Flexible workforce risk event impact surface assessment system and method

Abstract

This application discloses a system and method for assessing the impact of flexible employment risk events. The system includes an impact-surface agent and multiple basic agents. The system performs the following processes: receiving descriptive information about a flexible employment risk event input by a user; semantically parsing the descriptive information to identify user intent and extract core parameters; and performing the following processes based on the impact-surface agent: based on user intent and core parameters, invoking at least some basic agents to process the core parameters to obtain risk assessment data; and quantitatively analyzing the impact of the flexible employment risk event on at least two dimensions—legal, tax, financial, customer cooperation, and platform reputation—based on the core parameters and risk assessment data, obtaining impact assessment results for at least two dimensions, and generating a risk impact assessment report. This application enables automatic assessment of the impact of flexible employment risk events across multiple dimensions.

Description

Technical Field

[0001] This application relates to the fields of flexible employment risk management and artificial intelligence technology, and in particular to a system and method for assessing the impact of flexible employment risk events. Background Technology

[0002] As flexible employment models become increasingly prevalent across various industries, the complexity of their business scenarios, the diversity of participating entities, and the sophistication of policy supervision are constantly increasing, leading to a rise in the probability of various risk events. Flexible employment risk events encompass a variety of types, including contract disputes, tax violations, abnormal fund settlements, and customer breaches of contract. These events can not only directly result in legal proceedings and tax penalties, but may also have a cascading impact on the platform's financial health, customer relationships, and market reputation.

[0003] Currently, the impact assessment of risk events by flexible employment platforms mainly relies on manual cross-departmental collaboration. For example, the legal department assesses legal risks, the finance department calculates the amount of loss, and the operations department judges the impact on customers, ultimately integrating these findings to form an assessment conclusion. However, this traditional assessment model has the following problems: First, the cross-departmental collaboration process is cumbersome, communication costs are high, and the assessment cycle is long, making it difficult to meet the needs of rapid response and handling of risk events, and potentially leading to an expansion of the scope of losses due to delayed decision-making. Second, the assessment standards of different departments are not uniform, relying on the professional experience of personnel, which can easily lead to assessment bias and inaccurate quantification of risk impact. Third, the assessment dimensions are scattered, lacking a systematic integrated analysis of the impact of multiple dimensions such as legal, tax, financial, customer cooperation, and platform reputation, making it difficult to comprehensively present the overall impact of risk events. Summary of the Invention

[0004] In view of this, embodiments of this application provide a flexible employment risk event impact assessment system and method to solve at least one of the above-mentioned technical problems.

[0005] Firstly, embodiments of this application provide a flexible employment risk event impact assessment system. The system includes an impact-surface intelligent agent and multiple basic intelligent agents for specific risk assessments. After startup, the system performs the following processes: receiving descriptive information about a flexible employment risk event input by a user; performing semantic parsing on the descriptive information to identify the user's intent and extract core parameters, including the risk scenario, involved parties, risk type, scope of impact, and core demands; and performing the following processes based on the impact-surface intelligent agent: based on the user's intent and core parameters, calling at least some of the corresponding basic intelligent agents to process the core parameters, obtaining risk assessment data output by at least some of the basic intelligent agents; quantitatively analyzing the impact of the flexible employment risk event on at least two dimensions—legal, tax, financial, customer cooperation, and platform reputation—based on the core parameters and risk assessment data, outputting impact assessment results for at least two dimensions; and generating a risk impact assessment report based on the impact assessment results for at least two dimensions.

[0006] According to some embodiments of this application, optionally, the multiple basic intelligent agents include at least two of a legal intelligent agent, a policy expert intelligent agent, and a financial advisor intelligent agent; the legal intelligent agent is used to assess the legal risks of flexible employment risk events based on core parameters, a preset historical case library, a preset legal clause library, and an evidence list corresponding to the core parameters, to obtain the probability of winning the case, the legal clause basis, and the potential compensation amount; the policy expert intelligent agent is used to assess the tax risks of flexible employment risk events based on core parameters and a preset policy library, to obtain the tax risk assessment result and the potential penalty type; the financial advisor intelligent agent is used to assess the profit of the flexible employment project corresponding to the flexible employment risk event based on core parameters, to obtain the profit assessment result.

[0007] According to some embodiments of this application, optionally, the legal intelligence agent is specifically used to perform the following processes: querying similar historical cases that match the core parameters from the historical case database, and determining the initial probability of winning the flexible employment risk event based on the judgment results of similar historical cases; extracting target legal clauses that match the core parameters from the legal clause database, and calculating the matching degree score between the core parameters and the target legal clauses; calculating the evidence integrity score of the flexible employment risk event based on the evidence list corresponding to the core parameters; and performing a weighted calculation on the initial probability of winning, the matching degree score, and the evidence integrity score to obtain the probability of winning the flexible employment risk event.

[0008] According to some embodiments of this application, optionally, at least some of the basic intelligent agents include a legal intelligent agent; based on core parameters and risk assessment data, a quantitative analysis is performed on the impact of flexible employment risk events in at least two dimensions: legal, tax, financial, customer cooperation, and platform reputation, including: when the probability of winning is less than a preset probability threshold, calculating the cost of losing the case based on the potential compensation amount, statutory litigation cost standards, and industry attorney fee range; wherein, the impact assessment results of the legal dimension include the probability of winning, the legal basis, and the cost of losing the case when the probability of winning is less than the preset probability threshold.

[0009] According to some embodiments of this application, optionally, the policy expert agent is specifically used to perform the following processing: based on the risk type and scope, query the policy database for target-matching policies corresponding to the flexible employment risk event; based on the trained policy interpretation model, interpret the target-matching policies, and combine the scope, event progress, and policy interpretation results in the core parameters to determine the compliance of the flexible employment risk event, and determine whether there are tax violations and the corresponding potential penalty types; if the determination result is that there are tax violations, output the tax risk assessment result and potential penalty types for the existence of tax penalty risks.

[0010] According to some embodiments of this application, optionally, at least some of the basic intelligent agents include policy expert intelligent agents, and the scope of involvement includes the amount involved; based on core parameters and risk assessment data, a quantitative analysis is performed on the impact of flexible employment risk events on at least two dimensions: legal, tax, financial, customer cooperation, and platform goodwill, including: if the tax risk assessment result indicates the existence of tax penalty risk, then the amount of tax penalty is calculated based on the preset penalty ratio corresponding to the amount involved and the potential penalty type; the amount of supplementary tax and late payment fees are calculated based on the supplementary payment rules corresponding to the amount involved and the potential penalty type; based on the amount of tax penalty, supplementary tax, late payment fees, and preset tax rating impact standards, the impact of flexible employment risk events on the platform's tax rating is assessed; wherein, the impact assessment result of the tax dimension includes the tax risk assessment result, the potential penalty type, and the amount of tax penalty, supplementary tax, late payment fees, and the impact on the platform's tax rating when there is a tax penalty risk.

[0011] According to some embodiments of this application, optionally, the financial advisory agent is specifically used to perform the following processes: querying the budget cost, revenue ledger, project operating cash flow data, and project historical operating data of the flexible staffing project based on core parameters; predicting the expected profit value of the flexible staffing project under the influence of no risk events based on the budget cost, revenue ledger, and project historical operating data; and outputting the profit assessment result, which includes the expected profit value and project operating cash flow data.

[0012] According to some embodiments of this application, optionally, at least some of the basic intelligent agents include a financial advisor intelligent agent; based on core parameters and risk assessment data, a quantitative analysis is performed on the impact of flexible employment risk events in at least two dimensions: legal, tax, financial, customer cooperation, and platform goodwill, including: summarizing the risk expenditure amounts in at least two dimensions; calculating the capital occupation cost corresponding to the risk expenditure amounts in at least two dimensions based on project operating cash flow data and similar event loss benchmarks; and deducting the risk expenditure amounts in at least two dimensions and their corresponding capital occupation costs based on the expected profit value to obtain the final profit value of the flexible employment project; wherein, the impact assessment results in the financial dimension include the risk expenditure amounts, capital occupation costs, expected profit value, and final profit value in at least two dimensions.

[0013] Optionally, according to some embodiments of this application, the impact of flexible employment risk events on at least two dimensions—legal, tax, financial, customer cooperation, and platform reputation—is quantitatively analyzed based on core parameters and risk assessment data. This includes: obtaining customer cooperation attribute information corresponding to the involved entity, including customer cooperation level, customer cooperation duration, historical cooperation amount, and historical performance record; assessing the degree of impact of flexible employment risk events on the cooperation of the involved entity based on risk type, scope, and customer cooperation attribute information; and calculating the probability of churn risk for the involved entity based on preset customer cooperation level weights, risk type impact coefficients, and historical performance record scores. The impact assessment results for the customer cooperation dimension include the degree of cooperation impact and the probability of churn risk.

[0014] Optionally, according to some embodiments of this application, the impact of flexible employment risk events on at least two dimensions—legal, tax, financial, customer cooperation, and platform goodwill—is quantitatively analyzed based on core parameters and risk assessment data. This includes: assessing the propagation risk level of the flexible employment risk event based on the risk type, event progress, and scope of involvement in the core parameters; and predicting the amount of goodwill loss based on the propagation risk level, scope of involvement, and a preset goodwill loss benchmark coefficient. The impact assessment results for the platform goodwill dimension include the propagation risk level and the amount of goodwill loss.

