Intelligent analysis method and system for hazardous waste treatment data

Abstract

The invention relates to an intelligent analysis method and system for hazardous waste treatment data, and the method comprises the steps: collecting hazardous waste data of an intelligent hazardous waste bin, and carrying out the association with the warehouse identification information of the intelligent hazardous waste bin, and generating the hazardous waste treatment data; performing warehouse capacity change analysis and compliance detection on the intelligent hazardous waste warehouse based on the hazardous waste treatment data, and outputting warehouse analysis information; performing periodic inventory prediction on the warehouse analysis information and the hazardous waste treatment data to generate hazardous waste early warning information; and according to the hazardous waste early warning information, in combination with the hazardous waste treatment data and the warehouse analysis information, carrying out treatment scheme construction on each intelligent hazardous waste bin to obtain a hazardous waste treatment scheme. According to the invention, prospective hazardous waste early warning information can be generated, so that the system can identify inventory bottleneck and processing pressure in advance.

Description

Technical Field

[0001] This invention relates to the technical field of hazardous waste treatment, and in particular to an intelligent analysis method and system for hazardous waste treatment data. Background Technology

[0002] With the widespread application of IoT technology and intelligent sensing devices in hazardous waste storage management, how to deeply integrate and analyze massive amounts of hazardous waste data (including data on medical waste, chemical waste, dye residues, waste mineral oil, etc. listed in the "National Hazardous Waste List") and achieve dynamic inventory prediction, real-time compliance monitoring, and intelligent disposal scheduling has become a key issue for improving hazardous waste management and reducing environmental risks. Current hazardous waste data processing methods mostly focus on monitoring or post-event traceability at a single stage, such as simply recording hazardous waste entry or compiling periodic reports. These methods fail to form an integrated intelligent decision-making chain from data collection, multi-dimensional analysis, dynamic early warning to disposal plan generation, resulting in delayed system response, low resource scheduling efficiency, and difficulty in coping with the complex and ever-changing hazardous waste generation and treatment environment. Summary of the Invention

[0003] The main objective of this invention is to provide an intelligent analysis method and system for hazardous waste treatment data, which can generate forward-looking hazardous waste early warning information, enabling the system to identify inventory bottlenecks and treatment pressures in advance.

[0004] To achieve the above objectives, the present invention provides an intelligent analysis method for hazardous waste treatment data, comprising: Hazardous waste treatment data is generated by collecting hazardous waste data from intelligent hazardous waste storage facilities and associating it with the warehouse identification information of the intelligent hazardous waste storage facilities. Based on the hazardous waste treatment data, the intelligent hazardous waste warehouse is analyzed for changes in warehouse capacity and compliance is checked, and warehouse analysis information is output. Periodic inventory forecasting is performed on the warehouse analysis information and the hazardous waste treatment data to generate hazardous waste early warning information; Based on the hazardous waste early warning information, combined with the hazardous waste treatment data and the warehouse analysis information, a treatment plan is constructed for each of the intelligent hazardous waste warehouses to obtain a hazardous waste treatment plan.

[0005] Furthermore, the process of collecting hazardous waste data from the intelligent hazardous waste storage facility and associating it with the facility's identification information to generate hazardous waste treatment data includes: The data collected by multiple data acquisition terminals in the intelligent hazardous waste storage facility are standardized in format to form the hazardous waste data; Based on the warehouse identification information, the hazardous waste data is associated and bound with the smart hazardous waste warehouse in terms of storage location to generate hazardous waste warehouse location association data; Perform hazardous waste classification verification and hazardous waste parameter threshold calibration on the hazardous waste storage location associated data to obtain a compliant hazardous waste list; Based on the compliant hazardous waste list, an association mapping relationship is established between hazardous waste items and warehouse location identifiers, thereby generating hazardous waste treatment data.

[0006] Furthermore, the step of performing warehouse capacity change analysis and compliance testing on the intelligent hazardous waste warehouse based on the hazardous waste treatment data, and outputting warehouse analysis information, includes: Time series analysis was performed on the hazardous waste treatment data to obtain the hazardous waste stock trend component and the hazardous waste storage cycle component; Based on the hazardous waste inventory trend component and the hazardous waste inbound cycle component, the warehouse capacity change trend of the intelligent hazardous waste warehouse is predicted, and a warehouse capacity change prediction curve is generated. The warehouse capacity change prediction curve is dynamically matched based on a preset compliance threshold range to obtain a threshold deviation sequence. Based on the predicted capacity change curve and the threshold deviation sequence, abnormal operations of the intelligent hazardous waste warehouse are traced, and an event list report is generated. The status of the intelligent hazardous waste warehouse is assessed based on the warehouse capacity change prediction curve and the event list report to obtain warehouse analysis information.

[0007] Furthermore, the step of tracing abnormal operations of the intelligent hazardous waste warehouse based on the warehouse capacity change prediction curve and the threshold deviation sequence, and generating an event list report, includes: Based on a preset deviation threshold parameter, the deviation intervals of the threshold deviation sequence are extracted to obtain a set of deviation intervals; Based on the set of deviation intervals, operation records are extracted from the hazardous waste treatment data to obtain a candidate set of operation events; Identify the violation events in the candidate set of operational events and perform a violation deviation analysis with the warehouse capacity change prediction curve to obtain the impact data of the violation events; Based on the impact data of the aforementioned violations, a violation list is constructed, and an event list report is generated.

[0008] Furthermore, the step of performing periodic inventory forecasting on the warehouse analysis information and the hazardous waste treatment data to generate hazardous waste early warning information includes: The warehouse analysis information and the hazardous waste treatment data are input into a preset hazardous waste storage model, and the warehouse analysis information and the hazardous waste treatment data are integrated through the hazardous waste data feature fusion layer of the hazardous waste storage model to obtain integrated hazardous waste information. The core layer of inventory forecasting analyzes the inventory consumption patterns of the integrated hazardous waste information to obtain initial inventory forecast data. The initial inventory forecast data is subjected to compliance constraints and business rule corrections by the rule decision layer to obtain inventory forecast information; Based on the early warning signal generation layer, the inventory forecast information is compared at multiple levels and mapped to the early warning level to output the hazardous waste early warning information.

