Method, system, and medium for generating a task handling policy based on a regulation-guided framework

Abstract

This application discloses a method, system, and medium for generating task handling strategies based on a specification-guided framework, relating to the field of intelligent decision-making technology. The method includes: constructing multi-dimensional task feature vectors and object behavior profile vectors; using standardized prompts to constrain a large model and generate standardized candidate strategies; employing a four-layer progressive verification process based on legal compliance, cost-effectiveness, temporal feasibility, and ethical boundaries to complete strategy selection; real-time monitoring of object response signals after deployment to achieve dynamic control of strategy strength escalation / degradation, stage transitions, and emergency handling; automatically triggering strategy reconstruction when significant changes occur in the state; and achieving self-learning optimization based on experience rules accumulated from execution results. This application can improve strategy matching accuracy and deployment reliability, reduce execution risks, achieve end-to-end adaptive closed-loop and continuous iterative optimization, and is applicable to intelligent handling scenarios across multiple domains, exhibiting strong versatility and high stability.

Description

Technical Field

[0001] This application belongs to the field of computer intelligent decision-making and automated process control technology, specifically involving a task handling strategy generation method, system and medium based on a specification guidance framework. Background Technology

[0002] Task handling refers to the entire process of completing closed-loop processing and value realization for various business matters that have entered the pending handling state through multiple standardized execution methods. The specification guidance framework refers to the engineering constraint and positive guidance architecture built for the large model generation process. By pre-setting business rules, process specifications, timing constraints, and logical boundaries, it standardizes and structures the output behavior of the large model, ensuring that the model output meets the requirements of business logic correctness, process compliance, reasonable investment, and timing feasibility. This achieves reliable end-to-end control from automatic strategy generation and layered verification to implementation.

[0003] The existing task handling strategy generation and implementation process has the following significant technical defects: First, the formulation of traditional handling strategies relies heavily on the individual experience and subjective judgment of human operators. Different personnel may give very different handling and execution paths for the same business matter to be handled. There is a lack of a standardized and systematic strategy automatic matching mechanism based on the multi-dimensional characteristics of tasks and the behavioral profiles of interactive objects, which leads to large fluctuations in overall handling efficiency, low completion rate of business matters, and difficulty in forming a unified and reusable strategy generation paradigm.

[0004] Second, when using large models to directly assist in generating disposal strategies, the free generation of models is prone to defects such as non-compliant process specifications, uneconomical input ratios, and infeasible execution sequence logic. For example, it may recommend subsequent execution actions without meeting the pre-process conditions, recommend high-cost disposal paths for low-complexity tasks, or recommend subsequent parallel operations before the pre-process is completed. Existing conventional technologies lack a dedicated specification guidance architecture and a layered progressive verification mechanism for task disposal scenarios, and cannot intercept unqualified strategies generated by large models layer by layer from the rule layer, logic layer, and sequence layer, resulting in poor usability of output results and high implementation risks.

[0005] Third, the actual effectiveness of the handling strategy is highly influenced by the real-time behavioral feedback signals of the interacting objects. Existing technologies lack a strategy adaptive adjustment and path dynamic scheduling mechanism based on real-time feedback data streams, which can easily lead to mismatches such as executing high-intensity handling actions when the object is cooperative and using mild handling paths when the object is evasive. This not only increases compliance and public opinion risks but also causes the optimal handling execution window to be missed.

[0006] In summary, existing technologies suffer from several shortcomings, including high reliance on manual strategy generation, the ease with which invalid strategies can be generated in large models due to the lack of specification constraints, the absence of a hierarchical and reliable verification mechanism, and the inability to adaptively schedule based on real-time feedback during the execution phase. There is an urgent need to construct a set of technical solutions for intelligent generation, progressive verification, and dynamic adaptive control of task handling strategies based on a specification-guided framework. Summary of the Invention

[0007] The technical problem to be solved by this application is to provide a method, system and medium for generating task handling strategies based on a specification guidance framework, so as to solve the problem that the existing technology lacks effective engineering constraints and multi-level progressive verification mechanisms when generating task handling strategies using large models, resulting in defects in the output results in terms of logical consistency, execution feasibility and dynamic adaptability; at the same time, the existing system cannot adaptively adjust the control threshold based on real-time feedback data, making it difficult to ensure the continuous effectiveness and reliability of the control logic in dynamic environments.

[0008] To address the aforementioned technical problems, this application provides the following technical solution: Firstly, this application provides a method for generating task handling strategies based on a specification guidance framework, including: Obtain the multidimensional feature vector of the task to be processed and the behavioral profile vector of the task object; the multidimensional task feature vector includes task scale and structural features, target value and executability features, task object performance resource capability features, legal statute of limitations and legal status features, historical response features and associated guarantee chain features; the behavioral profile vector of the task object includes cooperation willingness score, confrontation tendency score, negotiation flexibility score, legal sensitivity score and compliance risk score. Based on the multidimensional feature vector and the task object behavior profile vector, a reduced structure prompt word is constructed, and the prompt word is input into a preset handling strategy to generate a large model, generating a set of candidate handling strategy schemes containing multiple candidate schemes; A progressive verification process is performed on each of the candidate disposal strategy options in the set to obtain disposal strategy options that pass the verification process. The progressive verification process includes a legal procedure compliance verification layer, a cost-effectiveness verification layer, a time sequence feasibility verification layer, and an ethical boundary verification layer, which are executed sequentially. Each layer of verification is only executed after the previous layer has passed. The strategy scheme that has passed the verification process is deployed to the task execution engine, and the response signals of the task object are monitored in real time during the execution process. The response signals include performance behavior signals, communication response signals, negotiation willingness signals, confrontation behavior signals, and legal procedure progress signals. Based on the response signal of the task object, the execution path of the handling strategy is dynamically adjusted.

[0009] Furthermore, the method also includes: triggering the regeneration of the handling strategy when the performance resource capability of the task object undergoes a significant change, or the deviation between the execution effect of the handling strategy and the expectation exceeds a preset threshold, or the behavioral profile vector of the task object undergoes a categorical change.

[0010] Furthermore, the method also includes: evaluating the effectiveness of the treatment strategy and optimizing the treatment strategy.

[0011] Furthermore, the calculation method for the value and enforceability characteristics of the target asset is as follows: it is obtained by multiplying the current market valuation of the target asset, the liquidity discount coefficient, and the legal enforceability coefficient; the liquidity discount coefficient is set to a preset value according to the type of the target asset; the legal enforceability coefficient is calculated by decreasing the number of legal obstacles existing on the target asset according to a preset obstacle discount factor.

[0012] Furthermore, the legal procedure compliance verification layer includes verification of legal preconditions, verification of the legality of preservation measures, verification of the timing of enforcement procedures, and verification of the compliance of disposal actions. The verification of legal preconditions includes verifying whether the statute of limitations is within its effective period, whether the object information is complete and deliverable, and whether the certificate of rights is sufficient. The verification of the compliance of disposal actions includes verifying whether the time of the action is within the legally permitted period, whether the frequency of the action exceeds the legal limit, and whether the object of the action is limited to the legal scope. The cost-benefit rationality verification layer includes cost-benefit ratio verification, marginal cost rationality verification, comparison verification with alternative solutions, and simplified handling verification for small-scale tasks. The cost-benefit ratio verification method is to calculate the expected net benefit after deducting the expected direct processing cost and resource occupation opportunity cost from the expected value recovery amount, and verify that it is a positive value. The marginal cost rationality verification method is to calculate the ratio of the cost of each stage to the increase in the expected value recovery rate of that stage, and verify that it does not exceed the preset maximum marginal cost-benefit ratio threshold. The time-series feasibility verification layer includes stage dependency verification, time window feasibility verification, and resource availability verification. The stage dependency verification method is to map the stage sequence of the strategy solution to a preset action dependency graph to check whether there is a stage arrangement that violates the dependency. The time window feasibility verification method is to calculate key time nodes based on the expected duration of each stage and verify whether they are within the statutory time window. The ethical boundary verification layer includes verification of protection of vulnerable objects, verification of basic right to survival, verification of information protection, and pre-assessment of compliance risks. The pre-assessment of compliance risks is based on the weighted product of the compliance risk score of the task object and the intensity coefficient and execution time of each stage of the strategy plan, and verifies that it does not exceed the preset compliance risk tolerance threshold.

