Judicial cooperation management mechanism oriented to enterprise environment conservation motivation and constraint and implementation method

Abstract

The invention discloses a judicial cooperation management mechanism and implementation method oriented to enterprise environment conservation motivation and constraint, and the mechanism comprises a multi-source environment conservation data center which is used for collecting and storing enterprise-related environment data from ecological environment departments, courts, procuratorials, public security and other related institutions in a real-time and standardized manner, the data at least comprises enterprise basic information, environment monitoring data, administrative penalty records, judicial referee information, rectification feedback information and a law-of-conservation credit rating. The problems of different evidence formats, information lag and repeated verification in an existing mechanism are solved through a unified evidence transfer rule, block chain evidence storage and a standardized mapping table, the collaboration period is shortened by 40% or above, the judicial credit collection accuracy is improved to 98% or above, and based on a three-dimensional layered label and an intelligent portrait model, the method is simple and convenient to implement. Differentiated excitation and pre-constraint are carried out on enterprises of different industries, scales and historical records, the law-keeping power of small, medium and micro enterprises is enhanced, and meanwhile potential risks are intervened in advance.

Description

Technical Field

[0001] This invention relates to the field of environmental governance and judicial collaborative management technology, specifically to a judicial collaborative management mechanism and implementation method for incentivizing and constraining corporate environmental compliance. Background Technology

[0002] The judicial collaborative management mechanism for incentivizing and constraining corporate environmental compliance is a systematic governance model aimed at enhancing the intrinsic motivation of enterprises for environmental governance and building a rule-of-law business environment. It integrates diverse judicial and administrative forces, including courts, procuratorates, public security bureaus, and environmental protection departments, through rule alignment, information sharing, procedural linkage, and functional complementarity. Its core lies in breaking through the limitations of traditional single-entity enforcement, guided by the principle of "warm incentives and rigid constraints." It both positively guides enterprises to proactively comply with the law—for example, the establishment of an "environmental compliance credit + judicial green channel" mechanism by judicial organs in conjunction with environmental protection departments, providing preferential support to long-term compliant enterprises in areas such as environmental dispute mediation, tolerance for minor violations, and judicial protection for green finance; and strengthens the effectiveness of reverse constraints through cross-departmental collaboration—for example, constructing a closed-loop chain of "standardized transfer of administrative enforcement evidence—prosecutorial public interest litigation to urge performance—professional adjudication of environmental resources trials by courts—rapid response of administrative and criminal law enforcement by public security departments," severely punishing malicious violations and data falsification according to law and publicly disclosing typical cases to create a deterrent effect. In terms of implementation, three key areas need to be addressed: First, institutional collaboration, including the development of a cross-departmental environmental judicial cooperation list to clarify case jurisdiction, mutual recognition of evidence, and unified standards for the application of law, eliminating issues such as inconsistent judgments in similar cases and regulatory vacuums. Second, technological empowerment, by establishing an environmental compliance data sharing platform to collect real-time information on corporate environmental impact assessments, monitoring, penalties, and rectification, and using big data analysis to identify high-risk entities, providing support for precise incentives or targeted constraints. Third, capacity building, by regularly conducting cross-departmental business training and case studies, cultivating a composite team that understands both environmental science and judicial rules, and guiding enterprises to improve their internal environmental governance systems through the issuance of judicial recommendations and compliance guidelines, ultimately promoting a virtuous cycle where "law-abiding citizens benefit and lawbreakers are restricted," and achieving a leap from "passive supervision" to "proactive co-governance" in environmental rule of law.

[0003] With the deepening of ecological civilization construction, judicial collaborative management of incentives and constraints for corporate environmental compliance has gradually become an important direction for environmental governance. In existing technologies, to enhance corporate initiative in environmental governance and build a rule-of-law business environment, various judicial collaborative management mechanisms and implementation methods for incentivizing and constraining corporate environmental compliance have been proposed. For example, by integrating the resources of courts, procuratorates, public security bureaus, and environmental protection departments, a systematic governance model with rule alignment, information sharing, and procedural linkage has been constructed. This includes establishing cross-departmental collaboration lists, data sharing platforms, credit evaluation, and judicial green channels, attempting to achieve the goal of "warm incentives and rigid constraints." However, existing technical solutions still have the following significant drawbacks in practical applications:

[0004] First, the depth and efficiency of cross-departmental collaboration are insufficient. While existing mechanisms emphasize multi-entity collaboration, the lack of unified operational norms and standards for assigning responsibilities leads to frequent instances of "collaboration without action" or "passing the buck" in practice. For example, when administrative enforcement evidence is transferred to judicial organs, inconsistent evidence collection standards and format requirements necessitate repeated verification or even re-collection of evidence. This not only prolongs the case processing cycle (averaging 30%-50% longer than single-department enforcement) but may also affect the accuracy of judicial decisions due to information loss. Furthermore, information sharing between departments largely relies on manual transmission or non-real-time interface connections, resulting in delayed data updates (some key information is delayed by 7-15 working days), making it difficult to support dynamic tracking and risk warning of corporate environmental behavior.

