An intelligent rule conflict detection method, system and program product

Abstract

This invention belongs to the field of artificial intelligence technology, specifically disclosing an intelligent rule conflict detection method, system, and program product. By transforming the traditionally tedious and time-consuming manual rule review into intelligent rule conflict detection, it greatly simplifies the verification process for business personnel in rule design and significantly improves the efficiency of rule review. It can comprehensively and deeply analyze the rule system, automatically identifying logical errors, overlaps, and contradictions that are difficult for the human eye to detect, effectively reducing the risk of rule defects and potential financial losses caused by human negligence. It deeply embeds artificial intelligence technology into the business operation process of rule configuration, ensuring the logical validity of rules once they are created, and promoting the upgrade of enterprise rule management from reliance on manual experience to intelligent automatic detection. Furthermore, it can intuitively display potential problems and conflicts between rules, making the entire rule verification process clearer and easier for users to understand and make decisions.

Description

Technical Field

[0001] This invention belongs to the field of artificial intelligence technology, specifically relating to an intelligent rule conflict detection method, system, and program product. Background Technology

[0002] With the continuous transformation of enterprise management models and the rapid development of business, enterprise rule systems are becoming increasingly complex and need to evolve constantly to adapt to new management requirements. In traditional enterprise rule management processes, due to limitations in information technology and automation, rule designers typically need to manually check complex rule logic relationships and verify the validity of each rule in conjunction with actual business conditions when configuring new rules or modifying existing ones. Given the diversity and complexity of business rule types, this reliance on manual review is not only inefficient but also prone to errors in rule design due to human negligence, increasing the workload of subsequent system testing and business verification. Summary of the Invention

[0003] The purpose of this invention is to provide an intelligent rule conflict detection method, system, and program product to solve the above-mentioned problems existing in the prior art.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: Firstly, an intelligent rule conflict detection method is provided, including: Retrieve the rule text uploaded by the user; The rule text is parsed to extract several rules to be detected, and each rule to be detected is converted into structured rule information. In response to the user's rule inspection command, the intelligent agent retrieves the professional prompt words from the rule center and inputs the structured rule information and professional prompt words into the large model, so that the large model can perform rule analysis on the structured rule information based on the professional prompt words; Receive the rule analysis results from the large model, and determine the logical conflicts between the contents of each rule to be detected in the rule text based on the rule analysis results; Based on the logical conflicts between the contents of each rule to be detected in the rule text, rule conflict warning information is generated, and rule conflict warning information and rule analysis results are output to the user.

[0005] In one possible design, before obtaining the user-uploaded rule text, the method further includes: Obtain the user's identity information; User permissions are verified based on their identity information.

[0006] In one possible design, before retrieving the specialized prompts from the rule center via the agent and inputting the structured rule information and specialized prompts into the large model, the method further includes: Configure the agent's comprehensive parameters according to the visual orchestration instructions in the rule center. The comprehensive parameters include the large model information to be called, professional prompt words, input parameter information, output parameter information and / or conditional branch processing information.

[0007] In one possible design, when the agent inputs the structured rule information and professional prompts into the large model, it connects to the corresponding large model through a standardized API interface based on the information of the large model to be called, and then inputs the structured rule information and professional prompts into the large model.

[0008] In one possible design, the professional prompts are used to guide the checking of whether there are logical errors, logical overlaps, or logical contradictions between the rules.

[0009] In one possible design, the logical conflict problem includes logical errors, logical overlaps, and / or logical contradictions.

[0010] In one possible design, the results of the rule analysis include logical conflict issues, potential risks, and optimization suggestions.

[0011] Secondly, an intelligent rule conflict detection system is provided, comprising a text acquisition unit, a rule extraction unit, a rule analysis unit, a result processing unit, and a result output unit, wherein: The text acquisition unit is used to acquire the rule text uploaded by the user; The rule extraction unit is used to parse the rule text, extract several rules to be detected, and convert each rule to be detected into structured rule information; The rule analysis unit is used to respond to the user's rule inspection command, retrieve the professional prompt words from the rule center through the intelligent agent, and input the structured rule information and professional prompt words into the large model, so that the large model can perform rule analysis on the structured rule information based on the professional prompt words; The results processing unit is used to receive the rule analysis results from the large model and determine the logical conflicts between the contents of each rule to be detected in the rule text based on the rule analysis results. The result output unit is used to generate rule conflict warning information based on the logical conflicts between the contents of each rule to be detected in the rule text, and output the rule conflict warning information and rule analysis results to the user.

