An industrial enterprise environment compliance dynamic evaluation method and system based on multi-source perception and bimodal data fusion

Abstract

The present application relates to the technical field of environmental protection informationization supervision, and discloses a kind of industrial enterprise environmental compliance dynamic evaluation method and system based on multi-source perception and bimodal data fusion.The method comprises the following steps: collecting the environmental management data of industrial enterprises, and constructing the evaluation system including source control, process control, end treatment, environmental management and hardware facilities supporting dimension;Through static evaluation model, constant static compliance score is generated in update cycle based on periodic inspection data;Through dynamic evaluation model, real-time identification of illegal operation events in the whole process of pollution production is realized by using AI visual analysis and sensor data fusion technology, and real-time deduction is executed;Dynamic evaluation model is configured with periodic reset mechanism, and dynamic score is automatically reset to preset full score value at the end of preset period;Finally, the comprehensive compliance score and multi-dimensional portrait are generated by combining static and dynamic scores.The present application solves the problems of existing supervision evaluation lag, process control loss and insufficient incentive mechanism, and realizes full-time, fine dynamic supervision of industrial enterprise environmental compliance.

Description

Technical Field

[0001] This invention relates to the field of environmental information supervision technology. Specifically, it relates to a dynamic evaluation method and system for monitoring industrial enterprises (especially those involved in volatile organic compound (VOC) emissions) throughout the entire process, creating multi-dimensional profiles, and providing real-time compliance scores using Internet of Things (IoT) sensing technology and artificial intelligence (AI) visual recognition technology. Background Technology

[0002] With increasingly stringent environmental regulations, refined management of industrial enterprises involved in volatile organic compound (VOC) emissions (such as auto repair, furniture manufacturing, and industrial coating) has become an industry consensus. Existing environmental compliance assessment systems face several technical and regulatory limitations in practical application:

[0003] The data sources for existing environmental credit rating or tiered management models mainly rely on periodic on-site inspections or supervisory monitoring by regulatory personnel. Because inspection cycles are typically long (e.g., annual or quarterly), this evaluation method is inherently static and fails to reflect the true management level of enterprises between inspections. Evaluation results often lag behind the actual performance of enterprises, failing to promptly detect management lapses or last-minute rectification attempts.

[0004] Existing regulatory methods typically focus on monitoring whether end-of-pipe emission concentrations meet standards, lacking effective control measures for fugitive emissions caused by improper operations during production. For example, unsealed work areas and unsealed hazardous waste bins often occur instantaneously and are difficult to detect through end-of-pipe monitoring equipment. This "results-oriented, process-neglecting" approach makes it difficult to incorporate many hidden violations into the evaluation system for quantitative assessment.

[0005] Existing evaluation systems typically lack positive incentive mechanisms with automatic recovery capabilities. Once a company is recorded for a violation, its evaluation results often remain low for a considerable period, failing to reflect subsequent corrective actions. This lack of real-time feedback means that companies lack the intrinsic motivation to immediately correct errors and restore compliance after a violation.

[0006] Existing corporate environmental management data is often fragmented. Static management data (such as environmental impact assessment procedures and hardware facility ledgers) and dynamic operational data (such as daily production activities and equipment operating status) are scattered across different management systems, making it difficult to form a unified, multi-dimensional compliance profile. This limits comprehensive and accurate hierarchical and categorized supervision of corporate environmental compliance levels. Summary of the Invention

[0007] The purpose of this invention is to solve the problems of evaluation lag, lack of process control, insufficient incentive mechanism and fragmented data dimensions in the existing technology, so as to provide a dynamic evaluation method and system for environmental compliance of industrial enterprises based on multi-source perception and dual-modal data fusion.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] On the one hand, this invention provides a dynamic evaluation method for the environmental compliance of industrial enterprises based on multi-source sensing and dual-modal data fusion. This method constructs a dual-modal evaluation model combining "static foundation" and "dynamic behavior," and introduces a discrete-time state transition mechanism to achieve full-time, quantitative supervision of enterprise compliance. Specifically, it includes the following steps:

[0010] Step 1: Multi-source environmental management data collection.