[0005] Secondly, the precision and suitability of incentive and constraint measures are lacking. Existing incentive methods mostly remain at the level of a broad-based "compliance equals preferential treatment" model, failing to design tiered and categorized incentive schemes that take into account the differentiated factors such as the industry characteristics, size, and historical compliance records of enterprises. As a result, some small and medium-sized enterprises lack the motivation to actively invest in environmental upgrades because "the cost of compliance is low but the incentive is weak." Constraint measures, on the other hand, focus on "post-event punishment" for illegal acts, with weak "pre-event prevention" and "in-event intervention" mechanisms for potential illegal risks. Furthermore, the connection between administrative and criminal law enforcement relies heavily on case-by-case coordination, lacking the ability to trace and jointly punish malicious violations and data falsification across the entire chain, making it difficult to create a sustained deterrent.

[0006] Third, the level of intelligence and integration of the technical support system is limited. Although some solutions mention building a data sharing platform, its functions are mostly limited to basic data collection, lacking intelligent risk assessment models based on big data analysis. It cannot automatically identify high-risk characteristics of corporate environmental behavior (such as abnormal emission fluctuations, logical contradictions in monitoring data, etc.) and generate graded early warnings. At the same time, existing technologies have not effectively integrated environmental science knowledge bases and judicial ruling rule bases, making it difficult to provide enterprises with integrated intelligent assistance of "compliance self-testing - risk diagnosis - rectification suggestions". This results in enterprises facing the dual obstacles of information asymmetry and high technical thresholds when independently improving their compliance capabilities.

[0007] Fourth, there is insufficient consistency in the coordination of systems and the application of law. Different departments have differences in the standards for identifying environmental violations (such as the definition of "serious circumstances") and the scale of legal liability (such as the range of fines). The existing coordination mechanism lacks a regular cross-departmental legal interpretation and consultation mechanism and case guidance mechanism, which can easily lead to problems such as "different judgments for similar cases" and "conflicts in regulatory standards," thus weakening the authority and credibility of judicial collaborative management.

[0008] Therefore, we propose a judicial collaborative management mechanism and implementation method for incentivizing and constraining corporate environmental compliance. Summary of the Invention

[0009] To achieve the above objectives, the present invention provides the following technical solution: a judicial collaborative management mechanism for incentivizing and constraining corporate environmental compliance, comprising:

[0010] The multi-source environmental compliance data center is used to collect and standardize the storage of enterprise-related environmental data from ecological and environmental departments, courts, procuratorates, public security organs and other relevant agencies in real time. The data includes at least basic enterprise information, environmental monitoring data, administrative penalty records, judicial rulings, rectification feedback information and compliance credit rating.

[0011] The cross-departmental collaborative scheduling engine is connected to the multi-source environmental compliance data center. It has built-in unified evidence transfer rules, legal application standard library and task flow protocol, which are used to automatically generate cross-departmental task instructions when a collaborative event is triggered, and allocate them to the corresponding agencies for execution according to the task type.

[0012] The intelligent compliance profile and risk warning module, based on the data from the data center, uses a machine learning model to calculate the enterprise's compliance index and violation risk score, and outputs hierarchical and classified incentive or constraint strategy suggestions based on the score results;

[0013] The differentiated incentive implementation subsystem automatically matches incentive measures based on the compliance index of the intelligent profiling module and the hierarchical classification tags of enterprises, and these measures take effect simultaneously in the judicial and administrative systems. The incentive measures include judicial green channels, priority approval for green finance, filing for minor violations, and public recommendation of compliant enterprises.

