Three-dimensional judgment and approval management method and system for temporary relaxation of hard constraints in an AI decision system

Abstract

The application discloses a three-dimensional judgment and approval management method and system for temporary relaxation of hard constraints in an AI decision system, and belongs to the technical field of constraint management related to AI decision. The application solves the problem of low safety and automation of the approval result in the traditional judgment and approval method in the AI decision system due to the lack of relaxation rationality judgment mechanism and life cycle management in the prior art; the application performs opportunity identification, risk assessment and compliance verification three-dimensional judgment on the non-hitter in parallel through the never-relaxed list filtering; after the three-dimensional judgment, a structured application package is automatically generated, and is routed to the conventional or emergency channel hierarchical approval according to the emergency symbol; after approval, the effect is valid and is attached with a mark, an effective period is set, and when the trigger condition is met, the system automatically rolls back and records logs, and the safe temporary relaxation of the hard constraint is realized. The application effectively improves the explainability and compliance of the relaxation decision, and can be applied to the automatic management of the intelligent agricultural decision system.

Description

Technical Field

[0001] This invention relates to the field of constraint management technology related to AI decision-making, specifically to a three-dimensional judgment and approval management method and system for temporarily relaxing hard constraints in an AI decision-making system. Background Technology

[0002] Currently, the existing technologies closest to this invention mainly include the following three categories: (1) Constraint exemption mechanism in rule engines (software field): Existing rule engines (such as Drools, IBM ODM, etc.) support defining "exceptional conditions" in business rules, and skip the execution of specific rules when specific conditions are met. Its technical features are: a. Exception conditions are defined through IF-THEN rule syntax; b. Exception conditions are at the same level as rules and exist in the form of static configuration; c. There is no validity period management mechanism, and exception conditions remain valid once they take effect; d. There is no approval process, and the rule base is directly modified by developers; e. There is no system-level protection mechanism of "never exempt"; (2) Approval workflow in business process management (BPM) systems: Existing BPM systems (such as Activiti, Camunda, etc.) support designing approval workflows for business operations. Its technical features are: a. A process engine that provides manual approval nodes; b. The approval object is business operations, not AI decision constraint parameters; c. It does not have AI system integration capabilities and cannot perceive the residual risk level of constraints; d. Real-time monitoring and automatic rollback mechanism after unconstrained relaxation; e. Automatic generation capability of unstructured relaxation application packages; (3) Security constraint framework of AI system (academic / engineering field): The technical characteristics of existing AI security frameworks (such as OpenAI's Constitution AI and DeepMind's security fence) are: a. Defining the boundary of absolutely prohibited behaviors in the form of hard coding or RLHF; b. Emphasizing the inviolability of constraints and having no temporary relaxation mechanism; c. Oriented towards general AI security and not for the management of constraint parameters for specific business scenarios; d. No constraint validity period management at the business decision level.

[0003] However, current technologies have several problems, specifically as follows: Problem 1: The "all or nothing" dilemma of hard constraints. In existing AI decision-making systems, once a hard constraint is set, it becomes an absolute prohibition. The system cannot distinguish between "safety red lines that can never be crossed" and "executable constraints that can be compliantly relaxed under specific conditions." When new opportunities arise (such as policy windows or the introduction of rare commodities), the system can only reject the optimal decision, leading to suboptimal decisions or even significant opportunity losses. Problem 2: Lack of a structured mechanism for judging the reasonableness of relaxation. Even if a human judgment requires temporary relaxation of a constraint, existing systems cannot automatically assess the reasonableness of the relaxation (such as whether the risk-reward ratio meets the standard, whether the residual risk is acceptable, and whether it violates higher-level laws). Relaxation decisions rely entirely on human experience and lack auditable judgment criteria. Problem 3: Lack of lifecycle management for relaxation operations. Existing systems lack an automatic management mechanism for the validity period of constraint relaxations: a. There is no control over the maximum validity period of a single relaxation (to prevent temporary relaxations from becoming permanent exemptions); b. There is no upper limit on the cumulative relaxation of the same constraint (to prevent systemic abuse of constraint circumvention); c. There is no automatic rollback mechanism upon expiration (relies on manual memory to restore constraints, which is easily forgotten); d. There is no real-time monitoring and early revocation trigger mechanism during the relaxation period; Technical issue four: lack of system-level protection of absolute security boundaries, the existing constraint exemption mechanism cannot technically distinguish between "exemptible constraints" and "absolutely non-exempt security red lines", there is a risk that core security constraints may be relaxed due to misoperation or malicious operation.

[0004] In summary, there is a need for a three-dimensional judgment and approval management method and system in AI decision-making systems that allows for temporary relaxation of hard constraints. Summary of the Invention

[0005] A brief overview of the invention is given below to provide a basic understanding of certain aspects of it. It should be understood that this overview is not an exhaustive summary of the invention. It is not intended to identify key or essential parts of the invention, nor is it intended to limit the scope of the invention. Its purpose is merely to present certain concepts in a simplified form as a prelude to the more detailed description that follows.

[0006] In view of this, in order to solve the problem that the judgment and approval methods in the existing AI decision-making system have low security and low degree of automation due to the lack of a mechanism for relaxing reasonableness judgment and life cycle management, the present invention provides a three-dimensional judgment and approval management method for temporarily relaxing hard constraints in the AI ​​decision-making system.

[0007] The technical solution is as follows: A three-dimensional judgment and approval management method for temporarily relaxing hard constraints in an AI decision-making system, comprising the following steps:

[0008] S1. Set up a never-relaxed list in the constraint management engine through the never-relaxed list module, use it as a system-level mandatory filtering mechanism, and execute it in priority over any relaxation application process to obtain constraints that are not hit by the never-relaxed list;

[0009] S2. For constraints that are not hit in the "Never Relax List" constraint, the three-dimensional precondition automatic judgment module automatically performs the judgment in three dimensions to filter out constraints that meet the conditions in all three dimensions.

