Rule conflict explanation generation and discretion control method for ai-based centralized procurement governance hub

Abstract

The embodiment of the application relates to the field of procurement governance, and particularly relates to a rule conflict explanation generation and discretionary control method of a centralized procurement governance hub based on AI. The method determines at least one discretionary rule corresponding to a target business. Corresponding discretionary schemes are respectively generated according to the discretionary rules corresponding to the target business. Whether conflicts exist between different discretionary rules of the target business is detected according to the corresponding discretionary schemes. In the case that conflicts exist between at least two discretionary rules, a target discretionary scheme is determined in the conflicting discretionary schemes, and explanation information of the target discretionary scheme is generated. The corresponding effective mode is determined according to the applicable conditions and risk levels of the explanation information. The validity of the explanation information is determined according to the effective mode. In the case that the explanation information is valid, the explanation information is stored and / or marked.

Description

Technical Field

[0001] This application relates to the field of procurement governance, and includes, but is not limited to, a method for generating and controlling rule conflicts in a centralized procurement governance hub based on AI. Background Technology

[0002] Centralized procurement, as an important means for enterprises or organizations to reduce costs, increase efficiency, and standardize procurement behavior, typically relies on a pre-established set of rules for its governance. These rules cover multiple dimensions, including compliance requirements, risk management, efficiency optimization, and historical experience summaries, collectively forming the basis for procurement decisions. In practice, a complex business objective often triggers multiple rules simultaneously. Due to differences in the goals, perspectives, and constraints of rule formulation, these rules may reach inconsistent or even conflicting conclusions regarding the same matter. For example, compliance rules may require prioritizing specific suppliers, while efficiency rules may favor lower-cost alternatives. Summary of the Invention

[0003] In view of this, the method for generating and controlling rule conflicts in a centralized procurement governance center based on AI provided in this application can automatically handle rule conflicts that occur during the procurement governance process.

[0004] The rule conflict interpretation generation and discretionary control method for the AI-based centralized procurement governance center provided in this application embodiment is implemented as follows: One aspect of this application provides a method for rule conflict interpretation, generation, and discretionary control in a centralized procurement governance center based on AI, the method comprising: Identify at least one discretionary rule corresponding to the target business; Generate corresponding discretionary solutions based on the discretionary rules corresponding to the target business; Based on the corresponding discretionary scheme, detect whether there are conflicts between different discretionary rules for the target business; In the event of a conflict between at least two discretionary rules, determine the target discretionary option among the conflicting discretionary options and generate explanatory information for the target discretionary option; The corresponding method of effectiveness is determined based on the applicable conditions and risk level of the explanatory information; The validity of the explanatory information is determined based on the method of its effectiveness. If the explanatory information is valid, store and / or tag the explanatory information.

[0005] In one possible implementation, the method also includes: Determine whether the explanatory information can be reused; Freeze the explanatory information if the explanatory information can be reused.

[0006] In one possible implementation, the method also includes: In response to receiving a similar business to the target business, the system invokes and interprets information to perform business discretion and obtain the corresponding business discretion result. Determine whether to convert the explanatory information into discretionary rules based on the results of the business discretion assessment.

[0007] In one possible implementation, a target discretionary option is identified among conflicting discretionary options, and explanatory information for the target discretionary option is generated, including: Based on the constraint information related to each conflicting discretionary option, generate candidate interpretations for each discretionary option. The target discretionary option is determined based on the candidate interpretations corresponding to each discretionary option, and the candidate interpretations of the target discretionary option are determined as explanatory information.

[0008] In one possible implementation, the constraint information includes compliance constraints, risk constraints, efficiency constraints, and historical business constraints.

[0009] In one possible implementation, the method also includes: Verify the consistency between the target discretionary scheme and the explanatory information.

[0010] In one possible implementation, the validity of the explanatory information is determined based on the method of effectiveness, including: If the target discretionary plan and the explanatory information are consistent, the explanatory information is deemed valid.

[0011] In one possible implementation, the validity of the explanatory information is determined based on the method of effectiveness, including: Upon receiving a confirmation instruction from the user regarding the validity of the explanation information, the system determines that the explanation information is valid.

