Dangerous waste intelligent supervision system and method based on Internet of Things

Abstract

The invention discloses a dangerous waste intelligent supervision system and method based on the Internet of Things, particularly relates to the field of solid dangerous waste supervision, is used for solving the problems of data consistency and anomaly detection in solid dangerous waste management, and aims at solving the problems of data consistency and anomaly detection in solid dangerous waste management by integrating multi-source Internet of Things sensor data and utilizing a long-short-term memory network model. Constructing a normal operation baseline in real time, and marking abnormal data; a multi-node data association network is constructed through partial mutual information and betweenness centrality indexes, a key node which has the greatest influence on data consistency is quickly identified, and a data mutation source is locked; identifying a root cause of data abrupt change by using a wavelet transform and partial directed coherence method in combination with a historical fault case library; isolation and repair operations are automatically executed according to a root cause analysis result, a backup monitoring device is called, and monitoring continuity and rapid recovery are ensured; and the stability and the reliability of the intelligent supervision system for the solid hazardous wastes are obviously enhanced.

Description

Technical Field

[0001] The present invention relates to the field of solid hazardous waste supervision, and more specifically, to an intelligent hazardous waste supervision system and method based on the Internet of Things. Background Art

[0002] With the widespread application of IoT technology in the management of solid hazardous waste, the collection, transportation and storage processes of hazardous waste have achieved a high degree of digitization and intelligence. By deploying a variety of sensor devices in each link, real-time data collection and monitoring are achieved, thereby improving the efficiency and safety of hazardous waste management. For example, the collection point is equipped with a weight sensor to monitor the amount of waste, the transportation status is tracked by GPS and load sensors during the transportation process, and the storage node uses environmental sensors to monitor key parameters such as temperature and humidity. This multi-node and multi-link data flow not only enhances the transparency of supervision, but also provides a solid foundation for data-driven decision-making. However, with the increase in system complexity, the consistency and accuracy of data have become one of the key factors affecting the overall supervision effect. In practical applications, problems such as sensor failure, communication interruption or human intervention will inevitably lead to data loss or mutation, which may lead to erroneous risk assessment and emergency response, and in severe cases may even lead to environmental pollution and safety accidents.

[0003] In a multi-node hazardous waste data chain, the identification of key nodes where data consistency mutations occur has become a technical problem that needs to be solved urgently. Specifically, in the multi-link data flow of hazardous waste collection, transportation and storage, factors such as sensor failure, human intervention or communication interruption may lead to the loss or mutation of key data. For example, when waste from multiple hazardous waste collection points is centrally transported to the same storage node, if there is an abnormal increase in the transport weight, it is difficult to quickly determine whether this mutation is caused by data distortion at a certain collection point or due to a deviation in the load distribution during transportation. In this case, there is a lack of effective algorithms and mechanisms to monitor and analyze data consistency in real time, which makes it difficult to quickly lock the mutation nodes, thereby affecting the reliability and response speed of the overall hazardous waste management system. Therefore, it is urgent to develop an innovative multi-node data consistency detection and key node identification method that can accurately locate abnormal data sources in complex data streams and distinguish the root causes of data mutations through intelligent analysis methods to ensure the stability and security of the hazardous waste management system.

[0004] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a hazardous waste intelligent supervision system and method based on the Internet of Things. By integrating multi-source Internet of Things sensor data and utilizing the long short-term memory network model, a normal operating baseline is constructed in real time, and abnormal data is accurately marked; a multi-node data association network is constructed through partial mutual information and betweenness centrality indicators, and the key nodes with the greatest impact on data consistency are quickly identified, and the source of data mutation is locked; wavelet transform and partial directed coherence method are used, combined with a historical fault case library, to accurately identify the root cause of data mutation, such as sensor hardware failure or communication link interruption; isolation and repair operations are automatically performed according to the root cause analysis results, and backup monitoring devices are called to ensure monitoring continuity and rapid recovery; thereby significantly enhancing the stability and reliability of the solid hazardous waste intelligent supervision system to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The intelligent supervision method of hazardous waste based on the Internet of Things includes the following steps:

[0008] S1: In each link of hazardous waste disposal, various sensors are deployed to collect various data of corresponding nodes and store them synchronously in the central database according to timestamps;

[0009] S2: Based on the data set in the central database, the long short-term memory network model is used to build the normal operation baseline of each node, and the data uploaded by the sensor in real time is predicted and compared. When the actual value deviates from the baseline prediction value beyond the preset threshold, the corresponding node data is marked as abnormal;

[0010] S3: For nodes marked as abnormal, by constructing a multi-node data association network and calculating the correlation and betweenness centrality index between each node, determine the key nodes that have the greatest impact on the overall data consistency in the corresponding abnormal event, and form a key node list;

[0011] S4: Perform Granger causality analysis on the nodes in the key node list and match them with the node anomaly information in the historical fault case library to identify the root cause of the data mutation;

[0012] S5: Based on the root cause identification results of key nodes, automatically perform isolation and repair operations and call backup monitoring devices.

[0013] In a preferred embodiment, step S2 includes the following contents:

[0014] First, for each node’s data set, the sliding window method is used to split the time series data into subsequences of fixed length as the input of the LSTM model; data of different scales are converted to a unified range through standardization:

[0015] Secondly, multivariate LSTM models are constructed, each of which is trained specifically on the dataset of one node;

[0016] Subsequently, in the real-time data monitoring stage, new data from each node is continuously received and input into the trained LSTM model to generate real-time prediction values. The residual between the predicted value and the actual observed value is used to calculate the anomaly score of each node using a dynamic threshold setting method based on information entropy.