[0013] Furthermore, the execution path of the dynamic adjustment strategy includes: when the positive response score exceeds a preset positive response threshold, reducing the intensity of the current stage and skipping the subsequent high-intensity stage; when the negative response score exceeds a preset negative response threshold, increasing the intensity of the current stage and shortening the waiting time to accelerate the entry into the next stage; and when signs of target transfer are detected, triggering the preservation procedure urgently.

[0014] Furthermore, the method for determining whether the task object's behavior profile has undergone a categorical change is as follows: calculate the cosine similarity between the direction of change of the current behavior profile vector and the preset category change direction vector. When the similarity exceeds a preset threshold, it is determined to be a categorical change. The category change includes changing from having the will but not the resources to having the resources but not the will, and changing from cooperating and negotiating to actively confronting.

[0015] Furthermore, the effectiveness of the aforementioned treatment strategy is evaluated, and the treatment strategy is optimized, including: When the actual effect of a certain strategy stage is significantly better than expected, the execution conditions and effects of that stage are recorded as positive empirical rules; when the actual effect of a certain strategy stage is significantly worse than expected, it is recorded as a negative empirical rule and an alternative solution is suggested; when the execution effect after dynamic adjustment of the strategy is better than the expected effect before adjustment, the adjustment rule is recorded as an empirical rule; the empirical rules are incorporated into the reduced structure prompts of subsequent strategy generation as prior knowledge.

[0016] Secondly, this application also provides a task handling strategy generation system based on a specification guidance framework, comprising: The data acquisition module is used to acquire the multi-dimensional feature vector of the task to be processed and the behavioral profile vector of the task object; the multi-dimensional task feature vector includes: task scale and structural features, target value and executability features, task object performance resource capability features, legal statute of limitations and legal status features, historical response features and associated guarantee chain features; the behavioral profile vector of the task object includes cooperation willingness score, confrontation tendency score, negotiation flexibility score, legal sensitivity score and compliance risk score. The strategy generation module is used to construct a reduced structure prompt word based on the multidimensional feature vector and the task object behavior profile vector, and input the prompt word into a preset disposal strategy generation model to generate a set of candidate disposal strategy schemes containing multiple candidate schemes; The strategy verification module performs a progressive verification process on each of the candidate disposal strategy solutions in the set of candidate disposal strategy solutions to obtain disposal strategy solutions that pass the verification process. The progressive verification process includes a legal procedure compliance verification layer, a cost-effectiveness verification layer, a time sequence feasibility verification layer, and an ethical boundary verification layer, which are executed sequentially. Each layer of verification is only executed after the previous layer has passed. The strategy deployment module is used to deploy the strategy scheme that has passed the verification process to the task execution engine, and to monitor the response signals of the task object in real time during the execution process. The response signals include performance behavior signals, communication response signals, negotiation willingness signals, confrontation behavior signals, and legal procedure progress signals. The strategy adjustment module dynamically adjusts the execution path of the handling strategy based on the response signal of the task object.

[0017] Thirdly, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the task handling strategy generation method based on the specification bootstrapping framework described above.

[0018] This application provides a task handling strategy generation method, system, and medium based on a specification guidance framework, the advantages of which are: This application constructs a specification guidance framework with structured constraints to engineer the generation behavior of large models, significantly improving the standardization and consistency of the generated control logic. Simultaneously, it employs multi-level engineering compliance and performance verification to progressively validate and screen candidate logic sets, intercepting defects such as logical contradictions, timing infeasibility, and resource unavailability at the source, ensuring the reliability and robustness of the deployed logic. Furthermore, based on real-time feedback data and environmental data, it dynamically and adaptively corrects the configurable adjustment thresholds of each control logic, and automatically triggers the regeneration of the large model when the cumulative offset exceeds a preset threshold, forming a closed-loop self-evolution mechanism. This effectively solves the technical problems of uncontrollable large model generation content and the difficulty of static rules adapting to dynamic changes, significantly enhancing the engineering deployment quality and continuous applicability of the control system in complex dynamic environments. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating a task handling strategy generation method based on a specification guidance framework in an embodiment of this application. Figure 2 This is a schematic diagram of the structure of a task handling strategy generation system based on a specification guidance framework in an embodiment of this application. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] Please see Figure 1 This application provides a task handling strategy generation method based on a specification-guided framework. Through quantitative modeling of task characteristics and task object behavior, large-scale model strategy generation, multi-level progressive verification, real-time control of the execution process, and experience-based closed-loop iteration, it achieves intelligent, standardized, and adaptive optimization of the entire task handling process. The method includes at least the following steps: S10. Obtain the multi-dimensional feature vector of the task to be processed and the behavioral profile vector of the task object; the multi-dimensional task feature vector includes task scale and structural features, target value and executability features, task object performance resource capability features, legal statute of limitations and legal status features, historical response features and associated guarantee chain features; the behavioral profile vector of the task object includes cooperation willingness score, confrontation tendency score, negotiation flexibility score, legal sensitivity score and compliance risk score.

[0023] It should be noted that the "task object" mentioned in this application refers to the traditionally defined responsible entity that bears the obligation to perform (i.e., the commonly referred to "credit-abnormal entity"). The term "task object" broadly refers to any entity that, according to agreement or legal provisions, is required to perform specific performance acts, respond to disposal instructions, or assume obligations and responsibilities, including but not limited to natural persons or legal persons. Hereafter, "task object" will be used to refer to such entities, and will not be elaborated further.

[0024] The "tasks pending disposal" mentioned in this application refer to the traditional "credit repayment obligations" that the task recipient must fulfill, broadly defined as "tasks pending disposal." These "tasks pending disposal" broadly refer to any obligation that, according to agreement or legal provisions, requires the task recipient to perform a specific action, pay a specific consideration, or assume a specific responsibility, including but not limited to fund repayment, service provision, asset transfer, and compliance rectification. Hereafter, "tasks pending disposal" will be used consistently to refer to such obligatory matters that need to be processed and completed, without further elaboration.

[0025] Specifically, this step first involves performing full-dimensional data abstraction and quantitative modeling of the task to be processed, generating multi-dimensional task feature vectors. ,in, The feature dimension and the task object behavior profile vector provide a unified and standardized data input for subsequent strategy generation, avoiding deviations in strategy generation due to missing information or unstructured descriptions.

