Multi-dispute management and court auxiliary platform based on block chain evidence storage

Abstract

The invention relates to the technical field of cloud fusion application operation support platforms, and discloses a multi-dispute management and court auxiliary platform based on block chain evidence storage, which breaks through a data barrier between a court and a multi-mediation organization and even other organizations by building a collaborative platform with an independent domain name, and improves the management efficiency of the court. And online closed-loop management of the full life cycle of the case is realized. According to the method, block chain type evidence storage and API interface combination are utilized, a trusted data flow penetrating through multiple subjects is constructed, and tampering resistance and traceability of the judicial cooperation process are ensured. Besides, an intelligent case allocation and outbound module converts an empirical rule into a data model, so that accurate matching and mediation assistance of people and cases are realized, and the specialization level and efficiency of dispute resolution are remarkably improved. On the whole, a scattered and low-efficiency offline cooperation mode is converted into an intensive, intelligent and credible online cooperation network, and the case management refinement level and judicial resource allocation efficiency are comprehensively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cloud fusion application running support platform, and particularly relates to a multi-element dispute management and court auxiliary platform based on block chain storage. BACKGROUND

[0002] At present, under the background of building a diversified dispute resolution mechanism, especially the "big mediation" work pattern, the coordination of cases between the court and various external mediation organizations has become the norm. However, the related information support system has significant defects.

[0003] Firstly, the case management is in a fragmented state, and the trial management system within the court, the mediation docking platform and the self-built system of various external mediation organizations are often independent of each other, forming multiple "information islands". Secondly, the data barriers between systems are strict, and due to the lack of safe and standardized data channels, key data such as case information, mediation records and results cannot be automatically transferred between the court and the mediation organization, relying on manual offline transmission and repeated input, which is not only inefficient, but also has a high error rate and difficulty in leaving traces throughout the process. From case assignment, mediation progress tracking to result feedback, each link seriously relies on traditional communication methods such as telephone and email, the process transparency is low, and the state is unknown, which leads to slow response of cross-organizational cooperation and difficulty in supervision and management. This series of technical bottlenecks seriously restricts the potential of the "big mediation" mechanism in improving the efficiency of dispute resolution and optimizing the allocation of judicial resources. SUMMARY

[0004] Therefore, the present application aims to provide a multi-element dispute management and court auxiliary platform based on block chain storage to solve the technical problems of scattered case management of existing mediation centers, incompatibility of internal and external system data, and low efficiency of process cooperation.

[0005] The present application discloses a multi-element dispute management and court auxiliary platform based on block chain storage, which comprises: An access and permission module is used to provide a login portal for different responsible subject users, and configure different data views and operation permissions according to the user roles; the responsible subjects include internal personnel of the court and external mediation organizations; A data receiving and processing module is used to receive case dispute data and determine the type identifier of the case according to the preset rules; the type identifier includes a mediation case identifier and a non-mediation case identifier; for the mediation responsible person, the operation permission includes uniformly assigning the mediation cases received by the account to the corresponding mediation organization; A data synchronization module is used to connect the internal information system of the mediation organization through a standard data interface, and encrypt and synchronize the mediation records of the case; The blockchain evidence storage module is used to generate corresponding digital fingerprints of business data at business process nodes executed by different responsible parties for the same dispute case, and submit the digital fingerprints to the blockchain network for evidence storage based on the order and relevance of the business processes.

[0006] Furthermore, the process of submitting digital fingerprints to the blockchain network for evidence storage based on the business process sequence and correlation specifically includes: In response to the first responsible entity completing the first type of business operation and obtaining the first business data, a first digital fingerprint corresponding to the first business data is generated and submitted to the blockchain network for storage. In response to the Nth responsible entity completing the Nth type of business operation based on the N-1th business data, obtaining the Nth business data, the N-1th digital fingerprint is associated with the Nth business data to generate the Nth digital fingerprint and submit it to the blockchain network for evidence storage; wherein, the first digital fingerprint to the Nth digital fingerprint form a sequential dependency relationship with the case business process through the blockchain evidence storage record; N is the number of responsible entities, N≥2.

[0007] Furthermore, after completing the digital fingerprint storage of the final mediation document, the blockchain storage module is also used to generate a judicial blockchain storage certificate; the storage certificate is configured as an electronic file attached to the final mediation document, including a unique storage number generated based on the storage information and a machine-readable identifier code for accessing the blockchain verification service.

[0008] Furthermore, the platform also includes an external verification service module; The external verification service module is used to receive evidence storage query requests from external verification parties. The evidence storage query request carries verification index information obtained from the judicial blockchain evidence storage certificate. The blockchain network is queried according to the verification index information to obtain the corresponding verification result information, which is then sent back to the external verification party. The verification result information includes the evidence storage status and the evidence storage time, but does not include the specific content data of the final mediation document.

[0009] Furthermore, the data synchronization module includes a data rule engine for storing and managing data transformation rule sets corresponding to the internal information systems of different mediation organizations; the data transformation rule sets include field mapping rules and data cleaning rules; wherein, the field mapping rules represent the correspondence between data fields within the platform and expected data fields in the target mediation organization's internal information system; the data cleaning rules include specific processing logic for judicial data characteristics.

