A mediation status monitoring system and method based on data analysis

Abstract

The present invention discloses a mediation status monitoring system and method based on data analysis, which relate to the technical field of mediation status monitoring. A mediation application keyword database is established, application keywords are extracted based on the mediation application of a newly added mediation case, historical mediation data of the mediated person is collected, mediators are screened according to the historical mediation data of the mediated person, preliminary mediators are screened, the degree of adaptation of the preliminary mediators to keywords is analyzed, pre-selected mediators are screened, the pre-selected mediators are screened according to their workload, and pre-selected mediators are supplemented from the mediators until the workload of the pre-selected mediators meets the requirements. The information of the pre-selected mediators is logged into the attached information tag of the newly added mediation case until the newly added mediation case is accepted, and then the attached information tag of the newly added mediation case is adjusted. The present invention improves the possibility of successful mediation and the operating efficiency of the mediation system through an intelligent matching process.

Description

Technical Field

[0001] The present invention relates to the technical field of mediation status monitoring, and in particular to a mediation status monitoring system and method based on data analysis. Background Art

[0002] Traditional mediation processes often face numerous practical challenges in resolving conflicts. The manual assignment of mediators presents significant limitations. It struggles to fully assess the match between case type, the parties' needs, and the mediator's expertise. It also lacks scientific data support, which can easily lead to resource mismatch and inefficient mediation. This not only fails to leverage professional expertise but can also impact mediation success rates due to inexperience. The traditional process lacks a dynamic adjustment mechanism, making it difficult to align mediators' workloads with case complexity in real time. This often leaves some mediators with backlogs and others with idle resources, reducing service quality and hindering the formation of a systematic closed-loop dispute resolution system. Furthermore, existing mediation systems exhibit significant shortcomings in data utilization and risk management. Most platforms focus solely on simple case recording and statistics, failing to fully tap into the value of historical data and provide precise guidance for mediator selection and case handling. For complex cases involving specific keywords, traditional methods struggle to quickly identify experienced mediators, leading to a time-consuming and trial-and-error mediation process. The imperfections in data security and authorization mechanisms also hinder the standardized development of mediation work. Some systems lack clear authorization processes when obtaining party information or mediator workload data, which not only poses a risk of privacy leakage but also affects the credibility of mediation services. The post-mediation failure handling mechanism is generally inadequate. Under the traditional model, if no agreement is reached in the first mediation, there is often a lack of effective alternative plans and resource replenishment mechanisms, which can easily lead to disputes being dragged on or conflicts escalating. In some cases, after mediation fails due to insufficient adaptability of mediators, the manual screening process needs to be restarted, which not only wastes the resources invested in the early stage, but may also exacerbate the parties' antagonism due to time delays. At the same time, the tracking and evaluation mechanism for mediation results is not sound, making it difficult to optimize mediation strategies through historical data, resulting in the recurrence of similar problems and the inability to form a sustainable path to improve service quality. Summary of the Invention

[0003] The object of the present invention is to provide a mediation status monitoring system and method based on data analysis to solve the problems raised in the above background technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: a mediation status monitoring method based on data analysis, comprising the following steps:

[0005] S1. Establish a keyword database for mediation applications and extract application keywords based on the mediation applications of newly added mediation cases;

[0006] S2. Collect the historical mediation data of the mediated person, screen the mediators based on the historical mediation data of the mediated person, and select the preliminary mediators;

[0007] S3. Based on the mediation history data, analyze the adaptability of the pre-selected mediators to the keywords and select the pre-selected mediators;

[0008] S4. Screen the pre-selected mediators according to their workload and add pre-selected mediators from the mediators until the workload of the pre-selected mediators meets the requirements;

[0009] S5. Log the pre-selected mediator's information into the newly added mediation case's attached information tab until the newly added mediation case is accepted;

[0010] S6. Adjust the attached information label of the newly added mediation case according to the completion status of the newly added mediation case.

[0011] Furthermore, in step S1, a mediation application keyword database is established, which includes pre-set keywords. When a new mediation case γ is received by the responsible person and the mediation application is uploaded, the keywords in the mediation application are monitored. The keyword set is {D1, D2, ..., D n ,…,D N}, marking the case type as α; establishing a keyword vocabulary for mediation applications and realizing automatic labeling of case types can significantly improve the standardization and efficiency of mediation work. By presetting keywords to cover high-frequency dispute points and legal requirements, the core elements of the case can be quickly captured, reducing the cost of manual screening, and enabling responsible personnel to complete case classification and diversion more efficiently. The dynamic update mechanism combines automatic learning and manual review to allow the vocabulary to continuously adapt to new types of disputes, avoid classification deviations caused by lagging terminology updates, and ensure the accuracy and timeliness of the labeling results. The keyword-based classification algorithm and multi-label processing logic can accurately match the multiple attributes of complex cases, ensuring the rapid positioning of single-type cases, and responding to the labeling needs of cross-domain disputes, reducing the risk of mislabeling. At the same time, the seamless connection between labeling results and business processes can trigger automatic resource allocation and risk warnings, promote the mediation work towards intelligence and standardization, improve the parties' satisfaction and trust in mediation services, and provide technical support for the efficient resolution of contradictions and disputes.