[0011] Environmental management data from industrial enterprises is collected, and this data is divided into static management data and dynamic operational data. The static management data comes from periodically entered on-site inspection records, administrative permits, and hardware facility ledgers; the dynamic operational data comes from real-time video stream data, equipment on / off status data, and operating parameter data collected by IoT sensing devices deployed on the production site.

[0012] Step 2: Construct a static evaluation model.

[0013] Based on the aforementioned static management data, a quantitative assessment of the enterprise's basic environmental management level is conducted to generate a static compliance score. This score represents a company's long-term compliance capabilities in areas such as hardware infrastructure and management system development. The score remains constant within a preset update cycle (such as quarterly or annually) and serves as the benchmark value for the evaluation system.

[0014] Step 3: Construct a dynamic evaluation model.

[0015] By leveraging artificial intelligence visual analysis and multi-source sensor data fusion technology, the entire production process is monitored in real time. The dynamic evaluation model constructed in this step comprises three progressive core logical modules: dynamic calculation of severity coefficients, real-time deduction value calculation, and discrete-time state transition.

[0016] First, for the identified i-th type of violation event, the system dynamically calculates the severity coefficient of the event based on its duration or frequency using a step-by-step aggravation model. The calculation formula is as follows:

[0017]

[0018] in, This refers to the real-time monitoring value of the violation. This is a preset tolerance threshold; To emphasize the tiered units; It is a single-step aggravation factor; This indicates the floor function. This formula ensures that the penalty coefficient increases non-linearly in a step-like manner as the violation becomes more severe.

[0019] Secondly, based on the calculated severity coefficient, the system performs real-time deduction calculations to determine the deduction value for each individual event. :

[0020]

[0021] in, This is the base deduction value for the i-th type of violation.

[0022] Finally, to achieve real-time updates and periodic resets of the scores, the model is configured with a discrete-time state transition mechanism. This mechanism will process the calculated deduction values... As input, the dynamic compliance score for the next time step t+1 is updated using the following three-branch state transition function. :

[0023]

[0024] in, The preset maximum score for dynamic scoring; The preset reset time point (preferably 00:00:00 every day); This function identifies a violation and triggers a point deduction instruction. It ensures that the dynamic score monotonically decreases due to violations during non-reset periods and is forced to bounce back upon reset, achieving a "daily settlement" of the company's compliance performance.

[0025] Step 4: Calculate the overall score.

[0026] Based on static and dynamic scoring, the comprehensive compliance score S of industrial enterprises is calculated using a pre-defined weighted algorithm:

[0027]

[0028] Here, α and β are the static weighting coefficient and the dynamic weighting coefficient, respectively. This step achieves the numerical fusion of long-term basic evaluation and real-time behavioral evaluation.

[0029] Step 5: Multi-dimensional portrait generation.

[0030] To visually demonstrate a company's compliance shortcomings, this invention constructs a profiling system encompassing at least five evaluation dimensions: source control, process management, end-of-pipe treatment, environmental management, and hardware infrastructure support. The system maps static and dynamic scoring items to corresponding dimensions and employs a dimension mapping normalization algorithm to calculate the dimensional score of the k-th evaluation dimension. :

[0031]

[0032] in, This is the set of all rating items mapped to the k-th dimension; This represents the current actual score of the j-th rating item in the set. This is the sum of the theoretical maximum scores for all scoring items in the set. Based on the scores of each dimension, a multi-dimensional compliance radar chart profile is generated.

[0033] On the other hand, this invention provides a dynamic evaluation system for environmental compliance of industrial enterprises based on multi-source sensing and dual-modal data fusion, which implements the above-mentioned method. The system includes:

[0034] Sensing Module: Deployed in industrial enterprise sites, including but not limited to one or more combinations of artificial intelligence cameras, door magnetic sensors and power monitors, for collecting multi-dimensional sensing data on site around the clock.