[0010] Furthermore, the data synchronization module also includes a verification unit for performing redundancy verification on data representing key business operations; The redundancy check process includes: At critical business nodes, an online confirmation process that runs parallel to automatic API synchronization is forcibly triggered; the online confirmation process provides a confirmation interface that is pre-loaded with data obtained through API synchronization. Receive confirmation data submitted by the responsible user through the confirmation interface; The confirmed data is compared with the data obtained synchronously through the API. If the comparison results are inconsistent, an alarm is triggered and the subsequent process is suspended until an authorized manual arbitration instruction is obtained.

[0011] Furthermore, the platform also includes an intelligent outbound calling module; The intelligent outbound call module includes a mediation strategy engine, which is used to match and generate mediation strategy prompts for the current outbound call task from a pre-set strategy rule library based on the case domain type, current process stage, and the parties' historical communication records, and dynamically display them on the outbound call interface; the strategy rules are associated with specific case characteristic conditions and corresponding strategy prompt content.

[0012] Furthermore, the intelligent outbound calling module also includes a recording transcription and transcript generation unit, which is used to convert the call audio stream into a text stream in real time after obtaining the consent of the party concerned and starting the call recording; Based on the mediation record template corresponding to the case type, key statements in the text stream are automatically identified and filled into the corresponding fields of the template, generating a pre-filled draft mediation record for mediators to review and modify.

[0013] Furthermore, the platform also includes a case allocation module, comprising a judge multi-dimensional profiling unit, a case feature analysis unit, and a recommendation unit; wherein, The judge multidimensional profile unit is used to construct and dynamically update the judge's professional competence profile based on the judge's historical case handling data; the profile includes the case type of expertise tag, the average trial time of historical cases, and the case complexity index handling ability coefficient; The case feature analysis unit is used to calculate the case complexity index based on the information of cases to be assigned; The recommendation unit is used to establish an optimization model with the goal of minimizing the global judge workload difference and maximizing the professional matching degree, and outputs a ranking list of recommended judges based on the multi-dimensional profile of judges and the case complexity index.

[0014] Furthermore, the case allocation module also includes a strategy interaction interface, which visually displays the sorting list to administrators with case allocation authority, and simultaneously displays the current workload heatmap and professional matching details of each candidate judge; The load heatmap visually displays the load value of each judge in each time period; the factors that determine the load value include the number of pending cases for each judge, the complexity index of each pending case, the scheduled court hearing time, and the estimated time for various administrative tasks.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention breaks down data barriers between courts, diverse mediation organizations, and even other institutions by establishing a collaborative platform with an independent domain name, achieving closed-loop online management of the entire case lifecycle. Specifically, it utilizes a combination of blockchain-based chain-of-sale evidence storage and API interfaces to construct a trusted data flow spanning multiple entities, ensuring the immutability and traceability of the judicial collaboration process. Furthermore, the intelligent case allocation and outbound call modules transform empirical rules into data models, enabling precise matching of cases and individuals and providing mediation assistance, significantly improving the professionalism and efficiency of dispute resolution. Overall, it transforms the fragmented and inefficient offline collaboration model into an intensive, intelligent, and reliable online collaborative network, comprehensively enhancing the precision of case management and the efficiency of judicial resource allocation. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the structure of a multi-faceted dispute management and court assistance platform based on blockchain evidence storage disclosed in Embodiment 1 of the present invention. Detailed Implementation

[0017] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0018] Example 1

[0019] This invention discloses a multi-faceted dispute management and court assistance platform based on blockchain-based evidence storage. This platform is a cloud-integrated application operation support platform, and in this invention, the platform uses an independent domain name. Please refer to [link / reference]. Figure 1 , Figure 1 This is a schematic diagram of the structure of a blockchain-based multi-faceted dispute management and court assistance platform disclosed in an embodiment of the present invention. The platform includes: The access and permissions module is used to provide login portals for users of different responsible entities and to configure different data views and operation permissions according to user roles; the responsible entities include internal court personnel and external mediation organizations; The data receiving and processing module is used to receive case dispute data and determine the case type identifier according to preset rules; the type identifier includes a mediation case identifier and a non-mediation case identifier; for the person in charge of mediation, the operation authority includes uniformly allocating the mediation cases received by the account to the corresponding mediation organization; The data synchronization module is used to connect with the mediation organization's internal information system through a standard data interface to encrypt and synchronize the mediation records of cases. The blockchain evidence storage module is used to generate corresponding digital fingerprints of business data at business process nodes executed by different responsible parties for the same dispute case, and submit the digital fingerprints to the blockchain network for evidence storage based on the order and relevance of the business processes.

[0020] In this embodiment of the invention, the aforementioned responsible entities include, but are not limited to, court personnel and external mediation organizations, and may also include third-party professional institutions. These third-party professional institutions include at least judicial appraisal institutions that have been reviewed and registered by the court. Their permissions on the platform are configured to securely receive appraisal requests matching their professional field, upload appraisal process documents and final appraisal reports, and synchronize data with the court's internal case processes through the platform interface.