[0014] The full-chain constraint and disposal subsystem is used to initiate rapid linkage of administrative law enforcement evidence collection, procuratorial public interest litigation supervision, court environmental and resource professional trial and public security administrative and criminal connection according to the preset process after risk warning is triggered or illegal behavior is confirmed, and writes the disposal results back to the data center to update the enterprise profile;

[0015] The compliance intelligent assistance platform provides enterprises with online environmental compliance self-testing, risk diagnosis, rectification suggestion push and legal knowledge Q&A services, and maintains two-way interaction with the data center to calibrate the self-test results.

[0016] Preferably, the multi-source environmental compliance data center adopts blockchain notarization technology to ensure the immutability and traceability of cross-institutional data, and adds a timestamp and source signature when writing data.

[0017] Preferably, the cross-departmental collaborative scheduling engine has a built-in evidence standardization mapping table that automatically converts the original evidence fields from different departments into a unified judicial acceptance format and performs integrity verification and missing information prompts before transfer.

[0018] Preferably, the intelligent compliance profile and risk warning module adopts a hybrid algorithm model that integrates environmental science indicators and judicial ruling rules. The model training data includes historical compliance / violation cases, environmental monitoring time series data, and judicial judgment documents.

[0019] Preferably, the differentiated incentive execution subsystem supports a three-dimensional hierarchical tagging system based on enterprise industry category, scale level, and historical compliance record, and sets independent incentive weights and fulfillment conditions for each tag level.

[0020] Preferably, the full-chain constraint and disposal subsystem has a pre-intervention node for illegal risks. When the risk score exceeds the first threshold but does not constitute a violation, the system automatically pushes risk warnings and rectification suggestions to the enterprise and regulatory authorities, and enters a pre-constraint state.

[0021] Preferably, the compliance intelligent assistance platform integrates a natural language processing engine, which can parse the environmental protection policy documents and monitoring reports submitted by enterprises, automatically compare them with the legal and regulatory database, and mark potential non-compliance points.

[0022] A method for implementing a judicial collaborative management mechanism for incentivizing and constraining corporate environmental compliance includes the following steps:

[0023] S1: Real-time aggregation and standardization of environmental data related to enterprises from various organizations through a multi-source environmental compliance data center;

[0024] S2: The cross-departmental collaborative scheduling engine receives collaborative events, generates cross-departmental task instructions according to unified rules, and distributes them.

[0025] S3: The intelligent compliance profile and risk warning module calculates the enterprise's compliance index and violation risk score based on the data from step S1, and outputs strategy suggestions;

[0026] S4: Based on the suggestion in step S3, the differentiated incentive execution subsystem automatically matches and executes incentive measures, or the full-chain constraint handling subsystem initiates the linkage constraint process;

[0027] S5: Write the execution result of step S4 back to the data center and update the enterprise profile;

[0028] S6: The compliance intelligent assistance platform provides enterprises with online compliance testing and rectification guidance, and interacts with the data center to exchange calibration data.

[0029] Preferably, in step S2, the collaborative events include changes in corporate credit rating, alarms for abnormal monitoring data, filing of administrative penalties, acceptance of judicial cases, or triggering of public complaints and investigations.

[0030] Preferably, the compliance index and risk score in step S3 adopt a weighted fusion algorithm, and the weights are dynamically adjusted by industry benchmark values, historical trends and external policy factors.

[0031] Compared with existing technologies, this invention provides a judicial collaborative management mechanism and implementation method for incentivizing and constraining corporate compliance with environmental laws, which has the following beneficial effects:

[0032] 1. This judicial collaborative management mechanism and its implementation method for incentivizing and constraining corporate environmental compliance solves the problems of inconsistent evidence formats, information lag, and duplicate verification in the existing mechanism by unifying evidence transfer rules, blockchain evidence storage, and standardized mapping tables. The collaborative cycle is shortened by more than 40%, and the accuracy rate of judicial acceptance is increased to more than 98%. Based on three-dimensional hierarchical tags and intelligent profiling models, differentiated incentives and pre-constraints are implemented for enterprises of different industries, sizes, and historical records to avoid "one-size-fits-all" approaches, enhance the motivation of small and medium-sized enterprises to comply with the law, and intervene in potential risks in advance to reduce the probability of major environmental incidents.

[0033] 2. This judicial collaborative management mechanism and implementation method for incentivizing and constraining corporate environmental compliance, through a hybrid algorithm model integrating environmental science and judicial rules, combined with the NLP analysis and self-testing functions of the compliance intelligent assistance platform, enables enterprises to obtain personalized compliance guidance at low cost, reducing information asymmetry and technical barriers. Through the legal application standard library and task flow protocol of the cross-departmental collaborative scheduling engine, it unifies the criteria for determining "serious circumstances", reduces inconsistent judgments in similar cases and regulatory conflicts, and enhances the authority and credibility of judicial collaboration.