[0012] Thirdly, an intelligent rule conflict detection system is provided, including: Memory, used to store instructions; A processor is configured to read instructions stored in the memory and execute the method described in any one of the first aspects above, according to the instructions.

[0013] Fourthly, a computer-readable storage medium is provided, wherein instructions are stored on the computer-readable storage medium, and when the instructions are executed on a computer, the computer performs any one of the methods described in the first aspect. A computer program product is also provided, which, when executed on a computer, performs any one of the methods described in the first aspect.

[0014] Beneficial Effects: This invention transforms the traditionally tedious and time-consuming manual rule review into intelligent rule conflict detection, greatly simplifying the verification process for business personnel in rule design and significantly improving the efficiency of rule review. Utilizing the powerful understanding and reasoning capabilities of large-scale models, it can comprehensively and deeply analyze the rule system, automatically identifying logical errors, overlaps, and contradictions that are difficult for the human eye to detect, effectively reducing the risk of rule defects and potential financial losses caused by human negligence. By deeply embedding artificial intelligence technology into the business operation process of rule configuration, the logical validity of rules is guaranteed once they are created, promoting the upgrade of enterprise rule management from reliance on manual experience to intelligent automatic detection. Furthermore, it can intuitively display potential problems and conflicts between rules, making the entire rule verification process clearer and easier for users to understand and make decisions. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the steps in the method of Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the system configuration in Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of the system configuration in Embodiment 3 of the present invention. Detailed Implementation

[0017] It should be noted that the descriptions of these embodiments are intended to aid in understanding the invention and do not constitute a limitation thereof. The specific structural and functional details disclosed herein are merely for describing exemplary embodiments of the invention. However, the invention may be embodied in many alternative forms and should not be construed as being limited to the embodiments described herein.

[0018] It should be understood that, unless otherwise explicitly specified and limited, the corresponding terms should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in the embodiments according to the specific circumstances.

[0019] Specific details are provided in the following description to provide a complete understanding of the exemplary embodiments. However, those skilled in the art will understand that the exemplary embodiments can be implemented without these specific details. For example, apparatus may be shown in block diagrams to avoid obscuring the examples with unnecessary details. In other embodiments, well-known processes, structures, and techniques may be omitted with non-essential details to avoid obscuring the embodiments.

[0020] Example 1: This embodiment provides an intelligent rule conflict detection method, which can be applied to corresponding rule detection servers, such as... Figure 1 As shown, the method includes the following steps: S1. Obtain the rule text uploaded by the user.

[0021] In practice, users can pre-edit the rule text to be tested, and then upload the edited rule text to the server through the corresponding client. The server then performs subsequent testing and analysis on the rule text. Furthermore, before the user uploads the rule text, user permission verification can be performed. That is, the user uploads identification information (such as an ID number) through the client, the server obtains the user's identification information, and then verifies the user's permissions based on the identification information to determine whether the user has the necessary access rights to the server system.

[0022] S2. Parse the rule text, extract several rule contents to be detected, and convert each rule content into structured rule information.

[0023] In practice, the server can parse the rule text uploaded by the user to extract several rules to be detected. For example, each rule to be detected includes: Rule 1: Basic Rules for Travel Expenses. For employees traveling on business, one-way transportation expenses will be reimbursed based on actual receipts, and accommodation expenses will be capped at 200 yuan per day.

[0024] Rule 2: Progressive rule for long-term business trips. If the business trip lasts more than 3 days, the daily accommodation fee cap increases from 200 yuan to 300 yuan.

[0025] Rule 3: Remote Area Subsidy Rules. If the business trip is to a remote area, the daily transportation subsidy will be increased by 50 yuan per person, and the daily accommodation fee cap will be raised to 280 yuan.