[0012] Furthermore, in step S2, after authorization by the mediator, the mediator includes {A1, A2, ..., A i ,…,A I}, where I represents the number of mediators, A i Denotes the ith mediator, analyzes the mediation history data of the mediator, and obtains the number of mediation cases involving the mediator, including the number of mediators A iThe number of mediation cases is X i , of which the number of mediation cases of the same type as the newly added mediation cases is Y i , set the mediator A i The demand for mediators is x i and mediator A i The demand for mediator type α is y i , when X i ≤X0, the mediator is judged to be a new mediator, where X0 is the maximum number of mediation cases for newly added mediators. i Demand for mediators x i =0, let the mediator A i Demand for mediator α type y i =0; when X i >X0, the mediator is not a new mediator, and mediator A is i Demand for mediators x i =X i , let the mediator A i Demand for mediator α type y i =Y i ;

[0013] Substitute i=1,2,…,I one by one, and get the demands of I mediators for investigators {x1,x2,…,x i ,…,x I}, we get the mediator’s demand {y1,y2,…,y i ,…,y I}, the total demand of the mediators for investigators is X, and the total demand of the mediators for mediators of type α is Y. Then, mediators with a history of mediation cases greater than X and a history of mediation of type α greater than Y are selected as preliminary mediators. The set of preliminary mediators is {C1, C2, …, C m ,…,C MA mechanism that analyzes the mediation data of the mediator to determine mediator needs can achieve precise allocation and efficient utilization of mediation resources. By distinguishing whether the mediator is newly added and matching it with the number of historical cases, the characteristics of entities frequently participating in mediation can be quickly identified, avoiding the blind assignment of new entities and reducing the cost of manual analysis and judgment. By setting typified requirements based on the number of similar cases, cases can be preferentially assigned to mediators familiar with the characteristics of disputes in that area, improving the relevance and success rate of mediation solutions. The total demand formed by the summation operation helps to balance the work plans of mediators from a global perspective, avoid overloading individual personnel or idle resources, and promote the dynamic optimization of mediation capabilities. The screening conditions combine historical experience and type matching to ensure that the professional capabilities of mediators are compatible with the difficulty of the case, and strengthen the reusability of mediation strategies through the accumulation of historical data. This helps to build a standardized and intelligent mediation resource scheduling system, ultimately improving the overall effectiveness of dispute resolution and enhancing the parties' recognition of the professionalism and fairness of mediation.

[0014] Furthermore, in step S3, for the keyword D n Conduct analysis and screen the preliminary mediator C m Historical mediation cases, the primary mediator C m The success rate of historical mediation cases is F m1 , screen the preliminary mediator C m The historical mediation cases contain the keyword D n The success rate of mediation cases is F m2 , set keyword weight k1, k1=β n / β, set the case weight k2, k2=(1-β n ) / β, where β represents the total number of historical mediation cases, β n Indicates that historical mediation cases contain keyword D n The total number of primary mediators C m Keyword D n Priority W m_n , W m_n =k1*F m1 +k2*F m2 , one by one into n = 1, 2, ..., N, to get the primary mediator C m For N keywords D n The priority of {W m_1 ,W m_2 ,…,W m_n ,…,W m_N}, sum to get the primary mediator C m The degree of adaptation W to the newly added mediation case γ m ;

[0015] Substitute m=1,2,…,M one by one to obtain the adaptability of M primary investigators to the newly added mediation case γ {W1,W2,…,W m ,…,W M}, set the number of pre-selected investigators to Q, when M≥M0, set the number of pre-selected investigators to Q=M0; when M<M0, set the number of pre-selected investigators to Q=M, where M represents the preset maximum number of pre-selected investigators, sort the pre-selected investigators from high to low according to the degree of adaptation, and select the Q pre-selected mediators with the highest degree of adaptation as the pre-selected mediators. The set of pre-selected mediators is {G1,G2,…,G q ,…,G Q}, where Q represents the number of pre-selected mediators, G q This represents the mediator suitability assessment mechanism for the qth pre-selected mediator, based on keywords and historical data. By analyzing the frequency of keywords in historical cases and assigning weights, this mechanism accurately identifies the core dispute points in the case. Combining the mediator's success rate in similar cases and overall caseload, it comprehensively calculates suitability, avoiding the one-sidedness of a single metric. This multi-dimensional assessment model considers both the mediator's overall mediation capabilities and their expertise in specific dispute areas, ensuring that complex cases are matched with mediation teams with both comprehensive competence and specialized expertise. Sorting pre-selected mediators by suitability dynamically optimizes resource allocation, prioritizing those with extensive experience handling key case elements, shortening the mediation cycle, and improving the relevance of solutions. Furthermore, this mechanism replaces the fuzzy judgment of traditional manual assignments with data-driven quantitative analysis, reducing human error and resource misallocation. This promotes a more scientific and refined mediation process, provides technical support for efficient dispute resolution, and contributes to the development of a more professional and credible mediation service system.