[0021] Specifically, court internal personnel refer to users who access the platform through the court's internal network or dedicated line. Their roles include at least personnel from the case filing division, the trial management office (trial administration office), judges from various business divisions, and judicial assistants. Their operational permissions and data views on the platform are configured according to their division, position, and responsibilities, mainly covering case filing import, process approval, task allocation, trial scheduling, case closure archiving, and overall process monitoring and data analysis. External mediation organizations refer to mediation institutions and their mediators that are certified and authorized by the court to access the platform. They access the platform after secure authentication via the internet, and their permissions and views are strictly limited to the cases they are assigned. Their main functions are to receive cases online, schedule mediation, record and upload the mediation process, synchronize mediation results, and achieve encrypted data exchange between their internal business systems and the platform through standard data interfaces.

[0022] This invention establishes an access and permission module to authenticate and isolate the rights and permissions of the aforementioned responsible parties, ensuring clear responsibilities and data security during internal and external collaboration in business processes. The blockchain evidence storage module generates a chain of evidence with sequential dependencies based on data submitted or confirmed by different responsible parties at key business nodes, thus solidifying the responsibilities and operational facts of each party.

[0023] Regarding the case dispute data reception method in the data reception and processing module, this platform is designed to be compatible with multiple heterogeneous data sources. For example, the head of the case filing division can export an Excel file conforming to a predetermined format from the superior litigation service network or trial system, and then upload the file through the case import interface provided on this platform. The system parses the Excel file, reads the preset fields (such as "case number," "parties," "cause of action," and "suitability for mediation"), and converts them into the system's internal standard data structure. Furthermore, the reception method is not limited to file import; it can also include: establishing a dedicated interface with the court's internal trial system through data exchange middleware for timed or real-time data synchronization, or receiving standard data packets conforming to JSON / XML format. This multi-mode reception design ensures that the platform can adapt to the access needs of courts with different levels of informatization.

[0024] Upon receiving data, the system first determines whether it falls within the scope of cases eligible for import into the platform based on the case source identifier or case nature field. Then, it assigns a type identifier to the case according to pre-defined case type identification rules. The core of these rules is to distinguish between cases suitable for mediation and those not requiring mediation or expert evaluation. Understandably, cases suitable for mediation typically refer to disputes with relatively clear facts, well-defined rights and obligations, and minimal contention, such as some marriage and family cases, neighborhood disputes, and small debt cases. Identifying these cases aims to prioritize their placement in non-litigation mediation procedures, diverting them through the platform to corresponding specialized mediation organizations to resolve conflicts efficiently and peacefully, thereby reducing the increase in litigation at its source.

[0025] Furthermore, the process of submitting digital fingerprints to the blockchain network for evidence storage based on the sequence and relevance of business processes specifically includes: In response to the first responsible entity completing the first type of business operation and obtaining the first business data, a first digital fingerprint corresponding to the first business data is generated and submitted to the blockchain network for storage. In response to the Nth responsible entity completing the Nth type of business operation based on the N-1th business data, obtaining the Nth business data, the N-1th digital fingerprint is associated with the Nth business data to generate the Nth digital fingerprint and submit it to the blockchain network for evidence storage; wherein, the first digital fingerprint to the Nth digital fingerprint form a sequential dependency relationship with the case business process through the blockchain evidence storage record; N is the number of responsible entities, N≥2.

[0026] Furthermore, after completing the digital fingerprint storage of the final mediation document, the blockchain storage module is also used to generate a judicial blockchain storage certificate. The storage certificate is configured as an electronic file attached to the final mediation document, including a unique storage number generated based on the storage information and a machine-readable identifier for accessing the blockchain verification service.

[0027] Furthermore, the platform also includes an external verification service module; The external verification service module is used to receive evidence query requests from external verification parties, and the requests carry verification index information obtained from the judicial blockchain evidence storage certificate. The blockchain network is queried according to the verification index information to obtain the corresponding verification result information, which is then sent back to the external verification party. The verification result information includes the evidence storage status and the evidence storage time, but does not include the specific content data of the final mediation document.

[0028] In this embodiment of the invention, the core of setting up the blockchain evidence storage module lies in constructing a digital trust chain that is completely mirrored and tamper-proof with the offline judicial collaborative business process. This process is not simply about independently putting multiple data on the chain, but rather about strongly linking the data and responsibilities of each stage in the business process.

[0029] Specifically, when a case enters the system, the primary responsible party (such as court case filing personnel) completes the first type of business operation—case registration and filing—generating structured electronic case information, i.e., the first business data. The system then calculates a unique hash value for this data packet, i.e., the first digital fingerprint H1, and packages it with information such as a timestamp, sending it to a blockchain network (such as a judicial consortium blockchain) for evidence storage. This creates a unique and trustworthy starting point for the digital process of the case on the blockchain, i.e., a trust anchor. When the case moves to the next stage, such as being assigned to an external mediation organization for mediation, when the secondary responsible party (mediation organization / mediator) completes the mediation and forms a record—the second type of business operation—generating the mediation record, i.e., the second business data, the system does not only store the record itself. To achieve a chain-locking of responsibility and process, the system performs a key operation: binding H1, representing the trustworthiness of the previous stage, with the current second business data to jointly generate a new and richer hash value, i.e., the second digital fingerprint H2, and then uploading it to the blockchain.

[0030] This binding involves treating H1 as plaintext or metadata, and participating in the hash calculation along with the second business data. This means that any tampering with the original case information, i.e., the data corresponding to H1 or the content of the mediation record, will cause the final calculated H2 to be inconsistent with the H2 recorded on the chain, thus instantly exposing the discrepancy.

