A digital and intelligent assessment method for false reporting and missing reporting of enterprise hazardous waste business conditions

Abstract

The application discloses a kind of enterprise dangerous waste management situation concealment and underreporting digital evaluation method, belong to enterprise dangerous waste management situation concealment and underreporting evaluation technical field, method includes: production monitoring arrangement and waste analysis model arrangement are carried out in each target enterprise;The waste analysis model is used to analyze the waste data reported by target enterprise, to determine whether the reported waste data is declared normally;Real-time monitoring is carried out in the production process, corresponding production monitoring data is obtained, the waste data reported by target enterprise is obtained, the waste data is analyzed by waste analysis model, and waste evaluation result is obtained;And according to production monitoring data, the corresponding additional analysis label is marked for waste evaluation result;Waste evaluation result is sent to corresponding user, and corresponding processing is carried out by user according to waste evaluation result.

Description

Technical Field

[0001] This invention belongs to the technical field of assessment of concealment and underreporting of enterprise hazardous waste management, specifically a digital assessment method for concealment and underreporting of enterprise hazardous waste management. Background Technology

[0002] In the industrial production sector, the management and disposal of hazardous waste is a crucial aspect of environmental protection. With rapid industrial development, the global annual generation of hazardous waste has reached hundreds of millions of tons. Simultaneously, the cost of hazardous waste disposal is exorbitant, and some companies, in an effort to reduce costs, fail to comply with relevant regulations during the hazardous waste treatment process, leading to frequent instances of concealment and underreporting of hazardous waste, seriously threatening ecological and environmental safety and public health. From the current technological standpoint, in the hazardous waste dynamic management system, the primary task of waste-generating enterprises is to periodically declare and register hazardous waste. However, current hazardous waste declarations suffer from numerous problems, including unreasonable and non-standard declaration data, and widespread concealment, misreporting, and underreporting.

[0003] While existing technologies offer some solutions for hazardous waste management, they also have significant limitations. On one hand, traditional regulatory methods rely primarily on self-reporting by enterprises and periodic inspections by environmental protection departments, making it difficult to grasp the real-time and comprehensive situation of hazardous waste generation and disposal. Enterprises may evade supervision by falsifying data or concealing production processes, making it difficult for environmental protection departments to detect underreporting in a timely manner. On the other hand, although some existing technologies utilize information technology for hazardous waste management, they lack intelligent assessment methods, making it difficult to accurately determine whether enterprises are underreporting or concealing information.

[0004] In order to solve the above problems, this invention provides a digital and intelligent assessment method for enterprises to conceal or fail to report their hazardous waste management. Summary of the Invention

[0005] To address the problems existing in the above solutions, this invention provides a digital and intelligent assessment method for enterprises to conceal or fail to report their hazardous waste management.

[0006] The objective of this invention can be achieved through the following technical solutions: A digital assessment method for detecting concealment or underreporting of hazardous waste management by enterprises, the method comprising: Step 1: Deploy production monitoring and waste analysis models at each target enterprise; the waste analysis model is used to analyze the waste data reported by the target enterprises to determine whether the reported waste data is normal. Furthermore, the establishment of the waste analysis model includes: Obtain historical waste monitoring data from the target enterprise, categorize the historical waste monitoring data according to production mode, obtain the corresponding classification data for each production mode, and label the classification data as mode material data; label each mode material data with whether it is normal or not; establish a waste analysis model based on the labeled mode material data, and the waste analysis model is expressed as follows: ; In the formula: s is the input data, representing the corresponding waste data, and the output data is the waste analysis value FP(s), which is 1 or 0; When the waste analysis value is 1, the assessment report is deemed abnormal. When the waste analysis value is 0, the assessment and application are normal.

[0007] Furthermore, analyze whether the target company has adjusted its production lines; When it is determined that the target company is adjusting its production line, the waste analysis model is adjusted accordingly. No action will be taken if it is determined that the target company has not adjusted its production line.

[0008] Furthermore, analyze whether the target company has adjusted its production lines, including: The system pre-sets and adjusts the related items of the production line, collects data from the target enterprise based on these related items, and obtains corresponding correlation analysis data. Based on the correlation analysis data, determine whether the target company has adjusted its production line.

[0009] Step 2: Conduct real-time monitoring during the production process to obtain relevant production monitoring data, acquire waste data reported by the target enterprise, analyze the waste data through a waste analysis model to obtain waste assessment results, and mark the waste assessment results with corresponding additional analysis labels based on the production monitoring data.