[0016] Furthermore, in step S4, after authorization, the current workload of the pre-selected mediators is analyzed, and the current workload set of the pre-selected mediators is {g1, g2, ..., g q ,…,g Q}, where g q represents the current workload of the qth pre-selected mediator, which represents the number of mediation cases that the qth pre-selected mediator needs to mediate when a new mediation case is logged in. If g q ≤g0, then retain the information of the qth pre-selected mediator; if g q>g0, then delete the information of the qth pre-selected mediator from the set of pre-selected mediators, where g0 represents the preset maximum workload of the mediator, and delete the information of a total of J pre-selected mediators. If M-M0≥q, then select the J pre-selected mediators with the highest degree of adaptability from the preliminary investigators who have never been selected as pre-selected mediators in the newly added mediation cases as pre-selected mediators, and perform workload analysis again; if 0<M-M0<J, then select the M-M0 pre-selected mediators with the highest degree of adaptability from the preliminary investigators who have never been selected as pre-selected mediators in the newly added mediation cases as pre-selected mediators, and select the J-(M-M0) pre-selected mediators from the mediators who have never been selected as pre-selected investigators in the newly added mediation cases as pre-selected mediators; if M-M0≤0, select the J pre-selected mediators from the mediators who have never been selected as pre-selected investigators in the newly added mediation cases as pre-selected mediators, until the workload of the Q pre-selected mediators is less than or equal to g0. The mechanism of dynamically adjusting the pre-selected mediators based on workload can realize the real-time optimization allocation and sustainable utilization of mediation resources. By analyzing the current workload and eliminating overloaded personnel, we can avoid case delays or reduced mediation quality caused by overburdened mediators, ensuring that each case receives sufficient attention. The hierarchical supplementation rules prioritize unselected personnel with high adaptability, maintaining a professional matching logic, while also enabling cross-selection when resources are insufficient, ensuring the integrity and flexibility of the mediation process. This closed-loop mechanism of screening, supplementation, and re-evaluation breaks the limitations of the traditional fixed allocation model, allowing mediation resources to fluctuate dynamically with case demand and personnel load, balancing workload while maintaining professionalism. Data-driven real-time scheduling can reduce subjective biases in manual allocation, avoid resource misallocation or idleness, and improve overall mediation efficiency. It also conveys a standardized and transparent service image to the parties, enhancing their trust in the fairness of the mediation process and laying the foundation for building an efficient and sustainable dispute resolution system.

[0017] Furthermore, in step S5, the information of the Q pre-selected mediators is logged into the newly added mediation case attached information tag, and the newly added mediation case attached information tag is hidden until any pre-selected mediator accepts the newly added mediation case.

[0018] Furthermore, in step S6, if the pre-selected mediator successfully mediates the newly added mediation case, the attached information tag of the newly added mediation case is deleted. Otherwise, the r most suitable mediators are selected from the remaining pre-selected mediators as the pre-selected mediators for secondary mediation, and the attached information tag of the newly added mediation case is re-logged in, waiting for the pre-selected mediators for secondary mediation to mediate the newly added mediation case. The r represents the preset number of pre-selected mediators for secondary mediation. If the mediation is successful, the attached information tag is deleted, which can clean up invalid data in time, keep the system simple and efficient, and avoid information redundancy interfering with subsequent processes. If the mediation fails, the secondary mediation is initiated based on the ranking of the remaining pre-selected mediators according to their degree of adaptability. This "tiered" resource calling mode not only avoids the repetitive work of screening from scratch, but also quickly locks in backup forces with high professional matching, and continues the consistency and pertinence of the mediation strategy. The pre-set number of pre-selected mediators for secondary mediation mechanism can concentrate superior resources to tackle complex cases within a limited range, reducing the communication costs and trust loss caused by the frequent replacement of mediators. At the same time, the mechanism ensures that cases are always in a follow-up state through closed-loop management, avoiding the intensification of conflicts caused by "interruption of mediation". It not only reflects the humanity and responsible attitude of mediation services, but also enhances the parties' confidence in dispute resolution through professional and continuous intervention, helping to build a more resilient and flexible mediation work system, and effectively improving the overall effectiveness of conflict resolution.

[0019] A mediation status monitoring system based on data analysis, comprising: a mediation application keyword extraction module, a mediator preliminary selection module, a keyword adaptation degree analysis and screening module, a mediator workload balance screening and supplementation module, a pre-selected mediator information login module, and a mediation case completion status ancillary information adjustment module;

[0020] The mediation application keyword extraction module is used to establish a mediation application keyword database and extract application keywords based on the mediation application of the newly added mediation case;

[0021] The mediator preliminary selection module is used to collect the historical mediation data of the mediator, screen the mediators according to the historical mediation data of the mediator, and select the preliminary mediators;

[0022] The keyword adaptation degree analysis and screening module is used to analyze the adaptation degree of the pre-selected mediators to the keywords based on the mediation history data, and screen out the pre-selected mediators;

[0023] The mediator workload balance screening and supplementation module is used to screen pre-selected mediators according to their workload and supplement pre-selected mediators from the mediators until the workload of the pre-selected mediators meets the requirements;

[0024] The pre-selected mediator information login module is used to log the pre-selected mediator's information into the newly added mediation case attached information tag until the newly added mediation case is accepted;

[0025] The mediation case completion status attached information adjustment module is used to adjust the attached information label of the newly added mediation case according to the completion status of the newly added mediation case.