[0031] By linking the digital fingerprints of business data at each stage onto the blockchain in a logical order, a tightly interlocking chain of trust is formed. This ensures that critical data throughout the entire process, from case filing to case closure, remains tamper-proof, and any changes made in intermediate stages are detected during final verification. This provides accurate and reliable technical evidence for judicial supervision and quality assessment. In loosely coupled collaborations involving courts, mediation organizations, and other parties, traditional methods struggle to prove that data has not been tampered with during transmission. This invention achieves automatic transfer and accumulation of trust by including the fingerprints of previous stages in the evidence stored at each subsequent stage. Data submitted by mediation organizations is trustworthy because it contains the fingerprint of the court's case filing, and the same applies to subsequent stages. This reduces reliance on any single centralized institution and establishes a decentralized foundation of collaborative trust.

[0032] The above process can be repeated according to the business workflow (N≥2). For example, when a judge conducts judicial confirmation (as a third party), H2 will be associated with the confirmation document data to generate H3 and uploaded to the blockchain. When the process reaches a key endpoint (such as generating a formal mediation agreement or judicial confirmation ruling), the blockchain evidence storage module will generate a judicial blockchain evidence storage certificate based on the evidence storage record of the final document. This certificate contains a unique number for this evidence storage and a machine-readable identifier (such as a QR code), becoming the digital identity card of the document in the blockchain network.

[0033] Furthermore, to facilitate the social application of documents, such as verification at real estate registration centers, the platform provides verification services through an external verification module. External institutions can scan the QR code on the certificate to initiate a verification request to the platform. The platform queries the blockchain based on the index information in the QR code, returning only the conclusion that the document was "certified at XX time and the content is complete" along with the certificate's date, without transmitting or returning the document's specific content. This proves the document's authenticity while fully protecting the privacy of the case.

[0034] In this embodiment of the invention, the generated judicial blockchain evidence storage certificate and external verification service enable mediation documents to be conveniently and efficiently verified online by other social institutions (such as banks and real estate registration centers) without exposing sensitive content. This greatly enhances the authority and circulation of judicial mediation documents, bridging the last mile in applying the results of litigation-mediation linkage to social governance, and is a direct manifestation of technology empowering judicial credibility. By combining evidence storage with automated business processes, evidence storage is achieved through operation, without the need for additional manual intervention. This not only improves efficiency but also ensures the rigor and authority of the judicial collaboration process through technological rigidity, providing a solid digital infrastructure for diversified dispute resolution mechanisms.

[0035] Furthermore, the data synchronization module includes a data rule engine for storing and managing data transformation rule sets corresponding to the internal information systems of different mediation organizations; the data transformation rule sets include field mapping rules and data cleaning rules; wherein, the field mapping rules represent the correspondence between data fields within the platform and expected data fields in the target mediation organization's internal information system; the data cleaning rules include specific processing logic for the characteristics of judicial data.

[0036] In this embodiment of the invention, the core of setting up the data synchronization module lies in realizing the secure, accurate, and automated exchange of case mediation records between this platform and the internal information systems of various external mediation organizations. Its encrypted synchronization process is an intelligent workflow integrating rule-driven mechanisms, data conversion, and secure transmission.

[0037] Specifically, before data is sent, the module calls its built-in data rule engine to perform standardized preprocessing on the platform's native data. This engine stores and manages data transformation rule sets that correspond one-to-one with different mediation organization systems. When data needs to be synchronized with a specific mediation organization, the engine automatically loads the corresponding rule set. Field mapping rules take effect first, automatically converting internal platform field names into the target system's expected field names, ensuring that both systems have a consistent understanding of the data semantics. Next, data cleaning rules are used to perform in-depth processing of the data content based on the characteristics of judicial data. For example, the internally used numerical codes of the "Case Type Provisions" (such as 101) are converted into standard text readable by the other system (such as divorce disputes); the parties' ID numbers are standardized into the standard format of "X digits + X check digits," etc. This preprocessing stage fundamentally solves the technical barriers caused by system heterogeneity and inconsistent data standards, enabling semantic alignment and format purification of cross-system data. This lays a solid foundation for subsequent automated exchange and greatly reduces the cost of manual cleaning and verification.

[0038] Standardized data packets, after being transformed and cleaned by the rules engine, enter the encrypted transmission stage. This invention employs a two-way authentication and hybrid encryption mechanism to ensure channel security. Specifically, a transport layer encrypted channel is established based on the TLS / SSL protocol, and the data packets themselves are encrypted at the application layer. Asymmetric encryption is performed using the public key provided by the target mediation organization, ensuring that only the legitimate recipient holding the corresponding private key can decrypt the data. Simultaneously, a digital signature generated based on the sender's private key is appended to the data packets, allowing the recipient to verify the authenticity and integrity of the data source and preventing data tampering or impersonation during transmission. This end-to-end deep encryption strategy ensures the confidentiality, integrity, and non-repudiation of sensitive judicial data transmitted in a public network environment, meeting the extremely high data security requirements of judicial collaboration.

