Automatic audit and inspection method and system based on emission source statistical data

Abstract

The present invention discloses an automatic audit and inspection method and system based on emission source statistical data, which relates to the technical field of statistical data auditing. The method first collects data through multimodal fusion and then pre-processes it through adaptive noise filtering and data enhancement. Fuzzy logic and genetic algorithms are used to dynamically generate optimization rules, and automatic auditing is carried out with the help of deep convolution and long short-term memory networks. Anomalies are detected and graded warnings are issued, and intelligent correction suggestions are provided. The results are stored in multiple dimensions and visually fed back. The method also covers security, privacy, and external collaboration steps. The present invention improves the efficiency and accuracy of emission source statistical data auditing through multimodal acquisition, intelligent auditing, and other technologies. It can dynamically optimize rules, provide graded warnings, and intelligently correct them. It has security protection and external collaboration functions, providing strong support for environmental management decisions.

Description

Technical Field

[0001] The present invention relates to the technical field of statistical data auditing, and in particular to an automatic auditing and inspection method and system based on emission source statistical data. Background Art

[0002] As global industrialization accelerates, environmental pollution is increasingly becoming a key factor hindering sustainable economic development and threatening human health. Emission source statistics, a crucial basis for environmental management and decision-making, are crucial for developing effective environmental policies and controlling pollutant emissions. Their accuracy and timeliness are crucial.

[0003] Traditionally, the audit and verification of emission source statistical data relies primarily on manual processes, requiring staff to manually collect, organize, and analyze vast amounts of data. This approach is not only inefficient and time-consuming, but also susceptible to human error, significantly compromising the accuracy and reliability of audit results. Faced with massive and complex emission data, manual audits often struggle to conduct comprehensive and detailed inspections, easily overlooking potential issues and anomalies. Furthermore, manual audits struggle to monitor and analyze data in real time, hindering timely identification of changing trends and potential risks at emission sources.

[0004] With the development of information technology, some enterprises and institutions have begun to try to use information technology to audit and verify emission source statistical data. However, most existing systems have problems such as single functions, lack of flexibility and adaptability. These systems can usually only perform simple data comparison and verification, and cannot conduct in-depth analysis and mining of complex emission data. Moreover, the existing audit rules are often fixed and cannot be dynamically optimized in time according to environmental changes, regulatory updates, and adjustments to corporate production processes, resulting in a certain deviation between the audit results and the actual situation. At the same time, in terms of data collection, most existing systems rely only on a single data source and cannot fully utilize the advantages of multimodal data, resulting in limited comprehensiveness and accuracy of the data. Therefore, there is an urgent need to develop an efficient, intelligent, and comprehensive automatic audit and inspection method and system based on emission source statistical data. Summary of the Invention

[0005] The present invention proposes an automatic audit and inspection method and system based on emission source statistical data to solve the problems mentioned in the above-mentioned prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an automatic audit and inspection method based on emission source statistical data, comprising:

[0007] Multimodal data fusion collection steps: Comprehensively utilize sensor networks, enterprise information system interfaces, and satellite remote sensing imagery for multimodal data collection. The sensor network adopts a distributed layout, with gas sensors and flow sensors deployed at emission nodes based on the characteristics of different emission sources to obtain real-time emission concentration and flow data. At the same time, through standardized data interfaces, it connects with the enterprise's production management system and environmental monitoring system to obtain production process data and emission record information. Satellite remote sensing imagery is used to monitor emission sources and obtain geographic spatial distribution and emission trend data.

[0008] Adaptive noise filtering and data enhancement preprocessing steps: Adaptive noise filtering is performed on the collected data; according to the characteristics of different data sources and the noise distribution law, the Kalman filter algorithm is used to dynamically filter the continuous monitoring data. For discrete enterprise report data, median filtering is used to remove outliers; at the same time, data enhancement technology is introduced to generate new simulated data by translating, rotating, and scaling the existing data. For emission data, virtual emission data samples are generated according to the proportion. The generation formula is: ,in is the original emission data, r is Randomly generated proportional coefficients within the interval;

[0009] Dynamic rule generation and optimization steps: Dynamically generate audit and inspection rules based on environmental laws and regulations, industry standards, and real-time updated environmental data; build a rule generation model that combines fuzzy logic reasoning and genetic algorithms for rule optimization; the rules include emission thresholds, emission concentration limits, and emission time rationality, and the rules will be adjusted in real time based on environmental changes and historical audit results. The monthly emission threshold T of emissions is dynamically adjusted based on the average emissions A of the past three months and the environmental carrying capacity coefficient k. The formula is: , where k is determined by the environmental monitoring department based on the regional environmental quality status;

[0010] Automatic audit steps based on deep learning: Use a model that combines deep convolutional neural networks (DCNN) and long short-term memory (LSTM) networks to audit pre-processed data; input the data into the trained model, and the model analyzes the data according to the rules to determine whether there are any anomalies; for emission data, the model will learn historical emission patterns and report when the deviation between actual emissions and model-predicted emissions exceeds a set threshold. When , marked as abnormal data; notify the environmental protection department and the company leader via SMS, phone, and email;