[0010] S3. Automatically extract information from the context of constraints that simultaneously meet the three-dimensional conditions, and automatically generate a standardized structured relaxation application package through the structured relaxation application package automatic generation module;

[0011] S4. Based on the required fields of the relaxed application package, conduct human-machine collaborative approval through the time-based hierarchical human-machine approval module, determine the approval level routing and approval permission control, and obtain the approval result;

[0012] S5. Once the approval result is approved, i.e. the temporary relaxation takes effect, the status is continuously monitored through the monitoring module, and validity period constraints, real-time monitoring requirements and automatic rollback are set to complete the relaxation effect evaluation.

[0013] Furthermore, in S1, the list of items that will never be relaxed includes five categories: personnel safety constraints, absolute legal red lines, ecological red lines, data security constraints, and ethical red lines.

[0014] When receiving a relaxation application, the list matching function is first called to perform a hash match on the ID of the constraint to be relaxed. If the constraint in the list is matched, a rejection signal is returned directly, and the process does not proceed to step S2.

[0015] The "Never Relax List" can only be modified by authorized system administrator accounts, and all changes are recorded in the audit log and cannot be deleted.

[0016] Furthermore, in S2, the three dimensions are dimension A, dimension B, and dimension C;

[0017] Dimension A is used for opportunity identification, determining the constraints that simultaneously satisfy the three sub-conditions of Dimension A, thus obtaining the constraints that satisfy Dimension A.

[0018] The three sub-conditions of dimension A are as follows:

[0019] 1) Sub-condition A1: The expected return of the new opportunity is quantitatively assessed > the current constraint cost is quantitatively assessed × preset threshold.

[0020] That is, the risk-reward ratio is greater than the preset threshold;

[0021] 2) Sub-condition A2: The new opportunity has a clearly defined effective time window;

[0022] 3) Sub-condition A3: It is not possible to achieve the same benefit by adjusting other non-hard constraint variables without relaxing this constraint;

[0023] Dimension B is used for risk assessment to determine the constraints that are simultaneously met by all three sub-conditions of Dimension B, thus obtaining the constraints that satisfy Dimension B.

[0024] The three sub-conditions of dimension B are as follows:

[0025] 1) Sub-condition B1: The system quantifies and rates the residual risk, and the residual risk level must be P1.

[0026] 2) Sub-condition B2: The application must include a specific and executable emergency plan, and the system will verify the completeness of the plan;

[0027] 3) Sub-condition B3: The risk monitoring plan must specify the review frequency and monitoring indicators, and the system verification review frequency shall not be less than once every 7 days;

[0028] Dimension C is used for compliance verification, determining the constraints that are simultaneously met by both sub-conditions of Dimension C, thus obtaining the constraints that satisfy Dimension C.

[0029] The two sub-conditions of dimension C are as follows:

[0030] 1) Sub-condition C1: Provide specific legal provisions for reference, and the system will match them with the compliance ontology to confirm the existence of interpretation space for higher-level laws;

[0031] The compliance ontology is a pre-built knowledge base of the system, which includes the legal clause ontology, interpretation space annotations and compliance precedents. It is maintained by the system administrator and records audit logs. The compliance check queries whether the legal references to be relaxed have interpretation space annotations through semantic matching.

[0032] 2) Sub-condition C2: Form written compliance evidence that can be explained to the regulator; the system automatically verifies the completeness of the compliance explanation text.

[0033] The results of the three dimensions are summarized using AND logic: if any dimension is not satisfied, the system returns a rejection signal and a description of the specific sub-condition that is not satisfied; when all three conditions are satisfied, the process proceeds to step S3.

[0034] Furthermore, in S3, the required fields for the relaxation application package include constraint unique identifier constraint_id, constraint name constraint_name, current value / condition current_value, proposed relaxed value / condition proposed_relaxed_value, application relaxation period duration_days, new opportunity description and benefit quantification opportunity_description, residual risk rating and contingency plan risk_assessment, compliance basis and regulatory reference compliance_basis, whether it is an urgent application urgency_flag, and minimum approval authority level approver_required_level.

[0035] Furthermore, in S4, the approval level routing is specifically as follows:

[0036] a. Regular applications: Approval will be completed within 72 hours, and the system will send a reminder 2 hours before the deadline;

[0037] b. Urgent applications: Approval completed within 4 hours, with real-time system notification;

[0038] Approval authority is controlled to a minimum level of approval, requiring senior experts in the field.

[0039] The specific process for handling approval results is as follows:

[0040] a. Approval: The approval metadata is merged with the relaxation application package and written into the constraint management engine, and the temporary relaxation takes effect; at the same time, all downstream decision outputs are automatically attached with relaxation labels. The content of the approval metadata includes the approver ID, approval time and approval reason, and the automatically attached relaxation labels include the name of the relaxed constraint, the approver, the expiration date and risk warning.

[0041] b. Reject: The constraint value remains unchanged, and the rejection reason is written to the audit log.

[0042] Furthermore, in S5, the continuous monitoring status is set as follows:

[0043] 1) The validity period constraints are as follows:

[0044] a. A one-time relaxation validity period is preset; if the maximum one-time relaxation validity period is exceeded, a new application must be submitted.

[0045] b. A preset upper limit is set for the total cumulative relaxation time of the same constraint. Once the total cumulative relaxation time limit is exceeded, the system will automatically reject new relaxation applications for the current constraint.

[0046] c. It is renewable, but renewal requires a complete approval process;

[0047] 2) The specific settings for real-time monitoring requirements are as follows:

[0048] a. Review frequency: A review will be automatically performed after each extension of the validity period, comparing the actual monitoring indicators with the risk assessment at the time of application.

[0049] b. Early withdrawal trigger: ① Monitoring data shows that the residual risk level has been upgraded to P0 level, ② Actual measured data exceeds the upper limit of the proposed relaxation value, ③ Authorize human experts to voluntarily withdraw;

[0050] 3) The automatic rollback process is as follows:

[0051] a. After the validity period expires or the early cancellation triggers, the system will automatically restore the constraint value to its original value before relaxation;

[0052] b. Record the rollback operation in a complete audit log. The audit log should include the rollback time, rollback reason, and a comparison of constraint values ​​before and after the rollback.

[0053] c. Notify relevant parties after the rollback, including the approver, applicant, and system administrator;

[0054] d. After rollback, within the single relaxation validity period limit, the system will prompt that the relaxation effect evaluation has been completed.