[0010] Furthermore, the waste data is analyzed using a waste analysis model, including: Obtain the production monitoring data corresponding to the waste data, determine the production mode corresponding to the waste data based on the production monitoring data, and label the waste data with the corresponding production mode tags. Waste data is input into the waste analysis model for analysis to obtain the corresponding waste analysis values. When the waste analysis value is 1, the waste assessment result is an abnormal declaration; When the waste analysis value is 0, the waste assessment result is considered normal.

[0011] Furthermore, based on production monitoring data, additional analytical labels are added to the waste assessment results, including: Obtain target company information and determine the company's reference classification in real time based on the target company information; Based on production monitoring data, the production mode of the target enterprise at the corresponding time is identified in real time, and a production mode diagram is generated based on the production mode at the corresponding time. The horizontal axis of the production mode diagram is time, and the vertical axis is production mode. The production model diagram is analyzed based on the enterprise reference classification to obtain production assessment results, which include normal production and abnormal production. Appropriate additional labels are added to the waste assessment results based on the production assessment results.

[0012] Furthermore, based on the target company information, the company's reference classification is determined in real time, including: The platform provider establishes a corporate information database, which is used to store various types of corporate information. The target company information is input into the company information database for matching analysis to obtain several reference companies, which form the target company's enterprise reference classification.

[0013] Furthermore, the target company information is input into the company information database for matching analysis, including: Establish a matching model; the expression for the matching model is: ; In the formula: (q, p) are the input data, where q represents the corresponding enterprise information in the enterprise information database and p is the target enterprise information; the output data is the matching evaluation value PR(q, p), which is 1 or 0. The matching model is used to analyze the target company information and the corresponding company information in the company information database to obtain the matching evaluation value of the corresponding company information. The reference companies are determined based on the information of companies with a matching evaluation value of 1.

[0014] Furthermore, the production model diagram is analyzed based on enterprise reference classification, including: Obtain the production model diagrams of each reference enterprise within the reference enterprise category and mark them as reference model diagrams; summarize and process each reference model diagram to obtain a representative model diagram. The production model diagram of the target company is analyzed based on the representative model diagram to obtain production evaluation results.

[0015] Step 3: Send the waste assessment results to the relevant users, who will then process the waste accordingly.

[0016] Furthermore, users will take appropriate measures based on the waste assessment results, including: When the waste assessment result is identified as an abnormal declaration, it shall be handled in accordance with the preset abnormal declaration handling method, such as on-site inspection, etc. When the waste assessment result is normal, identify the corresponding additional label. If the additional label indicates normal production, no action is taken. If the additional label indicates abnormal production, send a request for explanation to the target company.

[0017] Compared with the prior art, the beneficial effects of the present invention are: This invention breaks through the reliance of traditional regulatory methods on enterprise self-reporting and periodic inspections by relevant departments, achieving real-time and comprehensive monitoring of enterprises' hazardous waste management. Through a digital and intelligent assessment method, it can continuously collect and analyze relevant data on enterprise hazardous waste generation and disposal, allowing relevant departments to keep abreast of enterprise dynamics, promptly identify potential problems, and effectively prevent enterprises from evading supervision by falsifying data or concealing production processes, greatly improving the timeliness and coverage of supervision. Addressing the problem that existing technologies lack intelligent assessment methods and struggle to accurately determine whether enterprises are concealing or underreporting waste, the digital and intelligent assessment method provided by this invention can deeply analyze enterprise-reported data and actual operational data to conduct intelligent assessments. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0020] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0021] like Figure 1 As shown, a digital assessment method for detecting concealment or underreporting of hazardous waste management by enterprises is provided. The method includes: Step 1: Mark the enterprises that need to be monitored for concealment or underreporting of hazardous waste management as target enterprises, and set up production monitoring and waste analysis models at each target enterprise.

[0022] In one embodiment, according to relevant regulations, the company itself or a third-party organization hired by the company typically deploys monitoring equipment and waste analysis models on the production line to monitor waste information generated during the production process and analyze it using the waste analysis model to determine whether it is normal, such as comparing the estimated amount of waste generated with the actual amount of waste generated to determine whether there is any abnormality. The waste analysis model is generally built using the random forest algorithm; it can also be built using machine learning, deep learning algorithms, etc.

[0023] The standard procedure is as follows: internal audit (waste analysis model analysis) - automatic system push - proactive retrieval by regulatory authorities - third-party verification.