[0035] Data processing server: Communicatively connected to the sensing module, it deploys a cooperating static evaluation engine, a dynamic evaluation engine, and a comprehensive evaluation engine. The dynamic evaluation engine is specifically configured with a timed reset unit for executing the discrete-time state transition function to achieve periodic reset of the dynamic score.

[0036] Interactive terminal: used to visually display the comprehensive compliance score, the real-time fluctuation curve of the dynamic score, and the multi-dimensional compliance radar chart.

[0037] The present invention has the following beneficial effects:

[0038] Comprehensiveness and Scientific Rigor of Evaluation Dimensions: This invention innovatively proposes an evaluation architecture of "dual-modal data fusion," which establishes long-term compliance benchmarks for enterprises through a static evaluation model and captures real-time operational performance through a dynamic evaluation model. Combined with radar charts generated by a dimensional mapping normalization algorithm, it can comprehensively and thoroughly reflect the true level of environmental management of enterprises, avoiding the one-sidedness of single-dimensional evaluation.

[0039] Timeliness and accuracy of supervision: Unlike traditional lagging evaluations measured in years or quarters, this invention utilizes IoT and AI technologies to achieve millisecond-level violation identification and penalty. The introduction of a tiered severity model enables evaluation results to respond instantly to the severity of violations, elevating the granularity of supervision from "post-event accountability" to "in-process control."

[0040] The immediacy and positive impact of the incentive mechanism: By introducing a periodic reset mechanism based on a discrete-time state transition function, this invention breaks the rigid model of traditional credit evaluation where "one act of dishonesty leads to long-term restrictions." A company's violation points for today are automatically cleared and restored to full score the following day. This "instant restoration of compliance status" mechanism greatly reduces the psychological burden on companies, effectively incentivizing them to proactively identify and immediately rectify problems, thus forming a virtuous cycle of management.

[0041] Rigor and scalability of data logic: This invention establishes a standardized scoring calculation and dimension mapping logic, which is not only applicable to the automotive repair industry, but can also be widely applied to other industrial fields involving VOCs emissions, such as furniture manufacturing and industrial coating, by adjusting the feature library and mapping rules, and has broad application prospects. Attached Figure Description

[0042] Figure 1 This is a flowchart of a dynamic evaluation method for environmental compliance of industrial enterprises based on multi-source sensing and dual-modal data fusion, as proposed in this application.

[0043] Figure 2 This is a schematic diagram of the overall architecture of a dynamic evaluation system for environmental compliance of industrial enterprises based on multi-source sensing and dual-modal data fusion, as proposed in this application.

[0044] Figure 3 This is the main flowchart of the dual-modal evaluation method of this application;

[0045] Figure 4 This is the timing logic diagram of the event-triggered deduction and periodic reset mechanism in the dynamic evaluation model of this application;

[0046] Figure 5 This is a schematic diagram of the interactive interface for the multi-dimensional compliance profile and comprehensive scoring of the applicant company. Detailed Implementation

[0047] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. This embodiment takes an automobile repair company as an example, but those skilled in the art should understand that the method of the present invention is also applicable to other industrial enterprises involving volatile organic compound (VOC) emissions, such as furniture manufacturing, packaging printing, and industrial coating.

[0048] This embodiment provides a dynamic evaluation system and method for environmental compliance of industrial enterprises based on multi-source sensing and dual-modal data fusion. For example... Figure 2 As shown, the system mainly consists of three parts: a sensing module deployed at the industrial enterprise site, a data processing server deployed in the cloud, and an interactive terminal. The sensing module is responsible for collecting multi-dimensional sensing data from the site around the clock. In the automotive repair scenario, this specifically includes AI cameras deployed at key pollution-generating nodes such as paint booths, polishing workshops, paint mixing rooms, and hazardous waste storage rooms; door magnetic sensors installed on the doors of these work spaces; and power monitors installed on the power supply circuits of production facilities (such as paint booths) and waste gas treatment facilities. The data processing server, as the core brain of the system, deploys a coordinating static evaluation engine, a dynamic evaluation engine, and a comprehensive evaluation engine. The dynamic evaluation engine is specifically equipped with a timed reset unit. The interactive terminal is used to display comprehensive scores and multi-dimensional profiles to supervisors or enterprise managers.