[0009] Furthermore, the initial inventory forecast data obtained by analyzing the inventory consumption patterns of the integrated hazardous waste information by the core layer of inventory forecasting includes: The core layer of inventory forecasting performs sequence reconstruction and periodic pattern recognition on the integrated hazardous waste information to generate a periodic analysis dataset. The inventory consumption rate in the cycle analysis dataset is matched with a predefined transportation cycle schedule to establish a quantitative correlation between consumption and transportation batches, thereby estimating basic forecast data for future periods. Based on the geographical location and storage capacity attributes in the warehouse analysis information, the periodic analysis dataset is grouped into warehouses, and the independent inventory surplus change trend of each of the intelligent hazardous waste warehouses under the periodic model is calculated to obtain the storage surplus prediction data. The basic forecast data and the warehouse inventory forecast data are analyzed to generate initial inventory forecast data.

[0010] Furthermore, the step of performing multi-level comparison and early warning level mapping on the inventory forecast information based on the early warning signal generation layer, and outputting the hazardous waste early warning information, includes: The warning signal generation layer calibrates the warning threshold of the inventory forecast information based on the preset hazardous waste warehouse safety capacity parameters, and generates a multi-level threshold benchmark. Based on the multi-level threshold benchmark, the inventory forecast information is divided into risk intervals. The hazardous waste inventory value is compared with the threshold benchmark item by item to determine the risk level and obtain the risk interval data. By applying the persistence rule in the preset early warning triggering rules, candidate interval segments in the risk interval data that continuously trigger the persistence rule are identified; The data that triggers the rules in the candidate interval data are filtered according to the severity rules in the warning triggering rules, and then integrated into a target data fragment set; The warning intensity is assigned to the target data fragment set to obtain the warning level of each fragment. The target data fragment set and the fragment warning levels are then integrated by information encoding to generate the hazardous waste warning information.

[0011] Furthermore, the process of constructing treatment plans for each of the intelligent hazardous waste warehouses based on the hazardous waste early warning information, combined with the hazardous waste treatment data and the warehouse analysis information, to obtain a hazardous waste treatment plan includes: The hazardous waste early warning information and the hazardous waste treatment data are correlated and mapped to obtain a set of hazardous waste inventory parameters; Using the hazardous waste inventory parameter set as node attributes, the warehouse analysis information is reconstructed into a graph structure to generate a warehouse operation status map; Based on the warehouse operation status map, resource scheduling simulation is performed on each of the intelligent hazardous waste warehouses to obtain initial scheduling measures; The initial scheduling measures are subjected to bidirectional constraint verification with the hazardous waste early warning information. If the predicted inventory of any of the smart hazardous waste warehouses triggers a high-level early warning again after scheduling, the scheduling amount is adjusted retrospectively to generate optimized scheduling measures. Based on the optimized scheduling measures and the warehouse operation status map, the disposal path is planned to obtain the hazardous waste treatment plan.

[0012] Furthermore, the initial scheduling measures are obtained by simulating resource scheduling for each of the intelligent hazardous waste warehouses based on the warehouse operation status map, including: The remaining storage capacity, hazardous waste stock, and processing capacity parameters of each intelligent hazardous waste warehouse are extracted from the warehouse operation status map, classified and integrated to form a set of scheduling elements; The transfer requirements in the set of scheduling elements are matched with the resource attributes in the preset hazardous waste transfer network. The intelligent hazardous waste warehouse is assigned to each transfer requirement and a transfer route is planned to generate a scheduling configuration scheme. Based on the scheduling configuration scheme and the set of scheduling elements, load allocation and job configuration are performed to obtain the unit task allocation scheme and job nodes. Verify whether the load of each job node under the unit task allocation scheme exceeds its processing capacity threshold, reallocate and adjust the task parameters of the job nodes that exceed the limit until all job nodes meet the load constraints, and output the initial scheduling measures.

[0013] This invention also provides an intelligent analysis system for hazardous waste treatment data, applied to the intelligent analysis method for hazardous waste treatment data described in any one of the above claims, comprising: The identification module is used to collect hazardous waste data from the intelligent hazardous waste warehouse and associate it with the warehouse identification information of the intelligent hazardous waste warehouse to generate hazardous waste treatment data. The analysis module is used to perform warehouse capacity change analysis and compliance detection on the intelligent hazardous waste warehouse based on the hazardous waste treatment data, and output warehouse analysis information; The processing module is used to perform periodic inventory forecasting on the warehouse analysis information and the hazardous waste treatment data, and generate hazardous waste early warning information; The construction module is used to construct a treatment plan for each of the intelligent hazardous waste warehouses based on the hazardous waste early warning information, the hazardous waste treatment data, and the warehouse analysis information, thereby obtaining a hazardous waste treatment plan.

[0014] The present invention provides an intelligent analysis method and system for hazardous waste treatment data, which has the following beneficial effects: By collecting multi-source hazardous waste data from intelligent hazardous waste storage facilities and integrating it with warehouse identification information, a unified hazardous waste treatment data system was constructed. This system enables end-to-end data integration and traceability management of hazardous waste from warehousing to disposal, providing a complete and reliable data foundation for subsequent intelligent analysis. Based on hazardous waste treatment data, warehouse capacity change analysis and compliance testing can dynamically identify inventory change patterns and compliance risks, achieving real-time monitoring and anomaly warnings of hazardous waste storage facility operations, significantly improving the timeliness and accuracy of supervision. By inputting warehouse analysis information and hazardous waste treatment data into a hazardous waste storage model for periodic inventory prediction, forward-looking hazardous waste early warning information is generated, enabling the system to identify inventory bottlenecks and processing pressures in advance, providing a scientific basis for scheduling decisions. Based on hazardous waste early warning information and multi-dimensional data, the intelligent hazardous waste storage facilities are processed and analyzed to generate hazardous waste treatment solutions. This achieves optimized scheduling of disposal resources and intelligent planning of disposal paths, effectively improving the overall efficiency and responsiveness of hazardous waste treatment while reducing environmental risks and operating costs. Attached Figure Description

[0015] Figure 1 This is a flowchart of an intelligent analysis method for hazardous waste treatment data provided by the present invention; Figure 2 This is a structural diagram of an intelligent analysis system for hazardous waste treatment data provided by the present invention.