[0026] Rule 4: Executive Travel Rules. For company executives traveling on business, the daily accommodation allowance is capped at 300 yuan, with an additional 100 yuan per day for transportation allowance.

[0027] Then, the content of each rule to be detected is processed into text structure, which converts the content of each rule to be detected into the corresponding structured rule information, realizing the programmatic expression of the rule logic, so that the large model can perform natural language processing analysis in the future.

[0028] S3. In response to the user's rule inspection command, the agent retrieves the professional prompts from the rule center and inputs the structured rule information and professional prompts into the large model, so that the large model can perform rule analysis on the structured rule information based on the professional prompts.

[0029] In practice, users can perform rule checks by clicking on the corresponding user terminal, generating a rule check command that is sent to the server. The server responds to the user's rule check command by retrieving professional prompts from the rule center through a pre-configured agent. These prompts include information on whether there are logical errors, overlaps, or contradictions between the rules, such as "Please check whether there are logical errors, overlaps, or contradictions between the rules, and display the check results categorized by these three types." The server then inputs the structured rule information and the professional prompts into the large model, enabling the large model to perform rule analysis based on the professional prompts. The agent can be configured with parameters through the rule center's visual orchestration method. The server configures the agent's comprehensive parameters according to the rule center's visual orchestration instructions. These comprehensive parameters may include information about the large model to be invoked, professional prompts, input parameters, output parameters, and / or conditional branch processing information (used to trigger subsequent processing logic and response information based on different check results, such as meeting / not meeting conditions). Furthermore, fine-grained control parameters such as the AI ​​response information structure and context information when conditions are met / not met can also be configured. When the intelligent agent inputs the structured rule information and professional prompts into the large model, it connects to the corresponding large model through a standardized API interface based on the information of the large model to be called, and then inputs the structured rule information and professional prompts into the large model.

[0030] S4. Receive the rule analysis results from the large model and determine the logical conflicts between the contents of each rule to be detected in the rule text based on the rule analysis results.

[0031] In practical implementation, the intelligent agent utilizes the understanding and reasoning capabilities of the large model to conduct in-depth comparative analysis of the input rules across multiple dimensions, including logical errors, logical overlaps, and logical contradictions, combined with information from the existing rule knowledge base. The large model then provides rule analysis results, which may include logical conflict issues, potential risks, and optimization suggestions. The server can determine the logical conflict issues between the content of each rule to be detected based on the rule analysis results. These logical conflict issues include logical errors, logical overlaps, and / or logical contradictions. For example, for the four rules mentioned above, the logical conflict issues are as follows: 1. Logical Error: Rule 3 stipulates a maximum accommodation fee of 280 yuan for remote areas, lower than the 300 yuan maximum for long-term business trips in Rule 2. This is logically unreasonable because accommodation costs in remote areas are usually higher and should not be lower than the standard for long-term business trips in ordinary areas. 2. Logical Overlap: When the business trip exceeds 3 days and the location is a remote area, both Rule 2 and Rule 3 apply, but the maximum accommodation fee is different (300 yuan for Rule 2 and 280 yuan for Rule 3), causing an overlap and conflict in the rule coverage. 3. Logical contradiction: For senior executives whose business trips exceed 3 days and are located in remote areas, Rule 3 stipulates a maximum accommodation fee of 280 yuan, while Rule 4 stipulates a maximum of 300 yuan. The two rules contradict each other for the same scenario and cannot be satisfied at the same time.

[0032] S5. Generate rule conflict warning information based on the logical conflicts between the contents of each rule to be detected in the rule text, and output the rule conflict warning information and rule analysis results to the user.

[0033] In practice, the server can generate rule conflict warning information based on the logical conflicts between the contents of each rule to be detected in the rule text, and output the rule conflict warning information and rule analysis results to the user, making the entire rule verification process clear and visible.