[0015] According to some embodiments of this application, optionally, the impact-based intelligent agent further performs the following processing: scoring the impact assessment results of at least two dimensions based on preset quantitative scoring rules of at least two dimensions to obtain risk impact scores of at least two dimensions; performing weighted calculation on the risk scores of at least two dimensions to obtain a comprehensive risk impact score of the flexible employment risk event; determining the comprehensive risk impact level of the risk event based on the comprehensive risk impact score; and integrating the risk impact scores of at least two dimensions, the comprehensive risk impact score, and the comprehensive risk impact level into the risk impact assessment report.

[0016] According to some embodiments of this application, optionally, the impact-based intelligent agent also performs the following processing: for each of the at least two dimensions, based on the impact assessment results of that dimension, it calls the pre-set remedial strategy rule base of that dimension to generate candidate remedial measures; and integrates the candidate remedial measures of the at least two dimensions into the risk impact assessment report.

[0017] Secondly, embodiments of this application provide a method for assessing the impact of flexible employment risk events. This method is implemented based on the flexible employment risk event impact assessment system of any embodiment of the first aspect. The method includes: receiving descriptive information of a flexible employment risk event input by a user; performing semantic parsing on the descriptive information to identify the user's intent and extract core parameters, including the risk scenario, involved parties, risk type, scope of involvement, and core demands; performing the following processing based on the impact intelligence agent: based on the user's intent and core parameters, calling at least some of the corresponding basic intelligence agents to process the core parameters, obtaining risk assessment data output by at least some of the basic intelligence agents; quantitatively analyzing the impact of the flexible employment risk event on at least two dimensions in terms of law, taxation, finance, customer cooperation, and platform reputation based on the core parameters and risk assessment data, and outputting impact assessment results for at least two dimensions; and generating a risk impact assessment report based on the impact assessment results for at least two dimensions.

[0018] The flexible employment risk event impact assessment system and method provided in this application introduces an impact-surface agent to achieve a global analysis of the impact of flexible employment across multiple core dimensions, including legal, tax, financial, customer cooperation, and platform reputation. By quantitatively presenting the overall scope and severity of risk events, it helps platforms move beyond single-dimensional accounting and fragmented multi-dimensional understanding, accurately grasping the overall risk and providing a global decision-making basis for risk isolation and resource allocation. Furthermore, through a collaborative architecture of multiple basic agents conducting specialized assessments and the global integration of the impact-surface agent, each basic agent can be responsible for its respective specialized risk assessment area and output specialized risk assessment data, ensuring the professionalism of single-dimensional assessments. The impact-surface agent can uniformly perform correlation analysis and integrated calculations on multi-dimensional data, reducing the communication costs and subjective biases associated with manual cross-departmental collaboration. This collaborative model ensures both the professional depth of risk assessment and the efficiency of impact-surface analysis, shortens the risk assessment cycle, and solves the problem of delayed response in traditional models. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings of the embodiments of this application will be briefly described below.

[0020] Figure 1 This is a structural block diagram of a flexible employment risk event impact assessment system provided in an embodiment of this application.

[0021] Figure 2 This is a schematic diagram of the execution flow of the flexible employment risk event impact assessment system provided in the embodiments of this application.

[0022] Figure 3 This is a schematic diagram of the execution flow of a legal intelligent agent in the flexible employment risk event impact assessment system provided in the embodiments of this application.

[0023] Figure 4 This is a schematic diagram of the execution flow of a policy expert agent in the flexible employment risk event impact assessment system provided in the embodiments of this application.

[0024] Figure 5 This is a schematic diagram of the execution flow of a financial advisor agent in the flexible employment risk event impact assessment system provided in the embodiments of this application.

[0025] Figure 6 This is a schematic diagram of the execution flow of an impact surface agent in the flexible employment risk event impact surface assessment system provided in this application embodiment.

[0026] Figure 7 This is a schematic diagram of another execution flow of the impact surface agent in the flexible employment risk event impact surface assessment system provided in the embodiments of this application. Detailed Implementation

[0027] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0028] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0029] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0030] Various modifications and variations can be made to this application without departing from its spirit or scope, which will be apparent to those skilled in the art. Therefore, this application is intended to cover modifications and variations falling within the scope of the corresponding claims (the claimed technical solutions) and their equivalents. It should be noted that the implementation methods provided in the embodiments of this application can be combined with each other without contradiction.

[0031] Before describing the technical solutions provided in the embodiments of this application, in order to facilitate understanding of the embodiments of this application, this application first specifically explains the problems existing in the prior art: As flexible employment models become increasingly prevalent across various industries, the complexity of their business scenarios, the diversity of participating entities, and the sophistication of policy supervision are constantly increasing, leading to a rise in the probability of various risk events. Flexible employment risk events encompass a variety of types, including contract disputes, tax violations, abnormal fund settlements, and customer breaches of contract. These events can not only directly result in legal proceedings and tax penalties, but may also have a cascading impact on the platform's financial health, customer relationships, and market reputation.

[0032] Currently, the impact assessment of risk events by flexible employment platforms mainly relies on manual cross-departmental collaboration. For example, the legal department assesses legal risks, the finance department calculates the amount of loss, and the operations department judges the impact on customers, ultimately integrating these findings to form an assessment conclusion. However, this traditional assessment model has the following problems: First, the cross-departmental collaboration process is cumbersome, communication costs are high, and the assessment cycle is long, making it difficult to meet the needs of rapid response and handling of risk events, and potentially leading to an expansion of the scope of losses due to delayed decision-making. Second, the assessment standards of different departments are not uniform, relying on the professional experience of personnel, which can easily lead to assessment bias and inaccurate quantification of risk impact. Third, the assessment dimensions are scattered, lacking a systematic integrated analysis of the impact of multiple dimensions such as legal, tax, financial, customer cooperation, and platform reputation, making it difficult to comprehensively present the overall impact of risk events.

[0033] To address at least one of the aforementioned technical problems, this application provides a system and method for assessing the impact of flexible employment risk events.

[0034] The following section will first introduce the flexible employment risk event impact assessment system provided in this application.

[0035] Figure 1 This is a structural block diagram of a flexible employment risk event impact assessment system provided in an embodiment of this application. Figure 1 As shown, the flexible employment risk event impact assessment system 10 provided in this application embodiment may include an impact-surface intelligent agent 11 and multiple basic intelligent agents 12 for specific risk assessments. Each basic intelligent agent 12 is configured with a workflow for handling specific assessment tasks of flexible employment risk events, and processes specific assessment tasks in the corresponding field based on models, algorithms, and / or knowledge bases fine-tuned for flexible employment scenarios. In some embodiments, optionally, the multiple basic intelligent agents 12 may include at least two of legal intelligent agents 121, policy expert intelligent agents 122, and financial advisor intelligent agents 123. Of course, the multiple basic intelligent agents 12 may also include other basic intelligent agents for specific risk assessments, which is not limited in this application.

[0036] Figure 2 This is a schematic diagram of the execution flow of the flexible employment risk event impact assessment system provided in an embodiment of this application. Figure 2 As shown, after the flexible employment risk event impact assessment system 10 is started, it is used to perform the following steps S201 to S205.

[0037] S201: Receive description information of flexible employment risk events input by the user.

[0038] Users can input descriptive information about flexible employment risk events in natural language through the dialog-based window of the Flexible Employment Risk Event Impact Assessment System 10. This description can include the risk scenario, involved parties, risk type, event details, scope (such as the amount and / or quantity involved), and core demands. For example, a user might input the following description of a flexible employment risk event: Company A, in a campus part-time job project in cooperation with the platform, has failed to pay 300,000 yuan in service fees as stipulated in the contract and refuses to provide compliant invoices. The user requests an assessment of the relevant impact of this event and corresponding countermeasures.

[0039] S202: Perform semantic parsing on the description information, identify user intent and extract core parameters, including risk type, involved parties, scope of involvement and core demands.

[0040] After receiving the description information of a flexible employment risk event, the flexible employment risk event impact assessment system 10 can invoke a fine-tuned semantic matching model to perform semantic parsing on the user-input description information of the flexible employment risk event, identify the user's intent, and extract core parameters from the description information. These core parameters may include the risk scenario (e.g., campus part-time projects, community delivery projects), the parties involved (e.g., partner companies, platforms), the type of risk (e.g., contract breach, tax violations, fund arrears), the scope of involvement (e.g., the amount and / or quantity involved), and the core demands (e.g., loss assessment, evaluation of related impacts, provision of response solutions).

[0041] Based on the influence surface agent, the following steps S203 to S205 are performed.

[0042] S203: Based on user intent and core parameters, call at least some of the corresponding basic intelligent agents to process the core parameters and obtain risk assessment data output by at least some of the basic intelligent agents.

[0043] The impact agent can invoke at least some of the corresponding basic agents to perform specific risk assessment tasks based on user intent and core parameters, and summarize the risk assessment data output by at least some of the basic agents.