[0017] In a preferred embodiment, step S3 includes the following contents:

[0018] S3.1, first, based on the historical observation values ​​and abnormal marks of each node in the same period, a multi-dimensional cross-analysis matrix is ​​constructed; the partial mutual information method is used to remove the redundancy of the mutual information between each pair of nodes and eliminate the mixed interference caused by third-party nodes. The specific logic is as follows:

[0019] Set the time window of abnormal events to T, extract all abnormal nodes N={n 1 , n 2 , ..., n k The data set D = {d 1 , d 2 , ..., d k}, where each data point d i Contains node n i The values ​​of various parameters at time t;

[0020] For any two abnormal nodes n i and n j , given all other node sets C = N\{n i , n j}, the partial mutual information is defined as: Among them, P(d i , d j , d C ) is the joint probability distribution, P(d i , d j |d C ) is the conditional probability distribution; set the partial mutual information threshold θ PMI , only when PMI(n i ;n j |C)≥θ PMI When i and node n j Edge connections are established between them to form an adjacency matrix.

[0021] In a preferred embodiment, step S3 further includes the following contents:

[0022] S3.2, next, according to the value obtained from the partial mutual information, an undirected edge is assigned to each pair of nodes and an adjacency matrix is ​​constructed, that is, a multi-node data association network; then the network calculation betweenness centrality index is calculated to measure the importance of the node to the information flow in the network. The specific logic is as follows:

[0023] Based on the partial mutual information calculation results, a multi-node data association network G = (N, E) is constructed, where the node set N is the abnormal node set, and the edge set E consists of node pairs (n i , n j )composition;

[0024] In the constructed multi-node data association network, the betweenness centrality of each node is calculated to measure the importance of the node as an information bridge in the network;

[0025] S3.3, then, based on the calculated betweenness centrality values, all abnormal nodes are sorted from high to low according to betweenness centrality; nodes whose betweenness centrality values ​​exceed the betweenness centrality threshold are screened out to form a key node list.

[0026] In a preferred embodiment, step S4 includes the following contents:

[0027] S4.1, first, the time series data of each key node in the key node list is transformed by continuous wavelet transform to capture the time-frequency characteristics of the data; then, according to the scale parameter of the wavelet coefficient, the frequency range is divided into several frequency band sets, each frequency band corresponds to a specific subset of wavelet coefficients, so as to analyze the causal relationship between nodes at different frequencies;

[0028] S4.2, for each frequency band ω v , construct a multivariate model of the key node list, and calculate partial directed coherence to quantify the causal relationship between nodes. The specific process is as follows:

[0029] S4.2.1, for each frequency band, assume that the time series data of the key nodes satisfy the multivariate autoregressive model in the corresponding frequency band, expressed as: X(ω v , t) = A(ω v )X(ω v ,t-1)+∈(ω v , t); where X(ω v , t) is the key node in the frequency band ω v The state vector under v ) is the coefficient matrix, ∈(ω v , t) is the noise vector;

[0030] S4.2.2, by solving the multivariate autoregressive model, we obtain the frequency band ω v The transfer function matrix H(ω v ): H(ω v )=[IA(ω v )] -1 ; Where I is the unit matrix;

[0031] S4.2.3, for key node k o With key node k v In the frequency band ω v The following partially directed coherent WPDC is defined as: Among them, H ov (ω v ) is the oth row and vth column element of the transfer function matrix;

[0032] S4.2.4, a node k is considered critical only if it is partially directed coherent or equal to the partially directed coherent threshold. v In the frequency band ω v The upper Granger leads to the key node k o ; At this time, construct a directed edge (k v →k o ) indicates a cause-effect relationship;

[0033] S4.3, for each frequency band, construct a causal relationship matrix based on the partial directed coherence calculation results; perform a logical OR operation on the causal relationship matrices of all frequency bands to generate a comprehensive causal relationship matrix.

[0034] In a preferred embodiment, step S4 further includes the following contents:

[0035] S4.4, the fault case library contains comprehensive causal relationship matrix templates of various abnormal events that have been identified in history and their corresponding fault categories;

[0036] For the comprehensive causal relationship matrix of the current abnormal event, calculate the Jaccard similarity index between it and each historical fault case;

[0037] Traverse all historical fault cases and calculate the Jaccard similarity index value between the current comprehensive causal relationship matrix and each historical case; select the fault case with the highest Jaccard similarity index value that exceeds the set threshold, and use its corresponding fault category as the root cause of the current abnormal event; if all Jaccard similarity index values ​​do not exceed the set threshold, mark the current abnormal event as a new fault category and wait for the subsequent addition of the fault case library.

[0038] In a preferred embodiment, step S5 includes the following contents:

[0039] According to the list of key nodes and the root causes of the faults, the corresponding handling strategy script is loaded first. The script predefines the automated operation processes corresponding to different fault categories.