[0034] 3. The judicial collaborative management mechanism and implementation method for incentivizing and constraining corporate environmental compliance, along with the dynamic updating mechanism of the data center and profiling module, enable incentive and constraint measures to be adjusted in real time according to changes in corporate behavior, thus constructing a smart governance closed loop of "data collection - analysis and decision-making - execution feedback - re-optimization". Detailed Implementation

[0035] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] Example

[0037] An Example of a Judicial Collaborative Management Mechanism and Implementation Method for Incentives and Constraints on Corporate Environmental Compliance

[0038] A judicial collaborative management mechanism for incentivizing and constraining corporate environmental compliance includes:

[0039] The multi-source environmental compliance data center is used to collect and standardize the storage of enterprise-related environmental data from ecological and environmental departments, courts, procuratorates, public security organs and other relevant agencies in real time. The data includes at least basic enterprise information, environmental monitoring data, administrative penalty records, judicial rulings, rectification feedback information and compliance credit rating.

[0040] The cross-departmental collaborative scheduling engine is connected to the multi-source environmental compliance data center. It has built-in unified evidence transfer rules, legal application standard library and task flow protocol, which are used to automatically generate cross-departmental task instructions when a collaborative event is triggered, and allocate them to the corresponding agencies for execution according to the task type.

[0041] The intelligent compliance profile and risk warning module, based on the data from the data center, uses a machine learning model to calculate the enterprise's compliance index and violation risk score, and outputs hierarchical and classified incentive or constraint strategy suggestions based on the score results;

[0042] The differentiated incentive implementation subsystem automatically matches incentive measures based on the compliance index of the intelligent profiling module and the hierarchical classification tags of enterprises, and these measures take effect simultaneously in the judicial and administrative systems. The incentive measures include judicial green channels, priority approval for green finance, filing for minor violations, and public recommendation of compliant enterprises.

[0043] The full-chain constraint and disposal subsystem is used to initiate rapid linkage of administrative law enforcement evidence collection, procuratorial public interest litigation supervision, court environmental and resource professional trial and public security administrative and criminal connection according to the preset process after risk warning is triggered or illegal behavior is confirmed, and writes the disposal results back to the data center to update the enterprise profile;

[0044] The compliance intelligent assistance platform provides enterprises with online environmental compliance self-testing, risk diagnosis, rectification suggestion push and legal knowledge Q&A services, and maintains two-way interaction with the data center to calibrate the self-test results.

[0045] Specifically, the Multi-Source Environmental Compliance Data Center uses blockchain notarization technology to ensure the immutability and traceability of cross-organizational data, and adds timestamps and source signatures when writing data.

[0046] Specifically, the cross-departmental collaborative scheduling engine has a built-in evidence standardization mapping table that automatically converts the original evidence fields from different departments into a unified judicial acceptance format and performs integrity checks and missing information prompts before transfer.

[0047] Specifically, the intelligent compliance profile and risk warning module adopts a hybrid algorithm model that integrates environmental science indicators and judicial ruling rules. The model training data includes historical compliance / violation cases, environmental monitoring time series data, and judicial judgment documents.

[0048] Specifically, the differentiated incentive execution subsystem supports a three-dimensional hierarchical tagging system based on enterprise industry category, scale level, and historical compliance record, and sets independent incentive weights and redemption conditions for each tag level.

[0049] Specifically, the full-chain constraint and disposal subsystem has a pre-intervention node for illegal risks. When the risk score exceeds the first threshold but does not constitute a violation, risk warnings and rectification suggestions are automatically pushed to the enterprise and regulatory authorities, and the system enters a pre-constraint state.

[0050] Specifically, the compliance intelligent assistance platform integrates a natural language processing engine, which can parse the environmental protection policy documents and monitoring reports submitted by enterprises, automatically compare them with the legal and regulatory database, and mark potential non-compliance points.

[0051] A method for implementing a judicial collaborative management mechanism for incentivizing and constraining corporate environmental compliance includes the following steps:

[0052] S1: Real-time aggregation and standardization of environmental data related to enterprises from various organizations through a multi-source environmental compliance data center;

[0053] S2: The cross-departmental collaborative scheduling engine receives collaborative events, generates cross-departmental task instructions according to unified rules, and distributes them.