[0044] For example, if a user's intent is to assess the legal and tax implications of flexible employment risk events, the impact surface agent can invoke legal agent 121 and policy expert agent 122 to perform specific risk assessment tasks for the legal and tax dimensions, respectively, to obtain risk assessment data for those dimensions. As another example, if a user's intent is to assess at least the legal, tax, and financial implications of flexible employment risk events, the impact surface agent can invoke legal agent 121, policy expert agent 122, and financial advisor agent 123 to perform specific risk assessment tasks for the legal, tax, and financial dimensions, respectively, to obtain risk assessment data for those dimensions.

[0045] Legal Agent 121 can be used to assess the legal risks of flexible employment risk events based on core parameters, a preset historical case library, a preset legal clause library, and an evidence list corresponding to the core parameters, and obtain the probability of winning the case, the legal clause basis, and the potential compensation amount.

[0046] The policy expert agent 122 can be used to assess the tax risks of flexible employment risk events based on core parameters and a preset policy library, and obtain tax risk assessment results and potential penalty types.

[0047] The financial advisor AI agent 123 can be used to evaluate the profits of flexible employment projects corresponding to flexible employment risk events based on core parameters, and obtain profit evaluation results.

[0048] Thus, through the specialized division of labor among multiple basic intelligent agents such as legal agent 121, policy expert agent 122, and financial advisor agent 123, in-depth analysis across various assessment dimensions, including legal, tax, and financial aspects, can be achieved, effectively avoiding the knowledge blind spots and low accuracy issues of assessments conducted by single agents. Furthermore, multiple basic intelligent agents can operate in parallel to shorten the assessment cycle, addressing the core needs of rapid response and accurate decision-making in the flexible employment sector regarding risk events, and reducing secondary losses caused by assessment delays or biases.

[0049] S204: Based on core parameters and risk assessment data, conduct a quantitative analysis of the impact of flexible employment risk events on at least two dimensions: legal, tax, financial, customer cooperation, and platform reputation, and output impact assessment results for at least two dimensions.

[0050] The Impact Surface Agent 11, based on the risk assessment data and core parameters output by at least some of the basic agents, can quantitatively analyze the impact of flexible employment risk events on at least two dimensions: legal, tax, financial, customer cooperation, and platform reputation. In other words, it quantifies the impact on at least two dimensions to obtain impact assessment results for at least two dimensions. For example, the impact on the legal dimension can be quantified as the probability of winning a case, the amount of potential compensation or recovery, and the estimated cost of litigation. The impact on the tax dimension can be quantified as the amount of tax penalties, back taxes, late payment fees, and the degree of impact on tax ratings. The impact on the financial dimension can be quantified as the amount of project profit fluctuations, the cost of capital occupation, and the amount of net income or net loss. The impact on the customer cooperation dimension can be quantified as the probability of customer churn, the amount of potential revenue loss, and the cost of repairing cooperative relationships. The impact on the platform reputation dimension can be quantified as the estimated amount of reputational loss and the scope of negative publicity, etc., though this application does not limit the specific impact on these dimensions.

[0051] S205: Generate a risk impact assessment report based on the impact assessment results of at least two dimensions.

[0052] The impact assessment agent integrates the impact assessment results from at least two dimensions according to a preset report template to generate a risk impact assessment report. In some embodiments, the risk impact assessment report may also include a summary of core parameters, response plans, and assessment basis. The summary of core parameters may include key information such as the risk scenario, involved parties, and scope of impact, helping users quickly grasp the background of the event. The assessment basis may include the raw data used in the assessment, legal or policy basis, and detailed calculations.

[0053] The flexible employment risk event impact assessment system provided in this application introduces impact-surface intelligence agents to achieve a global analysis of the impact of flexible employment across multiple core dimensions, including legal, tax, financial, customer cooperation, and platform reputation. By quantifying the overall scope and severity of risk events, it helps platforms move beyond single-dimensional accounting and fragmented multi-dimensional understanding, accurately grasping the overall risk and providing a global decision-making basis for risk isolation and resource allocation. Furthermore, through a collaborative architecture of multiple basic intelligence agents conducting specialized assessments and a globally integrated impact-surface intelligence agent, each basic intelligence agent can be responsible for its respective specialized risk assessment area and output specialized risk assessment data, ensuring the professionalism of single-dimensional assessments. The impact-surface intelligence agent can uniformly perform correlation analysis and integrated calculations on multi-dimensional data, reducing the communication costs and subjective biases associated with manual cross-departmental collaboration. This division of labor and collaboration model ensures both the professional depth of risk assessment and the efficiency of impact-surface analysis, shortens the risk assessment cycle, and solves the problem of delayed response in traditional models.

[0054] To facilitate understanding, the following examples illustrate the impact assessment system for flexible employment risk events.

[0055] Figure 3 This is a schematic diagram of the execution flow of a legal intelligent agent in the flexible employment risk event impact assessment system provided in this application embodiment. Figure 3 As shown, according to some embodiments of this application, optionally, the legal intelligent agent can be used to perform the following steps S301 to S304.

[0056] S301: Search the historical case database for similar historical cases that match the core parameters, and determine the initial probability of winning the flexible employment risk event based on the judgment results of similar historical cases.

[0057] The historical case database stores historical legal cases related to the flexible employment sector. Each historical legal case may include information such as the risk scenario, the parties involved, the type of risk, the scope of the risk, the evidence, and the judgment.

[0058] The legal intelligence agent can retrieve similar historical legal cases from a historical case database based on core parameters such as risk scenarios (e.g., campus part-time projects, community delivery projects), attributes of the involved parties (e.g., company size, cooperation model), risk types (e.g., contract breach, tax disputes), and scope (e.g., amount involved, number of people involved). The agent then calculates the percentage of successful cases among the retrieved similar historical legal cases, using this percentage as the initial probability of success for flexible employment risk events. For example, if three highly similar historical legal cases are retrieved, with two losses and one win, the initial probability of success = (number of successful cases / total number of cases) × 100% = 1 / 3 × 100% ≈ 33.33%; if five highly similar cases are retrieved, with four wins and one loss, the initial probability of success = 4 / 5 × 100% = 80%.

[0059] S302: Extract target legal clauses that match the core parameters from the legal clause library and calculate the matching score between the core parameters and the target legal clauses.

[0060] The legal provisions database covers laws, administrative regulations, and judicial interpretations related to the flexible employment field. This content is categorized and indexed according to dimensions such as risk type and legal relationship. The legal intelligence agent can retrieve relevant target legal provisions from the database based on information such as the risk scenario corresponding to the core parameters, the attributes of the involved parties, the risk type, and the scope of application. Through semantic matching algorithms, the database analyzes the degree of matching between the facts described by the core parameters and the constituent elements and scope of application of the target legal provisions, calculating a matching score. A higher matching score indicates a closer alignment between the factual basis and legal provisions of the flexible employment risk event, and more sufficient legal support.

[0061] S303: Calculate the evidence completeness score for flexible employment risk events based on the evidence list corresponding to the core parameters.

[0062] For example, the evidence list corresponding to the core parameters can support various types of legal evidence for the facts of a risk event, such as written contracts, electronic communication records, payment vouchers, task acceptance documents, and qualification certificates. The legal intelligence agent can determine all the essential elements of the core facts in dispute based on the core parameters. For example, a contract breach dispute needs to cover elements such as contract formation, one's own performance, the other party's breach, and the existence of loss. Then, it sequentially verifies whether the evidence list corresponding to the core parameters contains supporting evidence for each element. In some specific examples, for each element, a quantitative score can be assigned based on whether there is direct evidence (e.g., 10 points), indirect evidence (e.g., 6-8 points), insufficient evidence (e.g., 3-5 points), or no relevant evidence (e.g., 0 points). After summing the scores of all elements, a conversion expression is used to obtain the evidence completeness score. For example, the preset conversion expression can be: Evidence Completeness Score = Sum of Scores for All Elements / Total Number of Elements × 10. A higher evidence completeness score indicates a more solid chain of evidence and stronger probative value.

[0063] S304: The initial probability of winning, the matching score, and the evidence integrity score are weighted and calculated to obtain the probability of winning the flexible employment risk event.

[0064] In S304, the legal intelligence agent can normalize the matching score and the evidence integrity score. Then, based on preset weight coefficients, it performs a weighted calculation on the initial probability of winning, the normalized matching score, and the normalized evidence integrity score to obtain the probability of winning the flexible employment risk event.

[0065] For example, in some examples, the weighting coefficient for the initial probability of winning is 0.3, the weighting coefficient for the matching score is 0.4, and the weighting coefficient for the evidence completeness score is 0.3. These weighting coefficients are merely examples and do not constitute a limitation of this application. Taking an initial probability of winning of 33.33%, a matching score of 90, and an evidence completeness score of 85 as an example, the legal intelligence agent can first normalize the matching score and evidence completeness to probability coefficients ranging from 0% to 100%. For example, the normalized probability coefficient for a matching score of 90 is 90%, and the normalized probability coefficient for an evidence completeness score of 85 is 85%. Based on the preset weighting coefficients, the initial probability of winning (e.g., 33.33%), the normalized matching score (e.g., probability coefficient 90%), and the normalized evidence completeness score (e.g., probability coefficient 85%) are weighted and calculated to obtain the final probability of winning = 33.33% × 0.3 + 90% × 0.4 + 85% × 0.3 ≈ 69.5%.