[0016] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments.

[0019] Reference Figure 1 As shown, this invention provides an intelligent analysis method for hazardous waste treatment data, comprising: Step S1: Collect hazardous waste data from the intelligent hazardous waste storage facility and associate it with the warehouse identification information of the intelligent hazardous waste storage facility to generate hazardous waste treatment data; Step S2: Based on hazardous waste treatment data, perform warehouse capacity change analysis and compliance testing on the intelligent hazardous waste warehouse, and output warehouse analysis information; Step S3: Perform periodic inventory forecasting based on warehouse analysis information and hazardous waste treatment data to generate hazardous waste early warning information; Step S4: Based on the hazardous waste early warning information, combined with hazardous waste treatment data and warehouse analysis information, construct treatment plans for each intelligent hazardous waste warehouse to obtain hazardous waste treatment plans.

[0020] Based on the steps described above, the detailed process is as follows: Step S1: IoT data acquisition terminals deployed in the intelligent hazardous waste storage facility acquire real-time hazardous waste storage data, including hazardous waste weight, type code, physical state, storage time, and chemical characteristic parameters. The acquisition terminals transmit raw data to a central processing unit via a dedicated communication protocol. The central processing unit standardizes the multi-source heterogeneous data according to predefined data specifications, eliminating differences in data dimensions and unit inconsistencies. The IoT data acquisition terminals in the intelligent hazardous waste storage facility include intelligent solid waste scales, intelligent hazardous waste scales, and intelligent solid waste cabinets.

[0021] The standardized data is linked and bound to warehouse identification information, which employs a unique coding mechanism. This coding structure includes metadata such as the warehouse's geographical location, storage capacity level, and environmental control characteristics. The linking process is implemented through a data mapping algorithm, establishing a one-to-one correspondence between hazardous waste data records and warehouse storage locations, thus forming hazardous waste storage location-linked data.

[0022] A hazardous waste classification and verification process is implemented, referencing the national hazardous waste list classification standards. A rule engine is used to match the hazardous waste type codes for compliance, while a sensor data calibration module performs threshold comparisons of hazardous waste parameters to ensure that monitored values ​​such as temperature, humidity, and pressure are within safe ranges. Finally, hazardous waste treatment data is generated, containing a mapping relationship between detailed information on hazardous waste entries and warehouse location identifiers. This data is stored in a structured format in a distributed database for subsequent analysis.

[0023] Step S2: The time-series data in hazardous waste treatment is decomposed to extract the trend component of hazardous waste stock change and the cycle component of warehousing operations. Time series analysis is used to identify the long-term trend and seasonal patterns of stock fluctuations. Based on the extracted trend and cycle components, a multidimensional prediction model is constructed. The model input includes historical stock data, warehousing frequency, and external environmental factors, and the output is a prediction curve of storage capacity changes for a specific future period.

[0024] The compliance monitoring module dynamically matches the warehouse capacity change prediction curve with a preset compliance threshold range. This threshold range is dynamically generated based on warehouse design capacity, environmental regulations, and operational strategies. The matching process calculates the deviation between the prediction curve and the threshold range and generates a threshold deviation sequence. The abnormal operation tracing module locates abnormal time intervals based on the threshold deviation sequence, performs event correlation analysis by combining it with operation log records from hazardous waste treatment data, identifies the types of violations and their impact scope, and generates an event list report including event time, operation type, deviation degree, and impact assessment.

[0025] By integrating warehouse capacity change prediction curves and event list reports through a status assessment algorithm, warehouse analysis information covering warehouse operation status, risk level, and compliance status is generated.

[0026] Step S3: Warehouse analysis information and hazardous waste treatment data are input into a pre-defined hazardous waste storage model. This model employs a multi-layered data processing architecture, using a feature fusion layer to integrate multi-source information from the input warehouse analysis and hazardous waste treatment data. The feature fusion layer utilizes an attention-based data weighting algorithm, assigning different weights based on the reliability and real-time nature of the data source to eliminate data redundancy and conflicts, generating integrated hazardous waste information encompassing time, space, and attribute dimensions.

[0027] The core layer of inventory forecasting performs in-depth analysis of integrated hazardous waste information, extracts inventory consumption patterns through time-series pattern recognition algorithms, identifies cyclical and trend characteristics at the daily / weekly / monthly levels, and combines external factors, including transportation plans, processing capacity constraints, and changes in environmental policies, to perform multi-dimensional correlation forecasting and generate initial inventory forecast data.

[0028] The rule decision layer loads a pre-set compliance constraint library and business rule library. The constraint library includes national hazardous waste storage standards, enterprise safety operating procedures and local environmental protection requirements. It performs rule verification and logical correction on the initial inventory forecast data to ensure that the forecast results meet regulatory requirements and operational realities, and outputs optimized inventory forecast information.

[0029] The early warning signal generation layer establishes a multi-level threshold comparison mechanism, dynamically configuring early warning threshold parameters according to warehouse type, hazardous waste category and seasonal characteristics, matching inventory forecast information with threshold parameters level by level, and generating hazardous waste early warning information containing risk level, early warning time window and expected deviation degree through risk level mapping algorithm.

[0030] Step S4: Establish a correlation mapping relationship between hazardous waste early warning information and hazardous waste treatment data, and extract key inventory parameters, including overcapacity risk value, emergency treatment priority and resource demand index, through data association algorithm to form a hazardous waste inventory parameter set.