[0040] The intelligent monitoring system for hazardous waste based on the Internet of Things includes: data collection module, anomaly detection module, key identification module, root cause analysis module and automatic response module;

[0041] Data collection module: In each link of hazardous waste disposal, various sensors are arranged to collect various data of corresponding nodes, and the data is synchronously stored in the central database according to the timestamp. The collected data is transmitted to the anomaly detection module;

[0042] Anomaly detection module: Based on the data set in the central database, the long short-term memory network model is used to build the normal operation baseline of each node, and the data uploaded by the sensor in real time is predicted and compared. When the actual value deviates from the baseline prediction value beyond the preset threshold, the corresponding node data is marked as abnormal, and the node data marked as abnormal is passed to the key identification module;

[0043] Key identification module: For nodes marked as abnormal, by constructing a multi-node data association network and calculating the correlation and betweenness centrality index between each node, the key nodes with the greatest impact on the overall data consistency in the corresponding abnormal event are determined, a key node list is formed, and the key node list is passed to the root cause analysis module;

[0044] Root cause analysis module: performs Granger causality analysis on the nodes in the key node list, matches them with the node abnormality information in the historical fault case library, identifies the root cause of the data mutation, and transmits the root cause identification results to the automatic response module;

[0045] Automatic response module: Based on the root cause identification results of key nodes, it automatically performs isolation and repair operations and calls backup monitoring devices.

[0046] The technical effects and advantages of the hazardous waste intelligent supervision system and method based on the Internet of Things of the present invention are as follows:

[0047] The present invention integrates multi-source IoT sensor data and utilizes the long short-term memory network model to build a normal operating baseline in real time and accurately mark abnormal data; constructs a multi-node data association network through partial mutual information and betweenness centrality indicators, quickly identifies the key nodes that have the greatest impact on data consistency, and locks the source of data mutation; uses wavelet transform and partial directed coherence method, combined with a historical fault case library, to accurately identify the root cause of data mutation, such as sensor hardware failure or communication link interruption; automatically executes isolation and repair operations based on the root cause analysis results, and calls the backup monitoring device to ensure monitoring continuity and rapid recovery; thereby significantly enhancing the stability and reliability of the solid hazardous waste intelligent supervision system. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a flow chart of the method for intelligently supervising hazardous waste based on the Internet of Things of the present invention;

[0049] Figure 2 It is a structural schematic diagram of the hazardous waste intelligent supervision system based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0051] Embodiment 1: Figure 1 The present invention provides a hazardous waste intelligent supervision method based on the Internet of Things, including:

[0052] S1: In each link of hazardous waste disposal, various sensors are deployed to collect various data of corresponding nodes and store them synchronously in the central database according to timestamps;

[0053] S2: Based on the data set in the central database, the long short-term memory network model is used to build the normal operation baseline of each node, and the data uploaded by the sensor in real time is predicted and compared. When the actual value deviates from the baseline prediction value beyond the preset threshold, the corresponding node data is marked as abnormal;

[0054] S3: For nodes marked as abnormal, by constructing a multi-node data association network and calculating the correlation and betweenness centrality index between each node, determine the key nodes that have the greatest impact on the overall data consistency in the corresponding abnormal event, and form a key node list;

[0055] S4: Perform Granger causality analysis on the nodes in the key node list and match them with the node anomaly information in the historical fault case library to identify the root cause of the data mutation and clarify the anomaly type;

[0056] S5: Based on the root cause identification results of key nodes, automatically perform isolation and repair operations and call backup monitoring devices.

[0057] With the widespread application of Internet of Things (IoT) technology in the management of solid hazardous waste, the collection, transportation and storage of hazardous waste have achieved a high degree of digitization and intelligence. By deploying weight sensors, GPS modules and environmental sensors in each link, key data are collected in real time, which improves the efficiency and safety of waste management. However, in the face of multi-node and multi-link data integration, problems such as data noise, inconsistent formats and time synchronization still restrict the overall performance of the system and the accuracy of data analysis. To this end, this solution proposes an innovative multi-source data integration and preprocessing technology, which aims to ensure the high quality and consistency of data in each link through precise data processing processes, and provide a solid data foundation for subsequent anomaly detection and key node identification.

[0058] Step S1 includes the following contents:

[0059] In each link of hazardous waste collection, transportation and storage, the deployed sensors, including weight sensors, GPS modules and environmental sensors (including temperature, humidity and harmful gas concentration sensors), are first used to collect the weight, location information and environmental parameter data of each node in real time. The collected data is first subjected to dynamic noise suppression through an adaptive Kalman filter to ensure the authenticity and stability of the weight and environmental data. For the location information obtained by the GPS module, the geographically weighted regression GWR algorithm is applied to perform spatial error correction to improve the accuracy of the location data.

[0060] Next, the missing values ​​in the data are filled using the polynomial regression interpolation method to ensure the integrity of the data set. In view of the differences in data formats generated by different sensors, a unified data conversion protocol is designed and implemented to convert all data into a structured unified format (such as JSON) to achieve seamless integration in the central database. In addition, a distributed time synchronization algorithm based on the Precision Time Protocol (PTP) is used to calibrate the timestamps of the data of all nodes to ensure the high consistency of the timing of the data in each link.

[0061] In order to achieve efficient data storage and retrieval, a distributed database system (such as Apache Cassandra) is selected and a database architecture specifically designed for hazardous waste management needs is designed to support high-throughput writing and low-latency queries of large-scale data. Data compression and partitioning strategies are implemented to optimize storage space utilization and access performance. At the same time, a real-time data stream processing pipeline based on Apache Kafka and Apache Flink is built to ensure that data can be quickly transmitted to the analysis stage and achieve real-time data processing and monitoring.