[0066] Thus, by weighting the initial probability of victory, the matching score, and the evidence completeness score, the probability of winning a case in a flexible employment risk event is obtained. This approach draws on judicial experience from similar historical cases while also fully considering the legal suitability and the solidity of evidence in this specific case. It avoids the one-sidedness of judging from a single dimension, such as underestimating the probability of winning simply because similar cases have failed. Compared to traditional subjective manual assessments, the evaluation results obtained through the above steps are more objective, helping the platform accurately determine the probability of victory and thus rationally choose response strategies such as litigation or negotiation.

[0067] According to some embodiments of this application, optionally, the legal intelligence agent can be used to comprehensively calculate the potential compensation amount based on information such as the amount involved, risk type, and breach of contract in the core parameters, combined with the relevant provisions on compensation liability and liquidated damages calculation in the matching target legal clauses, as well as the compensation judgment standards of similar historical cases.

[0068] For example, if the core parameter is that the partner in a flexible employment project fails to pay the service fee as stipulated in the contract, the amount involved is 500,000 yuan, the risk type is contract breach, and the breach is due payment for more than 90 days, the legal intelligence agent first matches the relevant provisions in the target legal clauses regarding the calculation of liquidated damages for overdue payments in sales contracts. Then, it retrieves similar historical cases where court judgments on payments overdue for 90 days typically support calculating liquidated damages at 1.5 times the Loan Prime Rate (LPR). Combining the above legal basis and case judgment standards, the potential compensation amount corresponding to the liquidated damages for overdue payments and the unpaid principal is calculated, resulting in a potential compensation amount of 523,000 yuan for the flexible employment risk event.

[0069] Optionally, according to some embodiments of this application, S204: Quantitatively analyzing the impact of flexible employment risk events on at least two dimensions—legal, tax, financial, customer cooperation, and platform goodwill—based on core parameters and risk assessment data may include the following steps: When the probability of winning the case is less than a preset probability threshold, the cost of losing the case is calculated based on the potential amount of compensation, the statutory litigation fee standard, and the industry range of attorney fees.

[0070] Specifically, the impact intelligence agent can receive risk assessment data in the legal dimension output by the legal intelligence agent, such as the probability of winning a case in flexible employment risk events, the legal basis for the claims, and the potential compensation amount. When the probability of winning is less than a preset probability threshold, the impact intelligence agent can use the potential compensation amount as the calculation base, determine the litigation costs with reference to the statutory litigation cost standards, determine the attorney's fees with reference to the attorney's fee range and according to the proportion of the amount involved, and add up the potential compensation amount, litigation costs, and attorney's fees to obtain the cost of losing the case.

[0071] Accordingly, the legal impact assessment results include the probability of winning, the legal basis, and the cost of losing when the probability of winning is less than a preset probability threshold.

[0072] In this way, the legal risks of losing a case in flexible employment can be transformed into specific and calculable cost figures. This allows the platform to intuitively grasp the actual economic impact of losing a case, quickly determine whether to take measures such as negotiation and settlement to mitigate losses, reduce additional costs caused by blind litigation, and effectively save on the legal risk handling costs of flexible employment business.

[0073] Figure 4 This is a schematic diagram illustrating the execution flow of a policy expert agent in the flexible employment risk event impact assessment system provided in this application embodiment. Figure 4 As shown, according to some embodiments of this application, optionally, the policy expert agent may be used to perform the following steps S401 to S403.

[0074] S401: Based on the risk type and scope, search the policy database for target-matching policies corresponding to flexible employment risk events.

[0075] The policy database can store tax policy texts related to flexible employment, such as the Tax Collection and Administration Law, special tax policies for flexible employment, and detailed tax regulations for various regions. The policy expert agent can retrieve policy provisions from the policy database that match the core parameters of a flexible employment risk event based on information such as the risk type and scope of the risk. This information can then be used as the target matching policy. For example, if the risk type is the underpayment of individual income tax in flexible employment, and the scope involves the failure to file tax returns for the labor remuneration income of freelancers in region A, the policy expert agent can retrieve clauses on the tax return of labor remuneration, special notices on the collection and administration of taxes for flexible employment, and detailed regulations from the tax authorities in region A regarding individual income tax filing for flexible employment. These policy provisions can then be integrated as the target matching policy for this flexible employment risk event.

[0076] S402: Based on a well-trained policy interpretation model, interpret the target-matching policies and combine the scope, event progress, and policy interpretation results in the core parameters to determine the compliance of flexible employment risk events, and determine whether there are any tax violations and the corresponding potential penalty types.

[0077] The policy interpretation model can be a finely tuned version of tax policy texts, policy interpretation cases, and regulatory practice cases in the field of flexible employment. And / or, the policy interpretation model can be configured with a knowledge base of tax policy texts, policy interpretation cases, and regulatory practice cases in the field of flexible employment, enabling it to interpret the scope of application, definition of violations, and penalty standards of policy provisions.

[0078] In S402, the policy expert agent can invoke a large-scale policy interpretation model to interpret policies that match the target audience, such as identifying the applicable subjects, the standards for defining violations, the elements for determining violations, and the penalty clauses. Taking the underpayment of individual income tax as an example, the applicable subjects can include flexible employment platforms or employing companies. The standards for defining violations can include, for example, failing to complete the individual income tax declaration and payment for freelancers whose labor remuneration exceeds 500 yuan within the statutory period. The elements for determining violations can include the elements or conditions that need to be met for the determination of violations, such as simultaneously meeting the requirements of failure to declare, failure to pay on behalf of, and the amount involved reaching the threshold. The penalty clauses can include the types of penalties and discretionary standards corresponding to different violations.

[0079] Then, the policy expert AI agent assesses the compliance of flexible employment risk events based on the scope, event progress, and policy interpretation results in the core parameters, determining whether the flexible employment risk events involve tax violations. Event progress can include information such as the duration of the violation, the amount not yet rectified, and whether a warning has been issued by the tax authorities. Specifically, based on the violation definition standards and violation identification elements in the policy interpretation results, it verifies whether the scope and event progress meet the violation identification conditions, such as whether the amount involved reaches the violation threshold, whether the platform engages in prohibited activities, and whether the behavior meets the violation criteria. If the violation identification conditions are met, the flexible employment risk event is determined to involve tax violations.

[0080] If a tax violation is determined to exist in a flexible employment risk event, the potential penalty type corresponding to the tax violation will be determined based on the penalty clauses in the policy interpretation results. For example, potential penalty types may include back tax payment, late payment fees, proportional fines, and / or tax rating downgrade.

[0081] S403: If the determination result is that there is a tax violation, output the tax risk assessment result and the potential penalty type.

[0082] If the assessment result indicates that the flexible employment risk event involves tax violations, the policy expert agent will output a tax risk assessment result indicating a risk of tax penalties and the corresponding potential penalty types. If the assessment result indicates that there are no tax violations, it will output a tax risk assessment result indicating no risk of tax penalties.

[0083] In this way, the policy expert AI has automated the assessment of tax compliance in flexible employment scenarios, resolving the errors caused by misunderstandings of policies and omissions of the latest regulatory details that can easily occur with manual assessments. This improves the efficiency and accuracy of tax-related assessments of flexible employment risk events. Furthermore, the tax risk assessment results and potential penalty types output by the policy expert AI can be directly used for subsequent quantitative analysis of tax-related impacts. This helps the platform identify tax penalty risks in advance, take timely corrective measures, and reduce economic losses and business qualification risks caused by tax violations.

[0084] Optionally, according to some embodiments of this application, S204: Based on core parameters and risk assessment data, a quantitative analysis of the impact of flexible employment risk events on at least two dimensions in legal, tax, financial, customer cooperation and platform goodwill may be performed, which may include the following steps one to three.

[0085] Step 1: If the tax risk assessment results indicate that there is a risk of tax penalties, calculate the amount of tax penalties based on the preset penalty ratio corresponding to the amount involved and the type of potential penalty.

[0086] Specifically, the impact agent can receive tax risk assessment results and potential penalty types output by the policy expert agent. If the tax risk assessment result indicates the existence of tax penalty risk, the impact agent calculates the tax penalty amount based on the amount involved and the preset penalty ratio corresponding to the potential penalty type, such as: Tax penalty amount = Amount involved × Preset penalty ratio. Different potential penalty types may correspond to different preset penalty ratios. For example, if the potential penalty type only involves back taxes and late payment fees, the corresponding preset penalty ratio can be 5% to 10% of the amount involved.

[0087] Step 2: Calculate the back taxes and late payment fees based on the rules for supplementary payments corresponding to the amount involved and the type of potential penalty.

[0088] The impact intelligence agent can extract tax payment rules corresponding to potential penalty types from the policy interpretation results output by the policy expert intelligence agent. These rules can include the type of tax to be paid, the statutory tax rate, and the late payment penalty collection standard. Then, using the amount involved in the core parameters as the calculation base, the amount of tax to be paid is calculated according to the type of tax to be paid and the statutory tax rate. Combining this with the number of overdue days in the event's progress, the late payment penalty is calculated according to the late payment penalty collection standard, such as late payment penalty = tax to be paid × number of overdue days × statutory daily collection rate.

