Data execution method and system based on AI client

Abstract

The invention discloses a data execution method and system based on an AI client, and the method comprises the steps: receiving input to-be-replied data through an AI reply client, and carrying out the networking processing of the to-be-replied data, so as to return text data; analyzing the text data by the local knowledge base to obtain a text analysis result; the executor matches an event corresponding to the text analysis result in an event library, executes the event and returns an execution state to the AI reply client; and the AI replies the client to generate an event suggestion text according to the execution state. In this way, a complete closed loop of data input, intelligent analysis, instruction execution and dynamic feedback is established, general knowledge can be obtained through networking processing, information lag of a pure localization system is avoided, secondary analysis is carried out in combination with a local knowledge base, it can be ensured that an output result conforms to a specific service rule, and the service quality is improved. The execution state real-time feedback mechanism enables the system to diverge and extend the data processing logic, so that the data execution efficiency is improved, and the complex processing degree of data is increased.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic data execution, and in particular to a data execution method and system based on an AI client. Background Art

[0002] Current AI systems face three major bottlenecks in data interaction processing: 1. General AI relies on cloud data and is difficult to adapt to local enterprise rules, resulting in output results being out of touch with business scenarios. 2. Existing solutions mostly remain at the text generation stage and lack a closed-loop mechanism to convert semantic instructions into executable operations; 3. Traditional systems only return error codes when an execution fails and are unable to dynamically generate response strategies based on the context. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a data execution method and system based on an AI client, which can improve the efficiency of data execution and increase the complexity of data processing.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is: A data execution method based on an AI client, comprising the steps of: The AI ​​reply client receives the input data to be replied, processes the data to be replied on the network, and returns text data; Analyzing the text data returned by the local knowledge base to obtain a text analysis result; The executor matches the event corresponding to the text analysis result in the event library, executes the event, and returns the execution status to the AI ​​reply client; The AI ​​reply client generates event suggestion text based on the execution status.

[0005] In order to solve the above technical problems, another technical solution adopted by the present invention is: A data execution system based on an AI client includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step of the above-mentioned data execution method based on the AI ​​client is implemented.

[0006] The beneficial effects of the present invention are as follows: the AI ​​reply client receives the input data to be replied, processes the data to be replied on the network to return text data; the local knowledge base analyzes the text data to obtain the text analysis result; the executor matches the event corresponding to the text analysis result in the event library, executes the event and returns the execution status to the AI ​​reply client; the AI ​​reply client generates event suggestion text according to the execution status. In this way, a complete closed loop of data input, intelligent analysis, instruction execution and dynamic feedback is established, which can obtain general knowledge through network processing and avoid the information lag of purely localized systems. The combination with the local knowledge base for secondary analysis can ensure that the output results comply with specific business rules. The real-time feedback mechanism of the execution status enables the system to diverge and extend the logic of data processing, thereby improving the efficiency of data execution and increasing the complexity of data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 This is a flowchart of a data execution method based on an AI client according to an embodiment of the present invention; Figure 2 Schematic diagram of a data execution system based on an AI client according to an embodiment of the present invention; Figure 3 This is a business flow chart of a data execution method based on an AI client according to an embodiment of the present invention; Figure 4 This is an execution flow chart of a data execution method based on an AI client according to an embodiment of the present invention; Figure 5 This is a processing flow chart of the executor according to an embodiment of the present invention.

[0008] Description of labels: 1. A data execution system based on an AI client; 2. Memory; 3. Processor. DETAILED DESCRIPTION

[0009] To illustrate the technical content, achieved objectives and effects of the present invention in detail, the following description is given in conjunction with the embodiments and accompanying drawings.

[0010] Please refer to Figure 1 , an embodiment of the present invention provides a data execution method based on an AI client, comprising the steps of: The AI ​​reply client receives the input data to be replied, processes the data to be replied on the network, and returns text data; Analyzing the text data returned by the local knowledge base to obtain a text analysis result; The executor matches the event corresponding to the text analysis result in the event library, executes the event, and returns the execution status to the AI ​​reply client; The AI ​​reply client generates event suggestion text based on the execution status.

[0011] From the above description, it can be seen that the beneficial effects of the present invention are: the AI ​​reply client receives the input data to be replied, processes the data to be replied on the network to return text data; the local knowledge base analyzes the text data to obtain the text analysis result; the executor matches the event corresponding to the text analysis result in the event library, executes the event and returns the execution status to the AI ​​reply client; the AI ​​reply client generates event suggestion text according to the execution status. In this way, a complete closed loop of data input, intelligent analysis, instruction execution and dynamic feedback is established, which can obtain general knowledge through network processing and avoid the information lag of purely localized systems. The combination with the local knowledge base for secondary analysis can ensure that the output results comply with specific business rules. The real-time feedback mechanism of the execution status enables the system to diverge and extend the logic of data processing, thereby improving the efficiency of data execution and increasing the complexity of data processing.