[0011] Data correction and feedback steps: Relevant personnel verify and correct abnormal data based on abnormal reports; the system provides auxiliary correction functions, providing suggestions for correcting data through historical data comparison and reference to data from similar emission sources; when the emission data of an emission source is abnormal, the system automatically retrieves historical data for the same period and the normal emission range of the same type of emission source, and provides a reasonable correction range; if the data error is caused by a monitoring equipment failure, the system will automatically locate the faulty equipment and provide maintenance guidance and backup data collection plans; at the same time, the corrected data and abnormal processing results are fed back to the dynamic rule generation and optimization step to update and improve the audit rules;

[0012] Multi-dimensional result storage and visual feedback steps: Store audit inspection results, exception reports and corrected data, using a combination of relational and non-relational databases to store structured emission data and audit results, as well as unstructured exception descriptions and processing process information; at the same time, use big data technology to analyze the stored data to discover emission patterns and problems; and display charts, maps, and reports to industry departments and enterprises through a visual interface.

[0013] Furthermore, it also includes:

[0014] Data security and privacy protection steps: Use homomorphic encryption algorithm to encrypt the collected emission source statistical data. During data use, access control and audit mechanisms are used to strictly limit data access rights and record and audit data operations.

[0015] Steps for collaborative interaction with external systems: The system interacts and shares data with meteorological monitoring systems and geographic information systems; combines meteorological data to analyze the impact of meteorological conditions on emission diffusion and optimize audit rules.

[0016] Furthermore, transfer learning technology is introduced in the automatic audit step based on deep learning; the parameters of the deep learning model trained in industrial production process monitoring are used to initialize the model of this system; at the same time, a federated learning algorithm is used to conduct joint model training among emission source data owners to improve the generalization ability of the model.

[0017] Furthermore, in the hierarchical and classified abnormal warning step, an abnormal warning knowledge base is established; historical abnormal cases and processing experience are summarized and summarized to form a knowledge base; when a new abnormal situation occurs, the system automatically matches cases from the knowledge base, provides processing suggestions and decision support; at the same time, natural language processing technology is used to perform semantic analysis on the abnormal report, and automatically generate processing procedures and solutions.

[0018] Furthermore, storage node technology is introduced in the data correction and feedback steps; the process and results of data correction are recorded on the nodes. At the same time, the distributed ledger characteristics of the storage nodes are utilized to realize data sharing and collaborative processing among industry departments and enterprises.

[0019] Furthermore, in the multi-dimensional result storage and visualization feedback steps, a visualization platform based on virtual reality and augmented reality technologies is developed; users can view the distribution and audit results of emission sources through the device, or obtain emission-related information in real time through the device in real scenarios to provide support for environmental management decisions.

[0020] A system using the automatic audit and inspection method based on emission source statistical data comprises:

[0021] Multimodal data fusion acquisition module: This module is responsible for the comprehensive use of sensor networks, enterprise information system interfaces, and satellite remote sensing imagery multimodal data acquisition methods to initially integrate data from different sources to form an original data set. The module adopts a distributed sensor layout and selects sensors based on the characteristics of different emission sources.

[0022] Adaptive noise filtering and data enhancement preprocessing module: Adaptively filter the collected data for noise, select filtering algorithms based on the noise characteristics of different data sources, and use data enhancement technology to expand the data set and generate new simulated data;

[0023] Dynamic rule generation and optimization module: Dynamically generates audit and inspection rules based on environmental regulations, industry standards and real-time updated environmental data; optimizes the rules using a combination of fuzzy logic reasoning and genetic algorithms. The rules are adjusted in real time based on environmental changes and historical audit results to adapt to different emission situations.

[0024] The deep learning-based automatic audit module uses a model combining a deep convolutional neural network (DCNN) and a long short-term memory (LSTM) network to audit preprocessed data. It extracts spatial and temporal features of the data to determine whether there are any anomalies in the emissions data. For emissions data, if the deviation between actual emissions and model predictions exceeds a set threshold, the data is marked as an anomaly.

[0025] Hierarchical and classified abnormal warning module: When abnormal data is found, the system automatically triggers the hierarchical and classified warning mechanism; according to the severity and scope of the abnormality, the warning is divided into different levels to notify the operator; at the same time, classified warnings are carried out according to the type, scale and importance of the emission source.

[0026] Data correction and feedback module: Operators verify and correct abnormal data based on abnormal reports; the module provides auxiliary correction functions, and provides suggestions for correcting data through historical data comparison and reference to similar emission source data; if the data error is caused by a monitoring equipment failure, the system will automatically locate the faulty equipment and provide maintenance guidance and backup data collection plans; the corrected data and abnormal processing results will be fed back to the rule dynamic generation and optimization module for updating and improving the audit rules.