[0031] Based on the hazardous waste inventory parameter set, the warehouse analysis information is reconstructed. The graph construction technology is used to integrate the warehouse operation status, resource distribution and environmental constraints into a multi-dimensional warehouse operation status graph. The graph nodes represent warehouse units and resource elements, and the edges represent logistics relationships and constraint relationships.

[0032] The resource scheduling simulation engine loads the warehouse operation status map and uses a combination of constraint satisfaction algorithm and heuristic search algorithm to simulate multi-warehouse collaborative scheduling scenarios. It considers transportation costs, time windows, processing capacity and risk diffusion factors to generate initial scheduling measures including transportation routes, processing sequence and resource allocation.

[0033] The bidirectional constraint verification module iteratively verifies the initial scheduling measures against the hazardous waste early warning information. It uses a feasibility verification algorithm to check whether the scheduling measures meet the early warning constraints and adopts a dynamic adjustment mechanism to reallocate conflicting resources, outputting optimized scheduling measures.

[0034] By using a disposal path planning algorithm, combined with optimized scheduling measures and the spatial topological relationship of the warehouse operation status map, the optimal disposal path and emergency backup plan are calculated to form a complete hazardous waste treatment plan.

[0035] This invention provides an intelligent analysis method for hazardous waste treatment data. By collecting multi-source hazardous waste data from intelligent hazardous waste warehouses and integrating it with warehouse identification information, a unified hazardous waste treatment data system is constructed. This system achieves end-to-end data integration and traceability management of hazardous waste from warehousing to disposal, providing a complete and reliable data foundation for subsequent intelligent analysis. Based on hazardous waste treatment data, warehouse capacity change analysis and compliance detection can dynamically identify inventory change patterns and compliance risks, enabling real-time monitoring and anomaly warnings of hazardous waste warehouse operations, significantly improving the timeliness and accuracy of supervision. By inputting warehouse analysis information and hazardous waste treatment data into a hazardous waste storage model for periodic inventory prediction, forward-looking hazardous waste early warning information is generated, allowing the system to identify inventory bottlenecks and processing pressures in advance, providing a scientific basis for scheduling decisions. Based on the hazardous waste early warning information and multi-dimensional data, the intelligent hazardous waste warehouse is processed and analyzed to generate hazardous waste treatment solutions. This achieves optimized scheduling of disposal resources and intelligent planning of disposal paths, effectively improving the overall efficiency and responsiveness of hazardous waste treatment, and reducing environmental risks and operating costs.

[0036] In one embodiment, hazardous waste treatment data is generated by collecting hazardous waste data from intelligent hazardous waste storage facilities and associating it with the warehouse identification information of the intelligent hazardous waste storage facilities, including: The data collected from multiple acquisition terminals in the intelligent hazardous waste storage facility is standardized in format to form hazardous waste data. This process involves heterogeneous sensing devices deployed at various monitoring points in the hazardous waste storage facility, including weight sensors, chemical property analyzers, temperature and humidity monitoring devices, and image acquisition units. These devices generate raw monitoring data according to different sampling frequencies and data formats. The data format standardization module uses a multi-protocol parsing engine to parse the raw data. During the parsing process, the data dimensions, units of measurement, and encoding formats are standardized and converted according to predefined data specifications.

[0037] Weight data was uniformly converted to kilograms, chemical concentration data was measured in ppm, and time data was stored in the ISO 8601 standard format. Image data was converted into structured descriptive information, including the morphological characteristics of hazardous waste containers and label recognition results, through feature extraction algorithms.

[0038] The data verification unit performs integrity checks and outlier filtering on the standardized data, removing erroneous data records that exceed the physical limits. The final hazardous waste data includes standardized fields such as timestamps, hazardous waste type codes, physical parameter measurements, and quality indicators, and is stored in a temporary buffer in a unified data format for subsequent processing.

[0039] Based on warehouse identification information, hazardous waste data is associated and bound to the storage location of intelligent hazardous waste warehouses, generating hazardous waste warehouse location association data. The warehouse identification information adopts a hierarchical coding structure, with the code including warehouse geographic coordinates, warehouse number, storage partition code, and warehouse location level identifier. The data association engine reads the standardized hazardous waste data stream and establishes associations between data records and corresponding warehouse storage locations through a spatiotemporal matching algorithm.

[0040] The spatiotemporal matching algorithm aligns the data collection timestamps with the time series of warehouse usage records, and combines this with hazardous waste container movement trajectory data obtained through RFID technology to determine the specific storage location corresponding to each data record. During the association process, distributed indexing technology is used to establish a mapping table between hazardous waste data records and warehouse location codes. This mapping table includes metadata such as the hazardous waste entry ID, warehouse location code, and association time.

[0041] For cases of conflicting multi-source data, a data fusion strategy based on the principle of closest time is adopted to ensure that each hazardous waste data record uniquely corresponds to a storage location. The generated hazardous waste storage location association data is stored in key-value pairs, where the key is the hash value of the hazardous waste data record and the value is a structured data object containing the storage location code and association confidence level, providing a location-aware data foundation for subsequent processing.

[0042] Hazardous waste classification verification and hazardous waste parameter threshold calibration are performed on the hazardous waste storage location associated data to obtain a compliant hazardous waste list. This process employs a dual verification mechanism. The hazardous waste classification verification module calls the real-time update interface of the National Hazardous Waste List database to obtain the latest classification standards and coding rules. The verification engine performs pattern matching between the hazardous waste type codes in the hazardous waste storage location associated data and the standard list. It identifies coding deviations and classification errors through a semantic similarity-based classification algorithm. For hazardous waste materials with unclear classifications, a multi-feature fusion decision method is used to determine their classification.