[0062] Through the above steps, S1 has achieved comprehensive integration and efficient preprocessing of multi-source data in the collection, transportation and storage of hazardous waste, eliminated noise and inconsistency in the data, and ensured the integrity and time synchronization of the data. This technical logic not only lays a solid data foundation for subsequent real-time anomaly detection and key node identification, but also significantly improves the data processing capacity and monitoring accuracy of the overall hazardous waste management system, providing key support for the efficient operation of the smart supervision system.

[0063] After the multi-source data integration and preprocessing step (S1) ensures the high quality and consistency of the weight, location and environmental data from the collection, transportation and storage links, the further challenge is how to effectively identify and mark the abnormal points in these data so that timely countermeasures can be taken. To this end, step S2 proposes an intelligent anomaly detection method based on the long short-term memory network (LSTM). By building a normal operation baseline model of each node, the real-time data is predicted and compared, thereby achieving accurate abnormal data marking. This method not only improves the accuracy and real-time performance of anomaly detection, but also provides reliable data support for subsequent key node identification.

[0064] Step S2 includes the following contents:

[0065] After integrating and storing the weight, location and environmental data from hazardous waste collection, transportation and storage in the central database, step S2 aims to build a normal operation baseline for each node through a long short-term memory network model and perform real-time anomaly detection. The specific technical processing logic is as follows:

[0066] First, for each node's data set, the sliding window method is used to split the time series data into fixed-length subsequences as the input of the LSTM model. These subsequences include weight data, GPS location coordinates, and environmental parameters (temperature, humidity, harmful gas concentration, etc.). Through standardization (for example, z-score standardization), data of different scales are converted to a unified range to improve the training efficiency and prediction accuracy of the model.

[0067] Secondly, a multivariate LSTM model is constructed, and each model is trained specifically for a node's data set. The input layer of the model receives the preprocessed time series data, which is captured by multiple layers of LSTM units to capture time dependencies and sequence features, and finally outputs the predicted value for the next time step through the fully connected layer. During the training process, the mean square error is used as the loss function, and the model parameters are optimized through the back propagation algorithm to ensure that the model can accurately predict the data trend of each node under normal operating conditions.

[0068] Subsequently, in the real-time data monitoring phase, new data from each node is continuously received and input into the trained LSTM model to generate real-time prediction values. The residual between the predicted value and the actual observed value is used to calculate the anomaly score of each node using a dynamic threshold setting method based on information entropy. Specifically, the multivariate residuals are weighted using the entropy weight method to form a comprehensive anomaly score indicator. When the anomaly score of a node exceeds the preset threshold, the data of the corresponding node is marked as abnormal.

[0069] Finally, the node data marked as abnormal is passed to the subsequent step S3 for key node correlation analysis and positioning. At the same time, the results and related parameters of each abnormal detection are recorded for continuous optimization and adjustment of the subsequent model. Step S2 not only realizes efficient monitoring and accurate abnormality detection of real-time data, but also lays a solid foundation for the intelligent improvement of the overall hazardous waste management system.

[0070] Step S2 systematically realizes accurate monitoring and anomaly detection of real-time data of each node of hazardous waste management by constructing and applying a multivariate long short-term memory network model. Step S2 ensures high-accuracy marking of abnormal data through sliding window data segmentation, standardization processing, multivariate LSTM model training and prediction, and dynamic threshold setting based on information entropy. This technical logic not only effectively identifies potential data anomalies, but also provides reliable anomaly marking for subsequent key node correlation analysis (step S3), significantly improving the overall intelligence level and responsiveness of the solid hazardous waste intelligent supervision system based on the Internet of Things, and ensuring that the system maintains a high degree of stability and security in a complex and changing operating environment.

[0071] After completing the anomaly detection based on the long short-term memory network model (corresponding to step S2), the real-time data of each node has been marked as abnormal. Since the hazardous waste management system has the characteristics of multi-node collaborative work, once a data anomaly occurs in a certain node, it is very likely to have a collateral impact on the data information of other nodes. How to quickly identify the key nodes that pose the greatest threat to the overall data consistency in the complex node relationships has become another key link to ensure the safety and stability of the system. To this end, step S3 proposes a technical method based on multi-node data association network and topological analysis, using high-order correlation and betweenness centrality indicators to quantify the influence and criticality of each node in abnormal events from a global perspective, thereby forming a list of key nodes, laying a solid data foundation for subsequent root cause analysis and fault handling.

[0072] Step S3 includes the following contents:

[0073] After completing the abnormal marking of each node data in step S2, the system passes these marks together with the original time series data to the multi-node data association analysis module to build a multi-node data association network and determine the key nodes that are most susceptible to overall data consistency. The specific processing logic is as follows:

[0074] S3.1, first, the system constructs a multi-dimensional cross-analysis matrix based on the historical observations and anomaly marks of each node in the same period. In order to avoid misleading influences on the network topology, the partial mutual information method is used to deduplicate the mutual information between each pair of nodes and eliminate the mixed interference caused by third-party nodes. Partial mutual information measures the degree of feature sharing between two nodes after controlling other potential correlation factors. The higher the value, the stronger the direct correlation between the two nodes. This method can effectively reduce false correlations caused by link redundancy or multicollinearity. The specific logic is as follows:

[0075] Set the time window of abnormal events to T, extract all abnormal nodes N in this time window 1 , n 2 , ..., n k The data set D = {d 1 , d 2 , ..., d k}, where each data point d i Contains node n i The values ​​of various parameters at time t.