[0026] Among them, the multidimensional task feature vector is used to perform structured characterization based on the attributes of the task itself, specifically including: Task Scale and Structural Characteristics Used to characterize the size, structural complexity, and degree of delay of a task, providing a basis for selecting the strength of a strategy; Specifically, in one embodiment, the task size and structural characteristics are calculated using the following formula: ; in, This serves as the baseline value for the remaining obligations; This is the amount of the extension surcharge; The total amount of additional costs incurred beyond the due date. This represents the number of overdue days. This feature is a four-dimensional vector, reflecting the absolute size of the debt, the proportion of interest burden, the proportion of punitive fees, and the severity of the overdue period.

[0027] Value and enforceability of the subject matter It is calculated by multiplying the current valuation of the target asset, the liquidity discount factor, and the legal enforceability factor, and can objectively reflect the realizability of the target asset and the degree of enforcement obstacles.

[0028] Specifically, in one embodiment, the formula for calculating the value of the subject matter and its enforceability characteristics is as follows: ; in, The current market value of the subject matter; This is a liquidity discount factor, reflecting the degree of discount on the realization of the underlying asset under compulsory disposal scenarios (value range). Real estate takes preset values Movable property takes preset value Financial assets take a preset value ); This is the legal enforceability coefficient, reflecting whether there are legal obstacles to the collateral (such as prior seizure, leasehold disputes, co-ownership disputes, etc.), and its value range is... The value is 1 when there is no obstacle; for each obstacle, a preset obstacle discount factor is applied. Decreasing. Without a target object. The range is The higher the value, the higher the recyclable value of the object.

[0029] Task object's resource capability characteristics Used to quantify the resource reserves available to an object to complete a task, and to determine its actual feasibility.

[0030] Specifically, in one embodiment, the formula for calculating the resource capability characteristics of the task object is as follows: ; in, The current monthly disposable resources of the task target (the remaining amount after deducting basic living expenses and priority obligation expenses) are multiplied by the preset number of months of resource capitalization. ; Valuation of the realizable assets under the name of the task recipient (including but not limited to bank deposits, securities assets, vehicles, and non-primary residences); For the balance of other obligations taking precedence over this obligation (if any secured). The range is... A positive value indicates that the task object has net performance resources, while a negative value indicates that the task object's resources are insufficient to fulfill its obligations.

[0031] Statute of Limitations and Characteristics of Legal Status Used to identify the legal process stage, remaining time limit, and preservation status of a task, in order to avoid violations of policy triggering timing.

[0032] Specifically, in one embodiment, the legal statute of limitations and legal status characteristics include the following sub-indicators: Remaining days of the statute of limitations The remaining number of days of the statute of limitations, calculated from the last valid contact (the act that interrupted the statute of limitations); Legal proceedings have commenced. The code is represented by discrete status values ​​(0 = no prosecution, 1 = prosecution but no judgment, 2 = judgment but no execution, 3 = execution proceedings have commenced, 4 = execution suspended / terminated). Preservation measures status : Encoded as discrete state values ​​(0 = not preserved, 1 = some executable objects have been restricted, 2 = all executable objects have been restricted).

[0033] Historical response characteristics: used to record the feedback patterns of task objects to historical actions, providing historical basis for strategy prediction.

[0034] Specifically, in one embodiment, the historical response characteristics include the following sub-indicators: Historical contact success rate The percentage of historical attempts to successfully reach the target of the task; Historical commitment fulfillment rate The percentage of commitments actually fulfilled by the task recipients; The number of days since the most recent effective performance ; Historical complaint rate The frequency with which the task recipient raises objections to the handling of the matter.

[0035] Related guarantee chain features: used to characterize task-related relationships, joint obligations and third-party performance capabilities, and expand the scope of task disposal.

[0036] Specifically, in one embodiment, the associated guarantee chain characteristics include the following sub-indicators: The number and value of other outstanding obligations for the same task object in this system. The number and value of guarantees provided by the task object as a related party to others. ; Assessment value of the performance resource capabilities of the related parties of the task object (Calculation method is the same) However, this applies to related parties.

[0037] The task subject behavior profile vector is used to dynamically score task subjects from dimensions such as behavioral motivation, response pattern, and risk propensity, specifically including: Willingness to cooperate rating This reflects the degree to which the recipient is inclined to actively cooperate, communicate, and fulfill their commitments.

[0038] Specifically, in one embodiment, the willingness score is calculated using the following formula: ; in, Scoring of historical performance behavior (derived by normalization of indicators such as the number of days since the most recent effective performance and the frequency of historical partial performance); The response attitude score is obtained by normalizing indicators such as call answering rate, communication cooperation level, and whether effective information is provided. The scoring is based on proactive communication (normalized from the frequency of the task subject proactively contacting the system for negotiation). ; in, For the preset weights, satisfy The value range is [0,1], and the higher the value, the stronger the willingness to repay.

[0039] Confrontational Tendency Score This reflects the possibility that the subject may avoid, refuse, delay, or even maliciously obstruct the progress of the task.

[0040] Specifically, in one embodiment, the antagonistic tendency score is calculated using the following formula: ; in, The asset transfer behavior is scored (based on the changes in assets under the name before and after the abnormal status is formed, and whether there is a low-price transfer or gratuitous donation, etc., normalized); The contact information invalidity score is obtained by normalization based on indicators such as the invalidity rate of registered phone numbers and the frequency of changes in registered addresses. Social relationship breakdown score (obtained by normalization based on indicators such as emergency contact loss rate and failure to notify banks of changes in work units); The preset weights are used. The value range is [0,1], and the higher the value, the stronger the tendency to evade debt.

[0041] Negotiation Flexibility Scoring This reflects the extent to which the target party is willing to make adjustments, concessions, and renegotiates during the communication of the proposed solution.

[0042] Specifically, in one embodiment, the negotiation resilience score is calculated using the following formula: ; in, Acceptance rate of historical negotiation proposals; The frequency with which counterproposals are made to the task recipients (reflecting their enthusiasm for participating in negotiations); The plan's conditional flexibility score is obtained by normalizing information such as the acceptable performance period range and the minimum reduction / exemption amount for the task object. This is the preset weight. The value range is [0,1], and a higher value indicates a larger negotiation space.

[0043] Legal sensitivity score This reflects the degree of respect the subject has for legal procedures and coercive measures, as well as their sensitivity to changes in behavior.

[0044] Specifically, in one embodiment, legal sensitivity scoring The following calculation formula is used: ; in, Performance evaluation of changes in behavior following historical legal proceedings (e.g., whether to proactively contact the client after receiving a formal letter, or to expedite compliance after legal proceedings have been initiated). Exposure to legally enforceable assets (the proportion of assets under one's name that can be restricted to the total size of obligations); The degree of social impact of the legal action on the target group (such as whether they have a specific social status, including public officials, heads of institutions, professionals with travel needs, etc.). The preset weights are used. The value range is [0,1], and the higher the value, the stronger the deterrent effect of legal measures on the target.

[0045] Compliance risk score : Used to predict the potential risk level that may lead to objections, complaints or external intervention during the handling process.

[0046] Specifically, in one embodiment, compliance risk scoring The following calculation formula is used: ; in, This is a normalized value representing the historical complaint frequency. A score is given for awareness of rights protection (based on normalized behaviors such as whether a lawyer is hired, whether legal provisions are cited, and whether audio recordings are made as evidence). The sensitivity score for public opinion is obtained by normalization based on factors such as the social media influence of the task subject and whether they have a media background. This is a preset weight. The value range is [0,1], and the higher the value, the greater the risk of triggering complaints during the handling process.

[0047] S20. Based on the multidimensional feature vector and the task object behavior profile vector, construct a reduced structure prompt word, and input the prompt word into a preset handling strategy to generate a large model, thereby generating a set of candidate handling strategy schemes containing multiple candidate schemes.