[0026] Compared with the existing technology, the beneficial effects achieved by the present invention are: on the one hand, the mediator screening model based on keyword thesaurus and historical data can accurately locate suitable candidates and greatly improve the professionalism and pertinence of the mediation work. When the system receives a new case, it first marks the case type through the keywords of the mediation application, and then analyzes the mediator's needs for the mediator in combination with the mediator's historical participation, and then screens out mediators with a high degree of adaptability from the initial selection to the pre-selection. This intelligent matching process changes the blindness of traditional manual allocation, allowing the mediator's professional expertise to fully match the characteristics of the case. It not only reduces the time cost of manual screening, but also relies on the mediator's rich experience in similar cases to increase the possibility of successful mediation, laying the foundation for the efficient resolution of disputes;

[0027] On the one hand, the dynamically adjusted workload management mechanism effectively balances the mediators' workload and ensures the quality and efficiency of mediation services. The solution introduces current workload analysis in the pre-selection stage of mediators, promptly eliminates overloaded personnel, and supplements suitable candidates from the candidate group according to their degree of adaptability, ensuring that everyone involved in mediation is within a reasonable work intensity range. This dynamic optimization method avoids problems such as case backlogs or hasty processing due to excessive tasks of individual mediators, allowing mediation resources to be more evenly distributed. At the same time, the secondary mediation pre-selection mechanism set up for mediation failures can quickly initiate alternative plans, continue the continuity of the mediation process, avoid resource waste due to a single failure, and form an efficient closed loop from case access to successful resolution, which comprehensively improves the operational efficiency of the mediation system;

[0028] On the other hand, through standardized data processing processes and authorization mechanisms, a more scientific mediation service system is established while ensuring data security. From the analysis of the historical data of the mediated party to the acquisition of the mediator's workload, every link involving data use emphasizes the prerequisite of authorization, fully respects user privacy and information security, and lays a foundation of trust for the practical application of the system. The comprehensive evaluation based on multi-dimensional data, such as keyword weight, historical success rate, and case type matching, makes the selection of mediators more comprehensive and objective, reducing the interference of subjective factors. This model of deeply integrating data analysis into the mediation process not only improves the quality of case handling, but also, through long-term data accumulation and optimization, drives the entire mediation service system towards intelligent and precise development, providing strong technical support for the construction of an efficient and fair dispute resolution mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0030] Figure 1 It is a structural diagram of a mediation status monitoring system based on data analysis of the present invention;

[0031] Figure 2 The present invention is a flow chart of a mediation status monitoring method based on data analysis. DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0033] See also Figure 1 and Figure 2 The present invention provides a technical solution: a mediation status monitoring method based on data analysis, comprising the following steps:

[0034] S1. Establish a keyword database for mediation applications and extract application keywords based on the mediation applications of newly added mediation cases;

[0035] S2. Collect the historical mediation data of the mediated person, screen the mediators based on the historical mediation data of the mediated person, and select the preliminary mediators;

[0036] S3. Based on the mediation history data, analyze the adaptability of the pre-selected mediators to the keywords and select the pre-selected mediators;

[0037] S4. Screen the pre-selected mediators according to their workload and add pre-selected mediators from the mediators until the workload of the pre-selected mediators meets the requirements;

[0038] S5. Log the pre-selected mediator's information into the newly added mediation case's attached information tab until the newly added mediation case is accepted;

[0039] S6. Adjust the additional information tags of the newly added mediation cases according to the completion status of the newly added mediation cases. In step S1, a mediation application keyword database is established. The mediation application keyword database includes pre-set keywords. When the newly added mediation case γ is received by the responsible person and the mediation application is uploaded, the keywords in the mediation application are monitored. The keyword set is {D1, D2, ..., D n ,…,D N}, marking the case type as α; establishing a keyword vocabulary for mediation applications and realizing automatic labeling of case types can significantly improve the standardization and efficiency of mediation work. By presetting keywords to cover high-frequency dispute points and legal requirements, the core elements of the case can be quickly captured, reducing the cost of manual screening, and enabling responsible personnel to complete case classification and diversion more efficiently. The dynamic update mechanism combines automatic learning and manual review to allow the vocabulary to continuously adapt to new types of disputes, avoid classification deviations caused by lagging terminology updates, and ensure the accuracy and timeliness of the labeling results. The keyword-based classification algorithm and multi-label processing logic can accurately match the multiple attributes of complex cases, ensuring the rapid positioning of single-type cases, and responding to the labeling needs of cross-domain disputes, reducing the risk of mislabeling. At the same time, the seamless connection between labeling results and business processes can trigger automatic resource allocation and risk warnings, promote the mediation work towards intelligence and standardization, improve the parties' satisfaction and trust in mediation services, and provide technical support for the efficient resolution of contradictions and disputes.