[0027] Multi-dimensional result storage and visual feedback module: stores audit inspection results, abnormality reports and corrected data using a combination of relational and non-relational databases; uses big data technology to analyze the stored data, and displays charts, maps and reports to industry departments and enterprises through a visual interface, providing a basis for environmental management and decision-making.

[0028] Furthermore, it also includes:

[0029] The data security and privacy protection module uses a homomorphic encryption algorithm to encrypt the collected emission source statistical data, strictly limits data access rights through access control and audit mechanisms, and records and audits data operations.

[0030] Collaborative interaction module with external systems: The module is responsible for data interaction and sharing with the meteorological monitoring system and geographic information system, combining meteorological data and geographic information to optimize audit rules and provide information for environmental management.

[0031] Compared with the existing technology, the beneficial effects of the present invention are:

[0032] In terms of data collection, multimodal data fusion is employed, leveraging multiple data sources such as sensor networks, enterprise information system interfaces, and satellite remote sensing imagery. This significantly improves the comprehensiveness and accuracy of data, providing a more comprehensive picture of the actual emission sources. During data preprocessing, adaptive noise filtering and data enhancement techniques effectively remove noise interference from the data, expanding the size and diversity of the dataset, providing a higher-quality data foundation for subsequent audits and verifications, and enhancing the reliability of audit results. The dynamic rule generation and optimization module dynamically adjusts audit rules based on environmental regulations, industry standards, and real-time environmental data. By combining fuzzy logic reasoning and genetic algorithms for optimization, the rules are more adaptable and accurate, better suited to varying emission scenarios and environmental changes. The deep learning-based automated audit module utilizes advanced deep convolutional neural networks and long short-term memory networks to accurately extract spatial and temporal features of the data, enabling intelligent auditing of emission data and significantly improving audit efficiency and accuracy. A hierarchical and classified anomaly warning mechanism provides targeted alerts based on anomaly severity and emission source type, ensuring that relevant personnel receive timely and accurate warning information and take effective action. The Intelligent Data Correction and Feedback Module provides intelligent assisted correction capabilities, integrating blockchain technology to ensure data immutability and traceability, improving the efficiency and credibility of data correction. The Multidimensional Result Storage and Visual Feedback Module leverages big data mining and visualization technologies to provide an intuitive and comprehensive basis for environmental management and decision-making. Furthermore, the Data Security and Privacy Protection Module, as well as the Interaction Module with External Systems, further enhance the system's security and practicality, providing strong support for environmental protection and sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a schematic block diagram of the automatic audit and inspection system based on emission source statistical data proposed by the present invention;

[0034] Figure 2 This is a schematic block diagram of the automatic audit and inspection method based on emission source statistical data proposed by the present invention;

[0035] Figure 3 This is a schematic diagram of comparing emissions from different emission source types based on emission source statistical data proposed in the present invention;

[0036] Figure 4 This is a schematic block diagram showing how the audit accuracy of the automatic audit inspection method based on emission source statistical data proposed in the present invention changes over time. DETAILED DESCRIPTION

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0038] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like to indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0039] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the said features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined. In addition, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. The present invention will be further described in detail below with reference to the accompanying drawings.

[0040] Reference Figures 1-4 : A specific implementation method of an automatic audit and inspection method based on emission source statistical data and a system.

[0041] 1. Overall system architecture

[0042] This automated audit and verification system for emission source statistics primarily consists of a multimodal data fusion acquisition module, an adaptive noise filtering and data enhancement preprocessing module, a dynamic rule generation and optimization module, a deep learning-based automated audit module, a hierarchical classification anomaly warning module, an intelligent data correction and feedback module, a multidimensional result storage and visualization feedback module, a data security and privacy protection module, and a module for collaborative interaction with external systems. These modules collaborate to achieve efficient and accurate audit and verification of emission source statistics.

[0043] 2. Specific implementation methods of each module

[0044] (1) Multimodal data fusion acquisition module This module is responsible for the comprehensive use of multiple data collection methods to obtain comprehensive and accurate emission source statistical data.

[0045] Sensor network: Various high-precision sensors are distributed at key emission source locations, such as factory chimneys and sewage treatment outlets. For gas emissions, gas concentration sensors and flow sensors are installed to monitor the type, concentration, and flow rate of emissions in real time. For liquid emissions, water quality monitoring sensors are installed to detect pollutant levels. Sensor data is transmitted in real time to a data collection center via wireless transmission protocols (such as ZigBee and LoRa).

[0046] Enterprise information system interface: Connect with the enterprise's production management system, environmental monitoring system, etc. through standardized data interfaces. Obtain data such as the enterprise's production plan, raw material usage, and emission records. This data can reflect the inherent connection between the enterprise's production activities and emissions.