[0012] In this embodiment, the method determines at least one discretionary rule corresponding to a target business. A corresponding discretionary scheme is generated based on each discretionary rule for the target business. Conflicts are detected between different discretionary rules of the target business based on the corresponding discretionary schemes. If a conflict exists between at least two discretionary rules, a target discretionary scheme is determined from the conflicting discretionary schemes, and explanation information for the target discretionary scheme is generated. In the case of multiple discretionary rules applicable to the same target business, this application automatically detects whether conflicts exist between different discretionary rules. If a conflict exists, a discretionary scheme is automatically selected to resolve the conflict, and a reasonable and structured explanation of the resolution process is provided. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 The flowchart illustrates a method for rule conflict interpretation generation and discretionary control in an AI-based centralized procurement governance center according to an embodiment of this application. Figure 2 This diagram illustrates a rule conflict interpretation generation and discretionary control process for an AI-based centralized procurement governance hub according to an embodiment of this application. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the specific technical solutions of this application will be further described in detail below with reference to the accompanying drawings of the embodiments of this application. The following embodiments are used to illustrate this application, but are not intended to limit the scope of this application.

[0016] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0017] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0018] It should be noted that the terms "first, second, third" used in the embodiments of this application are used to distinguish similar or different objects and do not represent a specific order of objects. It can be understood that "first, second, third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0019] The rule conflict interpretation generation and discretionary control method for the AI-based centralized procurement governance center in this application embodiment can be executed by any electronic device, including but not limited to mobile phones, wearable devices (such as smartwatches, smart bracelets, smart glasses, etc.), tablets, laptops, in-vehicle terminals, PCs (Personal Computers), etc. The functions implemented by this method can be achieved by a processor in the electronic device calling program code. Of course, the program code can be stored in a computer storage medium. Therefore, the electronic device includes at least a processor and a storage medium.

[0020] The AI-based centralized procurement governance method described in this application can be used in any centralized procurement scenario where discretion is possible. Examples include centralized procurement scenarios in large enterprise or group centralized procurement centers, centralized procurement scenarios on government / public institution procurement platforms, and centralized procurement scenarios in heavily regulated industries such as financial institutions, energy, and healthcare.

[0021] In the process of centralized procurement governance, when dealing with situations where multiple rules apply in parallel, related technologies typically employ simple prioritization or default to executing a single rule. This approach lacks the ability to proactively identify, reasonably interpret, and coordinate conflicts between rules. When the system encounters rule conflicts, it often manifests as execution interruption, contradictory results, or direct manual intervention. This approach has significant drawbacks: First, it cannot automatically and clearly explain the root cause of the conflict or why the system (or final decision) chooses to apply a particular rule over others, leading to a "black box" effect in the decision-making process and insufficient transparency. Second, relying on manual conflict resolution is inefficient and influenced by personal experience and cognition, making it difficult to unify discretionary standards and easily leading to operational risks and compliance vulnerabilities. Finally, the interpretations and judgments generated during manual discretion are mostly unstructured information, difficult to effectively record, reuse, and transform into system knowledge, hindering the continuous optimization and intelligent evolution of the governance system.

[0022] Therefore, the technical problem solved by the embodiments of this application is how to automatically identify conflicts in discretionary rules in a centralized procurement scenario, generate structured interpretations based on multi-dimensional constraints, and perform intelligent control over the interpretation results throughout their entire lifecycle.

[0023] The following describes in detail the governance scheme of the AI-based centralized procurement governance center according to the embodiments of this application, with reference to the accompanying drawings.

[0024] Figure 1 This diagram illustrates a flowchart of a rule conflict interpretation generation and discretionary control method for an AI-based centralized procurement governance center, according to an embodiment of this application. Figure 1As shown, the rule conflict interpretation generation and discretionary control method of the AI-based centralized procurement governance center in this application embodiment may include the following steps S10-S70.

[0025] For ease of description, the rule conflict interpretation generation and discretionary control method of the AI-based centralized procurement governance center in this application embodiment is described using an electronic device as the execution subject. It should be understood that the execution subject in this application embodiment can also be a processor or chip in an electronic device, and this application embodiment does not impose any limitations.

[0026] Step S10: Determine at least one discretionary rule corresponding to the target business.

[0027] In one possible implementation, the target business is acquired via electronic devices. This target business is a business matter at any stage of the centralized procurement process, including initiation, approval, or execution. The target business can be uploaded through human-computer interaction between the business execution personnel and the electronic devices. At different stages, the business execution personnel may include procurement applicants, procurement executors, approvers, etc.

[0028] Optionally, the target business has corresponding business characteristic information, used to characterize the characteristics of the target business in a structured data manner. This business characteristic information may include at least one of the following: business amount characteristics, time urgency characteristics, standard procurement rule matching characteristics, and historical similar business characteristics. The aforementioned characteristic information can be uploaded by the user, matched against a preset procurement rule base based on the content of the target business, or obtained through electronic devices.