[0040] In step S2, after authorization by the mediator, the mediator includes {A1, A2, ..., A i ,…,A I}, where I represents the number of mediators, A i Denotes the ith mediator, analyzes the mediation history data of the mediator, and obtains the number of mediation cases involving the mediator, including the number of mediators A i The number of mediation cases is X i , of which the number of mediation cases of the same type as the newly added mediation cases is Y i , set the mediator A i The demand for mediators is x i and mediator A i The demand for mediator type α is y i , when X i ≤X0, the mediator is judged to be a new mediator, where X0 is the maximum number of mediation cases for newly added mediators. i Demand for mediators x i =0, let the mediator A i Demand for mediator α type y i =0; when X i >X0, the mediator is not a new mediator, and mediator A is i Demand for mediators x i =X i , so that the mediator A i Demand for mediator α type y i =Y i ;

[0041] Substitute i=1,2,…,I one by one, and get the demands of I mediators for investigators {x1,x2,…,x i ,…,x I}, we get the mediator’s demand {y1,y2,…,y i ,…,y I}, the total demand of the mediators for investigators is X, and the total demand of the mediators for mediators of type α is Y. Then, mediators with a history of mediation cases greater than X and a history of mediation of type α greater than Y are selected as preliminary mediators. The set of preliminary mediators is {C1, C2, …, C m ,…,C M A mechanism that analyzes the mediation data of the mediator to determine mediator needs can achieve precise allocation and efficient utilization of mediation resources. By distinguishing whether the mediator is newly added and matching it with the number of historical cases, the characteristics of entities frequently participating in mediation can be quickly identified, avoiding the blind assignment of new entities and reducing the cost of manual analysis and judgment. By setting typified requirements based on the number of similar cases, cases can be preferentially assigned to mediators familiar with the characteristics of disputes in that area, improving the relevance and success rate of mediation solutions. The total demand formed by the summation operation helps to balance the work plans of mediators from a global perspective, avoid overloading individual personnel or idle resources, and promote the dynamic optimization of mediation capabilities. The screening conditions combine historical experience and type matching to ensure that the professional capabilities of mediators are compatible with the difficulty of the case, and strengthen the reusability of mediation strategies through the accumulation of historical data. This helps to build a standardized and intelligent mediation resource scheduling system, ultimately improving the overall effectiveness of dispute resolution and enhancing the parties' recognition of the professionalism and fairness of mediation.

[0042] In step S3, for keyword D n Conduct analysis and screen the preliminary mediator C m Historical mediation cases, primary mediator C m The success rate of historical mediation cases is F m1 , screen the preliminary mediator C m The historical mediation cases contain the keyword D n The success rate of mediation cases is F m2 , set keyword weight k1, k1=β n / β, set the case weight k2, k2=(1-β n ) / β, where β represents the total number of historical mediation cases, β n Indicates that historical mediation cases contain keyword D n The total number of primary mediators C m Keyword D n Priority W m_n , W m_n =k1*Fm1 +k2*F m2 , one by one into n = 1, 2, ..., N, to get the primary mediator C m For N keywords D n The priority of {W m_1 ,W m_2 ,…,W m_n ,…,W m_N}, sum to get the primary mediator C m The degree of adaptation W to the newly added mediation case γ m ;

[0043] Substitute m=1,2,…,M one by one to obtain the adaptability of M primary investigators to the newly added mediation case γ {W1,W2,…,W m ,…,W M}, set the number of pre-selected investigators to Q, when M≥M0, set the number of pre-selected investigators to Q=M0; when M<M0, set the number of pre-selected investigators to Q=M, where M represents the preset maximum number of pre-selected investigators, sort the pre-selected investigators from high to low according to the degree of adaptation, and select the Q pre-selected mediators with the highest degree of adaptation as the pre-selected mediators. The set of pre-selected mediators is {G1,G2,…,G q ,…,G Q}, where Q represents the number of pre-selected mediators, G q This represents the mediator suitability assessment mechanism for the qth pre-selected mediator, based on keywords and historical data. By analyzing the frequency of keywords in historical cases and assigning weights, this mechanism accurately identifies the core dispute points in the case. Combining the mediator's success rate in similar cases and overall caseload, it comprehensively calculates suitability, avoiding the one-sidedness of a single metric. This multi-dimensional assessment model considers both the mediator's overall mediation capabilities and their expertise in specific dispute areas, ensuring that complex cases are matched with mediation teams with both comprehensive competence and specialized expertise. Sorting pre-selected mediators by suitability dynamically optimizes resource allocation, prioritizing those with extensive experience handling key case elements, shortening the mediation cycle, and improving the relevance of solutions. Furthermore, this mechanism replaces the fuzzy judgment of traditional manual assignments with data-driven quantitative analysis, reducing human error and resource misallocation. This promotes a more scientific and refined mediation process, provides technical support for efficient dispute resolution, and contributes to the development of a more professional and credible mediation service system.