[0076] For any two abnormal nodes n i and n j , given all other node sets C = N\{n i , n j}, the partial mutual information is defined as: Among them, P(d i , d j , d C ) is the joint probability distribution, P(d i , d j |d C ) is the conditional probability distribution.

[0077] By calculating partial mutual information, the indirect correlation caused by other nodes can be effectively eliminated, and only the direct information dependency between the two nodes is retained.

[0078] Set the partial mutual information threshold θ PMI , only when PMI(n i ;n j |C)≥θ PMI When i Establish edge connections between nodes and form an adjacency matrix A, that is:

[0079] S3.2, next, according to the value obtained from the partial mutual information, an undirected edge is assigned to each pair of nodes and an adjacency matrix is ​​constructed, that is, a multi-node data association network. Whenever the partial mutual information value exceeds the partial mutual information threshold, it is considered that there may be coupling in the abnormal event. After obtaining the complete multi-node data association network, the network calculation betweenness centrality index is calculated to measure the importance of the node to the information flow in the network. Specifically, the betweenness centrality counts the frequency of the shortest path passing through each node. The higher the value, the more influential the node is in the process of abnormal information propagation, and the more likely it is to affect the overall data consistency. The specific logic is as follows:

[0080] Based on the partial mutual information calculation results, a multi-node data association network G = (N, E) is constructed, where the node set N is the abnormal node set, and the edge set E consists of node pairs (n i , n j ). This network structure reflects the direct correlation between abnormal nodes and forms a graph model for subsequent network analysis.

[0081] In the constructed multi-node data association network, the betweenness centrality of each node is calculated to measure the importance of the node as an information bridge in the network. The betweenness centrality is defined as: Among them, σ sd represents the total number of shortest paths from node s to node d, σ sd (n i ) indicates that through node n iThe total number of shortest paths. A high betweenness centrality value indicates that the node plays a key bridge role in the network and has a greater influence on the flow of information.

[0082] S3.3, then, based on the calculated betweenness centrality values, all abnormal nodes are sorted from high to low according to their betweenness centrality. Set the betweenness centrality threshold θ BC , filter out the nodes whose betweenness centrality values ​​exceed the betweenness centrality threshold, and form a key node list K = {k 1 , k 2 , ..., k m}, where m≤k. The nodes in this list are considered to be the key nodes that have the greatest impact on the overall data consistency in the current abnormal event. The betweenness centrality ranking reflects the role of the node as a bridge for the propagation of abnormalities in the network, while the partial mutual information value can verify the strength of the direct correlation between nodes. The combination of the two can quickly lock the dominant node that is most destructive to the overall data consistency among multiple abnormal nodes.

[0083] S3.4, pass the generated key node list and its topological information in the multi-node data association network to step S4 for further causal analysis and root cause identification. At the same time, record the network structure and key node information of the current abnormal event as reference data for subsequent model optimization and algorithm adjustment, to ensure that the system can handle similar abnormal events more efficiently in the future.

[0084] Step S3 constructs an efficient and accurate multi-node data association network by combining partial mutual information and betweenness centrality indicators, which can quickly identify the key nodes that have the greatest impact on the overall data consistency in complex and changeable abnormal events. The partial mutual information method is used to eliminate high-order correlation interference to ensure the authenticity of direct correlations between nodes; the betweenness centrality indicator is used to quantify the importance of nodes in information flow and accurately lock key nodes. This technical logic not only improves the accuracy and efficiency of key node identification, but also provides a clear goal for subsequent root cause analysis and fault handling, significantly enhancing the intelligence and responsiveness of the solid hazardous waste smart supervision system based on the Internet of Things, and ensuring the efficient and stable operation of the system in the face of multi-node anomalies.

[0085] Step S3 successfully identifies the key nodes that have the greatest impact on the overall data consistency by constructing a multi-node data association network and calculating the correlation and betweenness centrality indicators. However, after identifying the key nodes, it is still necessary to deeply analyze the causal relationship between these nodes to clarify the specific root cause of the data mutation. To this end, step S4 proposes an innovative frequency domain causal analysis method based on wavelet transform and partial directed coherence, combined with a historical fault case library for matching, to achieve accurate identification of the root cause of abnormal events. This method improves the accuracy and sensitivity of causal relationship identification through frequency domain analysis, provides a scientific basis for system fault diagnosis and prevention, and significantly enhances the intelligence level and response capability of the hazardous waste management system.

[0086] Step S4 includes the following contents:

[0087] After generating a list of key nodes and their associated network topology information in step S3, step S4 aims to accurately identify the root cause of data mutations by combining wavelet transform and partial directed coherence method with a historical fault case library. The specific technical processing process is as follows:

[0088] S4.1, first, the time series data of each key node in the key node list is subjected to continuous wavelet transform to capture the time-frequency characteristics of the data.

[0089] Subsequently, the frequency range is divided into several frequency band sets according to the scale parameters of the wavelet coefficients, each of which corresponds to a specific subset of wavelet coefficients, so as to analyze the causal relationship between nodes at different frequencies.