[0047] Satellite remote sensing imagery: Regularly acquire satellite remote sensing imagery data and utilize image processing and analysis techniques to conduct macroscopic monitoring of emission sources over large areas. For example, by analyzing the spectral characteristics of the imagery, the geographic spatial distribution of emission sources and emission trends can be identified. This multimodal data is initially integrated to form a raw data set, providing a foundation for subsequent processing and analysis.

[0048] (2) The adaptive noise filtering and data enhancement preprocessing module preprocesses the collected raw data to improve the quality and usability of the data.

[0049] Adaptive noise filtering: Different filtering algorithms are used based on the noise characteristics of different data sources. For continuously monitored sensor data, the Kalman filter algorithm is used for dynamic filtering. Kalman filtering is a recursive optimal estimation algorithm that filters data in real time based on the system's dynamic model and the statistical characteristics of measurement noise, effectively removing noise interference. For discrete enterprise report data, the median filter algorithm is used to remove potential outliers. By sorting the data sequence and taking the median value as the filtering result, the median filter effectively suppresses impulse noise.

[0050] Data enhancement: Data enhancement techniques are used to expand the size and diversity of the dataset. For emissions data, the formula Generate new simulated data, where is the original emission data, Randomly generate proportional coefficients within the interval. This can simulate different emission conditions and improve the generalization ability of the model.

[0051] (3) The rule dynamic generation and optimization module dynamically generates and optimizes audit and inspection rules based on environmental protection laws and regulations, industry standards and real-time updated environmental data.

[0052] Rule generation: Construct a rule generation model, combining fuzzy logic reasoning and genetic algorithms. Fuzzy logic reasoning takes into account the uncertainty and ambiguity of emission data, and sets fuzzy rules for different types of emission sources and emission indicators. For example, when the concentration of a certain emission is high and the emission volume continues to grow, it is more likely to be judged as abnormal emission. The rules include emission thresholds, emission concentration limits, and the rationality of emission time. For a certain type of emission, the monthly emission threshold T is dynamically adjusted based on the average emission volume A of the past three months and the environmental carrying capacity coefficient k. The formula is: , where k is determined by the environmental monitoring department based on the regional environmental quality status.

[0053] Rule optimization: Genetic algorithms iteratively optimize rule parameters by simulating natural selection and inheritance mechanisms. In each generation, excellent rule individuals are selected based on the rule's fitness function (such as audit accuracy and false positive rate) for crossover and mutation operations to generate a new rule population, gradually improving the rule's adaptability and accuracy.

[0054] Construction of the rule generation model: The rule generation model takes "environmental regulations - emission characteristics - dynamic adjustment" as its core logic and realizes the automatic generation of rules through a three-layer architecture:

[0055] Input layer: Receives multi-source data, including real-time emission data (concentration, flow, time series), environmental protection regulations (such as concentration limits for different industries in the "Comprehensive Emission Standards of Air Pollutants"), regional environmental quality data (such as real-time PM2.5 concentration, water pH value) and historical audit results (anomaly type, processing feedback).

[0056] The logic layer builds a rule generation engine based on fuzzy logic reasoning, converting input data into executable audit rules. For example, for an audit of SO2 emissions from a chemical company, the logic layer first fuzzifies parameters such as "emission concentration," "emission duration," and "regional environmental carrying capacity" into three-level fuzzy sets: "low / medium / high." It then generates preliminary rules using pre-set fuzzy rules (e.g., "If concentration is high and carrying capacity is low, then the abnormal risk is high").

[0057] Output layer: Outputs structured audit rules, including specific indicator thresholds (such as "a company's daily emissions must not exceed 500kg"), verification logic (such as "emissions exceeding the threshold for three consecutive days trigger an alert"), and adjustment conditions (such as "the threshold is lowered by 10% when regional environmental quality declines").

[0058] Fuzzy logic reasoning is used to handle the uncertainty and ambiguity of emission data and optimize rule adaptability through the following steps:

[0059] Fuzzy processing: define membership functions (such as triangular function, Gaussian function) for key parameters (such as emission concentration, flow rate, environmental carrying coefficient) and convert precise values ​​into fuzzy sets. For example, fuzzify "emission concentration" into "low (0-100mg / m 3 )"Medium (100-300mg / m 3 )"High (300mg / m 3 Each value corresponds to the membership of a different fuzzy set (e.g. 200mg / m 3 This corresponds to a membership of 0.8 for “medium” and 0.2 for “high”).

[0060] Rule base construction: Based on environmental protection laws and regulations and expert experience, an initial fuzzy rule base is established, including "if-then" type rules. For example: Rule 1: If the emission concentration is "high" and the environmental carrying capacity is "low", then the abnormality level is "serious";

[0061] Rule 2: If the emission concentration is "medium" and the emission duration is "continuously exceeding the standard", the abnormality level is "medium".

[0062] Fuzzy reasoning and defuzzification: For real-time input data, the fuzzy reasoning engine matches the rules in the rule base, calculates the trigger strength of each rule, and then converts the fuzzy results into precise audit thresholds using weighted average methods (such as the center of gravity method). For example, after combining multiple rules, "abnormal risk" is defuzzified into a specific emission threshold (such as "300 kg / day").