[0024] For example, regulations stipulate that entities discharging pollutants must install standard-compliant discharge and monitoring equipment and maintain their normal operation. Enterprises, as one of the main entities responsible for environmental monitoring, are required to conduct real-time monitoring of pollutant emissions during their production processes.

[0025] In one embodiment, the establishment of a waste analysis model includes: Identify the production modes of the target company. Production modes refer to the production methods adopted with different production efficiencies. Different production modes correspond to different normal waste ranges. Therefore, it is necessary to conduct a differential analysis based on production simulation. Generally, this is determined based on the target company's production line, but it can also be set directly by the company itself. Obtain historical waste monitoring data from the target enterprise, categorize the historical waste monitoring data according to production mode, obtain the corresponding category data for each production mode, and label it as mode material data; label each mode material data with whether it is normal or not; build a waste analysis model based on the labeled mode material data. The waste analysis model is expressed as follows: ; In the formula: s is the input data, representing the corresponding reported waste data, such as the types of waste and their corresponding quantities; for enterprises, it is the waste data determined by actual monitoring; training is performed using labeled pattern material data; the output data is the waste analysis value FP(s), which is 1 or 0; if s exceeds the normal range requirements of the corresponding production mode, the normal standard can be adjusted as needed, such as allowing a certain error, i.e., exceeding the normal range by a certain percentage is considered normal, and the specific settings are based on relevant regulations; When the waste analysis value is 1, the assessment report is deemed abnormal. When the waste analysis value is 0, the assessment and application are normal.

[0026] In one embodiment, the system analyzes in real time whether the target company is adjusting its production line, such as adjusting the production process or changing the production line. When it is determined to adjust the production line, the waste analysis model is adjusted to adapt it to the current production line analysis.

[0027] If it is determined that no production line adjustments have been made, the waste analysis model will not be adjusted.

[0028] In one embodiment, real-time analysis of whether a target company is adjusting its production line can be performed based on the relevant application data submitted by the target company. This is because, according to regulations, when a target company needs to adjust its production line, it must submit relevant application data to the relevant departments.

[0029] In one embodiment, real-time analysis of whether a target company is adjusting its production line includes: The data performance items of the production line are preset and marked as related items, such as equipment changes, personnel adjustments, production data, market feedback (decrease in defect rate, change in unit cost, supplier change, etc.); data is collected from the target enterprise based on the related items to obtain corresponding correlation analysis data. Generally, periodic collection is used, and real-time collection is not required. The analysis is based on correlation analysis data to determine whether the target company has adjusted its production line; for example, various performance characteristics corresponding to production line adjustment are preset, and matching analysis is performed according to the performance characteristics to determine whether the target company has adjusted its production line.

[0030] Step 2: Conduct real-time monitoring during the production process to obtain relevant production monitoring data and acquire waste data reported by the target company, including the corresponding time period, for comparative analysis; analyze the waste data through a waste analysis model to obtain waste assessment results; and mark the waste assessment results with corresponding additional analysis labels based on the production monitoring data.

[0031] In one embodiment, waste data is analyzed using a waste analysis model, including: Obtain production monitoring data corresponding to waste data, determine the production mode corresponding to waste data based on production monitoring data, and conduct staged analysis with multiple production modes, and finally summarize and analyze; label waste data with corresponding production mode tags; input waste data as input data into the waste analysis model for analysis to obtain corresponding waste analysis values; When the waste analysis value is 1, the waste assessment result is an abnormal declaration; When the waste analysis value is 0, the waste assessment result is considered normal.

[0032] In one embodiment, additional analytical labels are added to the waste assessment results based on production monitoring data, including: Obtain target company information and determine the company reference classification in real time based on the target company information. The company reference classification includes various companies that have a reference function for the production of the target company. The production mode is determined in real time based on production monitoring data, and a corresponding production mode diagram is generated based on the production mode at the corresponding time. The horizontal axis of the production mode diagram is time, and the vertical axis is production mode. The production model diagram is analyzed based on the enterprise reference classification to obtain the corresponding production assessment results, which include normal production and abnormal production. Based on the production assessment results, add appropriate additional labels to the waste assessment results, such as production anomaly labels.

[0033] In one embodiment, the enterprise reference classification is determined in real time based on the target enterprise information. Since the enterprise reference classification remains basically unchanged when the enterprise does not adjust its production line, the enterprise reference classification can be set manually for each target enterprise. Subsequently, the enterprise reference classification can be used to determine the target enterprise's current industry's normal production mode, production efficiency trend and other relevant data, and then assess whether the target enterprise's production is abnormal.