[0055] Technical Solution 2: A three-dimensional judgment and approval management system for temporarily relaxing hard constraints in an AI decision-making system, used to execute the three-dimensional judgment and approval management method for temporarily relaxing hard constraints in an AI decision-making system described in Technical Solution 1, including a never-relaxed list module, a three-dimensional precondition automatic judgment module, a structured relaxation application package automatic generation module, a time-based graded human-machine approval module, and a monitoring module connected in sequence.

[0056] The beneficial effects of the present invention are as follows: (1) The present invention solves the dilemma of "all or nothing" hard constraints and improves the decision optimization capability: By introducing a structured temporary relaxation mechanism, the AI ​​decision system can capture the time-sensitive optimal decision opportunity under the premise of ensuring the safety boundary, avoiding the systematic suboptimal decision caused by the absolute rigidity of the constraints. In the smart agriculture scenario verification, it effectively avoids the loss of major annual opportunities caused by constraint blocking; (2) The three-dimensional judgment mechanism of the present invention ensures the interpretability and compliance of the relaxation decision: In the traditional way, the constraint relaxation decision relies on human experience and has no standardized basis. The present invention automatically judges the three-dimensional preconditions and provides a quantifiable and auditable judgment basis (risk-reward ratio, residual risk level, compliance reference) for each relaxation decision, meeting the requirements of compliant delivery for audit traceability. (3) The present invention adopts an expiration period management mechanism to prevent temporary relaxation from evolving into permanent circumvention: The present invention sets a single 30-day limit and a cumulative 90-day limit, and with the automatic rollback mechanism upon expiration, it ensures the temporary nature of the relaxation of constraints from a technical perspective, and prevents the permanent downgrading of constraints caused by human forgetfulness or subjective will; (4) The present invention designs a list that will never be relaxed to provide a system-level guarantee of absolute security boundaries: Through a system-level hard interception mechanism, the present invention strictly distinguishes between "unbreakable security red lines" and "compliance-relaxable execution constraints" from a technical architecture perspective, ensuring that core constraints such as personnel safety, regulatory red lines, and ecological red lines cannot be relaxed under any circumstances, and eliminating the security risks of human error and malicious operation; (5) The present invention adopts time-sensitive graded approval to reduce opportunity loss: By identifying new time-sensitive opportunities (time window < 48 hours) and automatically upgrading to a 4-hour approval channel, while ensuring the compliance of manual approval, the present invention maximizes the reduction of opportunity loss caused by excessively long approval cycles, and takes into account the balance between security and efficiency. Attached Figure Description

[0057] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0058] Figure 1 This is a flowchart illustrating a three-dimensional judgment and approval management method for temporarily relaxing hard constraints in an AI decision-making system.

[0059] Figure 2 This is a schematic diagram of an embodiment of a three-dimensional judgment and approval management method for temporarily relaxing hard constraints in an AI decision-making system;

[0060] Figure 3 This is a logical diagram illustrating the determination of three-dimensional preconditions.

[0061] Figure 4 This is a schematic diagram of the validity period and monitoring state machine;

[0062] Figure 5 A flowchart illustrating the process of relaxing approval constraints on agricultural decision-making;

[0063] Figure 6 This is a schematic diagram of a three-dimensional judgment and approval management system that temporarily relaxes hard constraints in an AI decision-making system.

[0064] Attached reference numerals: 1. Never-relaxed list module; 2. Three-dimensional precondition automatic judgment module; 3. Structured relaxation application package automatic generation module; 4. Time-sensitive graded human-machine approval module; 5. Monitoring module. Detailed Implementation

[0065] To make the technical solutions and advantages of the embodiments of the present invention clearer, the exemplary embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not an exhaustive list of all embodiments. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0066] Example 1: Reference Figures 1-5 This embodiment details a three-dimensional judgment and approval management method for temporarily relaxing hard constraints in an AI decision-making system, specifically including the following steps:

[0067] S1. Set up a never-relaxed list in the constraint management engine through the never-relaxed list module, use it as a system-level mandatory filtering mechanism, and execute it in priority over any relaxation application process to obtain constraints that are not hit by the never-relaxed list;

[0068] S2. For constraints that are not hit in the "Never Relax List" constraint, the three-dimensional precondition automatic judgment module automatically performs the judgment in three dimensions to filter out constraints that meet the conditions in all three dimensions.

[0069] S3. Automatically extract information from the context of constraints that simultaneously meet the three-dimensional conditions, and automatically generate a standardized structured relaxation application package through the structured relaxation application package automatic generation module;

[0070] S4. Based on the required fields of the relaxed application package, conduct human-machine collaborative approval through the time-based hierarchical human-machine approval module, determine the approval level routing and approval permission control, and obtain the approval result;

[0071] S5. Once the approval result is approved, i.e. the temporary relaxation takes effect, the status is continuously monitored through the monitoring module, and validity period constraints, real-time monitoring requirements and automatic rollback are set to complete the relaxation effect evaluation.

[0072] Specifically, this invention can be applied to the following fields: 1) Smart agricultural decision-making system: In the agricultural production process, the AI ​​system automatically manages hard compliance constraints such as pesticide residue constraints, arable land protection constraints, and irrigation water quota constraints. Simultaneously, it needs to provide temporary flexible handling for compliance of some hard constraints during policy windows, the introduction of new varieties, and extreme weather events; 2) Industrial process control system: In industries such as manufacturing, chemicals, and energy, the AI ​​system maintains production safety constraints and quality standard constraints, and needs to provide a structured constraint exemption application and management mechanism during specific process improvement windows; 3) Government regulatory compliance AI system: In government services and policy implementation scenarios... AI decision-making systems need to provide standardized temporary exemption procedures for certain enforceable hard constraints, while ensuring that core security red lines are never breached, provided that they comply with higher-level laws; 4) Financial risk control decision-making systems: In credit and investment decision-making scenarios, some hard constraints of AI risk control systems (such as loan limit caps and risk exposure ratios) may need to be temporarily adjusted under special market opportunity windows. This invention provides a standardized approval and rollback mechanism; 5) Medical AI-assisted decision-making systems: Under safety restrictions such as drug dosage and contraindications, temporary exemption applications and management are provided for special scenarios such as rare diseases and emergency treatment;

[0073] In this embodiment, taking the rice planting season of a 10,000-mu smart farm as an example, the deep integration of the present invention with the agricultural scenario is illustrated. The farm has 10,000 mu of paddy fields, which are divided into 200 management units, each with an area of ​​50 mu. During the planting process, the farm must follow three types of hard constraints, namely, pesticide residue limits in accordance with GB 2763 standards, irrigation water quotas allocated by the government, and the red line for farmland protection clearly defined in the national land space planning. At the same time, it is equipped with an AI decision-making system, namely, an agricultural operation scheduling system built based on meteorological forecasts, soil moisture, and pest and disease monitoring data.