[0063] Genetic algorithms are used to iteratively optimize rule parameters and improve rule accuracy. The specific process is as follows:

[0064] Encoding: Encode key parameters of the audit rules (such as thresholds, weights, and adjustment coefficients) as binary chromosomes. For example, encode the "monthly emission threshold T" as an 8-bit binary number (range 0-255kg) and the "environmental carrying capacity factor k" as a 4-bit binary number (range 0.5-1.5).

[0065] Fitness function design: Using "audit accuracy," "false positive rate," and "rule coverage" as metrics, calculate the fitness of each rule. For example, fitness = (accuracy × 0.6) + (1 - false positive rate) × 0.3 + (coverage × 0.1). A higher value indicates better rule performance.

[0066] Selection-crossover-mutation operation: Selection: Use the roulette wheel method to select the rule with high fitness from the current rule population as the parent;

[0067] Crossover: Perform a single-point crossover on the parent chromosome to generate offspring rules. For example, if the threshold encoding of parent 1 is "10100110" and parent 2 is "11001001", the offspring may be "10101001" after crossover.

[0068] Mutation: Randomly flip 1-2 genes of the offspring chromosome (for example, mutate "10101001" to "10101011") to introduce a new combination of rule parameters.

[0069] Iterative optimization: Repeat the above steps (usually 50-100 generations) until the rule fitness stabilizes.

[0070] The rule generation model automatically updates rules based on real-time data, for example:

[0071] When environmental monitoring data in a certain area shows that "PM2.5 concentration has exceeded the standard for seven consecutive days", the system uses fuzzy logic reasoning to determine that the "environmental carrying capacity coefficient k" should be lowered to 0.8 (original k = 1.0), and triggers the genetic algorithm to re-optimize the emission thresholds of all enterprises in the area (for example, the original threshold of 500kg / day is adjusted to 400kg / day);

[0072] Combined with historical audit results, if the misjudgment rate of a rule exceeds 10% for three consecutive months, it will be automatically included in the focus of the next round of genetic algorithm optimization, and the applicability of the rule will be improved by adjusting parameters (such as relaxing the time window and modifying the membership function).

[0073] By combining fuzzy logic reasoning with genetic algorithms, the rule generation model can not only handle the ambiguity and uncertainty of emission data, but also continuously improve the accuracy and adaptability of the rules through iterative optimization, ensuring that the audit rules always keep dynamic matching with environmental protection laws and regulations, environmental changes and corporate emission characteristics.

[0074] (4) The automatic review module based on deep learning uses a model that combines deep convolutional neural networks (DCNN) and long short-term memory networks (LSTM) to review the preprocessed data.

[0075] Transfer learning technology aims to leverage knowledge from existing domains to solve problems in new domains. By reusing parameters from pre-trained models, the system's model training process accelerates and improves performance. Specific steps include: pre-trained model selection and parameter transfer. A deep learning model (e.g., a pre-trained DCNN-LSTM hybrid model) trained in industrial process monitoring (e.g., equipment operating parameters and emission monitoring data in the chemical and power industries) is selected. This model already has the ability to extract spatiotemporal features from industrial data. The parameters of the layers in the pre-trained model related to emission data features (e.g., convolutional layers in the DCNN and memory layers in the LSTM) are transferred to the system's automated audit model as initial parameters. For example, the parameters of the layers used to extract "concentration-time series correlation features" and "correlation features between equipment operating status and emissions" are retained, while only the output layer is replaced to adapt to the specific classification tasks of emission source audits (e.g., "normal emissions," "minor anomalies," and "serious anomalies"). Model fine-tuning and adaptation involves fine-tuning the transferred model based on the system's emission source dataset (pre-processed multimodal data). By setting a small learning rate, the model gradually adapts to the unique patterns of emission source data (such as the fluctuation cycles of emission concentrations in different industries and the impact of regional environmental differences on emissions) while retaining the transferred knowledge. New network layers (such as additional convolutional layers or fully connected layers) are added to conduct specialized learning for features in newly collected emission source data not covered by the pre-trained model (such as unique emissions from specific industries and geospatial features in satellite remote sensing imagery) to ensure the model's relevance to the target task.