[0039] After the data arrives at the mediation organization's internal system and is successfully decrypted and verified, it is written to its local database, completing the synchronization of case dispute data with the mediation organization. The reverse synchronization logic for the mediation organization's mediation records and results is the same as the above process, and will not be repeated here. The entire synchronization process can be automatically triggered by events (such as mediators submitting records) or executed periodically according to preset cycles. This module, through a combination of rule engine preprocessing and high-strength encrypted transmission, not only achieves seamless interoperability of data between different heterogeneous systems, but also builds a secure, reliable, and automated data pipeline. It completely changes the inefficient and error-prone data exchange mode that previously relied on manual export, format conversion, and email sending, raising the accuracy, timeliness, and security of cross-organizational data collaboration to a new level. It is a key technological support for breaking down information silos and realizing the vision of one-stop online services.

[0040] Furthermore, the data synchronization module also includes a verification unit for redundancy verification of data representing critical business operations. The redundancy verification process includes: At critical business nodes, an online confirmation process that runs parallel to automatic API synchronization is forcibly triggered; the online confirmation process provides a confirmation interface that is pre-loaded with data obtained through API synchronization. Receive confirmation data submitted by the responsible user through the confirmation interface; The confirmed data is compared with the data obtained synchronously through the API. If the comparison results are inconsistent, an alarm is triggered and the subsequent process is suspended until an authorized manual arbitration instruction is obtained.

[0041] To ensure the absolute accuracy and authority of data at critical business nodes and to avoid serious consequences due to occasional errors, network anomalies, or momentary inconsistencies in the underlying data state that may occur during automatic system synchronization, the data synchronization module of this invention further integrates a verification unit and implements a redundant verification mechanism. The core of this mechanism lies in generating a separate channel for data representing the critical business status (such as the terms of a finalized mediation agreement or judicial confirmation results), in addition to the automated API synchronization channel, based on confirmation by the responsible user. This dual-channel data comparison ensures the accuracy of the final data stored in the database.

[0042] The mandatory confirmation process creates a clear record of responsible actions, which, combined with blockchain evidence storage, makes the entire collaborative process not only credible in its outcome but also traceable at every key confirmation step, greatly enhancing the foundation of trust between the court and the mediation organization.

[0043] Furthermore, as a preferred implementation, the data synchronization module is also used for dynamic synchronization based on confidence level. This operation can adaptively optimize the data collaboration mode with different mediation organizations and intelligently allocate system resources while ensuring data accuracy. Specifically, the system maintains and dynamically updates a data synchronization confidence index for each external mediation organization that connects. This index is a comprehensive quantitative value, and its calculation mainly depends on the organization's historical synchronization behavior data. Core parameters include, but are not limited to, the frequency of data conflict alarms triggered in the redundancy verification process, the proportion of synchronized data overwritten due to manual correction, and the format standardization of data submission and the pass rate of the first pass through the rule engine cleaning.

[0044] By weighting these parameters and calculating the resulting confidence level, the system automatically executes differentiated synchronization strategies. For mediation organizations with a confidence level consistently above the high threshold, the system determines that their data submissions are of stable and reliable quality. Therefore, it adopts an efficiency-first API synchronization model with key node sampling checks. In this model, most routine data flows are automatically completed through efficient API interfaces. The system only randomly triggers redundant verification processes for sampling and review at the most critical business nodes, thereby ensuring the integrity of critical data while minimizing the interference of manual confirmation on collaboration efficiency. Conversely, for newly joined organizations with a confidence level below the low threshold or organizations with frequent historical conflicts, the system automatically activates a full-process dual-channel synchronization mode. In this mode, the synchronization of all business data of the organization, regardless of whether the node is critical, is forcibly triggered in parallel with API synchronization and online confirmation processes. A complete comparison is performed by the verification unit to build a comprehensive calibration mechanism.

[0045] Through the above operations, the embodiments of the present invention intelligently solve the problem of balancing collaborative efficiency and data security, enabling system resources to be accurately allocated to the links that require the most supervision, thereby achieving a comprehensive improvement in the security, efficiency and adaptability of the cross-organizational data collaboration system.

[0046] Furthermore, the platform also includes an intelligent outbound calling module; The intelligent outbound calling module includes a mediation strategy engine, which matches and generates mediation strategy prompts for the current outbound calling task based on the case type, current process stage, and the parties' historical communication records, and dynamically displays these prompts on the outbound calling interface. The strategy rules are associated with specific case characteristics and corresponding strategy prompts.

[0047] It is understandable that the mediation strategy engine integrated into the intelligent outbound call module of this invention focuses on transforming implicit mediation experience and expert knowledge into explicit strategy rules that can be automatically invoked, matched, and executed by the system, thereby providing mediators with real-time intelligent assistance. The aforementioned pre-built strategy rule library, based on in-depth analysis and deconstruction of massive historical mediation cases, extracts high-frequency communication points, common risks, and successful mediation strategies for different case areas (such as labor disputes, property disputes, and consumer rights protection) and different process stages (such as initial contact, fact verification, and solution negotiation). Secondly, it incorporates the practical experience of senior mediation experts and judges, manually refining and solidifying handling principles and communication suggestions for specific complex situations into rules. This diverse knowledge is ultimately abstracted and encoded into structured rule pairs, stored in the strategy rule library. For example, the condition section of a rule (specific case characteristics) might be defined as "Case field type = labor dispute & party role = employer & current stage = first outbound call", while the conclusion section might be "Note: Focus on verifying the specific reasons and procedural legality of the termination of the labor contract, note the relevant legal consequences, and pay attention to securing evidence."