[0074] The following are typical scenarios that trigger temporary relaxation:

[0075] Trial planting of new varieties during the policy window period (Scenario A):

[0076] a. A new disease-resistant rice variety has been granted a temporary trial planting permit through the Ministry of Agriculture and Rural Affairs' "green channel" (valid for 6 months).

[0077] b. The recommended planting density for this variety exceeds the soil carrying capacity constraints of conventional varieties (Method dimension).

[0078] c. The system detected that the expected return of the new variety is greater than 1.8 times the return of the conventional variety, and the trial planting permit has a time limit;

[0079] d. Triggering the temporary relaxation process of this invention: Apply to relax the "maximum planting density per season" from X to Y, with a validity period of 90 days;

[0080] Scenario B: Irrigation quota adjustment under extreme weather conditions:

[0081] a. 30 consecutive days of high temperatures and drought triggered a drought warning based on soil moisture monitoring data;

[0082] b. The government activates a drought emergency response, allowing temporary exceedances of water quotas (Environment dimension).

[0083] c. System assessment: Failure to relax irrigation constraints will result in crop failure, with a residual risk of P1 (with contingency plans in place).

[0084] d. Trigger the emergency approval channel for this invention: complete the approval process for relaxing the "daily irrigation volume limit" within 4 hours;

[0085] Scenario C: Conflict between farmland protection constraints and soil improvement:

[0086] a. A certain management unit needs to carry out deep plowing improvement (breaking through the hard constraint of "topsoil protection");

[0087] b. Soil testing data shows that this unit has a risk of heavy metal contamination and requires emergency replacement with fresh soil;

[0088] c. System determination: Deep plowing operations violate the red line for arable land protection, but fall under the category of "ecological red line", and are included in the list of areas that will never be relaxed → direct rejection;

[0089] d. After evaluation by human experts, apply for special government approval through compliant channels (outside the system process).

[0090] Furthermore, in S1, the list of items that will never be relaxed includes five categories: personnel safety constraints (any operational restrictions that may result in injury or death), absolute legal red lines (absolute clauses that are explicitly prohibited by laws and regulations), ecological red lines (destructive operations prohibited within protected areas), data security constraints (access restrictions involving sensitive data), and ethical red lines (operational restrictions that violate ethical guidelines).

[0091] When receiving a relaxation application, the list matching function is first called to perform hash matching on the constraint ID to be relaxed. If the constraint matches the list, a rejection signal is returned directly without proceeding to step S2, ensuring that system-level protection cannot be bypassed.

[0092] The "Never Relax List" can only be modified by authorized system administrator accounts, and all changes are recorded in the audit log and cannot be deleted.

[0093] Furthermore, in S2, the three dimensions are dimension A, dimension B, and dimension C;

[0094] Dimension A is used for opportunity identification, determining the constraints that simultaneously satisfy the three sub-conditions of Dimension A, thus obtaining the constraints that satisfy Dimension A.

[0095] The three sub-conditions of dimension A are as follows:

[0096] 1) Sub-condition A1: The expected return of the new opportunity is quantitatively assessed > the current constraint cost is quantitatively assessed × 1.5, that is, the risk-reward ratio is > 1.5;

[0097] 2) Sub-condition A2: The new opportunity has a clearly defined time window (the system requires applicants to provide the window deadline);

[0098] 3) Sub-condition A3: It is impossible to achieve the same benefit without relaxing this constraint by adjusting other non-hard constraint variables (path exclusivity verification).

[0099] Dimension B is used for risk assessment to determine the constraints that are simultaneously met by all three sub-conditions of Dimension B, thus obtaining the constraints that satisfy Dimension B.

[0100] The three sub-conditions of dimension B are as follows:

[0101] 1) Sub-condition B1: The system quantifies and rates the residual risk, and the residual risk level must be P1 (automatic rejection is given if P0 residual risk exists).

[0102] The residual risk level adopts a five-level classification system from P0 to P4. Among them, P0 level is a fatal risk that absolutely cannot be relaxed, and P1 level is a low risk that can be completely controlled by increasing the frequency of detection or emergency plans. In the three-dimensional judgment, only the constraints of residual risk level P1 are allowed to enter the relaxation approval process.

[0103] The residual risk level is a risk classification marker defined in this invention, which is divided into five levels from P0 to P4 based on the controllability of the risk, the scope of its impact, and the cost of recovery.

[0104] P0 level: There are uncontrollable and fatal risks that may lead to casualties, major property damage or irreversible ecological damage. Relaxation is absolutely prohibited.

[0105] Level P1: Low risk, limited impact, can be completely controlled by increasing testing frequency or implementing corresponding emergency plans;

[0106] Level P2: Medium risk, requires a specific emergency response plan and approval at the second level;

[0107] Level P3: Higher risk, requires a specific emergency response plan and Level 1 approval;

[0108] Level P4: High risk. In principle, restrictions will not be relaxed, except in cases of force majeure with special approval.

[0109] In the three-dimensional assessment of this invention, only when the residual risk level is ≤ P1 can the subsequent relaxation approval process proceed. Residual risk at level P0 triggers automatic rejection, and levels P2-P4 are generally not included in the relaxation process.

[0110] 2) Sub-condition B2: The application must include a specific and executable emergency plan, and the system will verify the completeness of the plan (including the three elements of triggering conditions, response steps, and recovery measures).