[0029] For example, in the case where the target business is "The core controller of production line A is damaged, and the estimated downtime loss is 100,000 yuan per hour. The existing supplier's standard delivery period is 15 days, which cannot be met," the business characteristic information can be determined as follows: the business amount characteristic is a purchase amount of 80,000 yuan; the time urgency characteristic can be a requirement for delivery and installation within 24 hours (extremely high urgency); the standard procurement rule matching characteristic is low; and the historical similar business characteristic can be that the system has retrieved 3 similar "emergency equipment repair procurement" records in the past six months, all of which were processed through the "emergency procurement report" discretionary strategy.

[0030] In some embodiments, if the electronic device determines that discretion is required, it can identify a mismatch between the procurement rules and the target business, or a conflict between the procurement rules and the target business. In this case, it needs to generate a discretionary plan corresponding to the target business based on business characteristic information and a pre-defined discretionary rule library. The discretionary rule library includes at least one discretionary rule. The electronic device first retrieves at least one discretionary rule corresponding to the target business from the library. Different discretionary rules correspond to different constraint information. For example, if the target business has three discretionary rules, they can be compliance discretionary rules, efficiency discretionary rules, and risk discretionary rules, used to assess the target business from the perspectives of compliance, efficiency, and risk, respectively.

[0031] Optionally, the discretionary rules corresponding to the target business are used to develop emergency plans for the target business. Each discretionary rule includes authority boundary parameters for constraining the discretionary plan. The authority boundary parameters include at least one of the following: applicable amount range, time window, and applicable frequency. The amount range may include an upper limit and a lower limit for the single applicable amount. The time window may include a procurement operation window or a post-event remedial period. The applicable frequency may be, for example, a quarterly limit for the same supplier / category. Alternatively, the authority boundary parameters may also include other boundary conditions such as object boundaries, process boundaries, and risk boundaries.

[0032] Step S20: Generate corresponding discretionary schemes according to the discretionary rules corresponding to the target business.

[0033] In one possible implementation, after determining at least one discretionary rule corresponding to the target business, the electronic device can generate a corresponding discretionary scheme based on each discretionary rule. This discretionary scheme is an automatically generated, selectable, and structured exception handling scheme by the electronic device for that discretionary rule. Optionally, each of the above discretionary schemes is a predefined, standardized, and compliant special process within specific boundaries.

[0034] For example, electronic devices can generate corresponding discretionary schemes based on various discretionary rules, which may include special processes such as emergency procurement - post-procurement supplementary bidding process, competitive negotiation process, and direct procurement process with special approval from senior management. Each discretionary scheme also has corresponding authority boundary parameters. For instance, the discretionary scheme for the emergency procurement - post-procurement supplementary bidding process could be: allowing immediate procurement from pre-qualified suppliers to meet urgent needs, but requiring a complete bidding process to be completed within a specified period afterward, and incorporating this procurement into subsequent contracts. This discretionary scheme also has corresponding authority boundary parameters: amount range: 100,000 to 500,000 yuan; time window: supplementary bidding must be initiated within 30 days after procurement; applicable frequency: no more than once per quarter for the same supplier / same product category. Alternatively, the discretionary scheme generated by the electronic device based on various discretionary rules could also be "selection".

[0035] Step S30: Detect whether there are conflicts between different discretionary rules of the target business according to the corresponding discretionary scheme.

[0036] In one possible implementation, after generating discretionary plans for the target business according to various discretionary rules, the electronic device can further detect whether there are conflicts between the discretionary plans generated by different discretionary rules for the target business. For example, if the discretionary plan generated by the electronic device according to discretionary rule 1 includes "must purchase from a Class A supplier", the discretionary plan generated according to discretionary rule 2 includes "select the supplier B with the lowest price", and the discretionary plan generated according to discretionary rule 3 includes "supplier B has performance risks", the electronic device can determine that there is a conflict between discretionary rule 2 and discretionary rule 3, and there is also a conflict between discretionary rule 1 and discretionary rule 2.

[0037] Optionally, the conflict identification can be performed by inputting each discretionary option into a trained conflict detection model via an electronic device. Alternatively, a conflict identification result can be obtained by performing consistency detection based on the content of each discretionary option using preset conflict judgment rules. Furthermore, if the electronic device identifies that the content of at least two discretionary options conflict or the conclusions are inconsistent, a conflict is determined to exist between the discretionary options.