[0048] In actual operation, when a mediator initiates an outbound call for a specific case, the mediation strategy engine immediately launches a dynamic matching process. This process first acquires the case's domain type, current process stage, and historical communication records of the parties extracted from the case database as input features in real time. Subsequently, the engine performs pattern matching and weight calculation on the conditional parts of all rules in the strategy rule base. The matching process is based on the calculation of feature similarity and the ranking of rule priorities; for example, rules that are completely identical in domain and stage and whose historical communication records mention the focus of the conflict are prioritized. Upon successful matching, the engine pushes the corresponding strategy prompts (such as a list of key inquiries, risk warnings, a summary of legal basis, or suggested communication scripts) to the outbound call interface in real time and dynamically highlights them in a preset area of ​​the screen (such as a sidebar or pop-up window).

[0049] Through the above operations, mediators can gain knowledge before making a call, accurately applying past group experience to the current case, effectively improving the professionalism, standardization, and success rate of communication, while reducing fluctuations in work quality that may be caused by differences in the mediators' individual experience.

[0050] In a preferred embodiment, the mediation strategy engine is also used to record the actual processing operations and results of mediators' outbound call tasks, perform correlation analysis between the actual processing operations and results and the generated strategy prompt information, and optimize and adjust the strategy rule base based on the analysis results.

[0051] Furthermore, the intelligent outbound calling module also includes a recording transcription and transcript generation unit, which converts the call audio stream into a text stream in real time after obtaining the consent of the parties and starting call recording. Based on the mediation transcript template corresponding to the case type, key statements in the text stream are automatically identified and filled into the corresponding fields of the template, generating a pre-filled draft mediation transcript for the mediator to review and modify.

[0052] Furthermore, the platform also includes a case allocation module, comprising a multi-dimensional judge profiling unit, a case feature analysis unit, and a recommendation unit; among which, The judge multidimensional profile unit is used to construct and dynamically update the judge's professional competence profile based on the judge's historical case handling data; the profile includes the case type of expertise tag, the average trial time of historical cases, and the case complexity index handling ability coefficient; The case feature analysis unit is used to calculate the case complexity index based on the information of cases to be assigned; The recommendation unit is used to establish an optimization model with the goal of minimizing the global judge workload difference and maximizing the professional matching degree, and outputs a ranking list of recommended judges based on the multi-dimensional profile of judges and the case complexity index.

[0053] Specifically, the aforementioned case complexity index handling capacity coefficient is determined primarily by reviewing every concluded case handled by the judge in the past, and calculating the initial complexity index of each historical case at the time of allocation based on the case characteristics. Then, the matching relationship between the judge's actual handling of the case (such as trial duration, number of procedural transitions, etc.) and the initial complexity index is analyzed. This coefficient is essentially a correction factor; if a judge consistently handles high-complexity index cases with above-average efficiency and high quality, the coefficient is greater than 1; otherwise, it is less than 1.

[0054] As a preferred implementation, a configurable weighted quantification model is used to calculate the complexity index of cases to be assigned. The model decomposes case information into multiple feature dimensions and assigns values ​​to them. For example, the basic score for the cause of action represents a preset base score based on the average difficulty of handling various causes of action in judicial statistics and the complexity of legal application; the coefficient for the amount in dispute represents different coefficients corresponding to different ranges of the amount in dispute, with higher coefficients for larger amounts; the coefficient for the number and type of parties represents that the number of parties, whether there is a class action lawsuit, and the type of parties (such as whether government departments or foreign factors are involved) all affect the coefficient; the coefficient for the quantity and type of evidence materials represents the length of the evidence list and whether it involves professional evidence types such as expert opinions, audits, and electronic evidence; and the social attention or risk label. The scores or coefficients of each dimension are calculated using a preset weighted summation formula, ultimately outputting a quantified case complexity index. It should be noted that this index is not static and can be dynamically updated during the process as supplementary materials are added (such as new evidence or additional parties).

[0055] In this embodiment of the invention, the goal of the optimization model established by the recommendation unit is to optimize the overall management objectives within the court while satisfying the statutory case assignment rules (such as random case assignment as the main method).

[0056] Specifically, the model's input includes the current dynamic workload and professional competence profile of all candidate judges, as well as the complexity index and cause-of-fact labels of the cases to be assigned. The objective function includes, but is not limited to, minimizing workload variance and maximizing professional matching. Minimizing workload variance refers to minimizing the variance or Gini coefficient of the effective workload of all judges after assignment, promoting a balanced workload. Maximizing professional matching refers to maximizing the match between the cause-of-fact labels of the cases to be assigned and the judges' expertise labels, combined with the judges' handling ability coefficients, to make complex cases more likely to be assigned to judges who can handle them efficiently.

[0057] In practice, a linear programming approach combined with heuristic rules is preferred for solving the problem. For example, the multi-objective problem is transformed into a single-objective problem, and dynamically adjustable weights (λ and 1-λ) are assigned to the two objectives to generate a comprehensive objective function. Simulation training is conducted using historical case allocation data to find the optimal weight λ, ensuring that the simulated case allocation results outperform historical manual case allocation results in both load balancing and professional matching. The training process is essentially a parameter tuning process, and the model's final output is a recommended list of judges for the current pending cases, sorted in descending order of comprehensive scores. This ranking list is presented to the case allocation administrator (such as staff in the case management office). The administrator can make quick decisions based on the recommended list or fine-tune the model by incorporating unconventional factors (such as judges' temporary assignments or recusal situations). Each case allocation result and the subsequent case trial efficiency data are fed back into the system as feedback data to continuously optimize judge profiles and model parameters.