[0049] Based on the above system architecture, the evaluation method in this embodiment follows the following... Figure 1 The overall process shown covers five core steps from data acquisition to profile generation.

[0050] Step S1: Multi-source environmental management data collection. The data collected by the system can be divided into two categories: static management data and dynamic operational data. Static management data is periodically entered by regulatory personnel through interactive terminals or synchronized from government databases, covering long-term indicators such as the enterprise's environmental impact assessment procedures, pollutant discharge permits, and hardware facility ledgers. Dynamic operational data is collected in real time through the sensing module, including video stream data uploaded by AI cameras, on / off data from door magnetic sensors, and power data from power monitors.

[0051] Step S2: Construct a static evaluation model and generate a static score. The static evaluation engine in the data processing server receives the aforementioned static management data and quantitatively assesses the company's basic environmental management level based on preset evaluation criteria. Scoring items cover dimensions such as hardware infrastructure (e.g., whether adsorption concentration + catalytic combustion treatment processes are used) and environmental management systems (e.g., the establishment of hazardous waste ledgers). The system generates a static compliance score based on the scoring results. ).like Figure 3 As shown, once generated, the score remains constant until the next inspection cycle arrives and new data is entered, reflecting the company's long-term baseline level of environmental compliance.

[0052] Step S3: Construct a dynamic evaluation model and generate dynamic scores. The system utilizes AI visual analysis and sensor data fusion technology through its internal dynamic evaluation engine to monitor the entire production process in real time. This step specifically includes three logical processes: violation identification, real-time point deduction, and periodic reset.

[0053] During the violation identification process, the system has a built-in "violation event feature library" covering the entire pollution generation process. The dynamic evaluation engine identifies typical violation events in real time through logical cross-validation. For example, when the AI ​​detects that painting or sanding work is in progress and the door magnetic sensor shows that the work space door is open, it is determined as "failure of airtightness of pollution generation space"; when the AI ​​detects that the hazardous waste bin lid is still open after personnel have left the hazardous waste temporary storage room, it is determined as "non-standard hazardous waste storage"; when the power monitor shows that the production equipment is running, but the power of the waste gas treatment facility is lower than the preset effective operating threshold, it is determined as "abnormal operation of treatment facility".

[0054] During the real-time deduction calculation, once any of the above events is identified, the dynamic evaluation engine first dynamically calculates the severity coefficient of the event based on its duration or frequency using a stepped aggravation model. The calculation formula is:

[0055]

[0056] in, This refers to real-time monitoring values ​​of violations (such as the duration of the violation). This is a preset tolerance threshold; The unit of emphasis is δ; δ is the single-step emphasis factor. This indicates the floor function.

[0057] The system calculates the deduction value ΔS corresponding to a single event using the following formula:

[0058]

[0059] in, This is the base deduction value for the i-th type of violation.

[0060] Specifically, in this embodiment, for the typical violation event of "failure to maintain the airtightness of the pollution-generating space" (such as the side door being opened during paint spraying operations), the system sets the following parameters to reflect the hierarchical control strategy:

[0061] Basic deduction value Set at 5 points;

[0062] Tolerance threshold Set to 3 minutes (allowing necessary short periods of entry and exit for operators to avoid false alarms);

[0063] Heavy ladder unit Set to 10 minutes;

[0064] The single-step aggravation factor δ is set to 0.5.

[0065] Based on the above parameters, if the AI ​​detects the duration of the side door being continuously open during a painting operation... If the violation lasts 25 minutes, the effective violation duration is 25 - 3 = 22 minutes. According to the aforementioned formula, the escalation increment is ⌊22 / 10⌋ = 2 (meaning two aggravating escalators are triggered). At this point, the severity coefficient of the event... The calculation is 1 + 2 × 0.5 = 2.0. Therefore, the final real-time deduction value ΔS for this violation is 2.0 × 5 = 10 points. This calculation process intuitively realizes the multiplication of penalties for long-term violations, reflecting the accuracy of dynamic evaluation.