[0012] Furthermore, the data to be replied is processed online to return text data, including: The AI ​​reply client processes the data to be replied to via the network and returns the text data generated by the processing; The text data returned by the local knowledge base is analyzed to obtain text analysis results, including: The local knowledge base performs keyword matching on the data information to be replied in the text data, performs local rule processing according to the matched keywords, and obtains a text analysis result by combining the text data and the local rule processing result.

[0013] From the above description, it can be seen that through the dual processing of networking and local knowledge base, the local rule adaptation rate of text analysis results and the parsing accuracy of text analysis results are improved. At the same time, it helps to balance the conflict between general knowledge and private rules, ensuring that the output results comply with specific business rules.

[0014] Furthermore, the executor matches an event corresponding to the text analysis result in the event library, including: The executor logically decomposes the text analysis result to obtain a decomposed text; The disassembled text is matched with event instructions in an event library, and an event corresponding to the text analysis result is determined based on all matching results.

[0015] As can be seen from the above description, by logically decomposing the text analysis results and matching the decomposed text with the event instructions in the event library, the events to be executed can be automatically obtained, so as to improve the efficiency of data execution.

[0016] Furthermore, the AI ​​reply client generates event suggestion text according to the execution status, including: If the execution status is execution failure, the AI ​​reply client generates event suggestion text according to the execution status.

[0017] From the above description, it can be seen that the results of execution failure can automatically trigger the knowledge base to search and generate event suggestion text, further diverging and extending the logic of data processing, thereby increasing the complexity of data processing.

[0018] Furthermore, the AI ​​reply client generates event suggestion text according to the execution status, including: If the execution status is successful, the AI ​​will reply to the client to display a prompt text of successful execution.

[0019] As can be seen from the above description, by providing feedback on the execution success status, the subsequent operation error rate can be visualized based on the execution trajectory.

[0020] Please refer to Figure 2 Another embodiment of the present invention provides a data execution system based on an AI client, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the system implements the steps of the above-mentioned data execution method based on an AI client.

[0021] The AI ​​client-based data execution method and system described above are suitable for improving the efficiency of data execution and increasing the complexity of data processing. The following describes a specific implementation method: Please refer to Figure 1 and Figure 3 , embodiment 1 of the present invention is: A data execution method based on an AI client, characterized by comprising the steps of: S1. The AI ​​reply client receives input data to be replied, processes the data to be replied via the network, and returns text data.

[0022] The AI ​​reply client processes the data to be replied to via the Internet and returns the text data generated by the processing.

[0023] In this embodiment, the data to be replied is game data or data delivered by the game process, and the AI ​​reply client is a webpage or PC capable of AI reply. Specifically, the game data or game process is delivered to the AI ​​reply client, which performs the reply calculations over the network and returns the text data of the online reply.

[0024] S2. The local knowledge base analyzes the returned text data to obtain a text analysis result.

[0025] The local knowledge base performs keyword matching on the data information to be replied in the text data, performs local rule processing according to the matched keywords, and obtains a text analysis result by combining the text data and the local rule processing result.

[0026] In this embodiment, please refer to Figure 4 The knowledge base is used to connect AI clients to the internet and generate data through thinking. The knowledge base contains data, rules, and concepts. Data refers to the data used for keyword matching; rules refer to the rules for executing and judging data; and concepts refer to the concepts to which the data belongs.

[0027] Specifically, after AI responds to the client with general knowledge obtained through the Internet, it also generates text analysis results based on the local knowledge base.

[0028] For example, if the data to be responded to is data A and data B, the AI ​​response client calculates "A + B = C" through online calculations. Then, in conjunction with the local knowledge base, it combines data A and data B for analysis. Because the knowledge base has keywords, deep thinking considers the combination of data A and data B. The returned data is then combined with the knowledge base for further analysis / thinking, effectively rethinking the data locally. If the knowledge base defines "A = Notify," "B = Wait," and "+ = Merge Execution," the output after combining with the local knowledge base will be: Notify and Wait for the result.

[0029] S3. The executor matches the event corresponding to the text analysis result in the event library, executes the event, and returns the execution status to the AI ​​reply client.

[0030] The executor logically decomposes the text analysis result to obtain a decomposed text, matches the decomposed text with event instructions in an event library, and determines an event corresponding to the text analysis result based on all matching results.

[0031] In this embodiment, please refer to Figure 5 The event library is the event that needs to be executed, and the AI ​​responds to the client with the text analysis results. The format of the event command is: @[target] [operation] [parameter]. For example, "@test d 123.txt" means that the host test in the event @ needs to perform the delete d operation on the file 123.txt. The status must also be determined, that is, whether the link failed or succeeded, and whether the deletion was successful or failed, and the result is returned to the AI ​​to reply to the client.

[0032] In this embodiment, the text analysis results can trigger the binding between the knowledge base and the event library. For example, if data A and data B are input, and the AI ​​reply client returns data C via the network, data C is replaced by the knowledge base and becomes action 1. The AI ​​reply client does not execute the action, but only performs the logical transformation and converts it into text. The executor then retrieves the logic and helps the AI ​​reply client complete the logic.

[0033] S4. The AI ​​reply client generates event suggestion text based on the execution status.