[0034] This method transforms the traditionally tedious and time-consuming manual rule review into intelligent rule conflict detection, greatly simplifying the verification process for business personnel in rule design and significantly improving the efficiency of rule review. Leveraging the powerful understanding and reasoning capabilities of large-scale models, it can comprehensively and deeply analyze the rule system, automatically identifying logical errors, overlaps, and contradictions that are difficult for the human eye to detect, effectively reducing the risk of rule defects and potential financial losses caused by human negligence. By deeply embedding artificial intelligence technology into the business operation process of rule configuration, the logical validity of rules is guaranteed once they are created, driving the upgrade of enterprise rule management from reliance on manual experience to intelligent automatic detection. Furthermore, it can intuitively display potential problems and conflicts between rules, making the entire rule verification process clearer and easier for users to understand and make decisions.

[0035] Example 2: This embodiment provides an intelligent rule conflict detection system, such as Figure 2 As shown, it includes a text acquisition unit, a rule extraction unit, a rule analysis unit, a result processing unit, and a result output unit, wherein: The text acquisition unit is used to acquire the rule text uploaded by the user; The rule extraction unit is used to parse the rule text, extract several rules to be detected, and convert each rule to be detected into structured rule information; The rule analysis unit is used to respond to the user's rule inspection command, retrieve the professional prompt words from the rule center through the intelligent agent, and input the structured rule information and professional prompt words into the large model, so that the large model can perform rule analysis on the structured rule information based on the professional prompt words; The results processing unit is used to receive the rule analysis results from the large model and determine the logical conflicts between the contents of each rule to be detected in the rule text based on the rule analysis results. The result output unit is used to generate rule conflict warning information based on the logical conflicts between the contents of each rule to be detected in the rule text, and output the rule conflict warning information and rule analysis results to the user.

[0036] Example 3: This embodiment provides an intelligent rule conflict detection system, such as Figure 3 As shown, at the hardware level, it includes: The data interface is used to establish data communication between the processor and external data terminals; Memory, used to store instructions; The processor is configured to read instructions stored in the memory and execute the intelligent rule conflict detection method in Embodiment 1 according to the instructions: S1. Obtain the rule text uploaded by the user; S2. Parse the rule text, extract several rule contents to be detected, and convert each rule content to be detected into structured rule information; S3. In response to the user's rule inspection instruction, retrieve the professional prompt words from the rule center through the intelligent agent, and input each structured rule information and professional prompt words into the large model, so that the large model performs rule analysis on each structured rule information according to the professional prompt words; S4. Receive the rule analysis results fed back by the large model, and determine the logical conflict problems between the rule contents to be detected in the rule text according to the rule analysis results; S5. Generate rule conflict warning information according to the logical conflict problems between the rule contents to be detected in the rule text, and output the rule conflict warning information and rule analysis results to the user.

[0037] Optionally, the system also includes an internal bus, through which the processor, memory, and data interface can be interconnected. This internal bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc.

[0038] The memory may include, but is not limited to, random access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out (FIFO) memory, and / or first-in-last-out (FILO) memory. The processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0039] Example 4: This embodiment provides a computer-readable storage medium storing instructions. When the instructions are executed on a computer, the computer performs the intelligent rule conflict detection method in Embodiment 1: S1. Obtain rule text uploaded by the user; S2. Parse the rule text, extract several rule contents to be detected, and convert each rule content to be detected into structured rule information; S3. In response to the user's rule inspection instruction, retrieve professional prompt words from the rule center through an intelligent agent, and input each structured rule information and professional prompt words into a large model, so that the large model performs rule analysis on each structured rule information based on the professional prompt words; S4. Receive the rule analysis results fed back by the large model, and determine the logical conflict problems between the rule contents to be detected in the rule text based on the rule analysis results; S5. Generate rule conflict warning information based on the logical conflict problems between the rule contents to be detected in the rule text, and output the rule conflict warning information and rule analysis results to the user. The computer-readable storage medium refers to a carrier for storing data, which may include, but is not limited to, floppy disks, optical disks, hard disks, flash memory, USB flash drives and / or memory sticks, etc. The computer may be a general-purpose computer, a special-purpose computer, a computer network or other programmable device.