[0058] Through the above implementation methods, the case allocation module transforms judges' implicit experience, the diverse characteristics of cases, and the management's equilibrium goals into a computable, optimizable, and interpretable algorithmic model. This not only significantly reduces the arbitrariness and time-consuming manual work in case allocation but also macroscopically optimizes the allocation of judicial resources, improves trial efficiency and judges' professional level, and serves as a key technological support for achieving scientific judicial management.

[0059] Furthermore, the case allocation module also includes a strategy interaction interface, which visually displays the sorting list to administrators with case allocation authority, and simultaneously displays the current workload heatmap and professional matching details for each candidate judge. The workload heatmap intuitively displays the workload value of each judge in different time periods; the factors determining the workload value include, but are not limited to, the number of pending cases for the judge, the complexity index of each pending case, the scheduled court hearing time, and the estimated time consumption of various administrative tasks.

[0060] As another preferred implementation, the case allocation module of this invention provides three modes: rule-based allocation, designated allocation, and competitive assignment. Rule-based allocation is the default mode, where the system automatically or assistedly completes the allocation based entirely on a sorted list generated by an intelligent recommendation algorithm. Designated allocation mode grants the case allocation administrator the highest authority, allowing them to directly designate the presiding judge based on legally mandated recusal provisions, joint case handling needs, or other special circumstances; the system records this as a special case. The competitive assignment mode is specifically designed for major, complex, novel, or typical cases. When an administrator selects this mode, the system automatically matches the case's characteristics (such as cause of action and complexity index) with the judge's professional competence profile, accurately pushing the case's basic information and points of contention (with sensitive personal information concealed) to the individual workstations of all judges with matching professional profiles in the form of task announcements. Interested judges can review the details within a specified time limit and submit a formal assignment application through the system, outlining their preliminary trial plan, anticipated difficulties, and proposed solutions. The system aggregates all received applications and summaries of their proposed solutions to the administrator's decision-making interface. The administrator can then comprehensively compare the professional suitability, clarity, and feasibility of each judge's approach, rather than solely relying on workload, to make the final decision that best facilitates the high-quality handling of the case. This model introduces judges' initiative and professional judgment into the case assignment process, achieving intelligent decision-making through human-machine collaboration. It helps to unleash judges' potential and optimize the allocation of resources for handling major cases.

[0061] Furthermore, to enhance the scientific rigor and feasibility of scheduling and avoid hidden case backlogs due to improper scheduling, the scheduling function of this invention employs a dynamic stress testing strategy. When scheduling a hearing for a new case, the system does not simply query the judge's calendar for available time slots, but instead initiates a simulation. Specifically, based on information about all scheduled but unheard cases under the judge's name, combined with parameters such as the complexity index of each case, the average trial duration of similar historical cases, and the judge's average trial efficiency, an expected case completion time is calculated for each scheduled case. Subsequently, the simulation inserts the new case's pending hearing into a candidate slot in the judge's schedule. The simulation calculates whether this insertion will cause the judge's workload in subsequent time slots to exceed their acceptable threshold and recalculates the expected case completion time for all affected cases.

[0062] If a certain percentage of pending cases are found to cause significant and unacceptable delays in their expected completion time due to the new scheduling (e.g., delays exceeding a certain percentage of the statutory time limit), the stress test will fail. In this case, the system will not directly block the scheduling but will automatically generate intelligent suggestions. First, it will provide several alternative scheduling dates with less impact on the judge's existing schedule. Second, if adjusting the scheduling fails to resolve the issue, the system will generate a suggestion that "this case is under excessive scheduling pressure under the current judge's schedule; a reassessment of case allocation is recommended," while simultaneously providing several alternative judges with lighter workloads or higher processing capacity. This process elevates scheduling from a simple placeholder operation to a scenario-based simulation of the judge's overall future workload and case completion progress, thereby contributing to the smoothness and stability of the trial process from the outset.

[0063] Finally, it should be noted that the above-described embodiments include multiple parallel implementations of the present invention. Deleting or otherwise adjusting one or more implementations will not affect the implementation of the solution. Furthermore, the blockchain-based multi-dispute management and court assistance platform disclosed in the embodiments of the present invention is merely a preferred embodiment of the present invention and is only used to illustrate the technical solution of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-faceted dispute management and court assistance platform based on blockchain-based evidence storage, characterized in that: The platform includes: The access and permissions module is used to provide login portals for users of different responsible entities and to configure different data views and operation permissions according to user roles; the responsible entities include internal court personnel and external mediation organizations; The data receiving and processing module is used to receive case dispute data and determine the case type identifier according to preset rules; the type identifier includes a mediation case identifier and a non-mediation case identifier; for the person in charge of mediation, the operation authority includes uniformly allocating the mediation cases received by the account to the corresponding mediation organization; The data synchronization module is used to connect with the mediation organization's internal information system through a standard data interface to encrypt and synchronize the mediation records of cases. The blockchain evidence storage module is used to generate corresponding digital fingerprints of business data at business process nodes executed by different responsible parties for the same dispute case, and submit the digital fingerprints to the blockchain network for evidence storage based on the order and relevance of the business processes.