[0034] In one embodiment, determining the enterprise reference classification in real time based on the target enterprise information includes: The platform establishes a corporate information database to store various types of corporate information, including information on various target companies and other related companies. The corporate information includes relevant information such as production processes and production line information. The target company information is input into the company information database for matching analysis to obtain several reference companies, which form the target company's enterprise reference classification.

[0035] In one embodiment, target company information is input into a company information database for matching analysis. Matching can be performed in existing ways, such as judging whether the assessment of the target company's production mode and production efficiency trend is effective. If it is effective, it is considered to meet the matching requirements and is marked as a reference company. On this basis, matching is performed based on various existing assessment methods.

[0036] In one embodiment, the target company information is input into a company information database for matching analysis, including: Establish a matching model; the expression for the matching model is: ; In the formula: (q, p) are the input data, q represents the corresponding enterprise information in the enterprise information database, and p is the target enterprise information; q satisfies the matching requirement, which means that q has an effect on the evaluation of p's production mode and production efficiency trend. The conditions for evaluating various enterprise information can be preset, and the corresponding training set can be integrated and set for training; the output data is the matching evaluation value PR(q, p), and the matching evaluation value is 1 or 0. The matching model is used to analyze the target company information and the corresponding company information in the company information database to obtain the matching evaluation value of the corresponding company information. The reference companies are determined based on the information of companies with a matching evaluation value of 1.

[0037] In one embodiment, the matching model can also be built based on machine learning or deep learning algorithms.

[0038] In one embodiment, the analysis of the production pattern diagram based on enterprise reference classification includes: Obtain the production model diagrams of each reference enterprise corresponding to the reference enterprise category, and mark them as reference model diagrams; summarize and process each reference model diagram to determine the representative model diagram; Analyze the production mode diagram of the target company based on the representative mode diagram to obtain production evaluation results. For example, if the production mode does not correspond or the production efficiency is abnormal, the representative mode diagram shows that the reference company adopts full-production line full-efficiency production, while the target company adopts half-efficiency production for a long period of time without submitting corresponding explanations. On this basis, various non-conforming comparison features can be set in advance, and subsequent identification and comparison can be carried out. Alternatively, intelligent analysis can be carried out by combining machine learning and other methods.

[0039] In one embodiment, the various reference pattern diagrams are aggregated. This aggregation process is carried out in an existing manner, such as first removing abnormal reference pattern diagrams and then using methods such as mode and mean to determine representative pattern diagrams.

[0040] In one embodiment, the waste assessment results are labeled with corresponding additional analytical tags based on production monitoring data, including: The production mode is determined in real time based on production monitoring data, and a corresponding production mode diagram is generated based on the production mode at the corresponding time. The horizontal axis of the production mode diagram is time, and the vertical axis is production mode. The production model diagram is analyzed based on the target company's current industry environment. The rationality of its adoption of the corresponding production model and production efficiency is evaluated, and then the production evaluation results are determined. Appropriate additional labels are added to the waste assessment results based on the production assessment results.

[0041] Step 3: Send the waste assessment results to the relevant users, who will then process the waste accordingly.

[0042] In one embodiment, the user processes the waste according to the assessment results, in accordance with the management methods of the relevant department.

[0043] In one embodiment, the user processes the waste according to the waste assessment results, including: When the waste assessment result is identified as an abnormal declaration, it shall be handled in accordance with the preset abnormal declaration handling method, such as on-site inspection, etc. When the waste assessment result is normal, identify the corresponding additional label. If the additional label indicates normal production, no action is taken. If the additional label indicates abnormal production, send a request for explanation to the target company. If the explanation is unreasonable, proceed with subsequent review and processing.

[0044] The above formulas are all numerical calculations after removing dimensions. The formulas are obtained by software simulation based on a large amount of data and are closest to the real situation. The preset parameters and preset thresholds in the formulas are set by those skilled in the art according to the actual situation or obtained by simulation based on a large amount of data.

[0045] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A digital assessment method for detecting concealment or underreporting of hazardous waste management by enterprises, characterized in that the method... include: Production monitoring and waste analysis models were deployed at each target company. The waste analysis model is used to analyze the waste data reported by the target company to determine whether the reported waste data is submitted normally. Real-time monitoring is conducted during the production process to obtain relevant production monitoring data and waste data reported by the target enterprise. The waste data is then analyzed using a waste analysis model to obtain waste assessment results. Additional analysis labels are then added to the waste assessment results based on the production monitoring data. The waste assessment results are sent to the relevant users, who then process the waste accordingly.