[0089] Step 3: Based on the amount of tax penalties, back taxes, late fees, and preset tax rating impact standards, assess the impact of flexible employment risk events on the platform's tax rating.

[0090] Specifically, the criteria for influencing tax ratings can be set with reference to the tax authorities' rules for rating enterprises (such as A, B, M, C, and D levels), and in combination with the characteristics of tax violations in the flexible employment industry. The criteria can include the range of the total amount of tax violations, the correlation between the severity of the violations and the direction and magnitude of tax rating adjustments. The severity of the violations can be determined based on factors such as the duration of the violation, whether it was a first-time violation, and whether the enterprise was warned or interviewed by the tax authorities.

[0091] In step three, the impact agent can calculate the total amount of damages involved in this tax violation, namely the sum of tax penalties, back taxes, and late payment fees, which serves as the core basis for the rating impact assessment. Then, it retrieves information on the nature of the violation from the flexible employment platform, such as whether it is the platform's first tax violation, the duration of the violation, and whether the platform has received a warning or been ordered to rectify the violation by the tax authorities. Based on this information, the total amount of damages is adjusted. For example, for a first-time violation lasting less than 30 days, the assessment base is calculated at 80% of the total amount of damages; for subsequent violations or violations that have received a warning from the tax authorities, the assessment base is calculated at 120% of the total amount of damages.

[0092] Then, the influence agent compares the calibrated assessment base with the preset tax rating impact standards. It first determines the platform's current tax rating benchmark, and then matches the corresponding rating adjustment rules according to the tax rating impact standards. For example, if the platform is currently rated B, and the calibrated assessment base is below 100,000 yuan and it's the first minor violation, the rating is determined to remain unchanged, with only a tax warning record. If the platform is currently rated B, and the calibrated assessment base is between 100,000 and 500,000 yuan, the rating is determined to be downgraded by one level, from B to M. If the platform is currently rated B, and the calibrated assessment base is above 500,000 yuan, or although below 500,000 yuan, it's not the first violation and the platform has been interviewed by the tax authorities, the rating is determined to be downgraded by two levels or more, such as directly from B to C. If the assessment base is above 1 million yuan or there are serious violations such as tax evasion or issuing false invoices, the rating is determined to be directly downgraded to D, with the duration of the downgrade and the conditions for rectification noted.

[0093] Finally, the Impact Surface Intelligence Agent integrates the rating adjustment results of this flexible employment risk event (such as maintaining the same level, downgrading by n levels, or directly downgrading to D level), the core basis for the adjustment (such as the total amount of loss involved, non-first-time violation), and the key requirements for rating repair (such as completing the supplementary payment on schedule and applying for a re-evaluation after 12 consecutive months without any new violations) to obtain the impact assessment results on the platform's tax rating.

[0094] Accordingly, the impact assessment results from the tax dimension may include tax risk assessment results, potential penalty types, and the assessment results of the amount of tax penalties, back taxes, late payment fees, and impact on the platform's tax rating when there is a risk of tax penalties.

[0095] In this way, the abstract tax penalty risks in flexible employment scenarios can be transformed into quantifiable monetary data and rating impact results. This provides flexible employment platforms with intuitive risk references, helping them to quickly allocate funds to complete tax arrears and fine payments, formulate tax rating repair strategies in advance, and effectively reduce subsequent operational risks such as restrictions on invoice issuance, cancellation of tax incentives, and damage to business credit caused by tax rating downgrades. This enables precise control of tax risks in flexible employment businesses from a quantitative perspective, reducing economic losses and operational impacts caused by tax violations.

[0096] Figure 5 This is a schematic diagram of the execution flow of a financial advisor agent in the flexible employment risk event impact assessment system provided in this application embodiment. Figure 5 As shown, according to some embodiments of this application, optionally, the financial advisor agent may be used to perform the following steps S501 to S503.

[0097] S501: Based on core parameters, query the budget cost, revenue ledger, project operating cash flow data, and project historical operating data of flexible employment projects.

[0098] The financial advisor intelligent agent can extract identifying information such as project number, project type, cooperation period, and involved parties for flexible employment projects from core parameters. Based on this identifying information, it can query the budgeted costs, revenue ledgers, project operating cash flow data, and historical operating data of flexible employment projects from the flexible employment platform's business database, financial database, and / or operational database. For example, budgeted costs may include labor costs, service costs, and operating costs. Project operating cash flow data may include details of cash inflows and outflows, capital turnover cycle, and capital occupation. Historical operating data may include historical gross profit margin, cost-to-revenue ratio, and revenue growth rate.

[0099] S502: Based on budgeted costs, revenue ledgers, and historical project operating data, predict the expected profit of flexible staffing projects under the absence of risk events.

[0100] The financial advisory agent can use the contract revenue amount in the revenue ledger as the revenue base, deduct budgeted costs, and obtain the project's theoretical gross profit. This theoretical gross profit serves as the foundation for calculating expected profit. Then, based on historical gross profit margins and cost-to-revenue ratios from the project's historical operating data, the theoretical gross profit is calibrated. For example, assuming the project's theoretical gross profit is 400,000 yuan, and historical operating data shows that the project's actual gross profit margin has been stable at 35% and its cost-to-revenue ratio at 8%, the calibrated gross profit base, calculated based on the historical gross profit margin, is 1 million yuan × 35% = 350,000 yuan. Then, combining this with the historical cost-to-revenue ratio, the reasonable expenses to be deducted are calculated to be 1 million yuan × 8% = 80,000 yuan. Further calibration yields a calibrated gross profit of 350,000 yuan - 80,000 yuan = 270,000 yuan. Compared to the initial theoretical gross profit of 400,000 yuan, calibration using historical indicators effectively reduces the deviation between theoretical calculations and the project's actual operating conditions, resulting in a calibrated gross profit that better reflects the project's actual operating level.

[0101] Next, after deducting all reasonable expenses incurred during the project cooperation period (such as platform operation service fees, fund handling fees, compliance consulting fees, etc.), the expected profit value of the flexible employment project under the influence of no risk events is calculated according to the formula: Expected Profit Value = Calibrated Gross Profit - All Reasonable Expenses. Furthermore, the calculated expected profit value can be compared with the project's historical profit level for the same period and the reasonable profit range of similar projects of the same scale. If the expected profit value exceeds the reasonable profit range, the project's theoretical gross profit is recalibrated based on historical operating data, and the expected profit value is recalculated to ensure that the final output expected profit value highly matches the project's actual operating status, serving as a reliable benchmark for subsequent calculation of the profit impact of risk events.

[0102] S503: Output profit assessment results, which include expected profit value and project operating cash flow data.

[0103] The financial advisory agent can integrate information such as expected profit value, project operating cash flow data, the basis for calculating expected profit value, forecast period, and core accounting indicators to generate and output profit assessment results.

[0104] In this way, the financial advisory AI automatically predicts the expected profits of flexible employment projects under the absence of risk events. By calibrating forecast indicators based on historical project operating data, the predicted profit values ​​more closely align with the actual operating conditions of the projects, improving the accuracy of profit assessment. Furthermore, it helps the platform clearly understand the project's profitability under risk-free conditions and provides a precise reference for subsequently calculating the actual impact of risk events on project profits and cash flow. This facilitates the platform's accurate financial assessment of the economic losses from risk events and the development of targeted capital management and risk mitigation strategies.

[0105] According to some embodiments of this application, optionally, at least some of the basic intelligent agents may include a financial advisor intelligent agent. Accordingly, S204: Based on core parameters and risk assessment data, a quantitative analysis of the impact of flexible employment risk events on at least two dimensions—legal, tax, financial, customer cooperation, and platform goodwill—may include steps four through six.

[0106] Step 4: Summarize the risk expenditure amounts from at least two dimensions.

[0107] Specifically, the impact on the intelligent agent is summarized across at least two dimensions of risk expenditures: legal, tax, financial, customer partnerships, and platform goodwill. Risk expenditures refer to direct financial outlays resulting from flexible employment risk events, such as legal costs of losing a case, tax penalties, back taxes, and late payment fees.

[0108] Step 5: Based on the project's operating cash flow data and the benchmark for losses from similar events, calculate the capital occupation cost corresponding to the risk expenditure amount in at least two dimensions.

[0109] The cost of capital tied up is the potential loss or opportunity cost incurred when additional funds are tied up due to the risk of flexible employment, preventing these funds from being invested in other production and operation processes or generating normal investment returns.

[0110] In step five, at least two dimensions of the actual squeeze period for risk expenditures can be extracted from the project's operating cash flow data. This is the duration (denoted as T) from the date each risk expenditure is due until the project's working capital returns to normal turnover. Simultaneously, the project's daily cash flow efficiency data (denoted as E) is extracted to correct the capital occupation cost rate.

[0111] From the benchmark for losses in similar events, retrieve the benchmark for the cost rate of funds occupied (denoted as R), the weights of funds occupied for different dimensions of risk expenditure (denoted as W1 and W2), and the calibration coefficient for the period of funds occupied in similar events (denoted as K). The calibration coefficient for the period of funds occupied in similar events can be used to correct the deviation between the current period of funds occupied and the historical average. The value of K ranges from 0.9 to 1.1.