[0048] This step, based on the dual-vector data obtained from S10, constructs structured and strongly constrained prompt words through a reduction-guided framework. It uniformly encodes and inputs task features, object profiles, available disposal methods, generation rules, historical cases, and prior experience, enabling the large model to generate standardized, executable, and verifiable candidate strategies within the preset engineering boundaries.

[0049] Specifically, in one embodiment, step S20 includes: First, the Harness (reduction structure framework) construction of strategy-generated prompts. It should be noted that the "Harness" engineering paradigm described in this application is collectively referred to as the "specification guidance framework" in this application. The specification guidance framework refers to the engineering control and guidance of the generation behavior of large models through a predefined set of structured constraints (including path integrity constraints, quantifiable transformation condition constraints, cost ceiling constraints, time constraints, intensity progression constraints, and parallel restriction constraints, etc.), which limits its output to a deployable, verifiable, and reliable boundary, thereby ensuring the logical consistency, compliance, and feasibility of the generated control logic.

[0050] The system organizes the following information into Harness-styled cue words and inputs them into the large model: Input Area 1 (Asset Feature Summary): The values ​​of each dimension of the multidimensional feature vector A of the task to be disposed of and its quantile position among similar assets.

[0051] Input Area 2 (Task Object Profile Summary): The values ​​of each dimension of the task object behavior profile vector B and their meaning for strategy selection.

[0052] Input Area 3 (List of Available Disposal Measures): This section lists all predefined available disposal measures, their applicable conditions, expected costs, and expected timeframes. These measures include, but are not limited to: negotiation, formal correspondence, negotiation of restructuring plans, initiation of legal proceedings, application for asset preservation, enforcement, realization of the subject matter, transfer of tasks, and write-off processing.

[0053] Input Area 4 (Policy Generation Constraints – Harness Constraint Set): Path integrity constraint: The strategy must include a complete path from the current state to the final disposal completion, and "dangling" stages are not allowed (i.e., no subsequent stages after a certain stage and the final state is not reached). Clear constraints on phase transition conditions: The conditions for transitioning from each phase to the next phase must be expressed by quantifiable indicators (such as "failure to fulfill obligations within a preset number of days after negotiation" or "failure to voluntarily fulfill obligations within a preset number of days after the court judgment takes effect"). Cost constraint: The total expected disposal cost of the strategy (including personnel costs, legal fees, litigation costs, appraisal fees, auction commissions, etc.) shall not exceed a preset percentage limit of the expected recovery amount. ; Time Limitation: The execution timeline for each stage of the strategy plan must not result in the expiration of the statute of limitations; Escalation Constraint: The intensity of the measures taken in the strategy must be escalated in a progressive manner (i.e., it is not allowed to jump directly to high-intensity measures without trying mild measures), unless there is an emergency (such as discovering that the target is transferring assets). Parallelism Constraint: The number of actions executed in parallel within the same time period shall not exceed a preset limit. .

[0054] Input Area 5 (Historical Similar Case Reference): The system retrieves a preset number of cases from the historical case database that are most similar to the current case in terms of characteristics. We provide a list of closed cases, detailing their processing paths, timeframes at each stage, and final recovery rates for reference.

[0055] The similarity calculation method is as follows: ; in, This is the preset kernel width parameter.

[0056] Input Area Six (Priority Disposal Experience Rule Base): The system's accumulated disposal experience rules (sources are found in Step Four's experience rule generation mechanism), which serve as reference constraints when generating large model strategies.

[0057] Secondly, the structured output of the candidate disposal strategy set The large model generates candidate disposal strategies based on the above inputs. ,in The number of candidate solutions (preset to be no less than) (Several options are available for comparison). Each option... The structure is as follows: ; in, To handle path sequences, including H_m An orderly phase; This is a set of conditions for stage transition; A description of the specific actions to be performed at each stage; This is an estimate of the expected recovery rate (based on historical similar cases from the large model), with a value range of [value range missing]. ; This is an estimate of the expected total disposal cost; This is an estimate of the total expected processing time (in days).

[0058] Each stage The structure is as follows: ; in, The main actions to be taken in this phase (selected from the list of available measures); This is the expected execution duration (in days) for this phase. Resources required for this phase (including types of personnel, external service providers, and budget). The success criteria for this stage (such as "receiving full performance of obligations", "reaching and signing a restructuring agreement", "the court accepting and filing the case"); This refers to the handling method when the success criteria are not met in this stage (either proceeding to the next stage or triggering a branch path).

[0059] For example, for a task with a remaining obligation benchmark of 50 units, secured by real estate (currently valued at 80 units), 180 days overdue, a task recipient's willingness to cooperate score of 0.4, and a legal sensitivity score of 0.7, the large model generates a candidate solution. for: Phase 1: Negotiated telephone contact (expected duration 15 days). Success is defined as "the target party agrees to the negotiated implementation plan". If it fails, proceed to Phase 2. Phase 2: Sending formal letters and simultaneously preparing legal documents (expected duration 10 days). Success criteria: "The client will proactively contact us for negotiation within 7 days of receiving the formal letter." Failure will proceed to Phase 3. Phase 3: Initiate legal proceedings and apply for restrictions on the subject matter (expected duration 90 days). Success criteria are "obtaining a favorable ruling and the restrictions being effective". If unsuccessful (e.g., the restrictions fail), proceed to Phase 4B (obligation transfer assessment). Phase 4A (after the ruling takes effect): Apply for enforcement and promote the disposal of the subject matter (expected duration 120 days), the success criterion is "disposal completed and distribution completed"; Phase 4B (when restrictions fail): Assess the feasibility of transferring the obligation. If the transfer price is not lower than the preset minimum value recovery rate, then execute the transfer.

[0060] Expected value recovery rate Expected total cost Cost unit, expected total time sky.

[0061] Finally, the system will also output a comparison between the various treatment options, as follows: The large model outputs a comparison analysis matrix between multiple candidate solutions. The five comparison dimensions are: expected return rate ranking; expected cost ranking; expected execution time ranking; execution complexity ranking (based on the number of stages and parallel actions); and risk ranking (based on the worst-case loss when the strategy fails). A comprehensive recommended ranking and the reasons for the recommendation are also provided.

[0062] S30. Perform a progressive verification process on each of the candidate disposal strategy solutions in the set of candidate disposal strategy solutions to obtain disposal strategy solutions that pass the verification process. The progressive verification process includes a legal procedure compliance verification layer, a cost-effectiveness verification layer, a time sequence feasibility verification layer, and an ethical boundary verification layer, which are executed sequentially. Each layer of verification is only executed after the previous layer has passed.

[0063] In one embodiment, the legal procedure compliance verification layer includes legal precondition verification, preservation measure legality verification, execution procedure timing verification, and disposal behavior compliance verification. The legal precondition verification includes verifying whether the legal statute of limitations is within its effective period, whether the object information is complete and deliverable, and whether the rights certificate is sufficient. The disposal behavior compliance verification includes verifying whether the behavior time is within the legally permitted period, whether the behavior frequency exceeds the legal limit, and whether the behavior object is limited to the legal scope.