[0111] 3) Sub-condition B3: The risk monitoring plan must specify the review frequency and monitoring indicators, and the system verification review frequency shall not be less than once every 7 days;

[0112] Dimension C is used for compliance verification, determining the constraints that are simultaneously met by both sub-conditions of Dimension C, thus obtaining the constraints that satisfy Dimension C.

[0113] The two sub-conditions of dimension C are as follows:

[0114] 1) Sub-condition C1: Provide specific legal provisions for reference, and the system will match them with the compliance ontology to confirm the existence of interpretation space for higher-level laws;

[0115] The compliance ontology is a pre-built regulatory knowledge base in the system of this invention, and its internal structure includes:

[0116] Legal provisions on the subject: Relevant legal provisions are stored in an ontological form, with each provision marked with its scope of application, level of validity, and scope of interpretation;

[0117] Explanatory spatial annotation: For each legal clause, indicate whether there are explanatory clauses such as "scientific use of medicine", "special circumstances", or "approved for adjustment";

[0118] Compliance Case Law Database: Records historical compliance review cases and their review conclusions for the purpose of assisting in matching.

[0119] The compliance ontology is maintained by the system administrator, who regularly updates the regulatory versions and records all changes in the audit log. The phrase "matching with the compliance ontology" means that the system semantically matches the regulatory references to be relaxed with the clauses in the ontology, checks whether the clause has an explanatory space annotation, and if it does, the compliance check is deemed to have passed; otherwise, it is deemed to have failed.

[0120] 2) Sub-condition C2: Form written compliance evidence that can be explained to the regulator; the system automatically verifies the completeness of the compliance explanation text.

[0121] The results of the three dimensions are summarized using AND logic: if any dimension is not satisfied, the system returns a rejection signal and a description of the specific sub-condition that is not satisfied; when all three conditions are satisfied, the process proceeds to step S3.

[0122] Furthermore, in S3, the required fields for the relaxation application package include constraint unique identifier constraint_id (automatic), constraint name (natural language) constraint_name (automatic), current value / condition current_value (automatic), proposed relaxed value / condition proposed_relaxed_value (manual, AI-assisted suggestion), application relaxation days (≤30 days) duration_days (manual), new opportunity description and benefit quantification opportunity_description (manual, AI-assisted structured), residual risk rating and contingency plan risk_assessment (AI-generated, manual confirmation), compliance basis and regulatory citation compliance_basis (manual, AI-assisted matching), whether it is an urgent application urgency_flag (automatic / time window based), and minimum required approver authority level approver_required_level (automatic).

[0123] The application package is stored in JSON format, automatically written to the pending approval queue, and routed to the regular channel (72-hour SLA) or the emergency channel (4-hour SLA) based on whether it is an urgent application or the urgency_flag field.

[0124] Furthermore, in S4, the approval level routing is specifically as follows:

[0125] a. Regular application (urgency_flag=false): Approval completed within 72 hours (working days), with the system sending a reminder 2 hours before the deadline;

[0126] b. Urgent application (urgency_flag=true, trigger condition: new opportunity time window < 48 hours): approval completed within 4 hours, system push notification in real time;

[0127] Approval authority is controlled to a minimum level of approval authority, which is granted to senior domain experts (verified by the system based on role-based access management). General business personnel do not have approval authority.

[0128] The specific process for handling approval results is as follows:

[0129] a. Approval: The approval metadata is merged with the relaxation application package and written into the constraint management engine, and the temporary relaxation takes effect; at the same time, all downstream decision outputs are automatically attached with relaxation labels. The content of the approval metadata includes the approver ID, approval time and approval reason, and the automatically attached relaxation labels include the name of the relaxed constraint, the approver, the expiration date and risk warning.

[0130] b. Reject: The constraint value remains unchanged, and the rejection reason is written to the audit log.

[0131] Furthermore, in S5, the continuous monitoring status is set as follows:

[0132] 1) The validity period constraints are as follows:

[0133] a. The maximum validity period for a single relaxation is 30 days; if the validity period exceeds 30 days, a new application must be submitted.

[0134] b. The maximum total duration of relaxation for the same constraint is 90 days (calculated cumulatively across multiple applications). Once the limit is exceeded, the system will automatically reject new relaxation applications for the current constraint.

[0135] c. It is renewable, but renewal requires a complete approval process (it cannot be automatically renewed).

[0136] 2) The specific settings for real-time monitoring are as follows:

[0137] a. Review frequency: A review is automatically performed every 7 days, comparing the actual monitoring indicators with the risk assessment at the time of application.

[0138] b. Early cancellation of triggers (three types): ① Monitoring data shows that the residual risk level has been upgraded to P0, ② Actual data exceeds the upper limit of the proposed relaxation value, ③ Authorized human experts to voluntarily cancel;

[0139] 3) The automatic rollback process is as follows:

[0140] a. After the validity period expires or the early cancellation triggers, the system will automatically restore the constraint value to its original value before relaxation;

[0141] b. Record the rollback operation in a complete audit log. The audit log should include the rollback time, rollback reason, and a comparison of constraint values ​​before and after the rollback.

[0142] c. Notify relevant parties after the rollback, including the approver, applicant, and system administrator;

[0143] d. Within 7 days after the rollback, the system will prompt that the relaxation effect assessment has been completed, and the assessment results will be written into the historical case database for reference by subsequent similar applications.

[0144] For details, please refer to Figure 5 To relax the approval mechanism for AI-powered agricultural decision-making systems, this system primarily targets scenarios where pesticide use frequency exceeds the annual pesticide residue limit in "double-cropping rice + ratooning rice" three-season planting schemes. It establishes a closed-loop management system encompassing "AI initial review - risk filtering - manual approval - execution monitoring - expiration rollback." This system provides a compliant path for relaxed restrictions on high-yield, policy-aligned planting schemes, while ensuring agricultural product quality, safety, and compliance. Simultaneously, it achieves full traceability and risk control. Details are as follows:

[0145] I. Detailed Process Steps

[0146] (I) AI Decision Engine

[0147] 1) Conflict detection and triggering:

[0148] This stage is automatically completed by the AI ​​agricultural decision-making system, serving as the starting point for the process, and its core function is to perform preliminary verification of the compliance of the solution and identification of conflicts.