[0038] Step S40: In the event of a conflict between at least two discretionary rules, determine the target discretionary scheme from the conflicting discretionary schemes and generate explanation information for the target discretionary scheme.

[0039] In one possible implementation, when the electronic device detects a conflict between at least two discretionary options, it can determine that the discretionary rules that generated the conflicting options are conflicting. Further, the electronic device can filter out a target discretionary option from the conflicting options and simultaneously generate corresponding explanatory information. This explanatory information explains the conclusion and rationale for resolving the conflict. For example, it may include the conflicting discretionary rules, a reasoning summary for each discretionary option, a quantitative score for each option, a weight or priority ranking result, and control parameters for associating the explanatory explanation with its effectiveness, risk level, and applicable conditions.

[0040] In some embodiments, when a conflict is determined between at least two discretionary rules, the electronic device can generate candidate interpretations for each discretionary option based on constraint information related to each conflicting discretionary option. Then, a target discretionary option is determined based on the candidate interpretations for each discretionary option, and the candidate interpretations of the target discretionary option are identified as interpretation information. The constraint information may include compliance constraints, risk constraints, efficiency constraints, and historical business constraints, used to characterize the key focus of the corresponding discretionary rule when exercising discretion over the target business. Specifically, compliance constraints constrain the degree to which the discretionary option complies with laws, regulations, internal policies, and mandatory standards; risk constraints constrain the probability and extent of potential adverse consequences from the discretionary option; efficiency constraints constrain the degree to which the discretionary option can achieve resource savings, time reductions, or efficiency improvements; and historical business constraints constrain the degree to which the discretionary option maintains consistency with discretionary decisions and interpretations made in similar past situations.

[0041] Optionally, the electronic device can input at least two conflicting discretionary rules and their corresponding discretionary schemes into a trained interpretation generation model. This model will automatically extract and analyze the constraint information related to each discretionary scheme and output corresponding candidate interpretations. Further, after determining the candidate interpretations for each discretionary scheme, the electronic device can determine the target discretionary scheme and its corresponding interpretation information by weighting or prioritizing each candidate interpretation.

[0042] For example, the weighted calculation method can be executed through a trained comprehensive evaluation model. This involves inputting each candidate explanation into the trained model for scoring, resulting in a score for each explanation. Further, the candidate explanation with the highest score is identified as the explanatory information, and the corresponding discretionary solution is the target discretionary solution. This explanation score can be calculated from various constraint dimensions, such as evaluating compliance, risk, efficiency, and historical fit scores for each candidate explanation separately, and then weighted and summed to obtain the corresponding score. This candidate explanation scoring method is flexible, balanced, and quantifiable. It acknowledges that compromises and balances may be necessary between constraints, making it more suitable for handling complex discretionary scenarios without absolute priorities.

[0043] On the other hand, the priority ranking process can be achieved by first defining a clear sequence of judgment logic using electronic devices, and then judging whether each candidate explanation conforms to that sequence. This sequence includes multiple priority judgments; for example, if a compliance conflict exists, then an explanation ensuring compliance is generated first; if a high risk exists, then an explanation mitigating the risk is generated first; and so on, generating the most efficient explanation. Candidate explanations are filtered layer by layer according to a preset priority order, and the corresponding explanation is ultimately selected as the explanatory information. If multiple explanations remain, simple weighting or random selection can be used. This candidate explanation selection and evaluation method is clear, definite, and highly principled. It ensures that certain core principles are not sacrificed under any circumstances, and the decision-making logic is easier to understand and review.

[0044] In other embodiments, the electronic device in this application can also perform consistency verification after determining the target discretionary scheme and the explanatory information. This consistency verification can include checking the consistency between the target discretionary scheme and the explanatory information, i.e., checking whether there are logical contradictions or conflicts between the explanatory information and the target discretionary scheme, such as whether the reasoning steps in the explanation are self-consistent, and whether there are circular arguments or contradictory statements. Alternatively, the consistency verification can also include checking the consistency between the explanatory information and the procurement rules, i.e., checking whether the conclusions of the explanatory information violate the top-level non-negotiable compliance or security rules in the system. Furthermore, consistency verification can also include longitudinal verification of the consistency between the explanatory information and historical interpretation principles, i.e., checking whether the discretionary rules on which the explanatory information is based deviate significantly from the mainstream principles of similar historical cases.