[0040] This embodiment also provides a computer program product. When the computer program product runs on a computer, it executes the intelligent rule conflict detection method in Embodiment 1: S1. Obtain the rule text uploaded by the user; S2. Parse the rule text, extract several rule contents to be detected, and convert each rule content to be detected into structured rule information; S3. In response to the user's rule inspection command, retrieve the professional prompt words from the rule center through the intelligent agent, and input each structured rule information and professional prompt words into the large model, so that the large model performs rule analysis on each structured rule information according to the professional prompt words; S4. Receive the rule analysis results fed back by the large model, and determine the logical conflict problems between the rule contents to be detected in the rule text according to the rule analysis results; S5. Generate rule conflict warning information according to the logical conflict problems between the rule contents to be detected in the rule text, and output the rule conflict warning information and rule analysis results to the user. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0041] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method of intelligent rule conflict detection, the method comprising: The method comprises the following steps: obtaining rule text uploaded by a user; parsing the rule text to extract a plurality of to-be-detected rule contents, and converting each to-be-detected rule content into structured rule information; in response to a rule checking instruction of the user, calling professional prompt words of a rule center through an intelligent agent, and inputting each structured rule information and the professional prompt words into a large model, so that the large model performs rule analysis on each structured rule information according to the professional prompt words; receiving a rule analysis result fed back by the large model, and determining a logical conflict problem existing between each to-be-detected rule content of the rule text according to the rule analysis result; generating rule conflict warning information according to the logical conflict problem existing between each to-be-detected rule content of the rule text, and outputting the rule conflict warning information and the rule analysis result to the user.

2. The intelligent rule conflict detection method of claim 1, wherein, Before obtaining the rule text uploaded by the user, the method further comprises: obtaining identity information of the user; performing permission verification on the user according to the identity information.

3. The intelligent rule conflict detection method of claim 1, wherein, Before calling the professional prompt words of the rule center through the intelligent agent, and inputting each structured rule information and the professional prompt words into the large model, the method further comprises: configuring comprehensive parameters of the intelligent agent according to a visual arrangement instruction of the rule center, wherein the comprehensive parameters comprise to-be-called large model information, professional prompt words, input parameter information, output parameter information, and / or conditional branch processing information.

4. The intelligent rule conflict detection method of claim 3, wherein, When inputting each structured rule information and the professional prompt words into the large model, the intelligent agent connects a corresponding large model through a standardized API interface according to the to-be-called large model information, and then inputs each structured rule information and the professional prompt words into the large model.

5. The intelligent rule conflict detection method of claim 1, wherein, The professional prompt words are used to guide the checking of whether there is a logical error, logical coincidence or logical contradiction between rules.

6. The intelligent rule conflict detection method of claim 1, wherein, The logical conflict problem comprises a logical error, a logical coincidence and / or a logical contradiction.

7. The intelligent rule conflict detection method of claim 1, wherein, The rule analysis result comprises a logical conflict problem, a potential risk and an optimization suggestion.

8. An intelligent rule conflict detection system characterized by, The method comprises the following steps: obtaining rule text uploaded by a user; parsing the rule text to extract a plurality of to-be-detected rule contents, and converting each to-be-detected rule content into structured rule information; in response to a rule checking instruction of the user, calling professional prompt words of a rule center through an intelligent agent, and inputting each structured rule information and the professional prompt words into a large model, so that the large model performs rule analysis on each structured rule information according to the professional prompt words; receiving a rule analysis result fed back by the large model, and determining a logical conflict problem existing between each to-be-detected rule content of the rule text according to the rule analysis result; generating rule conflict warning information according to the logical conflict problem existing between each to-be-detected rule content of the rule text, and outputting the rule conflict warning information and the rule analysis result to the user.

9. An intelligent rule conflict detection system characterized by, The method comprises the following steps: a memory for storing instructions; a processor for reading the instructions stored in the memory, and executing the intelligent rule conflict detection method according to any one of claims 1-7.

10. A computer program product, characterised in that, When the computer program product is run on a computer, the intelligent rule conflict detection method according to any one of claims 1-7 is executed.

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