[0112] Then, calculate the cost of capital occupation for each dimension. For example, the cost of capital occupation for the legal dimension C1 = legal dimension risk expenditure amount A × (R × E × K) × (T1 / 365) × W1. The cost of capital occupation for the tax dimension C2 = tax dimension risk expenditure amount B × (R × E × K) × (T2 / 365) × W2. T1 represents the duration from the date the legal dimension risk expenditure is due to the date the project's working capital returns to normal turnover, and T2 represents the duration from the date the tax dimension risk expenditure is due to the date the project's working capital returns to normal turnover. R × E × K is used to calibrate the actual cost rate of capital occupation for the current project.

[0113] Step Six: Based on the expected profit value, deduct the risk expenditure amount and its corresponding capital occupation cost from at least two dimensions to obtain the final profit value of the flexible employment project.

[0114] The impact agent deducts risk expenditures and their corresponding capital costs from the expected profit value to arrive at the final profit value of the flexible staffing project. That is, final profit value = expected profit value - total risk expenditures - total capital costs. A negative final profit value indicates a net loss incurred by the flexible staffing project due to a risk event.

[0115] Accordingly, the impact assessment results from the financial dimension may include risk expenditure amounts, capital occupation costs, expected profit values, and final profit values ​​from at least two dimensions. In some embodiments, the impact assessment results from the financial dimension may also include detailed data for each deduction item, namely, the risk expenditure amount and capital occupation cost for each dimension, thereby facilitating users' understanding of the specific impact of each dimension of risk on project profits.

[0116] In this way, by linking multi-dimensional risk losses with the project's original expected profits, the platform can accurately grasp the actual impact of risk events on the project's profitability and clearly identify the proportion of financial losses for each risk dimension. The output financial impact assessment results include both direct risk expenditures from legal and tax dimensions, as well as capital occupation costs from legal and tax dimensions. This provides intuitive and accurate data support for the platform to formulate financial risk response strategies and capital allocation plans, helping the platform quickly assess the project's profitability feasibility and take timely loss-mitigation measures, effectively manage the financial and operational risks of flexible employment businesses, and ensure the stability of project financial returns.

[0117] Figure 6 This is a schematic diagram illustrating the execution flow of an impact surface agent in the flexible employment risk event impact surface assessment system provided in this application embodiment. For example... Figure 6 As shown, according to some embodiments of this application, optionally, S204: Quantitatively analyze the impact of flexible employment risk events on at least two dimensions in terms of legal, tax, financial, customer cooperation and platform reputation based on core parameters and risk assessment data, which may include the following steps S601 to S603.

[0118] S601: Obtain customer cooperation attribute information corresponding to the entity involved. Customer cooperation attribute information includes customer cooperation level, customer cooperation duration, customer historical cooperation amount and historical performance record.

[0119] In S601, the influence agent can retrieve customer cooperation attribute information corresponding to the involved entity from the customer management database of the flexible employment platform. This customer cooperation attribute information may include customer cooperation level, customer cooperation duration, historical cooperation amount, and historical performance records. For example, customer cooperation level may include Level 1, Level 2, and Level 3 customers, etc., but this application does not limit this. Historical performance records may include whether the customer has made payments on time, whether they have fulfilled their contractual obligations, and whether there have been any past cooperation disputes.

[0120] S602: Based on the risk type, scope, and client cooperation attributes, assess the degree of impact of flexible employment risk events on the cooperation of the parties involved.

[0121] The impact intelligence agent analyzes the risk type and scope, and combines customer cooperation attribute information to comprehensively assess the impact of flexible employment risk events on the cooperation of the involved parties from two dimensions: the severity of the risk and the stickiness of customer cooperation.

[0122] Specifically, based on the type of risk (such as tax violations, data falsification, contract breaches, substandard service delivery, abnormal fund settlement, etc.) and the scope of involvement (such as the amount involved, the number of projects affected by the dispute, the duration of the impact on cooperation progress, the scale of personnel involved, etc.), the severity of the risk can be quantified into multiple severity levels, such as high, medium, and low. Based on customer cooperation attribute information, and considering customer cooperation level, cooperation duration, historical cooperation amount, and historical performance records, customer cooperation stickiness is divided into at least two levels from strong to weak. Then, the severity of the risk is matched with customer cooperation stickiness to output a quantitative rating of the degree of impact on the cooperation, such as no impact, slight impact, moderate impact, severe impact, and extremely severe impact. For example, if the risk severity is high and the customer cooperation stickiness is weak, it is rated as extremely severe impact. If the risk severity is low and the customer cooperation stickiness is high, it is rated as slight impact.

[0123] S603: Calculate the probability of loss of the involved entity based on the preset customer cooperation level weight, risk type impact coefficient and historical performance record score.

[0124] For example, in some embodiments, the probability of loss of the involved entity can be calculated based on the following expression: P=Wc×αr×(100-Sh)×β (1) Where P represents the probability of churn risk for the involved entity. Wc represents the customer cooperation level weight, reflecting the moderating effect of customer importance on churn sensitivity; a higher weight indicates a more important customer and a greater overall impact of churn risk. For example, in some examples, the customer cooperation level weight for Tier 1 customers (core customers) is 0.4, for Tier 2 customers (important customers) it is 0.3, and for Tier 3 customers (ordinary customers) it is 0.2. αr is the risk type influence coefficient, characterizing the difference in the destructive power of different risk types on the cooperative relationship; a higher risk type influence coefficient indicates that the risk is more likely to lead to customer churn. Sh is the historical performance record score, quantified on a percentage basis based on the customer's historical performance behavior (such as payment timeliness, dispute records, etc.); a lower score indicates a worse performance record. β is the preset calibration coefficient. The churn risk probability ranges from 0 to 100%; a higher probability value indicates a greater likelihood of customer churn.

[0125] Accordingly, the impact assessment results for the customer cooperation dimension can include the degree of cooperation impact and the probability of churn risk.

[0126] In this way, the Impact Surface Intelligent Agent enables a quantitative assessment of the impact of flexible employment risk events on customer cooperation, solving the problem that traditional cooperation impact assessments only make qualitative judgments without quantitative basis. By extracting customer cooperation attribute information from multiple dimensions and combining it with the characteristics of the risk itself for comprehensive evaluation, the determination of the degree of cooperation impact and the probability of churn risk is more in line with actual cooperation scenarios and has higher accuracy. The quantitative assessment results of customer cooperation can provide accurate data support for the platform to formulate customer retention strategies and revenue loss compensation plans, reduce customer churn risk, and ensure the stability of customer resources for flexible employment businesses.

[0127] Figure 7 This is a schematic diagram illustrating another execution flow of the impact surface agent in the flexible employment risk event impact surface assessment system provided in this application embodiment. For example... Figure 7 As shown, according to some embodiments of this application, optionally, S204: Quantitatively analyze the impact of flexible employment risk events on at least two dimensions in terms of legal, tax, financial, customer cooperation and platform goodwill based on core parameters and risk assessment data, which may include the following steps S701 and S702.

[0128] S701: Assess the propagation risk level of flexible employment risk events based on the risk type, event progress, and scope of involvement in the core parameters.

[0129] Specifically, the impact intelligence agent can categorize risks into multiple levels of dissemination based on their external sensitivity and public attention, assigning corresponding base scores. For example, high dissemination (e.g., tax violations, group labor disputes) corresponds to 80-100 points, medium dissemination (e.g., individual contract breaches) corresponds to 50-79 points, and low dissemination (e.g., internal process oversights) corresponds to 0-49 points. Secondly, a score is awarded based on the degree of external exposure of the event: 80-100 points for media attention, 50-79 points for questions from major partners, and 0-49 points for information known only internally. Thirdly, a comprehensive score for the breadth of impact is calculated based on three sub-dimensions: the number of involved parties, the amount of money involved, and the scope of affected business areas, and this score is normalized to a range of 0-100 points. Next, based on the pre-set weighting coefficients for the three dimensions of risk type, event progress, and scope of impact, the scores for these three dimensions are weighted and summed to obtain the total dissemination risk score. Finally, based on the total dissemination risk score and the pre-set total score range, the dissemination risk level is determined. For example, a score of 0-39 indicates low transmission risk, 40-69 indicates medium transmission risk, 70-89 indicates high transmission risk, and 90-100 indicates extremely high transmission risk.

[0130] S702: Based on the level of risk of transmission, the scope of involvement, and the preset benchmark coefficient for goodwill loss, predict the amount of goodwill loss.

[0131] In S702, the basic goodwill loss benchmark coefficient can be matched based on the level of propagation risk. For example, low propagation risk corresponds to 0.1%~0.5%, medium propagation risk corresponds to 0.5%~1.5%, high propagation risk corresponds to 1.5%~3.5%, and extremely high propagation risk corresponds to 3.5%~7.0%. This application does not limit this.