[0045] Furthermore, in this embodiment, the electronic device can also perform acceptability verification on the explanatory information. This acceptability verification is used to assess the feasibility and potential impact of adopting and implementing the explanatory information in actual business scenarios. Optionally, the acceptability verification process may include threshold and boundary verification, business rationality verification, and side effect and chain reaction assessment. Threshold and boundary verification is used to check whether key quantitative indicators involved in the explanatory information, such as risk values ​​and cost savings rates, exceed preset business acceptable ranges. Business rationality verification is used to check whether the explanatory information conforms to industry common sense, business practices, or governance realities. Side effect and chain reaction assessment is used to check whether adopting the explanatory information will trigger other negative issues not covered by current conflict rules. The above verification process can generate reasonable and structured explanatory information under multiple constraints and ensure that the explanatory information is quantifiable, traceable, and reusable.

[0046] Once the electronic device determines that the explanatory information has passed verification, it associates the explanatory information with applicable conditions and a risk level. For example, it can add corresponding applicable conditions and risk levels to the explanatory information. The risk level is used to characterize the magnitude of the potential negative impact of adopting the explanatory information.

[0047] Step S50: Determine the corresponding activation method based on the applicable conditions and risk level of the explained information.

[0048] In one possible implementation, after determining the explanatory information, the electronic device can determine the corresponding activation method based on the applicable conditions and risk level of the explanatory information. This activation method is used to verify the validity of the explanatory information. The applicable conditions can be automatically matched, and the risk level can be obtained by conducting a risk assessment of the explanatory information. The electronic device can determine the activation method corresponding to the explanatory information based on either the applicable conditions or the risk level. This activation method can include automatic activation, activation upon confirmation, and use as a reference only.

[0049] For example, the explanation information is determined by the risk level corresponding to the corresponding activation method. If the risk level is low, the electronic device can determine that the verification method for the validity of the explanation information is automatic verification. If the risk level is medium, the electronic device can determine that the verification method for the validity of the explanation information is manual verification. If the risk level is high, the electronic device can determine that the verification method for the validity of the explanation information is no verification, and it is only for reference. Step S60: Determine the validity of the explanation information according to the activation method.

[0050] In one possible implementation, the electronic device can determine whether the conditions for the explanatory information to take effect are met based on the corresponding activation method, and determine the validity of the explanatory information if the conditions are met. For example, if the activation method is automatic confirmation, the explanatory information is directly determined to be valid. If the activation method is manual confirmation, the electronic device confirms the explanatory information is valid upon receiving a confirmation instruction from the user.

[0051] In other words, the electronic device can determine the validity of the explanation information after identifying it, and store and / or mark the explanation information if it is valid. The validity confirmation method for the explanation information can be at least one method. For example, if the target discretionary scheme and the explanation information are consistent, the electronic device can automatically determine that the explanation information is valid. Alternatively, the electronic device can also send or display the explanation information to the user, and determine that the explanation information is valid upon receiving a confirmation instruction from the user regarding the validity of the explanation information. Step S70: If the explanation information is valid, store and / or mark the explanation information.

[0052] In one possible implementation, after confirming the explanation information, the electronic device can record and save the explanation process and results. The saved content may include the identifier, content, effective method, effective time, related business item identifier, effective instruction, confirmation or modification record, business feedback, and the final decision-maker of the explanation information.

[0053] Furthermore, the electronic device can also determine whether the explanatory information can be reused. If the explanatory information can be reused, it freezes and marks the explanatory information. Then, when a similar business to the target business is received, the explanatory information is invoked to perform business discretion and obtain the corresponding business discretion result. Based on the business discretion result, it is determined whether to convert the explanatory information into a discretion rule. Optionally, the electronic device can collect and process multiple business discretion results obtained by applying the explanatory information within a preset period, and convert the explanatory information into a discretion rule if certain conditions are met. In other words, the electronic device can also determine whether the stored explanatory information is reusable. If it can be reused, the explanatory information that meets the conditions is marked, and subsequent similar businesses can directly invoke it. The decision on whether to convert it into a formal rule is based on the usage effect.

[0054] Figure 2 This diagram illustrates the governance process of an AI-based centralized procurement governance hub according to an embodiment of this application. Figure 2 As shown, the electronic device in this embodiment can first determine the discretionary rules of the target service, and then generate a corresponding discretionary scheme using at least one of the corresponding discretionary rules. Further, conflict detection is performed based on the discretionary schemes corresponding to each discretionary rule. If a conflict exists, an interpretation is triggered. Candidate interpretations are generated by extracting the constraints corresponding to the conflicting discretionary rules, and interpretation information is obtained. Then, consistency verification is performed on the interpretation information. The electronic device can confirm the interpretation information that passes the consistency verification. The interpretation information takes effect after confirmation and is stored as a record.