[0076] Federated learning algorithms prevent raw data leakage and improve the model's global adaptability by collaboratively training models among data owners (such as different enterprises and regional environmental protection departments). The specific process is as follows: Distributed model architecture design: The system establishes a "central server-local node" federated learning architecture. The central server is responsible for aggregating and distributing global model parameters. Each local node (such as an enterprise or regional monitoring station) retains raw emission data locally and participates only in model training and parameter updates. The model structure of the local node is the same as that of the central server (both are DCNN-LSTM hybrid models), but it is trained only on its own data. For example, a chemical enterprise node trains its local model using only its own plant sensor data and production records, while a regional environmental protection department node trains its local model using satellite remote sensing data and aggregated data from multiple enterprises within its jurisdiction (after desensitization). The collaborative model training process: Initialization: The central server distributes initial model parameters to all local nodes (which can be combined with pre-trained parameters obtained through transfer learning). Local training: Each node trains the model based on its own data, calculates parameter gradients, and uploads only the gradient information (not the original data) to the central server in an encrypted format. For example, enterprise nodes calculate the gradient of "emissions prediction deviation" using local emissions data, while regional nodes calculate the gradient of "match between remote sensing imagery and ground monitoring data." Parameter aggregation: The central server uses a federated averaging algorithm to weightedly aggregate the gradients uploaded by each node (weights are dynamically adjusted based on the amount and quality of node data) and update the global model parameters. Iterative optimization: The updated global parameters are distributed to each node, and the "local training - gradient upload - parameter aggregation" process is repeated until the model converges (e.g., anomaly recognition accuracy stabilizes). Security and privacy protection are enhanced by using a homomorphic encryption algorithm to encrypt uploaded gradient data, ensuring that the central server cannot parse the original gradient information. Differential privacy technology is also introduced to add subtle noise to the gradient calculation to prevent the inference of local data details from the gradient. A node access mechanism is established, allowing only authorized emission source data owners (such as compliant enterprises and official monitoring agencies) to join the federated learning network, preventing malicious nodes from interfering with model training.

[0077] Feature extraction: DCNN is used to extract spatial features of data, such as the geographic distribution and emission patterns of different emission sources. Through a combination of convolutional, pooling, and fully connected layers, the input data is mapped into a high-dimensional feature space, extracting representative feature vectors. LSTM is used to process time series data, capturing patterns in emission data over time. LSTM, with its memory cells and gating mechanism, effectively processes long sequences of data, avoiding vanishing and exploding gradients.

[0078] Abnormal judgment: The extracted feature vector is input into the trained model, and the model conducts a comprehensive analysis of the data according to the rules. For emission data, when the deviation between the actual emission and the emission predicted by the model exceeds the set threshold , ( is determined during the model training process), that is, , it is marked as abnormal data.

[0079] (V) When abnormal data is found in the hierarchical classification abnormal warning module, the system automatically triggers the hierarchical classification warning mechanism.

[0080] Tiered Alerts: Alerts are categorized into Level 1, Level 2, and Level 3 based on the severity of the anomaly. Level 1 alerts address emission anomalies that seriously violate environmental regulations and could cause significant harm to the environment, such as significant excesses of emission concentrations or sharp increases in emissions. The system immediately notifies environmental protection authorities and company officials via text messages, phone calls, and emails. Level 2 alerts address situations where emissions approach thresholds or concentrations fluctuate abnormally, notifying relevant personnel via system messages and emails. Level 3 alerts address minor anomalies, such as small fluctuations in data, and are only reported internally.

[0081] Classified early warning: Early warnings are issued based on the type, scale, and importance of emission sources. For large industrial emission sources and key polluting enterprises, stricter early warning standards and more timely notification methods are set; for small emission sources and residential emissions, a relatively relaxed early warning strategy is adopted.

[0082] (6) Personnel related to the intelligent data correction and feedback module shall verify and correct abnormal data based on abnormal reports.

[0083] Intelligent Assisted Correction: The system provides intelligent assisted correction functionality, providing suggestions for correcting data through historical data comparison and reference to data from similar emission sources. When the emission data of a particular emission source is abnormal, the system automatically retrieves historical data for the same period and the normal emission range for similar emission sources to provide a reasonable correction range.

[0084] Equipment Fault Handling: If data errors are caused by a monitoring device malfunction, the system automatically locates the faulty device and provides repair instructions and backup data collection solutions. For example, through sensor status monitoring and fault diagnosis algorithms, the system can determine the fault type and location and promptly notify maintenance personnel for resolution.

[0085] Feedback mechanism: Corrected data and exception handling results are fed back to the dynamic rule generation and optimization module to update and improve audit rules. Through continuous feedback and optimization, the system's audit accuracy and adaptability are improved.

[0086] (7) The multi-dimensional result storage and visual feedback module will store the audit test results, abnormality reports and corrected data in a multi-dimensional manner, and display them to relevant departments and enterprises through a visual interface.

[0087] Multidimensional storage: This approach uses a combination of relational databases (such as MySQL and Oracle) and non-relational databases (such as MongoDB and HBase) to store both structured emissions data and audit results, as well as unstructured exception descriptions and processing information. Big data technologies are used to deeply mine and analyze the stored data to identify emission patterns and potential problems.

[0088] Visual feedback: Results are presented to relevant departments and businesses through a visual interface in the form of charts, maps, and reports. For example, geographic information system (GIS) maps can be used to display the distribution of emission sources and abnormalities in different regions, facilitating regional supervision by environmental protection departments. Line charts and bar graphs can be used to display emission data trends and comparisons, providing decision support for businesses.