[0090] S4.2, for each frequency band ω v , construct a multivariate model of the key node list, and calculate partial directed coherence to quantify the causal relationship between nodes. The specific process is as follows:

[0091] S4.2.1, for each frequency band, assume that the time series data of the key nodes satisfy the multivariate autoregressive model in the corresponding frequency band, expressed as: X(ω v , t) = A(ω v )X(ω v ,t-1)+∈(ω v , t); where X(ω v , t) is the key node in the frequency band ω v The state vector under v ) is the coefficient matrix, ∈(ω v , t) is the noise vector.

[0092] S4.2.2, by solving the multivariate autoregressive model, we obtain the frequency band ω v The transfer function matrix H(ω v ): H(ω v)=[IA(ω v )] -1 ; Where I is the identity matrix.

[0093] S4.2.3, for key node k o With key node k v In the frequency band ω v The following partially directed coherent WPDC is defined as: Among them, H ov (ω v ) is the element in the oth row and vth column of the transfer function matrix. The higher the partial directional coherence value, the more critical the node k is. v In the frequency band ω v For key nodes k o The stronger the causal influence.

[0094] S4.2.4, set the partial directed coherence threshold, and consider the key node k only when the partial directed coherence is or equal to the partial directed coherence threshold. v In the frequency band ω v The upper Granger leads to the key node k o At this time, construct a directed edge (k v →k o ) indicates a cause-effect relationship.

[0095] S4.3, for each frequency band, construct the causal relationship matrix based on the partial directed coherence calculation results in: The causal relationship matrix of all frequency bands Perform logical OR operations to generate a comprehensive cause-effect relationship matrix Among them, V represents the logical OR operation, which means that as long as there is a causal relationship in any frequency band, the corresponding element in the comprehensive matrix is ​​1.

[0096] S4.4, the fault case library contains a comprehensive causal relationship matrix template for various abnormal events that have been identified in history And its corresponding fault category Where C is the number of historical failure cases.

[0097] For the comprehensive causal relationship matrix of the current abnormal event, calculate its Jaccard similarity index with each historical fault case. The Jaccard similarity index value range is [0,1], and the closer the value is to 1, the more similar the two causal relationship matrices are.

[0098] Traverse all historical fault cases and calculate the Jaccard similarity index value between the current comprehensive causal relationship matrix and each historical case. Select the fault case with the highest Jaccard similarity index value that exceeds the set threshold, and use its corresponding fault category as the root cause of the current abnormal event. If all Jaccard similarity index values ​​do not exceed the set threshold, mark the current abnormal event as a new fault category and wait for the subsequent addition of the fault case library.

[0099] S4.5, according to the matching result of step S4.4, classify the current abnormal event into one of the following specific root causes:

[0100] Sensor hardware failure: data anomalies caused by sensor damage and signal distortion.

[0101] Communication link interruption: data anomalies caused by network failure and data transmission errors.

[0102] System configuration error: data anomalies caused by software setting errors and parameter configuration errors.

[0103] The root cause classification results, the current comprehensive causal relationship matrix, the matched historical fault cases and their fault categories are all passed to step S5 for automated isolation and repair operations. At the same time, the detailed information of this root cause analysis is recorded in the fault case library as reference data for similar abnormal events in the future, supporting the continuous optimization of the long short-term memory network model and the multi-node data association network analysis method.

[0104] Step S4 introduces wavelet transform and partial directed coherence method to perform fine-grained analysis of the causal relationship between key nodes in the frequency domain, thereby capturing the complex dynamic coupling characteristics between nodes in different time scales and frequency ranges. Compared with traditional time domain methods, this method can simultaneously analyze short-term mutation characteristics and long-term trend associations, and is particularly suitable for the multi-dimensional characteristics of sensor data in hazardous waste management scenarios, such as weight, location, and environmental parameters, which may present nonlinear dependencies in different time and frequency bands. Combined with the scenario of the present invention, this method can effectively filter out false correlations caused by short-term noise or accidental interference, accurately identify the source of deep-level data mutations across links, and provide a highly reliable and accurate analysis basis for subsequent root cause identification and repair, thereby significantly improving the overall system's abnormality handling capabilities and decision-making efficiency.

[0105] In the IoT-based intelligent supervision system for solid hazardous waste, timely response and handling of abnormal events at key nodes are crucial to ensure the overall stability and security of the system. Step S4 uses Granger causality analysis to accurately identify the root causes of data mutations, including sensor hardware failure, human intervention, and communication link interruption. To further ensure the continuity and reliability of the system, step S5 designs and implements an automated isolation and repair mechanism based on the root cause identification results of key nodes, and calls backup monitoring devices to ensure that the system monitoring function is not affected during the isolation or repair of abnormal nodes. This innovative processing flow can not only quickly restore normal operating conditions, but also effectively prevent potential safety hazards and enhance the system's self-healing and risk resistance capabilities.

[0106] Step S5 includes the following contents:

[0107] S5.1, based on the key node list and fault root cause output in step S4, first load the corresponding handling strategy script, which predefines the automated operation process corresponding to different fault categories. Taking sensor hardware failure as an example, the system will perform the following mathematical model selection and action instruction release:

[0108] The system logically isolates abnormal sensors so that the data they report does not enter the normal data flow of the central database temporarily. o ∈{0, 1} indicates whether the key node o is isolated:

[0109]

[0110] Once a critical node k0 is diagnosed as having a hardware failure, Notify upper-layer applications to no longer use the key node data for real-time decision-making.

[0111] If redundant or backup sensors are configured in the deployment scenario, the pre-written switching rules f switch (·) to activate the corresponding backup device. Define the backup relationship g(k o ) is mapped to a faulty node → standby sensor, and execute: This ensures that the system still has basic monitoring capabilities after the failed node stops uploading data.