[0132] Next, based on the proportion of the amount involved to the platform's total revenue during the same period, the basic goodwill loss benchmark coefficient is calibrated, and its expression is as follows: Q2 = Q1 × (1 + log) 10 (r×10+1)) (2) Where Q2 represents the calibrated goodwill loss benchmark coefficient, Q1 represents the basic goodwill loss benchmark coefficient, and r represents the proportion of the amount involved to the platform's total revenue in the same period.

[0133] Next, using the platform brand valuation or core business annual revenue as the accounting base, the product of the accounting base and the calibrated goodwill loss benchmark coefficient is calculated to obtain the goodwill loss amount, and a fluctuation range (such as ±20%) can be given according to the uncertainty of risk.

[0134] The impact assessment results for the platform's reputation dimension can include the level of dissemination risk and the amount of reputational loss.

[0135] In this way, by determining the risk level of dissemination through multi-dimensional indicators, the prediction of goodwill loss is more closely aligned with the actual dissemination trend of the event. Combining industry benchmark coefficients with the platform's actual operating data to calculate the amount of loss ensures the professionalism and accuracy of the assessment results. Furthermore, the impact intelligence agent transforms the abstract risk of goodwill damage into concrete monetary values, allowing the platform to intuitively grasp the actual impact of risk events on brand value. This provides a quantitative reference for the platform to take timely countermeasures such as public opinion management, brand repair, and customer reassurance, effectively reducing the chain risks of customer churn and business expansion obstacles caused by goodwill damage, and ensuring the stability of the flexible employment platform's brand value and industry reputation.

[0136] According to some embodiments of this application, optionally, the influence surface agent may also perform steps seven to ten.

[0137] Step 7: Based on the preset quantitative scoring rules for at least two dimensions, score the impact assessment results for at least two dimensions to obtain risk impact scores for at least two dimensions.

[0138] Specifically, quantitative scoring rules can be pre-set for various dimensions such as legal, tax, financial, customer cooperation, and platform reputation. Each dimension's quantitative scoring rules directly correspond to the impact assessment results for that dimension. Examples include the cost of losing a legal case and the probability of winning; the total amount of violations in the tax dimension; the final profit value in the financial dimension; the degree of impact on customer cooperation and the probability of churn in the customer cooperation dimension; and the amount of reputational loss in the platform reputation dimension. Based on these quantitative scoring rules, the impact agent scores the impact assessment results for each dimension, resulting in a risk impact score for each dimension.

[0139] Step 8: Perform a weighted calculation on the risk scores of at least two dimensions to obtain the comprehensive risk impact score of flexible employment risk events.

[0140] In step eight, the impact agent can perform a weighted calculation of the risk scores for at least two of the aforementioned dimensions based on preset weight coefficients for each dimension, thereby obtaining a comprehensive risk impact score for the flexible employment risk event. The weight coefficients for each dimension can be flexibly set according to the importance of each dimension to the platform's operations; this application does not impose any limitations on this. For example, in some examples, the weight coefficient for the financial dimension is 0.3, the weight coefficient for the customer cooperation dimension is 0.25, the weight coefficient for the tax dimension is 0.2, the weight coefficient for the legal dimension is 0.15, and the weight coefficient for the platform's goodwill dimension is 0.1.

[0141] Step 9: Determine the overall risk impact level of the risk event based on the overall risk impact score.

[0142] In step nine, the impact agent can determine the comprehensive risk impact level of flexible employment risk events based on a preset comprehensive risk impact level classification standard. This standard classifies different risk levels according to the range of risk impact scores. For example, 0-20 points represent a low risk level, 21-40 points a relatively low risk level, 41-60 points a moderate risk level, 61-80 points a relatively high risk level, and 81-100 points an extremely high risk level. In some embodiments, each risk level can also be matched with corresponding risk handling priority requirements and handling recommendations.

[0143] Step 10: Integrate the risk impact scores of at least two dimensions, the overall risk impact score, and the overall risk impact level into the risk impact assessment report.

[0144] The impact agent can integrate risk impact scores, comprehensive risk impact scores, and comprehensive risk impact levels into a risk impact assessment report according to a pre-set standardized report template.

[0145] In this way, by integrating risk impact scores of at least two dimensions, comprehensive risk impact scores, and comprehensive risk impact levels into the risk impact assessment report, relevant personnel can quickly and intuitively grasp the overall severity of risk events, clearly identify the differences in the strength of risks in each dimension, help relevant personnel to accurately formulate differentiated handling strategies, improve the efficiency and pertinence of risk handling, effectively reduce the chain losses caused by the expansion of risk events, and achieve refined management of flexible employment risks.

[0146] According to some embodiments of this application, optionally, the influence surface agent may also perform steps eleven and twelve.

[0147] Step 11: For each of the at least two dimensions, based on the impact assessment results of that dimension, call the pre-set remedial strategy rule base for that dimension to generate candidate remedial measures.

[0148] The remedial strategy rule base stores remedial strategies for different impact assessment results across various dimensions. In step eleven, the impact surface agent can query the remedial strategy rule base for the corresponding remedial measures based on the impact assessment results for each dimension. For example, remedial measures for the legal dimension may include supplementing evidence, engaging professional lawyers to defend the case, and negotiating a settlement with the parties involved. Remedial measures for the tax dimension may include proactively paying back taxes and late fees, submitting compliance rectification statements, and improving tax filing procedures. Remedial measures for the financial dimension may include adjusting project budgets and optimizing cash flow allocation. Remedial measures for the customer cooperation dimension may include issuing compliance commitment letters, offering preferential rates, and arranging dedicated teams to maintain cooperation. Remedial measures for the platform reputation dimension may include issuing official statements to clarify risks, conducting compliance publicity, and strengthening platform risk control disclosure.

[0149] Step 12: Integrate candidate remedies from at least two dimensions into the risk impact assessment report.

[0150] The impact intelligence agent integrates remedial measures from various dimensions into the risk impact assessment report, making it easier for risk control personnel to clearly understand the targeted handling plans corresponding to each dimension of risk, quickly initiate loss-stopping and risk control operations, effectively reduce the chain losses of risk events, and improve the efficiency of risk handling decision-making.

[0151] Based on the flexible employment risk event impact assessment system 10 provided in any of the above embodiments, this application also provides a flexible employment risk event impact assessment method, which can be implemented based on the flexible employment risk event impact assessment system 10 as described in any of the above embodiments.

[0152] like Figure 2 As shown, the method for assessing the impact of flexible employment risk events provided in this application embodiment may include the following steps: S201: Receive user input describing the flexible employment risk event; S202: Perform semantic parsing on the description information, identify user intent and extract core parameters, including risk scenario, involved parties, risk type, scope of involvement and core demands; Based on the influence surface agent, the following processing is performed: S203: Based on user intent and core parameters, call at least some of the corresponding basic intelligent agents to process the core parameters and obtain risk assessment data output by at least some of the basic intelligent agents; S204: Based on core parameters and risk assessment data, conduct a quantitative analysis of the impact of flexible employment risk events on at least two dimensions: legal, tax, financial, customer cooperation, and platform reputation, and output impact assessment results for at least two dimensions. S205: Generate a risk impact assessment report based on the impact assessment results of at least two dimensions.

[0153] The specific processes of each of the above steps have been described in detail above and will not be repeated here.

[0154] The flexible employment risk event impact assessment method provided in this application introduces an impact-surface agent to achieve a global analysis of the impact of flexible employment across multiple core dimensions, including legal, tax, financial, customer cooperation, and platform reputation. By quantitatively presenting the overall scope and severity of risk events, it helps platforms move beyond single-dimensional accounting and fragmented multi-dimensional understanding, accurately grasping the overall risk and providing a global decision-making basis for risk isolation and resource allocation. Furthermore, through a collaborative architecture of multiple basic agents conducting specialized assessments and the global integration of the impact-surface agent, each basic agent can be responsible for its respective specialized risk assessment area and output specialized risk assessment data, ensuring the professionalism of single-dimensional assessments. The impact-surface agent can uniformly perform correlation analysis and integrated calculations on multi-dimensional data, reducing the communication costs and subjective biases associated with manual cross-departmental collaboration. This collaborative model ensures both the professional depth of risk assessment and the efficiency of impact-surface analysis, shortens the risk assessment cycle, and solves the problem of delayed response in traditional models.

[0155] It should be noted that the flexible employment risk event impact assessment method provided in this application embodiment has the same or corresponding technical features as the flexible employment risk event impact assessment system provided in any of the above embodiments, and can produce the same technical effects. For the sake of brevity, it will not be elaborated further here.

[0156] The flowcharts and / or block diagrams of methods, systems, and computer program products according to embodiments of this application have been exemplarily described above, and related aspects have been described. It should be understood that each block or combination thereof in the flowcharts and / or block diagrams may be implemented by computer program instructions, by dedicated hardware performing a specified function or action, or by a combination of dedicated hardware and computer instructions. For example, these computer program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to form a machine such that these instructions, which execute via such processor, enable the implementation of the function / action specified in each block or combination thereof in the flowcharts and / or block diagrams. Such a processor may be a general-purpose processor, a dedicated processor, a special-purpose application processor, or a field-programmable logic circuit.

[0157] The functional blocks shown in the structural block diagrams of this application can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc.; when implemented in software, they are programs or code segments used to perform the required tasks. Programs or code segments can be stored in memory or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. Code segments can be downloaded via computer networks such as the Internet or intranets.