[0054] S3: The intelligent compliance profile and risk warning module calculates the enterprise's compliance index and violation risk score based on the data from step S1, and outputs strategy suggestions;

[0055] S4: Based on the suggestion in step S3, the differentiated incentive execution subsystem automatically matches and executes incentive measures, or the full-chain constraint handling subsystem initiates the linkage constraint process;

[0056] S5: Write the execution result of step S4 back to the data center and update the enterprise profile;

[0057] S6: The compliance intelligent assistance platform provides enterprises with online compliance testing and rectification guidance, and interacts with the data center to exchange calibration data.

[0058] Specifically, in step S2, the collaborative events include changes in corporate credit rating, alarms for abnormal monitoring data, filing of administrative penalty cases, acceptance of judicial cases, or triggering of public complaints and investigations.

[0059] Specifically, in step S3, the compliance index and risk score are combined using a weighted fusion algorithm, with the weights dynamically adjusted based on industry benchmarks, historical trends, and external policy factors.

[0060] Through the above technical solutions, this invention addresses the problems of inconsistent evidence formats, information lag, and duplicate verification in existing mechanisms by unifying evidence transfer rules, blockchain evidence storage, and standardized mapping tables. This shortens the collaboration cycle by over 40% and increases the accuracy of judicial acceptance to over 98%. Based on three-dimensional hierarchical labels and intelligent profiling models, it implements differentiated incentives and pre-constraints for enterprises of different industries, sizes, and historical records, avoiding a "one-size-fits-all" approach and enhancing the legal compliance motivation of SMEs. Simultaneously, it intervenes in potential risks in advance, reducing the probability of major environmental incidents. By integrating a hybrid algorithm model that combines environmental science and judicial rules with the NLP analysis and self-testing functions of the compliance intelligent assistance platform, enterprises can obtain personalized compliance guidance at low cost, reducing information asymmetry and technical barriers. Through the legal application standard library and task flow protocol of the cross-departmental collaborative scheduling engine, it unifies the criteria for determining "serious circumstances," reducing inconsistent judgments in similar cases and regulatory conflicts, enhancing the authority and credibility of judicial collaboration. The dynamic update mechanism of the data center and profiling module allows incentive and constraint measures to be adjusted in real time according to changes in enterprise behavior, constructing a smart governance closed loop of "data collection—analysis and decision-making—execution feedback—re-optimization."

[0061] 1. System Overall Deployment and Hardware Environment

[0062] This mechanism can be deployed using a hybrid centralized + distributed architecture:

[0063] Central Node: Located in the provincial / municipal judicial collaboration management center, configured with high-performance cloud servers (CPU≥32 cores, memory≥128GB, storage≥100TB SSD+PB-level object storage), running the core services of a multi-source environmental compliance data center, a cross-departmental collaborative scheduling engine, an intelligent compliance profiling and risk warning module, a differentiated incentive execution subsystem, a full-chain constraint disposal subsystem, and a compliance intelligent auxiliary platform.

[0064] Access Nodes: Each collaborating unit (Environmental Protection Bureau, Court, Procuratorate, Public Security Bureau, Financial Regulatory Bureau, Key Enterprises) deploys an edge access gateway responsible for local data collection, preprocessing, and secure transmission. The recommended gateway hardware is a dual-machine hot-standby server (CPU ≥ 8 cores, memory ≥ 32GB), equipped with a national cryptographic algorithm encryption card to ensure transmission security.

[0065] Network and Security: Nodes are connected via a government private network or VPN, using a TLS 1.3 encrypted channel; firewalls and intrusion detection systems (IDS) are enabled inside the data center, and the PBFT consensus mechanism is used between blockchain nodes to ensure data consistency.

[0066] 2. Data collection and standardization process (corresponding to claim 2)

[0067] 2.1 Data Sources and Types

[0068] Ecological and environmental departments: Enterprise discharge permit information, online monitoring data (hourly average, daily average), supervisory monitoring reports, administrative penalty decisions, rectification notices, and acceptance opinions.

[0069] Courts: Judgments, enforcement information, and records of dishonesty in environmental civil / administrative / criminal cases.

[0070] Procuratorate: Procuratorial recommendations, information on the filing and closure of public interest litigation cases.

[0071] Public Security Bureau: Information on the filing, investigation, and transfer for prosecution of environmental crime cases.

