technical scheme and the plurality of comparison files, the target scheme comprises one or more key technical features, sequentially analyzing the technical feature coincidence degree or similarity of the plurality of comparison files and the target files in batches by utilizing the generated artificial intelligent model, and generating a differential analysis conclusion comprising a qualitative conclusion and/or a quantitative conclusion, wherein the qualitative conclusion comprises a similar or dissi