[0044] In step S4, after authorization, the current workload of the pre-selected mediators is analyzed. The current workload set of the pre-selected mediators is {g1, g2, ..., g q ,…,g Q}, where g qrepresents the current workload of the qth pre-selected mediator, which represents the number of mediation cases that the qth pre-selected mediator needs to mediate when a new mediation case is logged in. If g q ≤g0, then retain the information of the qth pre-selected mediator; if g q >g0, then delete the information of the qth pre-selected mediator from the set of pre-selected mediators, where g0 represents the preset maximum workload of the mediator, and delete the information of a total of J pre-selected mediators. If M-M0≥q, then select the J pre-selected mediators with the highest degree of adaptability from the preliminary investigators who have never been selected as pre-selected mediators in the newly added mediation cases as pre-selected mediators, and perform workload analysis again; if 0<M-M0<J, then select the M-M0 pre-selected mediators with the highest degree of adaptability from the preliminary investigators who have never been selected as pre-selected mediators in the newly added mediation cases as pre-selected mediators, and select the J-(M-M0) pre-selected mediators from the mediators who have never been selected as pre-selected investigators in the newly added mediation cases as pre-selected mediators; if M-M0≤0, select the J pre-selected mediators from the mediators who have never been selected as pre-selected investigators in the newly added mediation cases as pre-selected mediators, until the workload of the Q pre-selected mediators is less than or equal to g0. The mechanism of dynamically adjusting the pre-selected mediators based on workload can realize the real-time optimization allocation and sustainable utilization of mediation resources. By analyzing the current workload and eliminating overloaded personnel, we can avoid case delays or reduced mediation quality caused by overburdened mediators, ensuring that each case receives sufficient attention. The hierarchical supplementation rules prioritize unselected personnel with high adaptability, maintaining a professional matching logic, while also enabling cross-selection when resources are insufficient, ensuring the integrity and flexibility of the mediation process. This closed-loop mechanism of screening, supplementation, and re-evaluation breaks the limitations of the traditional fixed allocation model, allowing mediation resources to fluctuate dynamically with case demand and personnel load, balancing workload while maintaining professionalism. Data-driven real-time scheduling can reduce subjective biases in manual allocation, avoid resource misallocation or idleness, and improve overall mediation efficiency. It also conveys a standardized and transparent service image to the parties, enhancing their trust in the fairness of the mediation process and laying the foundation for building an efficient and sustainable dispute resolution system.

[0045] In step S5, the information of the Q pre-selected mediators is logged into the newly added mediation case attached information tag until any pre-selected mediator accepts the newly added mediation case, in which case the newly added mediation case attached information tag is hidden.

[0046] In step S6, if the pre-selected mediator successfully mediates the newly added mediation case, the attached information tag of the newly added mediation case is deleted. Otherwise, the r most suitable mediators are selected from the remaining pre-selected mediators as the pre-selected mediators for secondary mediation. The attached information tag of the newly added mediation case is re-logged in, and the pre-selected mediators for secondary mediation are allowed to mediate the newly added mediation case. The r represents the preset number of pre-selected mediators for secondary mediation. The attached information tag is deleted when the mediation is successful, which can timely clean up invalid data, keep the system simple and efficient, and avoid information redundancy interfering with subsequent processes. If the mediation fails, the secondary mediation is initiated based on the ranking of the remaining pre-selected mediators based on their degree of adaptability. This "tiered" resource call model not only avoids the repetitive work of screening from scratch, but also quickly locks in backup forces with high professional matching, maintaining the consistency and pertinence of the mediation strategy. The pre-set number of pre-selected mediators for secondary mediation mechanism can concentrate superior resources within a limited scope to tackle complex cases, reducing the communication costs and trust loss caused by the frequent replacement of mediators. At the same time, the mechanism ensures that cases are always in a follow-up state through closed-loop management, avoiding the intensification of conflicts caused by "interruption of mediation". It not only reflects the humanity and responsible attitude of mediation services, but also enhances the parties' confidence in dispute resolution through professional and continuous intervention, helping to build a more resilient and flexible mediation work system, and effectively improving the overall effectiveness of conflict resolution.