[0112] If the diagnosis result shows that the communication link is interrupted, the system will automatically restart and self-check the corresponding network interface, call the communication scheduling module and execute the link rerouting algorithm to transfer the node traffic that originally relied on the faulty link to the backup link. This operation maintains a fault link matrix C at the network management level. fault (k o ), if k oIf the failure rate of the communication path exceeds the threshold, it will automatically switch to other available links.

[0113] Step S5 realizes immediate response and processing of abnormal events at key nodes by automatically executing isolation and repair operations, combined with the call of backup monitoring devices. Specifically, the system automatically isolates the faulty node based on the root cause identified in step S4 to prevent abnormal data from further affecting the overall operation of the system. At the same time, the backup monitoring device is started to ensure the continuity of the system monitoring function and the integrity of the data during the period when the main monitoring device is affected. This step reduces the need for human intervention, shortens the fault handling time, and improves the response speed and processing efficiency of the system through preset automated processes. Step S5 significantly enhances the stability and security of the solid hazardous waste intelligent supervision system based on the Internet of Things, ensuring that the system can quickly resume normal operation and protect environmental and public safety when faced with complex and changeable abnormal situations.

[0114] Embodiment 2: Figure 2 The invention provides a hazardous waste intelligent supervision system based on the Internet of Things, including: a data acquisition module, an anomaly detection module, a key identification module, a root cause analysis module and an automatic response module:

[0115] Data collection module: In each link of hazardous waste disposal, various sensors are arranged to collect various data of corresponding nodes, and the data is synchronously stored in the central database according to the timestamp. The collected data is transmitted to the anomaly detection module;

[0116] Anomaly detection module: Based on the data set in the central database, the long short-term memory network model is used to build the normal operation baseline of each node, and the data uploaded by the sensor in real time is predicted and compared. When the actual value deviates from the baseline prediction value beyond the preset threshold, the corresponding node data is marked as abnormal, and the node data marked as abnormal is passed to the key identification module;

[0117] Key identification module: For nodes marked as abnormal, by constructing a multi-node data association network and calculating the correlation and betweenness centrality index between each node, the key nodes with the greatest impact on the overall data consistency in the corresponding abnormal event are determined, a key node list is formed, and the key node list is passed to the root cause analysis module;

[0118] Root cause analysis module: performs Granger causality analysis on the nodes in the key node list, matches them with the node abnormality information in the historical fault case library, identifies the root cause of the data mutation, and transmits the root cause identification results to the automatic response module;

[0119] Automatic response module: Based on the root cause identification results of key nodes, it automatically performs isolation and repair operations and calls backup monitoring devices.

[0120] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0121] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0122] It should be noted that, in this article, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including a..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0123] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A smart hazardous waste supervision method based on the Internet of Things, characterized in that: Includes steps: S1: In each link of hazardous waste disposal, various sensors are deployed to collect various data of corresponding nodes and store them synchronously in the central database according to timestamps; S2: Based on the data set in the central database, the long short-term memory network model is used to build the normal operation baseline of each node, and the data uploaded by the sensor in real time is predicted and compared. When the actual value deviates from the baseline prediction value beyond the preset threshold, the corresponding node data is marked as abnormal; S3: For nodes marked as abnormal, by constructing a multi-node data association network and calculating the correlation and betweenness centrality index between each node, determine the key nodes that have the greatest impact on the overall data consistency in the corresponding abnormal event, and form a key node list; S4: Perform Granger causality analysis on the nodes in the key node list and match them with the node anomaly information in the historical fault case library to identify the root cause of the data mutation; S5: Based on the root cause identification results of key nodes, automatically perform isolation and repair operations and call backup monitoring devices.

2. The method for intelligent management of hazardous waste based on the Internet of Things according to claim 1 is characterized in that: Step S2 includes the following contents: First, for each node’s data set, the sliding window method is used to split the time series data into subsequences of fixed length as the input of the LSTM model; data of different scales are converted to a unified range through standardization; Secondly, multivariate LSTM models are constructed, each of which is trained specifically on the dataset of one node; Subsequently, in the real-time data monitoring phase, new data from each node is continuously received and input into the trained LSTM model to generate real-time prediction values; The residual between the predicted value and the actual observed value is used to calculate the anomaly score of each node by applying a dynamic threshold setting method based on information entropy.

3. The method for intelligent management of hazardous waste based on the Internet of Things according to claim 2 is characterized in that: Step S3 includes the following contents: S3.1, first, based on the historical observation values ​​and abnormal marks of each node in the same period, a multi-dimensional cross-analysis matrix is ​​constructed; the partial mutual information method is used to remove the redundancy of the mutual information between each pair of nodes and eliminate the mixed interference caused by third-party nodes. The specific logic is as follows: Set the time window of abnormal events to T, extract all abnormal nodes N = {n1, n2, ..., n k } of the data set D = {d1, d2, ..., d k }, where each data point d i Contains node n i The values ​​of various parameters at time t; For any two abnormal nodes n i and n j , given all other node sets C = N\{n i , n j }, the partial mutual information is defined as: Among them, P(d i , d j , d C ) is the joint probability distribution, P(d i , d j |d C ) is the conditional probability distribution; set the partial mutual information threshold θ PMI , only when PMI(n i ;n j |C)≥θ PMI When i and node n j Edge connections are established between them to form an adjacency matrix.