[0072] For enterprises: self-reported environmental protection policy documents, monitoring equipment operation and maintenance records, and third-party testing reports.

[0073] 2.2 Acquisition Method and Frequency

[0074] Real-time data (such as online monitoring): pushed every 5 minutes using the MQTT protocol;

[0075] Batch data (such as penalty decisions and judgments): daily incremental synchronization at midnight;

[0076] Enterprises can submit reports independently: submit reports as needed via HTTPS API, and the system will asynchronously verify and store the data.

[0077] 2.3 Standardization and Blockchain Evidence Storage

[0078] ETL processing: Using Apache NiFi to build a data stream pipeline to complete field extraction, null value filling, and unit conversion (e.g., mg / m³). 3 →ppm), time format is standardized (ISO 8601).

[0079] Standardized mapping table: Define a unified data model (such as EnterpriseID, Timestamp, DataType, Value, SourceAgency, Signature) to ensure that data from different sources can be directly associated.

[0080] Blockchain-based evidence storage: Using the Hyperledger Fabric consortium blockchain, each piece of data generates a hash value and is timestamped and digitally signed by the source institution when it is written, forming an immutable chain of evidence.

[0081] 3. Cross-departmental collaborative scheduling engine operation mechanism (corresponding to claim 3)

[0082] 3.1 Classification and Triggering Conditions of Collaborative Events

[0083] Event types: changes in credit rating, abnormal monitoring data, filing of administrative penalty cases, acceptance of judicial cases, investigation of public complaints, and overdue rectification.

[0084] Triggering rules:

[0085] If the monitored data exceeds the threshold for three consecutive periods, a "data anomaly event" will be triggered.

[0086] A company's credit rating is downgraded from A to B, triggering a "rating change event".

[0087] Upon receiving a report with a real name and preliminary verification that it involves suspected illegal activities, a "report verification event" is triggered.

[0088] 3.2 Task Instruction Generation and Distribution

[0089] Rules engine: Drools is used to configure cross-departmental task flow protocols and match preset process templates according to event type (e.g., "data anomaly event" template = on-site verification by the Ecological and Environmental Protection Bureau → transfer to the procuratorate if there is evidence of illegality → court case filing preparation).

[0090] Evidence standardization mapping: Automatically convert source data fields into judicially acceptable formats (example: the "COD emission concentration" field of the Ecological and Environmental Protection Bureau is mapped to PollutantConcentration_COD_mgPerM3 in the judicial model), and perform integrity checks before transfer (if the missing rate of required fields is >5%, it will be returned for completion).

[0091] Time limit control: Set a processing time limit for each task node (e.g., on-site verification ≤ 48 hours, issuance of procuratorial suggestions ≤ 7 working days), and automatically upgrade to the superior supervision if the time limit is exceeded.

[0092] 4. Intelligent compliance profile and risk warning module (corresponding to claim 4)

[0093] 4.1 Model Construction

[0094] Feature engineering: Selecting input variables includes:

[0095] Historical law-abiding index (average score over the past 12 months);

[0096] The frequency and severity of environmental violations;

[0097] Timeliness of rectification;

[0098] Online monitoring of data volatility;

[0099] Industry average compliance level (normalized comparison value);

[0100] Judicial judgment results (win / lost / mediation).

[0101] Algorithm selection: XGBoost regression is used to predict the compliance index, and LSTM time series network is used to predict the probability of illegal risk in the next 3 months. Environmental science limit standards (such as the Integrated Wastewater Discharge Standard GB 8978) and judicial judgment rule base (such as sentencing guidelines) are integrated.

[0102] Training and Updates: The model is incrementally trained quarterly using the latest cases and monitoring data. Cross-validation is used to prevent overfitting. Model accuracy targets: Law compliance index prediction error ≤ ±3 points, risk probability classification accuracy ≥ 92%.

[0103] 4.2 Risk Classification and Strategy Output

[0104] Risk score range: low risk [0, 0.2], medium risk (0.2, 0.6], high risk (0.6, 1];

[0105] Strategy output:

[0106] Low risk + compliance index ≥ 85 → Incentive measures recommended;

[0107] Medium risk → Pre-constraint warning + rectification suggestions;

[0108] High risk → Initiate full-chain constraint process.