[0149] Solution Reception: The system receives the "double-cropping rice + ratooning rice" three-season planting plan generated by the AI ​​agricultural decision-making system. The plan is the optimal planting strategy generated based on multi-dimensional data such as plot conditions, meteorological data, and market conditions.

[0150] Violation detection of constraints: The system automatically verifies the match between the frequency of pesticide use in the plan and the preset "annual pesticide residue constraint limit". The core judgment condition is: the frequency of pesticide use in the plan > the annual pesticide residue constraint limit.

[0151] 2) Conflict Triggering and Traffic Diversion:

[0152] a. If the result is "yes" (pesticide frequency exceeds the limit): trigger the conflict detection process and push the conflict data to the risk control and compliance layer;

[0153] b. If the result is "No" (meets the constraints): proceed with the normal process and do not require further approval.

[0154] c. Risk control layer push: Send complete data such as conflict data, planting plan, and land information to the risk control and compliance layer for the next stage of filtering and evaluation.

[0155] (II) Risk control and compliance layer: A never-relaxed list filtering process + AI-powered three-dimensional preliminary review.

[0156] This stage is the core risk control checkpoint in the process. Through a dual mechanism of "strong rule filtering + AI intelligent evaluation", applications that meet the relaxation conditions are screened out, eliminating high-risk and non-compliant relaxation requests from the source.

[0157] 1. Never relax list filtering (strong rule interception):

[0158] This step involves rigid constraints with no room for leniency; applications that do not meet the requirements will be directly rejected.

[0159] a. Verification of the list of prohibited pesticides: Check whether the plan contains pesticides such as methamidophos, which are prohibited by the state. If so, terminate the request directly and refuse approval.

[0160] b. Organic Certification Site Verification: For planting sites that have already obtained organic certification, any application for relaxation of restrictions on chemical pesticides will be directly included in the list of sites that will never be relaxed, and the application will be terminated immediately.

[0161] c. If both verifications pass (no banned pesticides, no non-organic certified plots), then proceed to the AI ​​preliminary review stage.

[0162] 2. AI-powered 3D preliminary review (intelligent assessment):

[0163] The AI ​​system conducts a comprehensive and quantitative preliminary review of applications for relaxation of restrictions from three dimensions: opportunity identification, risk assessment, and compliance verification. Only when all three dimensions meet the requirements can an approval package be generated.

[0164] Dimension A. Opportunity Identification (Benefits and Policy Feasibility):

[0165] Profit verification: The expected profit from three-season planting should reach 1.6 times that of conventional double-season rice, and should be higher than the preset threshold of 1.5 times, to ensure that the profit increase brought about by relaxing the constraints has sufficient economic value.

[0166] Policy window verification: Must meet the local government's special subsidy policies such as the creation of "ton-grain fields", and the subsidy validity period must cover the current planting cycle (e.g., validity period until xxxx year xx month xx day);

[0167] Path exclusivity verification: It was confirmed that no other variety combination or planting mode can achieve the same benefits, proving that the current relaxation of constraints is the only feasible path to achieve this benefit target;

[0168] Dimension B. Risk Assessment (Safety and Controllability):

[0169] Residual risk level: The risk is divided into P1-P4 levels. Only applications at the P1 level (low risk, which can be completely controlled by increasing the frequency of testing) are approved.

[0170] Emergency response plan formulation: Two core emergency measures are defined: ① Mandatory rapid testing for pesticide residues will be conducted on each batch of agricultural products before they are put on the market; ② If the test results show that the pesticide residues exceed the standard, the batch of products will be immediately isolated and destroyed.

[0171] Risk tracking mechanism: Establish a sampling and testing system every 7 days, and record all test data on the blockchain in real time to achieve full traceability of risks;

[0172] Dimension C. Compliance Verification (Legality and Explainability):

[0173] Legal basis: Clarify the legal basis for this relaxation of restrictions, such as the scope for interpretation of the relevant clauses on "scientific use of pesticides" in the "Regulations on Pesticide Management";

[0174] Explanatory Support: Provide complete supporting materials such as pesticide use records, detailed calculations of safety intervals, and historical pesticide residue test reports to ensure that the approval process is explainable and traceable;

[0175] 3. Generation of structured approval packages

[0176] After the AI ​​initial review is passed, the system automatically generates a structured application package in standardized JSON format, which serves as the sole basis for manual review. Refer to Table 1 for the function names and meanings of the core fields. The core fields are as follows:

[0177] {

[0178] "constraint_id": "PRESIDUE_001",

[0179] "constraint_name": "Annual pesticide use frequency limit",

[0180] "current_value": "4 times / quarter",

[0181] "proposed_relaxed_value": "5 times / quarter",

[0182] "farm_unit": "Area A-001 to Area A-050",

[0183] "crop_type": "regenerated rice",

[0184] "duration_days": 90,

[0185] "opportunity_description": {

[0186] "type": "Window period for subsidies for high-yield farmland",

[0187] "expected_revenue_increase": "28%",

[0188] "policy_deadline": "2025-12-31"

[0189] },

[0190] "risk_assessment": {

[0191] "residual_risk_level": "P1",

[0192] "contingency_plan": "Batch testing + isolation for exceeding standards",

[0193] "monitoring_frequency": "7 days"

[0194] },

[0195] "compliance_basis": {

[0196] "regulation_reference": "Article X of the Regulations on Pesticide Management",

[0197] "interpretation_space": "Exceptions to Scientific Drug Use"

[0198] },

[0199] "urgency_flag": false,

[0200] "approver_required_level": "Senior Agronomist"

[0201] }

[0202] Table 1

[0203]

[0204] Once generated, the approval package will be pushed to the manual approval layer.

[0205] (III) Manual Approval Level:

[0206] 1) Advanced review and decision-making

[0207] This step is the final human decision-making node in the process, and is approved by qualified professionals to ensure the professionalism and compliance of the decision.

[0208] SLA Notification Received: The approver receives an approval notification sent by the system, which clarifies the approval items, time limit requirements (72-hour SLA for the regular channel), and core information of the application package.