4. The method for intelligent management of hazardous waste based on the Internet of Things according to claim 3 is characterized in that: Step S3 also Includes the following: S3.2, next, according to the value obtained from the partial mutual information, an undirected edge is assigned to each pair of nodes and an adjacency matrix is ​​constructed, that is, a multi-node data association network; then the network calculation betweenness centrality index is calculated to measure the importance of the node to the information flow in the network. The specific logic is as follows: Based on the partial mutual information calculation results, a multi-node data association network G = (N, E) is constructed, where the node set N is the abnormal node set, and the edge set E consists of node pairs (n i , n j )composition; In the constructed multi-node data association network, the betweenness centrality of each node is calculated to measure the importance of the node as an information bridge in the network; S3.3, then, based on the calculated betweenness centrality values, all abnormal nodes are sorted from high to low according to betweenness centrality; nodes whose betweenness centrality values ​​exceed the betweenness centrality threshold are screened out to form a key node list.

5. The method for intelligent management of hazardous waste based on the Internet of Things according to claim 4 is characterized in that: Step S4 includes the following contents: S4.1, first, perform continuous wavelet transform on the time series data of each key node in the key node list to capture the time-frequency characteristics of the data; Then, according to the scale parameter of the wavelet coefficients, the frequency range is divided into several frequency band sets, each of which corresponds to a specific subset of wavelet coefficients, so as to analyze the causal relationship between nodes at different frequencies; S4.2, for each frequency band ω v , construct a multivariate model of the key node list, and calculate partial directed coherence to quantify the causal relationship between nodes. The specific process is as follows: S4.2.1, for each frequency band, assume that the time series data of the key nodes satisfy the multivariate autoregressive model in the corresponding frequency band, expressed as: X(ω v , t) = A(ω v )X(ω v ,t-1)+∈(ω v , t); where X(ω v , t) is the key node in the frequency band ω v The state vector under v ) is the coefficient matrix, ∈(ω v , t) is the noise vector; S4.2.2, by solving the multivariate autoregressive model, we obtain the frequency band ω v The transfer function matrix H(ω v ): H(ω v )=[IA(ω v )] -1 ; Where I is the identity matrix; S4.2.3, for key node k o With key node k v In the frequency band ω v The following partially directed coherent WPDC is defined as: Among them, H ov (ω v ) is the oth row and vth column element of the transfer function matrix; S4.2.4, a critical node k is considered to be a critical node only if it is partially directed coherent or equal to the partially directed coherent threshold. v In the frequency band ω v The upper Granger leads to the key node k o ; At this time, construct a directed edge (k v →k o ) indicates a cause-effect relationship; S4.3, for each frequency band, construct a causal relationship matrix based on the partial directed coherence calculation results; perform a logical OR operation on the causal relationship matrices of all frequency bands to generate a comprehensive causal relationship matrix.

6. The method for intelligent management of hazardous waste based on the Internet of Things according to claim 5 is characterized in that: Step S4 also includes the following contents: S4.4, the fault case library contains comprehensive causal relationship matrix templates of various abnormal events that have been identified in history and their corresponding fault categories; For the comprehensive causal relationship matrix of the current abnormal event, calculate the Jaccard similarity index between it and each historical fault case; Traverse all historical fault cases and calculate the Jaccard similarity index value between the current comprehensive causal relationship matrix and each historical case; The fault case with the highest Jaccard similarity index value exceeding the set threshold is selected, and the corresponding fault category is used as the root cause of the current abnormal event; if all Jaccard similarity index values ​​do not exceed the set threshold, the current abnormal event is marked as a new fault category and will be added to the fault case library later.

7. The method for intelligent management of hazardous waste based on the Internet of Things according to claim 6 is characterized in that: Step S5 includes the following contents: According to the list of key nodes and the root causes of the faults, the corresponding handling strategy script is loaded first. The script predefines the automated operation processes corresponding to different fault categories.

8. An intelligent hazardous waste supervision system based on the Internet of Things, used to implement the intelligent hazardous waste supervision method based on the Internet of Things according to any one of claims 1 to 7, characterized in that: include: Data collection module, anomaly detection module, key identification module, root cause analysis module and automatic response module; Data collection module: In each link of hazardous waste disposal, various sensors are arranged to collect various data of corresponding nodes, and the data is synchronously stored in the central database according to the timestamp. The collected data is transmitted to the anomaly detection module; Anomaly detection module: Based on the data set in the central database, the long short-term memory network model is used to build the normal operation baseline of each node, and the data uploaded by the sensor in real time is predicted and compared. When the actual value deviates from the baseline prediction value beyond the preset threshold, the corresponding node data is marked as abnormal, and the node data marked as abnormal is passed to the key identification module; Key identification module: For nodes marked as abnormal, by constructing a multi-node data association network and calculating the correlation and betweenness centrality index between each node, the key nodes with the greatest impact on the overall data consistency in the corresponding abnormal event are determined, a key node list is formed, and the key node list is passed to the root cause analysis module; Root cause analysis module: performs Granger causality analysis on the nodes in the key node list, matches them with the node abnormality information in the historical fault case library, identifies the root cause of the data mutation, and transmits the root cause identification results to the automatic response module; Automatic response module: Based on the root cause identification results of key nodes, it automatically performs isolation and repair operations and calls backup monitoring devices.

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