[0089] (8) Data security and privacy protection module

[0090] Collected emission source statistics are encrypted using a homomorphic encryption algorithm to ensure data security and privacy during transmission and storage. During data use, access control and audit mechanisms strictly limit data access rights, and data operations are recorded and audited in detail. For example, only authorized personnel can access and process specific types of data. Each data operation is recorded, including the time, operator, and content, for traceability and auditing purposes.

[0091] (9) Collaborative interaction module with external systems: This module is responsible for data interaction and sharing with external systems such as meteorological monitoring systems and geographic information systems.

[0092] Meteorological data integration: Combined with meteorological data, we analyze the impact of meteorological conditions on emission dispersion and further optimize audit rules. In conditions of strong winds and favorable atmospheric diffusion conditions, we appropriately relax the concentration limits for certain emissions; in calm and stable weather conditions, we strengthen emission supervision.

[0093] Geographic Information Application: Utilizing GIS data, we can accurately locate and spatially analyze emission sources. Maps can be used to display the distribution of emission sources, analyze emission characteristics and environmental pressures in different regions, and provide more comprehensive information for environmental management.

[0094] 3. Characterization of Beneficial Effect Data

[0095]

[0096] It can be seen from the data in the above table that the automatic audit and inspection method and system based on emission source statistical data of this patent have significantly improved the audit accuracy, timeliness of anomaly detection, data processing efficiency, etc., while greatly reducing labor costs. It can also adjust the rules in real time to adapt to environmental changes, and has good application prospects and economic benefits.

[0097] The above are only preferred specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solutions and inventive concepts of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. An automatic audit and inspection method based on emission source statistical data, characterized in that: include: Multimodal data fusion collection steps: Comprehensively utilize sensor networks, enterprise information system interfaces, and satellite remote sensing imagery multimodal data collection methods; The sensor network adopts a distributed layout, with gas sensors and flow sensors placed at emission nodes based on the characteristics of different emission sources to obtain real-time emission concentration and flow data. At the same time, it connects with the company's production management system and environmental monitoring system through standardized data interfaces to obtain production process data and emission record information. Satellite remote sensing images are used to monitor emission sources and obtain geographic spatial distribution and emission trend data. Adaptive noise filtering and data enhancement preprocessing steps: Adaptive noise filtering is performed on the collected data; according to the characteristics of different data sources and the noise distribution law, the Kalman filter algorithm is used to dynamically filter the continuous monitoring data. For discrete enterprise report data, median filtering is used to remove outliers; at the same time, data enhancement technology is introduced to generate new simulated data by translating, rotating, and scaling the existing data. For emission data, virtual emission data samples are generated according to the proportion. The generation formula is D new =D old (1+r), where D old is the original emission data, r is the proportional coefficient randomly generated in the interval [-0.1, 0.1]; Dynamic rule generation and optimization steps: Dynamically generate audit and inspection rules based on environmental laws and regulations, industry standards, and real-time updated environmental data; construct a rule generation model, and combine fuzzy logic reasoning and genetic algorithms to optimize the rules; the rules include emission thresholds, emission concentration limits, and emission time rationality, and the rules are adjusted in real time based on environmental changes and historical audit results. The monthly emission threshold T of the emission is dynamically adjusted based on the average emission volume A of the past three months and the environmental carrying capacity coefficient k. The formula is T = A × k, where k is determined by the environmental monitoring department based on the regional environmental quality status; Automatic audit steps based on deep learning: Use a model that combines deep convolutional neural networks (DCNN) and long short-term memory (LSTM) networks to audit preprocessed data; input the data into the trained model, and the model analyzes the data according to the rules to determine whether there are any anomalies; for emission data, the model will learn historical emission patterns. When the deviation between the actual emissions and the model-predicted emissions exceeds the set threshold δ, that is, |N actual -N predicted |>δ, marked as abnormal data; notify the environmental protection department and the company leader via SMS, phone, or email; Data correction and feedback steps: Relevant personnel verify and correct abnormal data based on abnormality reports; the system provides auxiliary correction functions, providing suggestions for correcting data through historical data comparison and reference to data from similar emission sources; when the emission data of an emission source is abnormal, the system automatically retrieves historical data for the same period and the normal emission range of similar emission sources, and provides a reasonable correction range; If the data error is caused by a monitoring device failure, the system will automatically locate the faulty device and provide repair instructions and backup data collection plans. At the same time, the corrected data and exception handling results will be fed back to the dynamic rule generation and optimization step to update and improve the audit rules. Multi-dimensional result storage and visual feedback steps: Audit and inspection results, exception reports, and corrected data are stored using a combination of relational and non-relational databases to store structured emissions data and audit results, as well as unstructured exception descriptions and processing information. At the same time, big data technology is used to analyze the stored data to identify emission patterns and problems. Charts, maps, and reports are presented to industry departments and enterprises through a visual interface. Data security and privacy protection steps: Use homomorphic encryption algorithm to encrypt the collected emission source statistical data. During data use, access control and audit mechanisms are used to strictly limit data access rights and record and audit data operations.