[0066] During the periodic reset process, such as Figure 4 As shown in the timing logic diagram, the dynamic evaluation engine is configured with a timed task. In this embodiment, the reset period is set to a calendar day. The system uses a discrete-time state transition function to update the dynamic score. :

[0067]

[0068] in, The preset maximum score for dynamic scoring (set to 100 points in this embodiment); The preset reset time point (in this embodiment, it is 00:00:00 every day). The system identifies violations and triggers point deductions. This mechanism ensures that dynamic scores decrease monotonically due to violations during non-reset periods and are restored to full score at midnight each day, incentivizing companies to complete daily tasks on the same day.

[0069] Step S4: Comprehensive Score Calculation. The comprehensive evaluation engine obtains the current static and dynamic compliance scores in real time and calculates the company's comprehensive compliance score S based on a preset weighting strategy. The calculation formula is:

[0070]

[0071] Where α and β are the static weighting coefficient and the dynamic weighting coefficient, respectively, and α + β = 1. In this embodiment, α = 0.4 and β = 0.6 are set. If a company's static score is 90 points, but its dynamic score drops to 80 points on a given day, then its real-time comprehensive score is 84 points.

[0072] Step S5, Multi-dimensional Profile Generation. While calculating the comprehensive score, the system generates a pentagonal radar profile (e.g., ...) based on preset business logic mapping rules. Figure 5(As shown). The system maps each specific scoring item in the static and dynamic evaluation models to five evaluation dimensions: source control, process management, end-of-pipe treatment, environmental management, and hardware infrastructure support. A specific mapping example is as follows:

[0073] Source control: mapping items such as "whether the VOC content of raw and auxiliary materials meets the standards" (static) and "whether the paint spraying booth uses electric energy for heating" (static).

[0074] Process control: Mapping items such as "Failure of airtight seal in spray painting booth" (dynamic) and "Failure of airtight seal in grinding booth" (dynamic).

[0075] End-of-pipe treatment: This includes items such as "abnormal power output of treatment facilities" (dynamic) and "overdue replacement of activated carbon" (dynamic).

[0076] Environmental management: This includes items such as "non-standard hazardous waste storage" (dynamic) and "lack of management records" (static).

[0077] Hardware facilities support: mapping items such as "design air volume matching degree of treatment facilities" (static) and "construction standardization of work area" (static).

[0078] To eliminate the dimensional discrepancies caused by the inconsistent number of rating items across different dimensions, the system employs a dimensional mapping normalization algorithm to calculate the dimensional score of the k-th evaluation dimension. :

[0079]

[0080] in, Index representing evaluation dimensions ( ); It is the set of all rating items mapped to the k-th dimension (including static and dynamic rating items); This represents the current actual score of the j-th rating item in the set. This is the sum of the theoretical maximum scores for all scoring items in the set. Based on the scores of each dimension, a multi-dimensional compliance radar chart profile is generated.

[0081] like Figure 5 As shown, the interactive terminal renders a radar chart based on the above calculation results. The chart clearly shows the comparison between "current company performance" and "industry average level". For example, if a company frequently experiences "paint booth door left open" incidents, it will lead to a decline in its "process control" dimension. The value drops significantly, forming a noticeable dip on the radar chart. Based on this, regulatory authorities can accurately identify management loopholes in specific areas of the company and issue targeted rectification notices.