[0043] The hazardous waste parameter threshold calibration module loads a preset safety parameter threshold table. This table is dynamically configured based on the characteristics of the hazardous waste and includes parameters such as volatile organic compound (VOC) concentration limits, heavy metal content thresholds, and critical values ​​for corrosiveness indicators. The calibration process utilizes real-time data stream processing technology to compare the hazardous waste parameter values ​​collected by sensors with the threshold table item by item. A sliding window algorithm is used to calculate parameter fluctuation trends and identify abnormal data points exceeding safety thresholds. For parameter anomalies detected during calibration, a data recalibration process is initiated, using multi-sensor data fusion technology for cross-validation to eliminate false alarms caused by equipment malfunctions. The final generated compliant hazardous waste list contains detailed information on verified hazardous waste items, with each record appended with classification verification results and parameter calibration status identifiers, forming a standardized compliant data set.

[0044] Based on the compliant hazardous waste inventory, a mapping relationship is established between hazardous waste items and warehouse location identifiers to generate hazardous waste treatment data. This process employs distributed graph construction technology, using hazardous waste items in the compliant hazardous waste inventory as nodes and warehouse location identifiers as spatial anchors to construct a hazardous waste-warehouse association graph.

[0045] The system analyzes the hazardous waste characteristic data and warehouse location metadata in the compliant hazardous waste list, determines the optimal storage mapping relationship through spatial relationship reasoning algorithm, and establishes a multi-dimensional association relationship including hazardous waste code, warehouse location code, storage timestamp and environmental constraints.

[0046] The association mapping process employs a consistent hashing algorithm to allocate data storage locations, ensuring that data records of the same hazardous waste category maintain physical storage proximity in the distributed environment. The generated hazardous waste treatment data adopts a hierarchical storage structure: the bottom layer is the raw monitoring data layer, the middle layer is the association mapping relationship layer, and the top layer is the compliance verification result layer.

[0047] The data encapsulation format adopts the internationally recognized hazardous waste data exchange standard, including data check codes and timestamp signatures to ensure data integrity and immutability. The resulting hazardous waste treatment dataset possesses complete spatiotemporal correlation characteristics and compliance assurance mechanisms, providing a high-quality data foundation for subsequent intelligent analysis.

[0048] This embodiment achieves standardized integration of multi-source hazardous waste data by unifying the format of heterogeneous sensor data, eliminating data dimensional conflicts caused by equipment differences. A precise correlation between hazardous waste data and warehouse locations is established based on a hierarchical coding structure and a spatiotemporal matching algorithm, enhancing the spatial awareness and traceability accuracy of hazardous waste storage. A dual verification mechanism is employed to perform hazardous waste classification verification and dynamic calibration of parameter thresholds, ensuring that hazardous waste data complies with national hazardous waste list standards and safe storage requirements. A multidimensional mapping between hazardous waste entries and warehouse locations is established through distributed graph construction technology, forming a hazardous waste treatment dataset with complete spatiotemporal characteristics, providing a highly reliable data foundation for intelligent analysis.

[0049] In one embodiment, based on hazardous waste treatment data, the smart hazardous waste warehouse is analyzed for changes in warehouse capacity and compliance checks, and warehouse analysis information is output, including: Time series analysis was performed on hazardous waste treatment data to obtain the hazardous waste stock trend component and the hazardous waste ingestion cycle component. This process employed time series decomposition methods to extract multi-dimensional features from the standardized hazardous waste stock monitoring sequence.

[0050] In the data preprocessing stage, the original time-series data is smoothed and outliers are removed. A sliding window algorithm is used to calculate the local mean and variance to eliminate random fluctuations. In the trend extraction stage, polynomial fitting technology is applied, and the parameters of the best-fit curve are solved using the least squares method to obtain the trend component of hazardous waste inventory that reflects the long-term direction of change.

[0051] The periodic analysis phase employs spectral analysis, using Fast Fourier Transform to identify implicit periodic patterns in the time series, extracting characteristic parameters such as daily, weekly, and monthly cycles to form the hazardous waste storage periodic component. The entire analysis process utilizes an iterative optimization mechanism, dynamically adjusting the analysis window size and fitting order based on data characteristics to ensure that the decomposition results accurately reflect macroeconomic trends while retaining important periodic features.

[0052] Based on the trend component of hazardous waste inventory and the cycle component of hazardous waste inbound storage, the warehouse capacity change trend of intelligent hazardous waste warehouses is predicted, generating a warehouse capacity change prediction curve. The prediction process is based on a feature weighted fusion algorithm, which performs collaborative analysis of the trend component and the cycle component.

[0053] The trend extrapolation module uses a linear prediction method to deduce the future development direction based on the historical trend change rate; the cycle overlay module performs phase extension on the identified cycle patterns to generate a cycle fluctuation prediction; the prediction results of the two components are fused through an adaptive weighting algorithm, and the weight coefficients are dynamically adjusted according to the historical prediction accuracy.

[0054] The prediction results were validated for reasonableness constraints. Boundary conditions, including warehouse design capacity, historical extreme values, and operational standards, were considered, and predictions exceeding reasonable limits were corrected. The final generated warehouse capacity change prediction curve includes time-series predictions and corresponding confidence intervals, accurately reflecting the warehouse capacity change trend over a specific future period, providing a data foundation for subsequent compliance checks.

[0055] The warehouse capacity change prediction curve is dynamically matched based on a preset compliance threshold range to obtain a threshold deviation sequence. The compliance threshold range is dynamically generated according to the warehouse design capacity, environmental regulations, and operational strategies, and includes static capacity limits and dynamically adjusted thresholds. The dynamic matching process uses a sliding window comparison mechanism to compare the prediction curve data points with the threshold parameters of the corresponding time window in real time.

[0056] The comparison algorithm calculates the deviation between the predicted value and the upper limit of the threshold at each time point, generating an original deviation sequence. The sequence smoothing module uses a weighted moving average method to filter the original deviation, eliminating short-term fluctuations and highlighting significant deviation trends. The processed threshold deviation sequence is stored in time series format, with each data point containing a timestamp, deviation value, and deviation direction identifier, providing quantitative evidence for tracing the source of abnormal operations.