[0034] Among them, if the execution status is execution failure, the AI ​​reply client generates event suggestion text according to the execution status; if the execution status is execution success, the AI ​​reply client displays execution success prompt text.

[0035] Specifically, the AI ​​responds to the client by returning results and displaying text based on the knowledge base. For example, if the "@test d123.txt" executor succeeds, the AI ​​responds to the client with a success message. If the execution fails, the AI ​​returns the following text based on the knowledge base configuration: Continue trying or give up.

[0036] In summary, this embodiment combines the AI ​​client with a knowledge base, and then with an event library, to intelligently pair data with events. Specifically, AI generates text data, and then uses the data returned from the knowledge base and event library to form data context. This not only guides users but also improves execution efficiency, increases data divergence, allows for unlimited logic expansion, and increases the complexity of data processing.

[0037] Please refer to Figure 2 , the second embodiment of the present invention is: A data execution system 1 based on an AI client includes a memory 2, a processor 3, and a computer program stored in the memory 2 and executable on the processor 3. When the processor 3 executes the computer program, each step of an AI client-based data execution method according to the first embodiment is implemented.

[0038] In summary, the present invention provides a data execution method and system based on an AI client, in which the AI ​​reply client receives the input data to be replied, processes the data to be replied on the network, and returns text data; the local knowledge base analyzes the text data to obtain the text analysis result; the executor matches the event corresponding to the text analysis result in the event library, executes the event and returns the execution status to the AI ​​reply client; the AI ​​reply client generates event suggestion text according to the execution status. In this way, a complete closed loop of data input, intelligent analysis, instruction execution, and dynamic feedback is established, which can obtain general knowledge through network processing, avoid the information lag of a purely localized system, and combine with the local knowledge base for secondary analysis to ensure that the output results comply with specific business rules. The real-time feedback mechanism of the execution status enables the system to diverge and extend the logic of data processing, thereby improving the efficiency of data execution and increasing the complexity of data processing.

[0039] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's description and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A data execution method based on AI client, characterized in that: Including steps: The AI ​​reply client receives the input data to be replied, processes the data to be replied on the network, and returns text data; Analyzing the text data returned by the local knowledge base to obtain a text analysis result; The executor matches the event corresponding to the text analysis result in the event library, executes the event, and returns the execution status to the AI ​​reply client; The AI ​​reply client generates event suggestion text based on the execution status.

2. The data execution method based on AI client according to claim 1, characterized in that: Processing the data to be replied to on the Internet to return text data includes: The AI ​​reply client processes the data to be replied to via the network and returns the text data generated by the processing; The text data returned by the local knowledge base is analyzed to obtain text analysis results, including: The local knowledge base performs keyword matching on the data information to be replied in the text data, performs local rule processing according to the matched keywords, and obtains a text analysis result by combining the text data and the local rule processing result.

3. The data execution method based on AI client according to claim 1, characterized in that: The executor matches the events corresponding to the text analysis results in the event library, including: The executor logically decomposes the text analysis result to obtain a decomposed text; The disassembled text is matched with event instructions in an event library, and an event corresponding to the text analysis result is determined based on all matching results.

4. The data execution method based on AI client according to claim 1, characterized in that: The AI ​​reply client generates event suggestion text based on the execution status, including: If the execution status is execution failure, the AI ​​reply client generates event suggestion text according to the execution status.

5. The data execution method based on AI client according to claim 4, characterized in that: The AI ​​reply client generates event suggestion text based on the execution status, including: If the execution status is successful, the AI ​​will reply to the client to display a prompt text of successful execution.

6. A data execution system based on an AI client, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the following steps are implemented: The AI ​​reply client receives the input data to be replied, processes the data to be replied on the network, and returns text data; Analyzing the text data returned by the local knowledge base to obtain a text analysis result; The executor matches the event corresponding to the text analysis result in the event library, executes the event, and returns the execution status to the AI ​​reply client; The AI ​​reply client generates event suggestion text based on the execution status.

7. The data execution system based on AI client according to claim 6, characterized in that: Processing the data to be replied to on the Internet to return text data includes: The AI ​​reply client processes the data to be replied to via the network and returns the text data generated by the processing; The text data returned by the local knowledge base is analyzed to obtain text analysis results, including: The local knowledge base performs keyword matching on the data information to be replied in the text data, performs local rule processing according to the matched keywords, and obtains a text analysis result by combining the text data and the local rule processing result.

8. The data execution system based on AI client according to claim 6, characterized in that: The executor matches the events corresponding to the text analysis results in the event library, including: The executor logically decomposes the text analysis result to obtain a decomposed text; The disassembled text is matched with event instructions in an event library, and an event corresponding to the text analysis result is determined based on all matching results.

9. The data execution system based on AI client according to claim 6, characterized in that: The AI ​​reply client generates event suggestion text based on the execution status, including: If the execution status is execution failure, the AI ​​reply client generates event suggestion text according to the execution status.

10. The data execution system based on AI client according to claim 9, characterized in that: The AI ​​reply client generates event suggestion text based on the execution status, including: If the execution status is successful, the AI ​​will reply to the client to display a prompt text of successful execution.

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