[0082] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A dynamic evaluation method for environmental compliance of industrial enterprises based on multi-source sensing and dual-modal data fusion, characterized in that, Includes the following steps: Collect environmental management data from industrial enterprises, including static management data recorded through periodic inspections and dynamic operational data collected in real time through IoT sensing devices; A static evaluation model is constructed and run to assess the company's environmental management foundation based on the static management data and generate a static compliance score, which remains constant within a preset update cycle. A dynamic evaluation model is constructed and run, which is based on artificial intelligence visual analysis and sensor data fusion technology to identify violations in the production process of the industrial enterprise in real time. When the violation event is detected, the dynamic evaluation model performs real-time deduction calculations according to preset rules to generate a dynamic compliance score; The dynamic evaluation model is equipped with a periodic reset mechanism. At the end of each preset time period, the deduction records within that period are automatically cleared, and the dynamic compliance score is reset to the preset full score. Based on the static compliance score and the dynamic compliance score, the comprehensive compliance score of the industrial enterprise is calculated using a preset weighting algorithm. The scoring items in the static evaluation model and the dynamic evaluation model are mapped to preset evaluation dimensions to generate a multi-dimensional compliance radar profile of the industrial enterprise.

2. The method according to claim 1, characterized in that, The industrial enterprises mentioned are those involved in the emission of volatile organic compounds (VOCs); the violations cover the entire process of pollution generation in industrial enterprises, including but not limited to at least one violation in the source control, process management and control and end-of-pipe treatment stages; the violations include but are not limited to failure of the airtightness of the pollution-generating work space, abnormal operation of waste gas treatment facilities or non-standard management of hazardous waste.

3. The method according to claim 1, characterized in that, When performing real-time deduction calculations, the dynamic evaluation model uses a tiered aggravation model to calculate the severity coefficient of the violation event. And calculate the single deduction value accordingly. The calculation formula is as follows: ； ； in, This refers to the real-time monitoring value of the violation. This is a preset tolerance threshold; To emphasize the tiered units; It is a single-step aggravation factor; This indicates the floor function; This serves as the base deduction value for this type of violation.

4. The method according to claim 1, characterized in that, The periodic reset mechanism is configured to reset according to a preset time period; the periodic reset mechanism follows a discrete-time state transition function to update the next time step. Dynamic compliance scoring : ； in, The preset maximum score for dynamic scoring; This is the preset reset time point; The deduction value is the score triggered by a real-time violation.

5. The method according to claim 1, characterized in that, The formula for calculating the comprehensive compliance score is as follows: ; Where S represents the overall compliance score. For static compliance scoring, For dynamic compliance scoring, α and β are the static weighting coefficient and the dynamic weighting coefficient, respectively.

6. The method according to claim 1, characterized in that, The multi-dimensional compliance profile includes at least five evaluation dimensions: source control, process management, end-of-pipe treatment, environmental management, and hardware infrastructure support. Each specific scoring item in the static and dynamic evaluation models is mapped to at least one of the above evaluation dimensions based on its business attributes. The system uses a dimension mapping normalization algorithm to calculate the first... Dimensional scores for each evaluation dimension : ； in, This is the set of all rating items mapped to the k-th dimension; This represents the current actual score of the j-th rating item in the set. It is the sum of the theoretical maximum scores for all rating items in this set.

7. A dynamic evaluation system for environmental compliance of industrial enterprises based on multi-source sensing and dual-modal data fusion, characterized in that, include: The sensing module is deployed in industrial enterprises to collect video stream data and equipment status data; A data processing server, communicatively connected to the sensing module, is deployed on which: A static evaluation engine, configured to store and process static management data, generates static compliance scores that remain constant throughout the update cycle; A dynamic evaluation engine is configured to process the video stream data and device status data, identify violations and perform real-time deductions to generate a dynamic compliance score. The dynamic evaluation engine includes a timed reset unit, which is used to reset the dynamic compliance score when a preset time period is reached. The comprehensive evaluation engine is configured to combine the static compliance score and the dynamic compliance score to generate a comprehensive compliance score and a multi-dimensional compliance profile.

8. The system according to claim 7, characterized in that, The sensing module includes, but is not limited to, one or more combinations of an AI camera, a door magnetic sensor, and a power monitor; the dynamic evaluation engine is configured to perform logical cross-validation based on the visual flow data collected by the AI ​​camera and the status data collected by other sensors in the sensing module to determine whether the airtightness failure event or other violation operation event has occurred.

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