[0064] Specifically, verification layer one: legal procedure compliance verification. Legal procedure compliance verification ensures that the legal actions involved in the strategy plan meet legal procedure requirements. The system performs the following automated checks: Verification of Pre-litigation Conditions For stages in the strategy plan that include litigation actions, the system verifies whether the preconditions for filing a lawsuit are met: Is it within the statute of limitations? ); Does the defendant have clear information (the debtor's identity information is complete and the address is accessible)? Whether there are sufficient legal and obligation documents (loan agreement, loan disbursement certificate, overdue records, etc.); If the guarantor seeks recourse, does the recourse condition stipulated in the guarantee contract apply?

[0065] The verification method is as follows: The system maintains a legal precondition rule base, and compares the legal actions in the strategy plan with the rule base one by one. Each legal action must meet all its corresponding preconditions to pass the verification.

[0066] Verification of the legality of preservation measures For the stages of the strategy plan that include asset preservation actions, the system verifies: Whether the preservation application is within the statutory time limit (pre-litigation preservation must be filed within a predetermined number of days after the preservation); Whether the object of preservation falls within the scope of preservation (excluding the legal protection of the subject's necessities of life, the right to reside in their only residence, etc.); Is the amount of preservation equivalent to the amount claimed in the lawsuit (not exceeding the pre-set maximum preservation amount)? ).

[0067] Verification 3: Execution timing verification For phases in the strategy plan that include mandatory actions, the system verifies: Whether a legally effective document (judgment, mediation agreement, notarized debt instrument, etc.) has been obtained; Has the legally mandated period for automatic performance expired? Whether the enforcement application is within the statutory time limit for enforcement.

[0068] Verification of the Compliance of Handling Actions The system verifies the actions taken in the strategy plan: Whether the processing time is within the legally permitted time period (the preset permitted processing time period) ); Does the frequency of handling exceed the legal limit (the number of contacts per day does not exceed the preset limit)? ); Whether the targets of the action are limited to the person subject to the task and their legal contact (without harassing unrelated third parties); Does the wording used in handling this matter contain content prohibited by law?

[0069] In one embodiment, the cost-benefit rationality verification layer includes cost-benefit ratio verification, marginal cost rationality verification, comparison verification with alternative solutions, and simplified handling verification for small-scale tasks. The cost-benefit ratio verification method is to calculate the expected net benefit after deducting the expected direct processing cost and resource occupation opportunity cost from the expected value recovery amount, and verify that it is a positive value. The marginal cost rationality verification method is to calculate the ratio of the cost of each stage to the increase in the expected value recovery rate of that stage, and verify that it does not exceed the preset maximum marginal cost-benefit ratio threshold.

[0070] Specifically, verification layer two: cost-effectiveness verification layer Cost-benefit rationality verification ensures that the strategy is economically reasonable, i.e., the expected recovery benefits are greater than the disposal costs.

[0071] Test 1: Cost-benefit ratio verification Calculate the expected net recovery value of the strategy: ; in, The expected amount to be recovered; For expected direct disposal costs; The opportunity cost of capital occupation The interest rate is the preset opportunity cost.

[0072] Verification pass conditions: (The expected net recovery value is positive).

[0073] Verification of the reasonableness of marginal cost For each stage in the strategy plan, calculate the marginal cost-benefit ratio for that stage: ; in, For the first Expected costs of the stage For the first The expected recovery rate increment of the stage relative to the previous stage.

[0074] Validation pass criteria: for each stage, (Preset maximum marginal cost-benefit ratio threshold). This means that the cost invested in each stage should not exceed a reasonable proportion of the incremental recovery it brings.

[0075] In one specific embodiment, The value is set to 0.5 (meaning that the cost of each stage does not exceed 50% of the expected incremental recovery from that stage).

[0076] Test 3: Comparison and verification with alternative solutions The system calculates the net recovery value of two benchmark options: "direct write-off" and "direct transfer". The net recovery value of direct write-off is 0 (abandonment of recovery); The net recovery value of direct transfer is ,in, Based on the latest transaction discount rate estimates for similar credit anomaly packages in the market.

[0077] Validation pass criteria: candidate solution The economic performance of the candidate solution must be no less than the higher of the two benchmark solutions mentioned above. This means that the economic performance of the candidate solution is at least no worse than that of direct abandonment or direct transfer.

[0078] Verification of Simplified Handling of Small-Amount Credit Abnormal Obligations When the total amount of abnormal credit obligations is lower than the preset threshold for small abnormal credit obligations At that time, the system verifies whether the strategy plan adopts a simplified handling path (such as only including telephone handling and negotiation for reduction, excluding high-cost means such as litigation). If the strategy plan for small-amount abnormal credit obligations includes litigation actions, the system marks it as "economically unreasonable - high-cost handling of small-amount abnormal credit obligations".

[0079] In one embodiment, The value is set at 50,000 yuan.

[0080] In one embodiment, the timing feasibility verification layer includes stage dependency verification, time window feasibility verification, and resource availability verification. The stage dependency verification method is to map the stage sequence of the strategy scheme to a preset action dependency graph to check whether there is a stage arrangement that violates the dependency. The time window feasibility verification method is to calculate key time nodes based on the expected duration of each stage and verify whether they are within the statutory time window.

[0081] Specifically, the timing feasibility verification aims to ensure that the timing arrangement of each stage in the strategy plan is feasible in actual operation, including the following verifications: Verification of Stage Dependencies Dependency diagram between system maintenance and handling actions The nodes represent the types of actions to be taken, and the directed edges represent the prerequisite dependencies (e.g., "enforcement" depends on "obtaining an effective judgment", and "asset auction" depends on "completing the assessment").

[0082] The verification method is to map the phase sequence of the strategy to the dependency graph and check whether there is a phase arrangement that violates the dependency (i.e., the preceding dependent phase of a certain phase does not appear before it).

[0083] Verification of the feasibility of the time window For actions involving legally mandated time windows in the strategy plan, verify whether the time schedule is within the window: The time limit for filing a lawsuit after pre-litigation preservation (preset number of days after preservation) (The prosecution must be initiated). The time limit for applying for enforcement after the judgment takes effect (preset number of years after the judgment takes effect) (Application for execution must be submitted within the specified period). Auction announcement period (pre-set number of days before auction) (Announcement period).

[0084] The verification method is as follows: based on the cumulative expected duration of each stage, calculate whether the key time nodes are within the legal window. If the expected duration of a certain stage causes subsequent stages to exceed the time window, it is marked as "time sequence infeasible".

[0085] Verification 3: Resource Availability For each stage of the strategy plan, verify whether the resources are available during the expected execution period: Is the workload of the personnel within a manageable range (the number of cases handled by a single person at the same time does not exceed the preset limit)? ); Does the scheduling of external lawyers allow for the preparation of litigation materials within the expected timeframe? Does the court's case acceptance period match the timeline of the strategy plan?

[0086] The verification method is as follows: the system queries the resource availability data in the resource management module and matches it with the resource requirements of the strategy plan.

[0087] In one embodiment, the ethical boundary verification layer includes verification of protection of vulnerable objects, verification of basic right to survival, verification of information protection, and pre-assessment of compliance risks. The pre-assessment of compliance risks is based on the weighted sum of the compliance risk score of the task object and the intensity coefficient and execution time of each stage of the strategy plan, and verifies that it does not exceed the preset compliance risk tolerance threshold.

[0088] Specifically, ethical boundary verification ensures that the strategy does not violate social ethics and consumer rights protection requirements, including the following checks: Test 1: Verification of Protection for Vulnerable Groups When the task recipient belongs to a pre-defined vulnerable group category (including but not limited to people with disabilities, patients with serious illnesses, low-income households, pregnant women, and people over 70 years old), the system verifies whether the strategy has taken appropriate protective measures: High-pressure tactics must not be used against vulnerable groups; The performance restructuring plan should offer more favorable terms (longer extension, greater reduction). When enforcing a judgment, a living allowance higher than the general standard should be reserved.