[0047] A mediation status monitoring system based on data analysis, comprising: a mediation application keyword extraction module, a mediator preliminary selection module, a keyword adaptation degree analysis and screening module, a mediator workload balance screening and supplementation module, a pre-selected mediator information login module, and a mediation case completion status ancillary information adjustment module;

[0048] The mediation application keyword extraction module is used to establish a mediation application keyword database and extract application keywords based on the mediation application of newly added mediation cases;

[0049] The mediator preliminary selection module is used to collect the historical mediation data of the mediator, screen the mediators based on the historical mediation data of the mediator, and select the preliminary mediators;

[0050] The keyword adaptation degree analysis and screening module is used to analyze the adaptation degree of the pre-selected mediators to the keywords based on the mediation history data, and screen out the pre-selected mediators;

[0051] The mediator workload balance screening and supplementation module is used to screen pre-selected mediators according to their workload and supplement pre-selected mediators from the mediators until the workload of the pre-selected mediators meets the requirements;

[0052] The pre-selected mediator information login module is used to log the pre-selected mediator's information into the newly added mediation case attached information tag until the newly added mediation case is accepted;

[0053] The mediation case completion status ancillary information adjustment module is used to adjust the ancillary information labels of newly added mediation cases according to the completion status of the newly added mediation cases.

[0054] Example 1: We extract high-frequency keywords such as contract disputes, property disputes, and family conflicts to build a dynamically updated keyword library for mediation applications. When staff receive a new case, the system automatically scans the application content and quickly tags the case type through keyword matching. For example, if it recognizes terms such as "rent arrears" or "shop transfer," it automatically categorizes it as a contract dispute case, reducing the time and cost of manual classification.

[0055] Next, the system analyzes the parties' mediation history. For first-time mediation parties, the system assumes they have no specific mediator requirements. For repeat mediation parties, the system calculates their need for mediators in different fields based on the number of cases they have previously been involved in and the proportion of similar cases.

[0056] The system then further evaluates the suitability of the shortlisted mediators. Focusing on the core dispute points of the case, the system searches the mediator's past success rate in handling similar issues and combines this with their overall mediation performance to generate a comprehensive suitability score. For example, if a mediator has successfully resolved dozens of similar conflicts in contract disputes, the system will assign them a higher matching priority, ensuring a precise alignment of their expertise with the case's needs.

[0057] Before pre-selecting mediators, the system monitors their current workload in real time and automatically excludes those with full workloads to avoid case backlogs. If the number of available mediators is insufficient, the system will expand the selection process from highest to lowest suitability scores, prioritizing the use of specialized mediators from the reserve talent pool to ensure that each case is promptly assigned to a mediator with the appropriate workload.

[0058] If the initial mediation attempt is unsuccessful, the system automatically recommends backup mediators from among the remaining eligible mediators based on pre-set rules, creating a "tiered" mediation pipeline and minimizing the cost of repeated communication between the parties. Following a successful mediation, the system automatically archives the case information and simultaneously updates the mediator's competency tags and case handling data, providing a more accurate reference for subsequent resource allocation.

[0059] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. A mediation status monitoring method based on data analysis, characterized by: The method comprises the following steps: S1. Establish a keyword database for mediation applications and extract application keywords based on the mediation applications of newly added mediation cases; S2. Collect the historical mediation data of the mediated person, screen the mediators based on the historical mediation data of the mediated person, and select the preliminary mediators; S3. Based on the mediation history data, analyze the adaptability of the pre-selected mediators to the keywords and select the pre-selected mediators; S4. Screen the pre-selected mediators according to their workload and add pre-selected mediators from the mediators until the workload of the pre-selected mediators meets the requirements; S5. Log the pre-selected mediator's information into the newly added mediation case's attached information tab until the newly added mediation case is accepted; S6. Adjust the information labels attached to the newly added mediation cases based on the completion status of the newly added mediation cases; In step S2, after authorization by the mediator, the mediator includes {A1, A2, ..., A i ,…,A I }, where I represents the number of mediators, A i Represents the ith mediator, analyzes the mediation history data of the mediator, and obtains the number of mediation cases involving the mediator, including the number of mediators A i The number of mediation cases is X i , of which the number of mediation cases of the same type as the newly added mediation cases is Y i , set the mediator A i The demand for mediators is x i and mediator A i The demand for mediator type α is y i , when X i ≤X0, the mediator is judged to be a new mediator, and mediator A is ordered to i Demand for mediators x i =0, let the mediator A i Demand for mediator α type y i =0; when X i >X0, the mediator is determined to be a new mediator, and mediator A is i Demand for mediators x i =X i , let the mediator A i Demand for mediator α type y i =Y i； Substitute i=1,2,…,I one by one, and get the demands of I mediators for investigators {x1,x2,…,x i ,…,x I }, we get the mediator’s demand {y1,y2,…,y i ,…,y I }, the total demand of the mediators for investigators is X, and the total demand of the mediators for mediators of type α is Y. Then, mediators with a history of mediation cases greater than X and a history of mediation of type α greater than Y are selected as preliminary mediators. The set of preliminary mediators is {C1, C2, …, C m ,…,C M }; In step S4, after authorization, the current workload of the pre-selected mediators is analyzed, and the current workload set of the pre-selected mediators is {g1, g2, ..., g q ,…,g Q }, where g q represents the current workload of the qth pre-selected mediator, which represents the number of mediation cases that the qth pre-selected mediator needs to mediate when a new mediation case is logged in. If g q ≤g0, then retain the information of the qth pre-selected mediator; if g q >g0, then delete the information of the qth pre-selected mediator from the pre-selected mediator set, where g0 represents the preset maximum workload of the mediator, and delete the information of J pre-selected mediators in total. If M-M0≥q, then select the J pre-selected mediators with the highest degree of adaptability from the preliminary investigators who have never been selected as pre-selected mediators in the newly added mediation cases as pre-selected mediators, and perform workload analysis again; if 0<M-M0<J, then select the M-M0 pre-selected mediators with the highest degree of adaptability from the preliminary investigators who have never been selected as pre-selected mediators in the newly added mediation cases as pre-selected mediators, and select the J-(M-M0) pre-selected mediators from the mediators who have never been selected as pre-selected investigators in the newly added mediation cases as pre-selected mediators; if M-M0≤0, select the J pre-selected mediators from the mediators who have never been selected as pre-selected investigators in the newly added mediation cases, and so on until the workload of the Q pre-selected mediators is less than or equal to g. 0； In step S5, the information of the Q pre-selected mediators is logged into the "New Mediation Case Attached Information" tag, and the "New Mediation Case Attached Information" tag is hidden until any pre-selected mediator accepts the new mediation case. In step S6, if the pre-selected mediator successfully mediates the newly added mediation case, the attached information tag of the newly added mediation case is deleted; otherwise, the r most suitable mediators are selected from the remaining pre-selected mediators as the pre-selected mediators for secondary mediation, and the attached information tag of the newly added mediation case is re-logged in, waiting for the pre-selected mediators for secondary mediation to mediate the newly added mediation case, where r represents the preset number of pre-selected mediators for secondary mediation.