[0057] Based on the storage capacity change prediction curve and threshold deviation sequence, abnormal operations in the intelligent hazardous waste storage facility are traced, generating an event list report. The tracing process employs a multi-source data association analysis method. First, significant deviation periods are identified based on the threshold deviation sequence, establishing an abnormal time window index. The operation log analysis module retrieves hazardous waste treatment operation records within the corresponding time window, including inbound and outbound records, transfer operations, and treatment data. The correlation between operation records and storage capacity changes is analyzed, and a rule-matching algorithm identifies abnormal event types that violate operating procedures.

[0058] The impact assessment unit calculates the contribution of each anomalous event to changes in storage capacity, quantifying the capacity deviation and duration caused by the anomalous operation. The final event list report uses a structured data format, including the anomalous event number, occurrence time, operation type, description of the violation, and impact assessment data, forming a complete anomalous operation file.

[0059] The status of the intelligent hazardous waste warehouse is assessed based on the capacity change prediction curve and event list report to obtain warehouse analysis information. The status assessment adopts a multi-dimensional weighted scoring system, and the assessment dimensions include capacity status, compliance status, and degree of operational standardization.

[0060] The capacity status assessment module analyzes the overall trend and fluctuation characteristics of the forecast curve, and calculates the capacity utilization rate, trend stability index, and peak risk coefficient. The compliance status assessment module integrates the statistical characteristics of violation records and threshold deviation sequences in the event list report to generate a compliance score and risk level identifier.

[0061] The operational standardization assessment module calculates an operational standardization index based on the distribution and impact of operational types in the event list. The assessment results are then used to generate unified warehouse analysis information through a data fusion algorithm. This information employs a hierarchical data structure and includes basic status indicators, risk assessment results, and improvement suggestions, providing a comprehensive basis for subsequent decision-making.

[0062] This embodiment, through time-series decomposition and multi-dimensional feature extraction of hazardous waste treatment data, accurately identifies the long-term trends and cyclical changes in hazardous waste inventory, providing a reliable data foundation for warehouse capacity prediction. Based on the collaborative analysis of trend and cyclical components, a warehouse capacity change prediction curve is generated, enabling accurate prediction of the warehouse's future state and providing forward-looking guidance for resource allocation. By dynamically matching the prediction curve with compliance threshold ranges, capacity deviations are monitored in real time, effectively identifying potential overcapacity risks and violations. Combining the threshold deviation sequence with the prediction curve allows for the tracing of abnormal operations, accurately pinpointing violations and their impact, and establishing a traceable responsibility determination mechanism.

[0063] In one embodiment, abnormal operations of the intelligent hazardous waste warehouse are traced based on the warehouse capacity change prediction curve and threshold deviation sequence, generating an event list report, including: The deviation interval set is obtained by extracting deviation intervals from the threshold deviation sequence based on preset deviation threshold parameters. This process employs a multi-level threshold determination mechanism. The preset deviation threshold parameters are set hierarchically according to warehouse type and hazardous waste characteristics, including two levels: early warning threshold and alarm threshold. The deviation interval extraction algorithm scans the threshold deviation sequence, identifies sequence segments that continuously exceed the early warning threshold, and determines the start and end time points of the deviation interval through a boundary optimization algorithm.

[0064] For abnormal peak points exceeding the alarm threshold, peak clustering analysis is used to merge adjacent peaks into a comprehensive deviation interval. Each extracted deviation interval contains metadata such as start timestamp, end timestamp, maximum deviation value, average deviation intensity, and duration, forming a structured set of deviation intervals to provide a time range benchmark for subsequent operation record analysis.

[0065] Operation records are extracted from hazardous waste treatment data based on the deviation interval set to obtain a candidate set of operation events. The operation record extraction process is based on the principle of time window matching, aligning the time range of the deviation interval with the operation logs in the hazardous waste treatment database in terms of time sequence.

[0066] The extraction algorithm employs multi-index query technology to quickly locate all operation records that occurred within the off-range time period, including hazardous waste inbound, outbound, transfer, and mixing operations. For each operation record, detailed operation parameters are extracted, including operation time, operation type, quantity of hazardous waste involved, operator identification, and equipment number.

[0067] The extracted operation events undergo initial screening to remove obviously irrelevant records, forming a candidate set of operation events. This candidate set is stored using a time-series sorting structure to ensure events are arranged in chronological order, facilitating subsequent identification and analysis of violations.

[0068] Violation events in the candidate set of operational events are identified, and deviation analysis is performed on the violation event data against the storage capacity change prediction curve to obtain the impact data of the violation events. Violation event identification employs a combination of rule matching and pattern recognition. A predefined rule base contains violation operation types and their characteristic patterns from hazardous waste management procedures. The rule matching engine compares each record in the candidate set of operational events with the rule base to identify potential violations of storage specifications, operation time limits, or quantity restrictions.

[0069] For identified violations, a violation deviation analysis is performed: A correspondence is established between the time point of the violation and the predicted capacity change curve; correlation analysis is used to calculate the contribution of the violation to the capacity change; secondly, the capacity deviation caused by the violation is quantified by difference calculation, and the duration and scope of the deviation are analyzed; the impact of the violation on the subsequent prediction curve is assessed, and the risk diffusion coefficient is calculated. The analysis process generates a violation event impact dataset, including violation type codes, impact quantification indicators, risk level assessment, and time-related impact range parameters.

[0070] A violation list is constructed based on the impact data of violation events, generating an event list report. The violation list construction adopts a structured data assembly method, integrating the impact data of violation events according to time series.

[0071] Violation events are prioritized based on factors such as severity of impact, frequency of occurrence, and risk level. Next, event correlations are established to identify groups of violations with causal or temporal connections. Finally, a standardized event record format is generated, with each record including event number, occurrence time, operation type, description of the nature of the violation, quantitative impact data, and handling recommendations.