[0089] Verification of the right to life The execution of the verification strategy will not cause the task object to lose its basic survival conditions. If the task subject has only one residence and that residence is mortgaged, verify whether the strategy has considered alternative solutions such as "rent-to-own" or "retaining the right to reside". If enforcement results in the target group's monthly disposable income falling below the local minimum living standard, it is necessary to verify whether the strategy plan includes an enforcement exemption clause.

[0090] Verification 3: Information Protection The execution of the verification strategy does not involve the unauthorized use or disclosure of the personal information of the task recipients. During the handling process, the debt information of the task recipient must not be disclosed to any unrelated third parties; Notices regarding the disposal of such items must not be posted in public places; It is forbidden to use the social media information of the person being dealt with to exert pressure.

[0091] Inspection 4: Complaint Risk Pre-assessment Complaint risk scoring based on task object b_5 b5, Conduct a pre-assessment of complaint risks for the strategy plan: ; in, For the first The intensity coefficient of the staged handling action (preset value, the higher the intensity, the larger the coefficient).

[0092] Verification pass conditions: (Preset complaint risk tolerance threshold).

[0093] When the risk of complaints exceeds the threshold, the system suggests reducing the frequency or duration of high-intensity actions in the strategy plan.

[0094] It should be noted that the hierarchical relationship and processing mechanism of incremental verification are as follows: The above four layers of verification are performed progressively in the following order: 1. Legal procedure compliance verification (first layer): As a hard constraint, any plan that violates legal procedures will be directly rejected and will not proceed to the next verification layer; 2. Economic Feasibility Verification (Second Layer): Verify economic feasibility under the premise of legal compliance. Unreasonable solutions are marked as "needs adjustment" and returned to the large model for correction. 3. Time-series feasibility verification (third layer): Verify the feasibility of implementation under the premise of legal compliance and economic rationality. Adjust the time schedule or resource allocation for infeasible solutions. 4. Ethical Boundary Verification (Fourth Layer): Verify ethical compliance on the premise that the first three layers have passed. If the solution fails, reduce the intensity of the action or increase the protective measures.

[0095] The core principle of incremental verification is that each layer of verification is only executed after the previous layer has passed, thus avoiding wasting economically reasonable and time-feasible verification computational resources on legally non-compliant solutions.

[0096] When a candidate solution fails a certain validation layer, the system adds the specific reasons for the validation failure and suggested corrections as constraints to the prompt words, requiring the large model to be corrected and regenerated accordingly. The number of retries for correction shall not exceed a preset limit. Once the limit is exceeded, the system adopts a rule-based conservative strategy (selecting the most matching template from the preset standard handling template library).

[0097] S40. Deploy the strategy scheme that has passed the verification process to the task execution engine, and monitor the response signals of the task object in real time during the execution process. The response signals include performance behavior signals, communication response signals, negotiation willingness signals, confrontation behavior signals, and legal procedure progress signals.

[0098] Specifically, after the strategy is deployed to the execution engine, the system continuously monitors the following task response signals: Signals of fulfilling obligations This includes whether an obligation has been fulfilled (full / partial), the amount repaid, and the timing (whether it was within the promised timeframe).

[0099] Communication response signals This includes whether the caller answers the handling call, the call duration, the communication attitude (obtained by the handling personnel's markings or voice emotion analysis), and whether the caller proactively calls back.

[0100] Signals of willingness to negotiate This includes whether a plan for fulfilling obligations has been proposed, the reasonableness of the plan (whether the amount and timeframe are within acceptable limits), and whether an agreement has been signed.

[0101] Confrontational Behavior Signals This includes whether a complaint has been filed, whether a lawyer has been hired to defend oneself, whether negative information has been posted on social media, and whether assets have been transferred (discovered through asset monitoring).

[0102] Signals of progress in legal proceedings This includes whether the court accepted the case, whether the service of process was successful, whether the other party raised jurisdictional objections or counterclaims, the judgment result, and the progress of enforcement.

[0103] S50. Based on the response signal of the task object, dynamically adjust the execution path of the handling strategy.

[0104] In one embodiment, the execution path of the dynamic adjustment strategy includes: when the positive response score exceeds a preset positive response threshold, reducing the intensity of the current stage and skipping the subsequent high-intensity stage; when the negative response score exceeds a preset negative response threshold, increasing the intensity of the current stage and shortening the waiting time to accelerate entry into the next stage; and when signs of target transfer are detected, triggering the preservation procedure urgently.

[0105] Specifically, in this embodiment, the system evaluates the execution effect of the current strategy stage in real time based on the debtor's response signal and decides whether the strategy path needs to be adjusted. The triggering conditions include: Adjusting Trigger Condition 1: Positive Response Exceeds Expectations When a task recipient exhibits a more positive response than expected at the current stage (such as proactively proposing a full fulfillment of obligations during the pressure handling phase), the system triggers a strategy downgrade adjustment: Reduce the intensity of the current response by one level; Skip the subsequent high-intensity phase and proceed directly to the negotiation and settlement phase; Update the repayment willingness score in the behavioral profile of the task target.

[0106] The criteria for determining a positive response exceeding expectations are: ; in, This is the preset positive response threshold.

[0107] Adjusting trigger condition two: Negative response deteriorates When the task object exhibits obvious resistant or evasive behavior at the current stage, the system triggers a strategy upgrade and adjustment: The intensity of the current response will be increased by one level. Shorten the waiting time in the current stage and accelerate the transition to the next stage; If signs of asset transfer are detected, an emergency asset preservation procedure will be initiated (skipping the regular sequence of steps).

[0108] The criterion for determining a deterioration in the negative response is: ; in, This is the preset negative response threshold.

[0109] Adjustment trigger condition three: Phase timeout not met When the actual execution time of a certain stage exceeds the preset multiple of the expected time. If the success criteria are not met, the system triggers a phase transition: Execute the pre-defined failure handling actions for this stage. ; Record the actual execution results of this stage for use in the generation of subsequent empirical rules.

[0110] In one embodiment of this application, the method further includes: S60. When the performance resource capability of the task object undergoes a significant change, or the deviation between the execution effect of the disposal strategy and the expectation exceeds a preset threshold, or the behavior profile vector of the task object undergoes a categorical change, the disposal strategy scheme is regenerated.

[0111] Specifically, the system determines that the current strategy is no longer applicable and triggers the regeneration of the large model strategy when the following situations occur: Scenario 1: A significant change occurs in the performance capability of the task object (such as the discovery of new executable assets, or the existing assets being executed by other credit obligors first), resulting in a change in the asset feature vector A. or The change exceeds the preset threshold .

[0112] Scenario 2: When the strategy is implemented to a certain stage, the deviation between the actual recovery effect and the expected recovery rate exceeds a preset threshold. : ; Scenario 3: The behavioral profile of the task subject undergoes a categorical change (such as from "willing but lacking resources" to "having resources but lacking willingness", or from "cooperating and negotiating" to "actively resisting"), resulting in a switch in the dominant dimension of the behavioral profile vector B.

[0113] The method for determining categorical shifts is as follows: calculate the cosine similarity between the direction of change of the behavioral profile vector and the preset categorical shift direction vector. When the similarity exceeds a preset threshold... This was determined to be a categorical shift.