2. The automatic audit and inspection method based on emission source statistical data according to claim 1 is characterized in that: Also includes: Steps for collaborative interaction with external systems: The system interacts and shares data with meteorological monitoring systems and geographic information systems; Combined with meteorological data, the impact of meteorological conditions on emission diffusion is analyzed to optimize the audit rules.

3. The automatic audit and inspection method based on emission source statistical data according to claim 1 is characterized in that: In the automatic audit step based on deep learning, transfer learning technology is introduced; the parameters of the deep learning model trained in industrial production process monitoring are used to initialize the model of this system; at the same time, a federated learning algorithm is used to conduct joint model training among emission source data owners to improve the generalization ability of the model.

4. The automatic audit and inspection method based on emission source statistical data according to claim 1 is characterized in that: In the hierarchical and classified abnormal warning step, an abnormal warning knowledge base is established; historical abnormal cases and processing experience are summarized and summarized to form a knowledge base; when a new abnormal situation occurs, the system automatically matches cases from the knowledge base and provides processing suggestions and decision support; at the same time, natural language processing technology is used to perform semantic analysis on abnormal reports to automatically generate processing procedures and solutions.

5. The automatic audit and inspection method based on emission source statistical data according to claim 1 is characterized in that: In the data correction and feedback steps, storage node technology is introduced; the process and results of data correction are recorded on the nodes. At the same time, the distributed ledger characteristics of the storage nodes are utilized to realize data sharing and collaborative processing among industry departments and enterprises.

6. The automatic audit and inspection method based on emission source statistical data according to claim 1 is characterized in that: In the multi-dimensional result storage and visualization feedback step, a visualization platform based on virtual reality and augmented reality technologies is developed; users can view the distribution and audit results of emission sources through the device, or obtain emission-related information in real time through the device in real scenarios to provide support for environmental management decisions.

7. A system for implementing the automatic audit and inspection method based on emission source statistical data according to any one of claims 1 to 6, characterized in that: include: Multimodal data fusion acquisition module: This module is responsible for the comprehensive use of sensor networks, enterprise information system interfaces, and satellite remote sensing imagery multimodal data acquisition methods to initially integrate data from different sources to form an original data set. The module adopts a distributed sensor layout and selects sensors based on the characteristics of different emission sources. Adaptive noise filtering and data enhancement preprocessing module: Adaptively filter the collected data for noise, select filtering algorithms based on the noise characteristics of different data sources, and use data enhancement technology to expand the data set and generate new simulated data; Dynamic rule generation and optimization module: Dynamically generates audit and inspection rules based on environmental regulations, industry standards, and real-time updated environmental data. Fuzzy logic reasoning and genetic algorithms are used to optimize the rules, which are adjusted in real time based on environmental changes and historical audit results to adapt to different emission situations. The deep learning-based automatic audit module uses a model combining a deep convolutional neural network (DCNN) and a long short-term memory (LSTM) network to audit preprocessed data. It extracts spatial and time series features from the data to determine whether there are any anomalies in the emission data. For emission data, when the deviation between actual emissions and model-predicted emissions exceeds a set threshold, the data is marked as abnormal. Classified abnormal warning module: When abnormal data is detected, the system automatically triggers a classified warning mechanism. According to the severity and scope of the abnormality, the warning is divided into different levels and notified to the operator. At the same time, classified warnings are issued according to the type, scale and importance of the emission source. Data Correction and Feedback Module: Operators verify and correct abnormal data based on abnormality reports. The module provides auxiliary correction functions, providing suggestions for correcting data by comparing historical data with emission source data references. If the data error is caused by a monitoring device failure, the system will automatically locate the faulty device and provide maintenance instructions and backup data collection plans. The corrected data and exception handling results are fed back to the dynamic rule generation and optimization module to update and improve the audit rules; Multi-dimensional result storage and visual feedback module: stores audit inspection results, abnormality reports and corrected data using a combination of relational and non-relational databases; uses big data technology to analyze the stored data, and displays charts, maps and reports to industry departments and enterprises through a visual interface, providing a basis for environmental management and decision-making.

8. The system for automatic audit and inspection method based on emission source statistical data according to claim 7 is characterized in that: Also includes: Data security and privacy protection module: Use homomorphic encryption algorithm to encrypt the collected emission source statistical data, strictly limit data access rights through access control and audit mechanisms, and record and audit data operations.

9. The system for automatic audit and inspection method based on emission source statistical data according to claim 7 is characterized in that: Also includes: Collaborative interaction module with external systems: The module is responsible for data interaction and sharing with the meteorological monitoring system and geographic information system, combining meteorological data and geographic information to optimize audit rules and provide information for environmental management.

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