[0072] The event list report is organized in a hierarchical structure. The first layer contains summary information, including statistics on major violations and an overall risk assessment. The second layer is a detailed list of events, arranged chronologically with their analytical data. The third layer contains appendices, including relevant data charts and records of the analysis process. The report output format supports both machine-readable structured data formats and human-readable document formats, ensuring that subsequent processing stages can directly utilize the report data for decision analysis.

[0073] This embodiment employs a multi-level threshold judgment mechanism to accurately extract deviation intervals, effectively identifying the time range of abnormal fluctuations in storage capacity and providing an accurate time benchmark for subsequent operational analysis. Based on the time window matching principle, a candidate set of operational events is extracted, ensuring a comprehensive review of all relevant operational records and avoiding omissions of key violation clues. A method combining rule matching and pattern recognition is used to identify violation events, comprehensively covering various types of violations and improving the detection rate of violations. Violation deviation analysis quantifies the impact of violations on storage capacity changes, providing data support for liability determination and handling decisions.

[0074] In one embodiment, periodic inventory forecasting is performed based on warehouse analysis information and hazardous waste treatment data to generate hazardous waste early warning information, including: Warehouse analysis information and hazardous waste treatment data are input into a pre-defined hazardous waste storage model. The model's hazardous waste data feature fusion layer integrates this multi-source information to obtain integrated hazardous waste information. This process employs multi-source data fusion technology to process the input warehouse analysis information and hazardous waste treatment data. The warehouse analysis information includes warehouse status assessment results, compliance scores, and risk level data; the hazardous waste treatment data includes detailed information on hazardous waste items, warehouse location relationships, and real-time monitoring parameters.

[0075] The feature fusion layer performs spatiotemporal alignment on the two types of data, establishing relationships between them through timestamp matching and spatial location mapping. Subsequently, a feature weighted fusion algorithm is employed, assigning different weight coefficients based on the reliability and real-time nature of the data source. Numerical features are normalized, and categorical features are encoded uniformly.

[0076] During the data fusion process, special attention was paid to data conflicts, employing a weighted strategy based on the principle of closest time and source reliability to resolve data inconsistencies. The final integrated hazardous waste information is represented in the form of a multi-dimensional feature vector, incorporating integrated data across time, space, hazardous waste characteristics, and warehouse status dimensions, providing a unified data input for subsequent inventory forecasting.

[0077] The core layer of inventory forecasting analyzes the inventory consumption patterns of integrated hazardous waste information to obtain initial inventory forecast data. This analysis process is based on historical data mining and pattern recognition techniques to extract features from the time-series data in the integrated hazardous waste information, identifying the trend, periodic, and random components of inventory changes.

[0078] Warehouse capacity change analysis employs a sliding window statistical method to calculate the average rate of change and acceleration indicators at different time scales; periodic analysis uses spectrum detection technology to identify the existence and intensity of daily, weekly, and monthly periodic patterns; randomness analysis uses volatility calculation and outlier detection methods. Based on the extracted feature parameters, a relationship between inventory consumption patterns is established, which comprehensively considers the impact of internal factors (hazardous waste characteristics, warehouse operations) and external factors (seasonal changes, policy influences) on inventory consumption.

[0079] The forecasting process derives the inventory change trajectory for a specific future period based on historical patterns and current conditions, generating initial inventory forecast data that includes predicted values, confidence intervals, and risk indicators. The forecast results undergo a reasonableness check to ensure they conform to physical possibilities and business constraints.

[0080] The initial inventory forecast data is subject to compliance constraints and business rule corrections by the rule decision layer to obtain inventory forecast information. The rule decision layer loads predefined compliance rule bases and business rule bases. The compliance rule base contains clauses such as capacity limits, storage time limits, and compatibility requirements from national hazardous waste storage standards, local environmental regulations, and industry safety specifications; the business rule base contains rules such as internal enterprise operation strategies, processing priority settings, and resource allocation constraints.

[0081] The correction process involves matching and verifying the initial inventory forecast data against the rule base one by one. For any part of the forecast data that violates compliance rules, different correction strategies are adopted according to the severity of the rule: minor violations are handled by a smoothing adjustment algorithm, which makes fine adjustments while maintaining the forecast trend; serious violations are handled by a forced correction algorithm, which restricts the forecast value to the compliance range.

[0082] The revision of business rules primarily considers actual operational needs, including factors such as transportation plan coordination, capacity matching, and cost optimization, and adjusts the forecast results through a weighted optimization algorithm. The revised inventory forecast information meets both regulatory requirements and actual business needs, and includes the adjusted forecast value, revision record, and compliance status indicator.

[0083] The early warning signal generation layer performs multi-level comparisons and mapping of inventory forecast information to early warning levels, outputting hazardous waste early warning information. The early warning signal generation layer establishes a multi-level threshold system, dynamically configuring early warning threshold parameters based on factors such as warehouse type, hazardous waste category, and seasonal characteristics. The multi-level comparison process employs a step-by-step screening mechanism, initially comparing inventory forecast information with benchmark thresholds to identify potential risk periods; subsequently, a refined comparison is performed to analyze the duration, degree of deviation, and trend of risk periods; and a comprehensive risk assessment is conducted, considering the coupling effects of multiple factors.

[0084] The warning level mapping employs fuzzy reasoning, determining the warning level based on multiple dimensions such as deviation degree, risk duration, and impact scope through rule-based reasoning. The warning information generation module converts the mapping results into standardized hazardous waste warning information, including warning level, warning time window, expected deviation value, impact scope assessment, and disposal recommendations. The output format supports machine-readable data structures and human-readable document formats, ensuring that subsequent stages can directly utilize the warning information for decision-making and response.