[0114] It should be noted that the solution in this application can handle the automatic generation and accumulation of empirical rules, as detailed below. The system automatically generates and incorporates prior experience rules into the prior experience rule base under the following circumstances: Scenario 1: The strategy phase yields significantly better results than expected. When the actual results at a certain stage (such as the amount recovered or the success rate of negotiation) are significantly better than expected (exceeding the preset threshold for generating empirical rules), When the system records the execution conditions and effects of this stage as a positive empirical rule: "When the asset characteristics meet the [condition range] and the profile of the task object meets the [condition range], the expected effect of using [the action in this stage] is the [actual effect range]".

[0115] Scenario 2: The strategy phase results are significantly worse than expected. When the actual effect at a certain stage is significantly worse than expected (below the preset threshold for generating empirical rules) When this occurs, the system records it as a negative rule of thumb: "When the asset characteristics meet the [condition range] and the profile of the task object meets the [condition range], it is not recommended to use [action at this stage], and the recommended alternative is [alternative action]."

[0116] Scenario 3: Verification of the effect of dynamic adjustment When the execution effect after dynamic adjustment of the strategy is better than the expected effect before adjustment, the system records the adjustment rule as an empirical rule: "When the response signal of the task object meets the [signal pattern], the strategy should be adjusted from the [original action] to the [adjusted action]".

[0117] In one embodiment of this application, the method further includes: S70, evaluating the effectiveness of the treatment strategy and optimizing the treatment strategy.

[0118] Specifically, in this embodiment, firstly, a quantitative assessment of the treatment effect is conducted. The system operates according to a preset evaluation cycle. The overall effectiveness of the strategy plan will be quantitatively evaluated. The evaluation indicator system is as follows: Comprehensive performance evaluation (Collection Effectiveness Score): ; in: To assess the average recovery rate (actual recovered amount / total amount of outstanding obligations) over the evaluation period. To assess the average disposal cost rate (disposal cost / recovery amount) during the evaluation period; To assess the average processing time within the evaluation period, The processing time for the preset target; The complaint rate (number of complaints / total number of cases handled) during the assessment period. The preset evaluation weight coefficients satisfy... .

[0119] In one embodiment, The value is 0.40. The value is 0.20. The value is 0.20. The value is 0.20.

[0120] Secondly, a stratified analysis of the quality of the strategy solution. The system performs tiered analysis of the effectiveness of strategy solutions based on different combinations of asset characteristics and task object profiles: Cases will be categorized by the total amount of outstanding obligations: Cases will be divided into small-amount categories (below a preset threshold) based on the total amount of outstanding obligations. ), medium amount (between and Between), large amounts (higher than) The system is divided into three layers, and the comprehensive effectiveness score of each layer is calculated.

[0121] Cases are categorized by task target type: Based on the dominant dimension of the task target's behavioral profile, cases are divided into four categories: "willing and capable", "willing but incapable", "unwilling but capable", and "unwilling and incapable". The comprehensive handling efficiency score for each category is calculated.

[0122] Stratification by Disposal Path: Cases are classified according to the actual disposal path type (negotiation path, litigation path, transfer path, and hybrid path), and the effectiveness indicators of each path are calculated separately.

[0123] When the comprehensive collection efficiency score of a certain layer falls below the preset quality lower limit for a number of consecutive preset periods, the system will trigger the optimization of the template generation strategy for that layer: adjust the prompt word constraints of the corresponding layer, update the experience rule base of the corresponding layer, or adjust the verification threshold of the corresponding layer.

[0124] Finally, continuous monitoring of the generation quality of large model strategies. The system collects the following indicators according to a preset monitoring cycle: Progressive validation pass rate: The percentage of candidate solutions generated by the large model that pass all four layers of validation on the first attempt; Rejection rates at each validation layer: The distribution of rejection rates at each validation layer (reflecting on which dimension the large model is most likely to generate unacceptable solutions). Strategy dynamic adjustment frequency: The number of times the strategy is dynamically adjusted per unit of time (the lower the frequency, the stronger the adaptability of the initial strategy). Policy regeneration frequency: The number of times a policy is regenerated per unit of time; Accumulation rate and utilization rate of empirical rules: The number of newly added empirical rules and the proportion of empirical rules referenced in subsequent policy generation.

[0125] When the pass rate of incremental verification is lower than the preset quality limit for a consecutive preset number of cycles. When the system indicates that the generation quality of the large model strategy needs optimization, the following optimization actions are triggered: Analyze the distribution of validation rejection rates at each layer to pinpoint the main weaknesses of the large model; Provide additional constraints and examples in the prompts to address weaknesses; We have compiled and organized the rules of experience accumulated recently, and selected high-confidence rules to be included in the prior knowledge area of ​​the prompt words.

[0126] This application provides a method for generating task handling strategies based on a specification guidance framework, the advantages of which are: This application constructs a specification guidance framework with structured constraints to engineer the generation behavior of large models, significantly improving the standardization and consistency of the generated control logic. Simultaneously, it employs multi-level engineering compliance and performance verification to progressively validate and screen candidate logic sets, intercepting defects such as logical contradictions, timing infeasibility, and resource unavailability at the source, ensuring the reliability and robustness of the deployed logic. Furthermore, based on real-time feedback data and environmental data, it dynamically and adaptively corrects the configurable adjustment thresholds of each control logic, and automatically triggers the regeneration of the large model when the cumulative offset exceeds a preset threshold, forming a closed-loop self-evolution mechanism. This effectively solves the technical problems of uncontrollable large model generation content and the difficulty of static rules adapting to dynamic changes, significantly enhancing the engineering deployment quality and continuous applicability of the control system in complex dynamic environments.

[0127] Please see Figure 2 This application discloses a task handling strategy generation system 200 based on a specification guidance framework, comprising: The data acquisition module 201 is used to acquire the multi-dimensional feature vector of the task to be processed and the behavioral profile vector of the task object; the multi-dimensional task feature vector includes: task scale and structural features, target value and executability features, task object performance resource capability features, legal statute of limitations and legal status features, historical response features and associated guarantee chain features; the behavioral profile vector of the task object includes cooperation willingness score, confrontation tendency score, negotiation flexibility score, legal sensitivity score and compliance risk score; The strategy generation module 202 is used to construct a reduced structure prompt word based on the multidimensional feature vector and the task object behavior profile vector, and input the prompt word into a preset disposal strategy generation model to generate a set of candidate disposal strategy schemes containing multiple candidate schemes; The strategy verification module 203 performs a progressive verification process on each of the candidate disposal strategy solutions in the set of candidate disposal strategy solutions to obtain disposal strategy solutions that pass the verification process. The progressive verification process includes a legal procedure compliance verification layer, a cost-effectiveness verification layer, a time sequence feasibility verification layer, and an ethical boundary verification layer, which are executed sequentially. Each layer of verification is only executed after the previous layer has passed. The strategy deployment module 204 is used to deploy the strategy scheme that has passed the verification process to the task execution engine, and to monitor the response signals of the task object in real time during the execution process. The response signals include performance behavior signals, communication response signals, negotiation willingness signals, confrontation behavior signals and legal procedure progress signals. The strategy adjustment module 205 dynamically adjusts the execution path of the handling strategy based on the response signal of the task object.

[0128] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of generating a task handling strategy based on a specification bootstrapping framework as described above.