[0209] Advanced Review: The farm's technical director (who must hold a senior agronomist qualification) will conduct a comprehensive review of the AI ​​initial review results, application package contents, risk contingency plan, and compliance basis, with a focus on the following:

[0210] The authenticity of the benefit calculations and the effectiveness of the policies;

[0211] The feasibility of risk management measures and the completeness of emergency plans;

[0212] The legality of the compliance basis and the sufficiency of the supporting materials;

[0213] 2) Approval Decision-Making and Decentralization

[0214] Approval denied (No): The approver needs to provide adjustment suggestions, and the system will return the plan to AI;

[0215] The strategy engine, powered by AI, readjusts the planting plan and triggers the process again;

[0216] Approval Passed (Yes): The system generates an approval instruction, pushes it to the execution and monitoring layer, and initiates the subsequent execution process.

[0217] (iv) Execution and monitoring layer, i.e., system execution + full lifecycle management:

[0218] This stage is the implementation and closure of the process, covering system execution after approval, full-cycle process supervision, rollback upon expiration and effect evaluation, to ensure that the entire process of relaxing constraints is controllable and traceable;

[0219] 1. System execution (after approval)

[0220] a. Constraint Engine Update: The constraint management engine automatically updates the pesticide application frequency constraints for the corresponding plots (Area A-001 to Area A-050), temporarily increasing the original upper limit of 4 times / quarter to 5 times / quarter;

[0221] b. Schedule Regeneration: Based on the updated constraints, the agricultural scheduling system regenerates the complete agricultural operation plan for the plot, including pesticide application time, dosage, safety interval, etc.

[0222] c. Add traceability label: Add a special "Restriction Relaxation" label to the traceability code of all agricultural products produced in these 50 plots to achieve full life cycle traceability of products;

[0223] 2. Process monitoring (within the validity period)

[0224] a. Regular automatic review: The system automatically performs a rapid soil pesticide residue test every 7 days to monitor the pesticide residue level of the plot in real time and ensure that the risk is controllable;

[0225] b. Early Cancellation Trigger Mechanism: The system will automatically trigger early cancellation of constraints when any of the following situations occur: ① A batch of agricultural products exceeds the pesticide residue test standard; ② Meteorological disasters lead to the risk of crop failure in the plot; The original constraint upper limit will be restored immediately after cancellation;

[0226] c. Automatic rollback upon expiration: The validity period of this restriction relaxation is 90 days. After the expiration, the system will automatically restore the upper limit of pesticide use frequency of 4 times / season. The planting plan for the next season must be rescheduled according to the original restriction.

[0227] 3. Effectiveness Evaluation

[0228] After the process is completed, the system automatically conducts an effectiveness evaluation, comparing the actual income, pesticide residue test results, and agricultural product quality of the "relaxed restriction plots" and the "control plots", generating an evaluation report to provide data support for the subsequent optimization of the relaxed restriction rules.

[0229] III. Summary of the Core Process

[0230] 1. Full closed-loop management mechanism: Achieves a closed-loop process from "conflict triggering - risk filtering - AI preliminary review - manual approval - execution monitoring - rollback upon expiration", with no blind spots in management;

[0231] 2. AI + Human Dual Review Mode: AI-powered three-dimensional preliminary review improves approval efficiency, while human final review ensures professional decision-making, balancing efficiency and compliance;

[0232] 3. A constraint system that combines rigidity and flexibility: By maintaining a "never-relaxed list" to safeguard the bottom line of safety, flexible relaxation space is provided for high-value solutions that meet the conditions, thus balancing safety and efficiency;

[0233] 4. Full-process traceability: All operations, testing data, and approval records are stored on the blockchain, and traceability codes enable full life-cycle supervision of products, meeting the requirements for agricultural product quality and safety;

[0234] 5. Dynamic validity period management: A 90-day temporary relaxation period + periodic review + early cancellation mechanism is set to achieve dynamic adjustment of constraints and avoid the risks of long-term relaxation.

[0235] Example 2: Reference Figures 1-6 This embodiment describes a three-dimensional judgment and approval management system for temporarily relaxing hard constraints in an AI decision-making system. This system is used to execute the three-dimensional judgment and approval management method for temporarily relaxing hard constraints in an AI decision-making system described in Embodiment 1. It includes a never-relaxed list module 1, a three-dimensional precondition automatic judgment module 2, a structured relaxation application package automatic generation module 3, a time-sensitive graded human-machine approval module 4, and a monitoring module 5, all connected sequentially.

[0236] Although the invention has been described with reference to a limited number of embodiments, those skilled in the art will understand from the foregoing description that other embodiments are conceivable within the scope of the invention described herein. Furthermore, it should be noted that the language used in this specification has been chosen primarily for readability and instructional purposes, and not for the purpose of interpreting or limiting the subject matter of the invention. Therefore, many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the appended claims. The disclosure of the invention is illustrative and not restrictive, and the scope of the invention is defined by the appended claims.

Claims

1. A three-dimensional judgment and approval management method for temporarily relaxing hard constraints in an AI decision-making system, characterized in that, Includes the following steps: S1. Set up a never-relaxed list in the constraint management engine through the never-relaxed list module, use it as a system-level mandatory filtering mechanism, and execute it in priority over any relaxation application process to obtain constraints that are not hit by the never-relaxed list; S2. For constraints that are not hit in the "Never Relax List" constraint, the three-dimensional precondition automatic judgment module automatically performs the judgment in three dimensions to filter out constraints that meet the conditions in all three dimensions. S3. Automatically extract information from the context of constraints that simultaneously meet the three-dimensional conditions, and automatically generate a standardized structured relaxation application package through the structured relaxation application package automatic generation module; S4. Based on the required fields of the relaxed application package, conduct human-machine collaborative approval through the time-based hierarchical human-machine approval module, determine the approval level routing and approval permission control, and obtain the approval result; S5. Once the approval result is approved, i.e. the temporary relaxation takes effect, the status is continuously monitored through the monitoring module, and validity period constraints, real-time monitoring requirements and automatic rollback are set to complete the relaxation effect evaluation.