[0109] 5. Differentiated incentive execution subsystem (corresponding to claim 5)

[0110] 5.1 Three-dimensional hierarchical labeling system

[0111] Dimension 1: Industry category (chemical, metallurgy, electronics, food, etc., assigned different compliance difficulty coefficients);

[0112] Dimension Two: Size Level (Large / Medium / Micro, classified by output value or number of employees);

[0113] Dimension 3: Historical law-abiding record (no violations in the past 3 years = Category A, occasional minor violations = Category B, multiple violations = Category C).

[0114] Example of tag combination: Medium-sized Class A enterprises in the chemical industry → Incentive weight = 1.2; Small and micro Class C enterprises in the chemical industry → Incentive weight = 0.6.

[0115] 5.2 Automated Execution of Incentive Measures

[0116] The incentive configuration table is stored in the database, with fields including: TriggerCondition (compliance index + risk score + tag combination), MeasureType (green channel, credit priority, fault tolerance filing, public recommendation), and ApprovalFlow (automatic / manual review).

[0117] When the conditions are met, the system calls external interfaces (such as court case filing systems and financial regulatory approval systems) to automatically write incentive identifiers and generate traceable operation logs.

[0118] 6. Full-chain constraint handling subsystem (corresponding to claim 6)

[0119] 6.1 Process Nodes and Linkage Mechanisms

[0120] Administrative law enforcement evidence collection: The Ecological and Environmental Protection Bureau conducts on-site verification and generates a standardized evidence collection package (including photos, videos, sampling records, and monitoring data).

[0121] Procuratorial public interest litigation supervision: If public interests are harmed, the procuratorate will issue a procuratorial suggestion within 3 working days and follow up on rectification.

[0122] Environmental and resource trials in courts: The environmental and resource division of the court will hear cases as a specialized collegial panel, with priority scheduling and a conclusion within 60 days (complex cases may be extended to 90 days).

[0123] Police administrative and criminal investigation coordination: The police will decide whether to file a case within 10 working days after receiving and transferring suspected criminal leads.

[0124] 6.2 Pre-intervention mechanism

[0125] When the risk score is greater than 0.2 and less than 0.6, the system will send a "Risk Warning Letter" to the enterprise and the regulatory authorities, requiring them to submit a rectification plan and upload supporting materials within 15 days; if the rectification is not completed within the time limit, the system will automatically enter the formal constraint process.

[0126] 7. Compliance Intelligent Assistance Platform (corresponding to claim 7)

[0127] Functional modules:

[0128] Compliance self-assessment: Enterprises select industry and process type, and the system automatically generates a checklist based on the regulatory database (such as hazardous waste storage requirements and emission limits).

[0129] Risk Diagnosis: After the monitoring report is uploaded, the NLP engine extracts key values ​​and compares them with standards, marking items that exceed the standards and their legal basis.

[0130] Recommendations for improvement: Based on the type of problem, provide best practice cases and feasible technical solutions (such as installing online monitoring equipment or optimizing the process flow).

[0131] Legal Q&A: Based on knowledge graphs and semantic retrieval, it answers common questions from enterprises regarding environmental law, criminal liability, and litigation procedures.

[0132] Two-way interaction: Enterprises can voluntarily send their self-test results back to the data center as a basis for correcting their compliance profiles and improving the accuracy of the profiles.

[0133] 8. Closed-loop optimization and continuous improvement

[0134] After each incentive or constraint is implemented, the system writes the results (including enterprise feedback, rectification results, and judicial rulings) into the data center, triggering the profiling module to recalculate the index and risk score.

[0135] The management center generates a collaborative governance analysis report every month, which includes cross-departmental collaboration efficiency indicators (average processing time, task completion rate), incentive coverage, and trend of violation rate, to help decision-makers optimize rules and resource allocation.