2. The method for monitoring mediation status based on data analysis according to claim 1, characterized in that: In step S1, a mediation application keyword database is established. The mediation application keyword database includes pre-set keywords. When a new mediation case γ is received by the responsible person and the mediation application is uploaded, the keywords in the mediation application are monitored. The keyword set is {D1, D2, ..., D n ,…,D N }, and mark the case type as α.

3. The method for monitoring mediation status based on data analysis according to claim 2, characterized in that: In step S3, for keyword D n Conduct analysis and screen the preliminary mediator C m Historical mediation cases, the primary mediator C m The success rate of historical mediation cases is F m1 , screen the preliminary mediator C m The historical mediation cases contain the keyword D n The success rate of mediation cases is F m2 , set keyword weight k1, k1=β n / β, set the case weight k2, k2=(1-β n ) / β, where β represents the total number of historical mediation cases, β n Indicates that historical mediation cases contain keyword D n The total number of primary mediators C m Keyword D n Priority W m_n , W m_n =k1*F m1 +k2*F m2 , one by one into n = 1, 2, ..., N, to get the primary mediator C m For N keywords D n The priority of {W m_1 ,W m_2 ,…,W m_n ,…,W m_N }, sum to get the primary mediator C m The degree of adaptation W to the newly added mediation case γ m .

4. The method for monitoring mediation status based on data analysis according to claim 3, characterized in that: Substitute m=1,2,…,M one by one to obtain the adaptability of M primary investigators to the newly added mediation case γ {W1,W2,…,W m ,…,W M }, set the number of pre-selected investigators to Q, when M≥M0, set the number of pre-selected investigators to Q=M0; when M<M0, set the number of pre-selected investigators to Q=M, where M represents the preset maximum number of pre-selected investigators, sort the pre-selected investigators from high to low according to the degree of adaptation, and select the Q pre-selected mediators with the highest degree of adaptation as the pre-selected mediators. The set of pre-selected mediators is {G1,G2,…,G q ,…,G Q }, where Q represents the number of pre-selected mediators, G q represents the qth pre-selected mediator.

5. A mediation status monitoring system based on data analysis, wherein the system is applied to the mediation status monitoring method based on data analysis according to any one of claims 1 to 4, characterized in that: The system includes: a mediation application keyword extraction module, a mediator preliminary selection module, a keyword adaptation degree analysis and screening module, a mediator workload balance screening and supplement module, a pre-selected mediator information login module and a mediation case completion status ancillary information adjustment module; The mediation application keyword extraction module is used to establish a mediation application keyword database and extract application keywords based on the mediation application of the newly added mediation case; The mediator preliminary selection module is used to collect the historical mediation data of the mediator, screen the mediators according to the historical mediation data of the mediator, and select the preliminary mediators; The keyword adaptation degree analysis and screening module is used to analyze the adaptation degree of the pre-selected mediators to the keywords based on the mediation history data, and screen out the pre-selected mediators; The mediator workload balance screening and supplementation module is used to screen pre-selected mediators according to their workload and supplement pre-selected mediators from the mediators until the workload of the pre-selected mediators meets the requirements; The pre-selected mediator information login module is used to log the pre-selected mediator's information into the newly added mediation case attached information tag until the newly added mediation case is accepted; The mediation case completion status attached information adjustment module is used to adjust the attached information label of the newly added mediation case according to the completion status of the newly added mediation case.

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