[0158] It should be noted that this application is not limited to the specific configurations and processes described above or shown in the figures. The above descriptions are merely specific embodiments of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the described systems, devices, modules, or units can be referred to the corresponding processes in the method embodiments, and need not be repeated here. It should be understood that the scope of protection of this application is not limited thereto. Any person skilled in the art can conceive of various equivalent modifications or substitutions within the scope of the technology disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application.

Claims

1. A flexible employment risk event impact assessment system, characterized in that, The system includes an impact-area agent and multiple basic agents for specific risk assessments. After startup, the system performs the following processes: Receive user input describing flexible employment risk events; The descriptive information is semantically parsed to identify user intent and extract core parameters, including risk scenario, involved parties, risk type, scope of involvement and core demands. Based on the influence surface agent, the following processing is performed: Based on the user intent and the core parameters, at least some of the corresponding basic intelligent agents are invoked to process the core parameters, thereby obtaining the risk assessment data output by the at least some of the basic intelligent agents. Based on the core parameters and risk assessment data, a quantitative analysis is conducted on the impact of flexible employment risk events on at least two dimensions: legal, tax, financial, customer cooperation, and platform reputation, and the impact assessment results for the at least two dimensions are output. Based on the impact assessment results of at least two dimensions, a risk impact assessment report is generated.

2. The system according to claim 1, characterized in that, Multiple basic intelligent agents include at least two of the following: legal intelligent agent, policy expert intelligent agent, and financial advisor intelligent agent; The legal intelligence agent is used to assess the legal risks of flexible employment risk events based on the core parameters, a preset historical case library, a preset legal clause library, and an evidence list corresponding to the core parameters, so as to obtain the probability of winning the case, the legal clause basis, and the potential compensation amount. The policy expert agent is used to assess the tax risks of flexible employment risk events based on the core parameters and the preset policy library, and to obtain the tax risk assessment results and potential penalty types. The financial advisory agent is used to evaluate the profit of flexible employment projects corresponding to flexible employment risk events based on the core parameters, and obtain the profit evaluation result.

3. The system according to claim 2, characterized in that, The legal intelligence agent is specifically used to perform the following processes: Search the historical case database for similar historical cases that match the core parameters, and determine the initial probability of winning the flexible employment risk event based on the judgment results of similar historical cases. Extract target legal clauses that match the core parameters from the legal clause library, and calculate the matching score between the core parameters and the target legal clauses; Calculate the evidence completeness score for flexible employment risk events based on the evidence list corresponding to the core parameters; The probability of winning a case in a flexible employment risk event is obtained by weighting the initial probability of winning, the matching score, and the evidence integrity score.

4. The system according to claim 2 or 3, characterized in that, At least some basic intelligent agents include legal intelligent agents; Based on the aforementioned core parameters and risk assessment data, a quantitative analysis is conducted on the impact of flexible employment risk events on at least two dimensions: legal, tax, financial, customer cooperation, and platform reputation. When the probability of winning the case is less than a preset probability threshold, the cost of losing the case is calculated based on the potential compensation amount, the statutory litigation fee standard, and the industry lawyer fee range. The legal impact assessment results include the probability of winning, the legal basis, and the cost of losing when the probability of winning is less than a preset probability threshold.

5. The system according to claim 2, characterized in that, The policy expert agent is specifically used to perform the following processes: Based on the risk type and the scope involved, query the policy database for target matching policies corresponding to flexible employment risk events; Based on the trained policy interpretation model, the target matching policy is interpreted, and the compliance of flexible employment risk events is determined by combining the scope, event progress and policy interpretation results in the core parameters, to determine whether there are tax violations and the corresponding potential penalty types. If the determination result indicates that there is a tax violation, the output will show the tax risk assessment result and the potential penalty type.

6. The system according to claim 2 or 5, characterized in that, At least some of the basic intelligent agents include policy expert intelligent agents, and the scope of involvement includes the amount involved; Based on the aforementioned core parameters and risk assessment data, a quantitative analysis is conducted on the impact of flexible employment risk events on at least two dimensions: legal, tax, financial, customer cooperation, and platform reputation. If the tax risk assessment result indicates that there is a risk of tax penalty, then the amount of tax penalty is calculated based on the amount involved and the preset penalty ratio corresponding to the potential penalty type. Based on the supplementary payment rules corresponding to the amount involved and the potential penalty type, calculate the supplementary tax and late payment fees; Based on the amount of tax penalties, back taxes, late fees, and preset tax rating impact criteria, assess the impact of flexible employment risk events on the platform's tax rating. The impact assessment results from the tax dimension include tax risk assessment results, potential penalty types, and the amount of tax penalties, back taxes, late payment fees, and impact on the platform's tax rating when there is a risk of tax penalties.

7. The system according to claim 2, characterized in that, The financial advisor agent is specifically used to perform the following processes: Based on the core parameters, query the budget cost, revenue ledger, project operating cash flow data, and project historical operating data of the flexible employment project; Based on budgeted costs, revenue ledgers, and historical project operating data, predict the expected profit of the flexible staffing project under the condition of no risk events. Output the profit assessment results, which include the expected profit value and the project operating cash flow data.

8. The system according to claim 7, characterized in that, At least some basic intelligent agents include financial advisory agents; Based on the aforementioned core parameters and risk assessment data, a quantitative analysis is conducted on the impact of flexible employment risk events on at least two dimensions: legal, tax, financial, customer cooperation, and platform reputation. Summarize the risk expenditure amounts from at least two dimensions; Based on the project's operational cash flow data and the benchmark for losses from similar events, calculate the capital occupation cost corresponding to the risk expenditure amounts in the at least two dimensions. Based on the expected profit value, the final profit value of the flexible employment project is obtained by deducting the risk expenditure amount and the corresponding capital occupation cost of the at least two dimensions. The impact assessment results in the financial dimension include the risk expenditure amount, capital occupation cost, expected profit value, and final profit value in at least two of the aforementioned dimensions.

9. The system according to claim 1, characterized in that, Based on the aforementioned core parameters and risk assessment data, a quantitative analysis is conducted on the impact of flexible employment risk events on at least two dimensions: legal, tax, financial, customer cooperation, and platform reputation. Obtain customer cooperation attribute information corresponding to the entity involved, including customer cooperation level, customer cooperation duration, customer historical cooperation amount, and historical performance record; Based on the risk type, the scope of involvement, and the customer cooperation attribute information, assess the degree of impact of flexible employment risk events on the cooperation of the involved parties; Based on preset customer cooperation level weights, risk type impact coefficients, and historical performance record scores, the probability of loss of the involved entity is calculated. The impact assessment results for the customer cooperation dimension include the degree of cooperation impact and the probability of churn risk.

10. The system according to claim 1, characterized in that, Based on the aforementioned core parameters and risk assessment data, a quantitative analysis is conducted on the impact of flexible employment risk events on at least two dimensions: legal, tax, financial, customer cooperation, and platform reputation. Based on the risk type, event progress, and scope of involvement in the core parameters, assess the spread risk level of flexible employment risk events; Based on the aforementioned risk level of the spread, the aforementioned scope of involvement, and the preset benchmark coefficient for goodwill loss, the amount of goodwill loss is predicted; The impact assessment results for the platform's reputation dimension include the level of dissemination risk and the amount of reputational loss.

11. The system according to claim 1, characterized in that, Based on the influence surface, the agent also performs the following processing: Based on the preset quantitative scoring rules for the at least two dimensions, the impact assessment results of the at least two dimensions are scored to obtain the risk impact scores for the at least two dimensions; The risk scores of at least two dimensions are weighted and calculated to obtain the comprehensive risk impact score of flexible employment risk events; Based on the comprehensive risk impact score, the comprehensive risk impact level of the risk event is determined; The risk impact scores of at least two dimensions, the comprehensive risk impact score, and the comprehensive risk impact level are integrated into the risk impact assessment report.

12. The system according to claim 1, characterized in that, Based on the influence surface, the agent also performs the following processing: For each of the at least two dimensions, based on the impact assessment results of that dimension, the pre-defined remedial strategy rule base for that dimension is invoked to generate candidate remedial measures; Integrate the candidate remedies from at least two dimensions into the risk impact assessment report.

13. A method for assessing the impact of flexible employment risk events, characterized in that, The method is implemented based on the flexible employment risk event impact assessment system as described in any one of claims 1-12, and the method includes: Receive user input describing flexible employment risk events; The descriptive information is semantically parsed to identify user intent and extract core parameters, including risk scenario, involved parties, risk type, scope of involvement and core demands. Based on the influence surface agent, the following processing is performed: Based on the user intent and the core parameters, at least some of the corresponding basic intelligent agents are invoked to process the core parameters, thereby obtaining the risk assessment data output by the at least some of the basic intelligent agents. Based on the core parameters and risk assessment data, a quantitative analysis is conducted on the impact of flexible employment risk events on at least two dimensions: legal, tax, financial, customer cooperation, and platform reputation, and the impact assessment results for the at least two dimensions are output. Based on the impact assessment results of at least two dimensions, a risk impact assessment report is generated.

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