2. The three-dimensional judgment and approval management method for temporarily relaxing hard constraints in an AI decision-making system according to claim 1, characterized in that, The S1 list, which will never be relaxed, includes five categories: personnel safety constraints, absolute legal red lines, ecological red lines, data security constraints, and ethical red lines. When receiving a relaxation application, the list matching function is first called to perform a hash match on the ID of the constraint to be relaxed. If the constraint in the list is matched, a rejection signal is returned directly, and the process does not proceed to step S2. The "Never Relax List" can only be modified by authorized system administrator accounts, and all changes are recorded in the audit log and cannot be deleted.

3. The three-dimensional judgment and approval management method for temporarily relaxing hard constraints in an AI decision-making system according to claim 2, characterized in that, In S2, the three dimensions are dimension A, dimension B, and dimension C; Dimension A is used for opportunity identification, determining the constraints that simultaneously satisfy the three sub-conditions of Dimension A, thus obtaining the constraints that satisfy Dimension A. The three sub-conditions of dimension A are as follows: 1) Sub-condition A1: The expected return of the new opportunity is quantitatively assessed > the current constraint cost is quantitatively assessed × the preset threshold, that is, the risk-reward ratio is > the preset threshold; 2) Sub-condition A2: The new opportunity has a clearly defined effective time window; 3) Sub-condition A3: It is not possible to achieve the same benefit by adjusting other non-hard constraint variables without relaxing this constraint; Dimension B is used for risk assessment to determine the constraints that are simultaneously met by all three sub-conditions of Dimension B, thus obtaining the constraints that satisfy Dimension B. The three sub-conditions of dimension B are as follows: 1) Sub-condition B1: The system quantifies and rates the residual risk, and the residual risk level is P1. 2) Sub-condition B2: The application includes a specific and executable emergency plan, and the system verifies the completeness of the plan; 3) Sub-condition B3: The risk monitoring plan specifies the review frequency and monitoring indicators, and the system verification review frequency is no less than once every 7 days; Dimension C is used for compliance verification, determining the constraints that are simultaneously met by both sub-conditions of Dimension C, thus obtaining the constraints that satisfy Dimension C. The two sub-conditions of dimension C are as follows: 1) Sub-condition C1: Provide specific legal provisions for reference, and the system will match them with the compliance ontology to confirm the existence of interpretation space for higher-level laws; The compliance ontology is a pre-built knowledge base of the system, which includes the legal provisions ontology, interpretation space annotations and compliance precedents. It is maintained by the system administrator and records audit logs. Compliance verification uses semantic matching to query whether the legal references to be relaxed have interpretation space annotations. 2) Sub-condition C2: Form written compliance evidence that can be explained to the regulator; the system automatically verifies the completeness of the compliance explanation text. The results of the three dimensions are summarized using AND logic: if any dimension is not satisfied, the system returns a rejection signal and a description of the specific sub-condition that is not satisfied; when all three conditions are satisfied, the process proceeds to step S3.

4. The three-dimensional judgment and approval management method for temporarily relaxing hard constraints in an AI decision-making system according to claim 3, characterized in that, In S3, the required fields for the relaxation application package include constraint unique identifier constraint_id, constraint name constraint_name, current value / condition current_value, proposed relaxed value / condition proposed_relaxed_value, application relaxation period duration_days, new opportunity description and profit quantification opportunity_description, residual risk rating and contingency plan risk_assessment, compliance basis and regulatory reference compliance_basis, whether it is an urgent application urgency_flag, and minimum approval level required by the approver approver_required_level.

5. The three-dimensional judgment and approval management method for temporarily relaxing hard constraints in an AI decision-making system according to claim 4, characterized in that, In S4, the approval level routing is as follows: a. Regular Application: The system will send a reminder before the deadline set within the regular time frame for approval. b. Emergency Request: Set an emergency timeframe for approval, and the system will send a real-time notification. Approval authority is controlled to a minimum level of approval, requiring senior experts in the field. The specific process for handling approval results is as follows: a. Approval: The approval metadata is merged with the relaxation application package and written into the constraint management engine, and the temporary relaxation takes effect; at the same time, all downstream decision outputs are automatically attached with relaxation labels. The content of the approval metadata includes the approver ID, approval time and approval reason, and the automatically attached relaxation labels include the name of the relaxed constraint, the approver, the expiration date and risk warning. b. Reject: The constraint value remains unchanged, and the rejection reason is written to the audit log.

6. The three-dimensional judgment and approval management method for temporarily relaxing hard constraints in an AI decision-making system according to claim 5, characterized in that, In S5, the continuous monitoring status is set as follows: 1) The validity period constraints are as follows: a. A one-time relaxation validity period is preset; if the maximum one-time relaxation validity period is exceeded, a new application must be submitted. b. A preset upper limit is set for the total cumulative relaxation time of the same constraint. Once the total cumulative relaxation time limit is exceeded, the system will automatically reject new relaxation applications for the current constraint. c. It is renewable, but renewal requires a complete approval process; 2) The specific settings for real-time monitoring requirements are as follows: a. Review frequency: A review will be automatically performed after each extension of the validity period, comparing the actual monitoring indicators with the risk assessment at the time of application. b. Early withdrawal trigger: ① Monitoring data shows that the residual risk level has been upgraded to P0 level, ② Actual measured data exceeds the upper limit of the proposed relaxation value, ③ Authorize human experts to voluntarily withdraw; 3) The automatic rollback process is as follows: a. After the validity period expires or the early cancellation triggers, the system will automatically restore the constraint value to its original value before relaxation; b. Record the rollback operation in a complete audit log. The audit log should include the rollback time, rollback reason, and a comparison of constraint values ​​before and after the rollback. c. Notify relevant parties after the rollback, including the approver, applicant, and system administrator; d. After rollback, within the single relaxation validity period limit, the system will prompt that the relaxation effect evaluation has been completed.

7. A three-dimensional judgment and approval management system for temporarily relaxing hard constraints in an AI decision-making system, characterized in that, The method for implementing the three-dimensional judgment and approval management method for temporary relaxation of hard constraints in an AI decision-making system according to any one of claims 1-6 includes a never-relaxed list module (1), a three-dimensional precondition automatic judgment module (2), a structured relaxation application package automatic generation module (3), a time-based graded human-machine approval module (4), and a monitoring module (5) connected in sequence.

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