[0055] Based on the aforementioned technical features, this application's embodiments, through automatic identification of multiple rule application conflicts, generation of structured interpretations based on multi-dimensional constraints, and controlled implementation and full lifecycle governance of the interpretation results, have produced several beneficial effects: First, it realizes the transformation of discretionary governance from reliance on manual processes to intelligent automation, significantly improving decision-making efficiency and processing consistency; second, by generating quantifiable and traceable structured interpretations, it effectively solves the black-box problem of traditional system decision-making processes, significantly enhancing the transparency and credibility of discretionary decisions; third, the innovative interpretation freezing, reuse, and evolution mechanism enables the system to continuously accumulate and optimize discretionary knowledge, not only ensuring the consistent inheritance of historical experience but also promoting the adaptive evolution of the governance rule system, forming a sustainable intelligent governance capability; fourth, through dynamic implementation control and complete traceability of associated risk levels, it achieves a balance between automated discretion and risk management, ensuring the safety, controllability, and compliance of discretionary activities in complex business scenarios. Overall, this method provides an effective technical solution for building a transparent, consistent, evolvable, and risk-controllable intelligent centralized procurement governance system.

[0056] It should be understood that although the steps in the above flowcharts are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the above flowcharts may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0057] It should be understood that the phrases "one embodiment," "an embodiment," or "some embodiments" mentioned throughout the specification mean that a specific feature, structure, or characteristic related to an embodiment is included in at least one embodiment of this application. Therefore, phrases such as "in one possible implementation," "in one embodiment," or "in some embodiments" appearing throughout the specification do not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above-described embodiments are merely for descriptive purposes and do not represent the superiority or inferiority of the embodiments. The descriptions of the various embodiments above tend to emphasize the differences between the various embodiments; their similarities or commonalities can be referred to mutually, and for the sake of brevity, they will not be repeated here.

[0058] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three kinds of relationships. For example, object A and / or object B can represent three situations: object A exists alone, object A and object B exist simultaneously, and object B exists alone.

[0059] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0060] The methods disclosed in the several method embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments.

[0061] The above description is merely an embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for rule conflict interpretation, generation, and discretionary control in a centralized procurement governance center based on AI, characterized in that, The method includes: Identify at least one discretionary rule corresponding to the target business; Generate corresponding discretionary solutions based on the discretionary rules corresponding to the target business; Based on the corresponding discretionary scheme, detect whether there are conflicts between different discretionary rules for the target business; In the event of a conflict between at least two discretionary rules, a target discretionary scheme is determined from the conflicting discretionary schemes, and explanatory information for the target discretionary scheme is generated. The corresponding activation method is determined based on the applicable conditions and risk level of the aforementioned explanatory information; The validity of the explanatory information is determined according to the aforementioned activation method; If the explanation information is valid, store and / or tag the explanation information.

2. The method according to claim 1, characterized in that, The method further includes: Determine whether the explanatory information can be reused; If the explanatory information can be reused, the explanatory information is frozen.

3. The method according to claim 2, characterized in that, The method further includes: In response to receiving a similar service to the target service, the system calls upon the explanation information to perform service discretion and obtain the corresponding service discretion result. Based on the results of the business discretion assessment, determine whether to convert the explanatory information into discretionary rules.

4. The method according to claim 1, characterized in that, The step of determining a target discretionary option among conflicting discretionary options and generating explanatory information for the target discretionary option includes: Based on the constraint information related to each conflicting discretionary option, generate a candidate interpretation for each discretionary option; The target discretionary scheme is determined based on the candidate interpretations corresponding to each discretionary scheme, and the candidate interpretations of the target discretionary scheme are determined as interpretation information.

5. The method according to claim 4, characterized in that, The constraints include compliance constraints, risk constraints, efficiency constraints, and historical business constraints.

6. The method according to claim 1, characterized in that, The method further includes: Verify the consistency between the target discretionary scheme and the explanatory information.

7. The method according to claim 1, characterized in that, Determining the validity of the explanatory information according to the activation method includes: In response to the consistency between the target discretionary scheme and the explanatory information, the explanatory information is determined to be valid.

8. The method according to claim 1, characterized in that, Determining the validity of the explanatory information according to the activation method includes: In response to receiving a confirmation instruction from the user regarding the validity of the explanation information, the explanation information is determined to be valid.

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