2. The multi-faceted dispute management and court assistance platform based on blockchain evidence storage as described in claim 1, characterized in that, The process of submitting digital fingerprints to the blockchain network for evidence storage based on the business process sequence and correlation specifically includes: In response to the first responsible entity completing the first type of business operation and obtaining the first business data, a first digital fingerprint corresponding to the first business data is generated and submitted to the blockchain network for storage. In response to the Nth responsible entity completing the Nth type of business operation based on the N-1th business data, obtaining the Nth business data, the N-1th digital fingerprint is associated with the Nth business data to generate the Nth digital fingerprint and submit it to the blockchain network for evidence storage; wherein, the first digital fingerprint to the Nth digital fingerprint form a sequential dependency relationship with the case business process through the blockchain evidence storage record; N is the number of responsible entities, N≥2.

3. The blockchain-based multi-faceted dispute management and court assistance platform according to claim 1 or 2, characterized in that, After the digital fingerprint evidence of the final mediation document is completed, the blockchain evidence storage module is also used to generate a judicial blockchain evidence storage certificate; the evidence storage certificate is configured as an electronic file attached to the final mediation document, including a unique evidence storage number generated based on the evidence storage information and a machine-readable identifier code for accessing the blockchain verification service.

4. The multi-faceted dispute management and court assistance platform based on blockchain evidence storage as described in claim 3, characterized in that, The platform also includes an external verification service module; The external verification service module is used to receive evidence storage query requests from external verification parties. The evidence storage query request carries verification index information obtained from the judicial blockchain evidence storage certificate. The blockchain network is queried according to the verification index information to obtain the corresponding verification result information, which is then sent back to the external verification party. The verification result information includes the evidence storage status and the evidence storage time, but does not include the specific content data of the final mediation document.

5. The multi-faceted dispute management and court assistance platform based on blockchain evidence storage as described in claim 1, characterized in that, The data synchronization module includes a data rule engine for storing and managing data conversion rule sets corresponding to the internal information systems of different mediation organizations. The data conversion rule sets include field mapping rules and data cleaning rules. The field mapping rules represent the correspondence between data fields within the platform and expected data fields in the target mediation organization's internal information system. The data cleaning rules include specific processing logic for the characteristics of judicial data.

6. The multi-faceted dispute management and court assistance platform based on blockchain evidence storage as described in claim 5, characterized in that, The data synchronization module also includes a verification unit, which is used to perform redundancy verification on data representing key business operations. The redundancy check process includes: At critical business nodes, an online confirmation process that runs parallel to automatic API synchronization is forcibly triggered; the online confirmation process provides a confirmation interface that is pre-loaded with data obtained through API synchronization. Receive confirmation data submitted by the responsible user through the confirmation interface; The confirmed data is compared with the data obtained synchronously through the API. If the comparison results are inconsistent, an alarm is triggered and the subsequent process is suspended until an authorized manual arbitration instruction is obtained.

7. The multi-faceted dispute management and court assistance platform based on blockchain evidence storage as described in claim 1, characterized in that, The platform also includes an intelligent outbound calling module; The intelligent outbound call module includes a mediation strategy engine, which is used to match and generate mediation strategy prompts for the current outbound call task from a pre-set strategy rule library based on the case domain type, current process stage, and the parties' historical communication records, and dynamically display them on the outbound call interface; the strategy rules are associated with specific case characteristic conditions and corresponding strategy prompt content.

8. The multi-faceted dispute management and court assistance platform based on blockchain evidence storage as described in claim 7, characterized in that, The intelligent outbound calling module also includes a recording transcription and transcript generation unit, which is used to convert the call audio stream into a text stream in real time after obtaining the consent of the party concerned and starting the call recording. Based on the mediation record template corresponding to the case type, key statements in the text stream are automatically identified and filled into the corresponding fields of the template, generating a pre-filled draft mediation record for mediators to review and modify.

9. The multi-faceted dispute management and court assistance platform based on blockchain evidence storage as described in claim 1, characterized in that, The platform also includes a case allocation module, comprising a judge multi-dimensional profiling unit, a case feature analysis unit, and a recommendation unit; among which, The judge multidimensional profile unit is used to construct and dynamically update the judge's professional competence profile based on the judge's historical case handling data; the profile includes the case type of expertise tag, the average trial time of historical cases, and the case complexity index handling ability coefficient; The case feature analysis unit is used to calculate the case complexity index based on the information of cases to be assigned; The recommendation unit is used to establish an optimization model with the goal of minimizing the global judge workload difference and maximizing the professional matching degree, and outputs a ranking list of recommended judges based on the multi-dimensional profile of judges and the case complexity index.

10. The multi-faceted dispute management and court assistance platform based on blockchain evidence storage as described in claim 9, characterized in that, The case allocation module also includes a strategy interaction interface, which visually displays the sorting list to administrators with case allocation authority, and simultaneously displays the current workload heatmap and professional matching details of each candidate judge; The load heatmap visually displays the load value of each judge in each time period; the factors that determine the load value include the number of pending cases for each judge, the complexity index of each pending case, the scheduled court hearing time, and the estimated time for various administrative tasks.

Images