2. The digital assessment method for detecting concealment or underreporting of hazardous waste management by enterprises according to claim 1, characterized in that, The establishment of the waste analysis model includes: Obtain historical waste monitoring data from the target enterprise, categorize the historical waste monitoring data according to production mode, obtain the corresponding classification data for each production mode, and label the classification data as mode material data; label each mode material data with whether it is normal or not; establish a waste analysis model based on the labeled mode material data, and the waste analysis model is expressed as follows: ； In the formula: s is the input data, representing the corresponding waste data, and the output data is the waste analysis value FP(s), which is 1 or 0; When the waste analysis value is 1, the assessment report is deemed abnormal. When the waste analysis value is 0, the assessment and application are normal.

3. The digital assessment method for detecting concealment or underreporting of hazardous waste management by enterprises according to claim 1, characterized in that, Analyze whether the target company has adjusted its production lines; When it is determined that the target company is adjusting its production line, the waste analysis model is adjusted accordingly. No action will be taken if it is determined that the target company has not adjusted its production line.

4. The digital assessment method for detecting concealment or underreporting of hazardous waste management by enterprises according to claim 3, characterized in that, Analyze whether the target company has adjusted its production lines, including: The system pre-sets and adjusts the related items of the production line, collects data from the target enterprise based on these related items, and obtains corresponding correlation analysis data. Based on the correlation analysis data, determine whether the target company has adjusted its production line.

5. The digital assessment method for detecting concealment or underreporting of hazardous waste management by enterprises according to claim 1, characterized in that, Waste data is analyzed using a waste analysis model, including: Obtain the production monitoring data corresponding to the waste data, determine the production mode corresponding to the waste data based on the production monitoring data, and label the waste data with the corresponding production mode tags. Waste data is input into the waste analysis model for analysis to obtain the corresponding waste analysis values. When the waste analysis value is 1, the waste assessment result is an abnormal declaration; When the waste analysis value is 0, the waste assessment result is considered normal.

6. The digital assessment method for detecting concealment or underreporting of hazardous waste management by enterprises according to claim 1, characterized in that, Based on production monitoring data, appropriate additional analysis labels are added to the waste assessment results, including: Obtain target company information and determine the company's reference classification in real time based on the target company information; Based on production monitoring data, the production mode of the target enterprise at the corresponding time is identified in real time, and a production mode diagram is generated based on the production mode at the corresponding time. The horizontal axis of the production mode diagram is time, and the vertical axis is production mode. The production model diagram is analyzed based on the enterprise reference classification to obtain production assessment results, which include normal production and abnormal production. Appropriate additional labels are added to the waste assessment results based on the production assessment results.

7. The digital assessment method for detecting concealment or underreporting of hazardous waste management by enterprises according to claim 6, characterized in that, The enterprise reference classification is determined in real time based on the target enterprise information, including: The platform provider establishes a corporate information database, which is used to store various types of corporate information. The target company information is input into the company information database for matching analysis to obtain several reference companies, which form the target company's enterprise reference classification.

8. The digital assessment method for detecting concealment or underreporting of hazardous waste management by enterprises according to claim 7, characterized in that, Input the target company information into the company information database for matching analysis, including: Establish a matching model; the expression for the matching model is: ； In the formula: (q, p) are the input data, where q represents the corresponding enterprise information in the enterprise information database and p is the target enterprise information; the output data is the matching evaluation value PR(q, p), which is 1 or 0. The matching model is used to analyze the target company information and the corresponding company information in the company information database to obtain the matching evaluation value of the corresponding company information. The reference companies are determined based on the information of companies with a matching evaluation value of 1.

9. The digital assessment method for detecting concealment or underreporting of hazardous waste management by enterprises according to claim 6, characterized in that, The production model diagram is analyzed based on the enterprise reference classification, including: Obtain the production model diagrams of each reference enterprise within the reference enterprise category and mark them as reference model diagrams; summarize and process each reference model diagram to obtain a representative model diagram. The production model diagram of the target company is analyzed based on the representative model diagram to obtain production evaluation results.

10. The digital assessment method for detecting concealment or underreporting of hazardous waste management by enterprises according to claim 1, characterized in that, Users will handle the waste according to the assessment results, including: When the waste assessment result is identified as an abnormal declaration, it shall be handled in accordance with the preset abnormal declaration handling method; When the waste assessment result is normal, identify the corresponding additional label. If the additional label indicates normal production, no action is taken. If the additional label indicates abnormal production, send a request for explanation to the target company.

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