[0129] In this embodiment, the computer-readable storage medium can be a non-volatile storage medium or a volatile storage medium. For example, the computer storage medium may include, but is not limited to, various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0130] In all examples shown and described herein, any specific values ​​should be interpreted as merely exemplary and not as limitations; therefore, other examples of exemplary embodiments may have different values.

[0131] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0132] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that, in alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0133] In addition, the functional modules or units in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0134] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a terminal device (which may be a smartphone, personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0135] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes 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.

Claims

1. A method for generating task handling strategies based on a specification-guided framework, characterized in that, include: Obtain the multidimensional feature vector of the task to be processed and the behavioral profile vector of the task object; The multidimensional task feature vector includes task scale and structural features, target value and executability features, task object's performance resource capabilities features, legal statute of limitations and legal status features, historical response features, and associated guarantee chain features; the task object's behavioral profile vector includes cooperation willingness score, confrontation tendency score, negotiation flexibility score, legal sensitivity score, and compliance risk score. Based on the multidimensional feature vector and the task object behavior profile vector, a reduced structure prompt word is constructed, and the prompt word is input into a preset handling strategy to generate a large model, generating a set of candidate handling strategy schemes containing multiple candidate schemes; A progressive verification process is performed on each of the candidate disposal strategy options in the set to obtain disposal strategy options that pass the verification process. The progressive verification process includes a legal procedure compliance verification layer, a cost-effectiveness verification layer, a time sequence feasibility verification layer, and an ethical boundary verification layer, which are executed sequentially. Each layer of verification is only executed after the previous layer has passed. The strategy scheme that has passed the verification process is deployed to the task execution engine, and the response signals of the task object are monitored in real time during the execution process. The response signals include performance behavior signals, communication response signals, negotiation willingness signals, confrontation behavior signals, and legal procedure progress signals. Based on the response signal of the task object, the execution path of the handling strategy is dynamically adjusted.

2. The method according to claim 1, characterized in that, The method further includes: When the performance capability of the task object undergoes a significant change, or the deviation between the execution effect of the disposal strategy and the expectation exceeds a preset threshold, or the behavior profile vector of the task object undergoes a categorical change, the disposal strategy scheme is regenerated.

3. The method according to claim 2, characterized in that, The method further includes: evaluating the effectiveness of the treatment strategy and optimizing the treatment strategy.

4. The method according to claim 1, characterized in that, The calculation method for the value and enforceability characteristics of the subject matter is as follows: it is obtained by multiplying the current market valuation of the subject matter, the liquidity discount factor, and the legal enforceability factor; the liquidity discount factor is a preset value according to the type of the subject matter; the legal enforceability factor is calculated by decreasing the number of legal obstacles existing on the subject matter according to a preset obstacle discount factor.

5. The method according to claim 1, characterized in that, The legal procedure compliance verification layer includes verification of legal preconditions, verification of the legality of preservation measures, verification of the timing of enforcement procedures, and verification of the compliance of disposal actions. The verification of legal preconditions includes verifying whether the statute of limitations is within its effective period, whether the object information is complete and deliverable, and whether the rights certificates are sufficient. The verification of the compliance of disposal actions includes verifying whether the time of the action is within the legally permitted period, whether the frequency of the action exceeds the legal limit, and whether the object of the action is limited to the legal scope. The cost-benefit rationality verification layer includes cost-benefit ratio verification, marginal cost rationality verification, comparison verification with alternative solutions, and simplified handling verification for small-scale tasks. The cost-benefit ratio verification method is to calculate the expected net benefit after deducting the expected direct processing cost and resource occupation opportunity cost from the expected value recovery amount, and verify that it is a positive value. The marginal cost rationality verification method is to calculate the ratio of the cost of each stage to the increase in the expected value recovery rate of that stage, and verify that it does not exceed the preset maximum marginal cost-benefit ratio threshold. The time-series feasibility verification layer includes stage dependency verification, time window feasibility verification, and resource availability verification. The stage dependency verification method is to map the stage sequence of the strategy solution to a preset action dependency graph to check whether there is a stage arrangement that violates the dependency. The time window feasibility verification method is to calculate key time nodes based on the expected duration of each stage and verify whether they are within the statutory time window. The ethical boundary verification layer includes verification for the protection of vulnerable groups, verification for the guarantee of basic right to life, verification for information protection, and pre-assessment of compliance risks. The compliance risk pre-assessment method is based on the sum of the weighted product of the compliance risk score of the task object and the intensity coefficient and execution time of each stage of the strategy plan, and verifies that it does not exceed the preset compliance risk tolerance threshold.

6. The method according to claim 1, characterized in that, The execution path of the dynamic adjustment strategy includes: when the positive response score exceeds the preset positive response threshold, reducing the intensity of the current stage and skipping the subsequent high-intensity stage; when the negative response score exceeds the preset negative response threshold, increasing the intensity of the current stage and shortening the waiting time to accelerate the entry into the next stage; and when signs of object transfer are detected, triggering the preservation procedure urgently.

7. The method according to claim 2, characterized in that, The method for determining whether the behavior profile of the task object has undergone a category change is as follows: calculate the cosine similarity between the direction of change of the current behavior profile vector and the preset category change direction vector. When the similarity exceeds the preset threshold, it is determined to be a category change. The category change includes changing from having the will but not the resources to having the resources but not the will, and changing from cooperating and negotiating to actively confronting.

8. The method according to claim 3, characterized in that, The effectiveness of the proposed treatment strategy is evaluated, and the treatment strategy is optimized, including: When the actual effect of a certain strategy stage is significantly better than expected, the execution conditions and effects of that stage are recorded as positive empirical rules; when the actual effect of a certain strategy stage is significantly worse than expected, it is recorded as a negative empirical rule and an alternative solution is suggested; when the execution effect after dynamic adjustment of the strategy is better than the expected effect before adjustment, the adjustment rule is recorded as an empirical rule; the empirical rules are incorporated into the reduced structure prompts of subsequent strategy generation as prior knowledge.

9. A task handling strategy generation system based on a specification-guided framework, characterized in that, include: The data acquisition module is used to acquire the multi-dimensional feature vector of the task to be processed and the behavioral profile vector of the task object; The multidimensional task feature vector includes: task scale and structural features, target value and executability features, task object's performance resource and capability features, legal statute of limitations and legal status features, historical response features, and associated guarantee chain features; the task object's behavioral profile vector includes cooperation willingness score, confrontation tendency score, negotiation flexibility score, legal sensitivity score, and compliance risk score. The strategy generation module is used to construct a reduced structure prompt word based on the multidimensional feature vector and the task object behavior profile vector, and input the prompt word into a preset disposal strategy generation model to generate a set of candidate disposal strategy schemes containing multiple candidate schemes; The strategy verification module performs a progressive verification process on each of the candidate disposal strategy solutions in the set of candidate disposal strategy solutions to obtain disposal strategy solutions that pass the verification process. The progressive verification process includes a legal procedure compliance verification layer, a cost-effectiveness verification layer, a time sequence feasibility verification layer, and an ethical boundary verification layer, which are executed sequentially. Each layer of verification is only executed after the previous layer has passed. The strategy deployment module is used to deploy the strategy scheme that has passed the verification process to the task execution engine, and to monitor the response signals of the task object in real time during the execution process. The response signals include performance behavior signals, communication response signals, negotiation willingness signals, confrontation behavior signals, and legal procedure progress signals. The strategy adjustment module dynamically adjusts the execution path of the handling strategy based on the response signal of the task object.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the task disposal strategy generation method based on the specification bootstrapping framework as described in any one of claims 1-8.

Images