[0136] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A judicial collaborative management mechanism for incentivizing and constraining corporate environmental compliance, characterized by: include: The multi-source environmental compliance data center is used to collect and standardize the storage of enterprise-related environmental data from ecological and environmental departments, courts, procuratorates, public security organs and other relevant agencies in real time. The data includes at least basic enterprise information, environmental monitoring data, administrative penalty records, judicial rulings, rectification feedback information and compliance credit rating. The cross-departmental collaborative scheduling engine is connected to the multi-source environmental compliance data center. It has built-in unified evidence transfer rules, legal application standard library and task flow protocol, which are used to automatically generate cross-departmental task instructions when a collaborative event is triggered, and allocate them to the corresponding agencies for execution according to the task type. The intelligent compliance profile and risk warning module, based on the data from the data center, uses a machine learning model to calculate the enterprise's compliance index and violation risk score, and outputs hierarchical and classified incentive or constraint strategy suggestions based on the score results; The differentiated incentive implementation subsystem automatically matches incentive measures based on the compliance index of the intelligent profiling module and the hierarchical classification tags of enterprises, and these measures take effect simultaneously in the judicial and administrative systems. The incentive measures include judicial green channels, priority approval for green finance, filing for minor violations, and public recommendation of compliant enterprises. The full-chain constraint and disposal subsystem is used to initiate rapid linkage of administrative law enforcement evidence collection, procuratorial public interest litigation supervision, court environmental and resource professional trial and public security administrative and criminal connection according to the preset process after risk warning is triggered or illegal behavior is confirmed, and writes the disposal results back to the data center to update the enterprise profile; The compliance intelligent assistance platform provides enterprises with online environmental compliance self-testing, risk diagnosis, rectification suggestion push and legal knowledge Q&A services, and maintains two-way interaction with the data center to calibrate the self-test results.

2. The judicial collaborative management mechanism for incentivizing and constraining corporate environmental compliance as described in claim 1, characterized in that: The multi-source environmental compliance data center uses blockchain notarization technology to ensure the immutability and traceability of cross-organizational data, and adds timestamps and source signatures when writing data.

3. The judicial collaborative management mechanism for incentivizing and constraining corporate environmental compliance as described in claim 1, characterized in that: The cross-departmental collaborative scheduling engine has a built-in evidence standardization mapping table that automatically converts the original evidence fields from different departments into a unified judicial acceptance format and performs integrity verification and missing information prompts before transfer.

4. The judicial collaborative management mechanism for incentivizing and constraining corporate environmental compliance as described in claim 1, characterized in that: The intelligent compliance profile and risk warning module adopts a hybrid algorithm model that integrates environmental science indicators and judicial ruling rules. The model training data includes historical compliance / violation cases, environmental monitoring time series data, and judicial judgment documents.

5. The judicial collaborative management mechanism for incentivizing and constraining corporate environmental compliance as described in claim 1, characterized in that: The differentiated incentive execution subsystem supports a three-dimensional hierarchical tagging system based on enterprise industry category, size level, and historical compliance record, and sets independent incentive weights and redemption conditions for each tag level.

6. The judicial collaborative management mechanism for incentivizing and constraining corporate environmental compliance as described in claim 1, characterized in that: The full-chain constraint and handling subsystem has a pre-intervention node for illegal risks. When the risk score exceeds the first threshold but does not constitute a violation, it automatically pushes risk warnings and rectification suggestions to enterprises and regulatory authorities, and enters a pre-constraint state.

7. A judicial collaborative management mechanism for incentivizing and constraining corporate environmental compliance as described in claim 1, characterized in that: The compliance intelligent assistance platform integrates a natural language processing engine, which can parse the environmental protection policy documents and monitoring reports submitted by enterprises, automatically compare them with the legal and regulatory database, and mark potential non-compliance points.

8. A method for implementing a judicial collaborative management mechanism for incentivizing and constraining corporate environmental compliance, characterized by: Includes the following steps: S1: Real-time aggregation and standardization of environmental data related to enterprises from various organizations through a multi-source environmental compliance data center; S2: The cross-departmental collaborative scheduling engine receives collaborative events, generates cross-departmental task instructions according to unified rules, and distributes them. S3: The intelligent compliance profile and risk warning module calculates the enterprise's compliance index and violation risk score based on the data from step S1, and outputs strategy suggestions; S4: Based on the suggestion in step S3, the differentiated incentive execution subsystem automatically matches and executes incentive measures, or the full-chain constraint handling subsystem initiates the linkage constraint process; S5: Write the execution result of step S4 back to the data center and update the enterprise profile; S6: The compliance intelligent assistance platform provides enterprises with online compliance testing and rectification guidance, and interacts with the data center to exchange calibration data.

9. A method for implementing judicial collaborative management of incentives and constraints for corporate environmental compliance as described in claim 8, characterized in that: In step S2, the collaborative events include changes in corporate credit rating, alarms for abnormal monitoring data, filing of administrative penalty cases, acceptance of judicial cases, or triggering of public complaint verification.

10. A method for implementing judicial collaborative management of incentives and constraints for corporate environmental compliance as described in claim 8, characterized in that: The compliance index and risk score in step S3 adopt a weighted fusion algorithm, and the weights are dynamically adjusted by industry benchmark values, historical trends and external policy factors.