[0085] This embodiment effectively integrates multi-source information through a hazardous waste data feature fusion layer, eliminating information silos caused by differences in data sources and providing a complete and reliable data foundation for inventory forecasting. Initial forecast data generated based on inventory consumption pattern analysis accurately reflects future trends in hazardous waste inventory, providing forward-looking guidance for warehouse management. Compliance constraints and business rule corrections at the rule-based decision-making layer ensure that forecast results comply with both regulatory requirements and actual operational needs, improving the practicality and reliability of the forecasts. Hazardous waste early warning information generated using a multi-level comparison and early warning level mapping mechanism can accurately identify warehouse conditions at different risk levels, providing a basis for developing differentiated disposal strategies. The entire forecasting and early warning process forms a closed-loop management system, achieving intelligent processing across the entire process from data integration, pattern analysis, rule correction to early warning output, significantly improving the precision of hazardous waste warehouse management.

[0086] In one embodiment, the inventory forecasting core layer analyzes the inventory consumption patterns of the integrated hazardous waste information to obtain initial inventory forecast data, including: The core layer of inventory forecasting performs sequence reconstruction and periodic pattern recognition on integrated hazardous waste information to generate a periodic analysis dataset. This process uses time series reconstruction technology to structure the multi-dimensional data in the integrated hazardous waste information. In the sequence reconstruction stage, the original time series data is aligned to eliminate data granularity differences caused by different collection frequencies, and a timestamp normalization method is used to unify various types of data to the same time reference.

[0087] Feature dimensions are reorganized by arranging key indicators such as inventory level, inbound frequency, and outbound rate according to time series to form a multi-dimensional time series matrix. Periodic pattern recognition employs a combination of spectral analysis and pattern matching, using Fast Fourier Transform to detect implicit periodic features in the sequence and identify regular fluctuation patterns such as daily, weekly, and monthly cycles.

[0088] For the identified periodic patterns, feature parameters such as period length, amplitude variation, and phase shift are extracted, and a periodic pattern feature library is established. The final generated periodic analysis dataset contains reconstructed time series data, periodic pattern feature parameters, and pattern confidence indices, providing basic data support for subsequent predictive analysis.

[0089] Key parameters were extracted from the periodic analysis dataset, primarily the average inventory consumption rate curves for different hazardous waste categories within typical periods (e.g., daily, weekly, monthly). These consumption rate curves, with time as the horizontal axis, clearly demonstrate the downward trend and fluctuation characteristics of inventory levels over time. Simultaneously, a predefined transportation cycle schedule was invoked, which explicitly specifies fixed transportation batch plans, including departure times, cycle spans, and regular transportation volumes.

[0090] This involves precisely aligning and matching the inventory depletion rate curve with the transportation cycle timeline on a time axis. This matching operation aims to find the spatiotemporal correspondence between consumption patterns and transportation events. Specifically, it analyzes the effect of each transportation batch arriving at the warehouse on the increase in inventory levels, and the subsequent decrease in inventory levels due to daily consumption until the next batch arrives. By analyzing this "replenishment-consumption" cycle over multiple cycles, a quantitative correlation between consumption and transportation batches is established. For example, it determines the proportional relationship between the total consumption of a certain hazardous waste and the transportation scale of that cycle within a specific transportation cycle, or identifies whether the inventory depletion rate exhibits a specific pattern before and after the arrival of transportation batches (e.g., accelerated consumption before arrival, and stable consumption after arrival). This quantitative correlation is defined in a calculable form, possibly as a mapping table or a set of rules that dynamically correlates the time and scale of transportation batches with the expected consumption in the subsequent consumption cycle.

[0091] After successfully establishing quantitative correlations, the basic forecast data for future periods enters the execution phase. The extrapolation process is based on the confirmed correlation model and inputs future transportation schedules. For each planned transportation batch in the future, the established quantitative correlations are applied to predict the expected change trajectory of inventory levels from the arrival of that batch of goods until the arrival of the next batch. This trajectory not only predicts the inventory level at the end of the cycle but also depicts the dynamic reduction path of inventory over time. For inventory forecasts prior to the first transportation batch in the future period, the basis is a combination of the current inventory level and historical in-phase consumption rates.

[0092] By sequentially connecting the forecast segments corresponding to each independent transportation cycle on a timeline, a continuous basic forecast data curve covering a specific future outlook period is formed. This basic forecast data reflects the future evolution of the overall warehouse inventory, assuming that historical consumption patterns and established transportation plans remain unchanged.

[0093] Warehouse analysis information is the key basis for grouping. Among them, the geographical location attribute is used to identify the physical or logical distribution of warehouses (such as being located in area A or area B, belonging to collection points or transit stations), while the warehouse capacity attribute defines the static capacity characteristics of each warehouse (such as maximum safety stock capacity, design processing capacity).

[0094] Based on these attributes, the periodic analysis dataset is grouped into warehouses. The grouping strategy, based on the analysis objectives, groups warehouses that are geographically close and have similar storage capacity levels together, or creates independent groups for each warehouse to conduct the most granular analysis. The grouping operation cuts the global historical periodic data into subsets corresponding to each warehouse or warehouse group. After data grouping, for each warehouse group (or individual smart hazardous waste warehouse), its independent inventory balance change trend under the periodic pattern is calculated. This calculation does not simply allocate the global basic forecast data proportionally, but rather re-identifies its unique consumption periodic pattern based on a subset of the warehouse's own historical data. Due to differences in the service scope, waste source characteristics, and operational strategies of different warehouses, their inventory consumption cycles may deviate from the global average.

[0095] The calculation process analyzes the warehouse's historical inventory records to identify its own consumption peaks and troughs, combining this with its storage capacity attributes (such as current inventory levels and remaining capacity). For example, a warehouse with smaller capacity may have a faster inventory turnover and shorter cycle; a warehouse located in an industrial area may experience a more significant decrease in its weekend consumption rate than a warehouse located in a continuous production area. Through analysis, an independent inventory balance change model reflecting the specific operational patterns of each warehouse is established. This model predicts the expected value of its inventory balance (i.e., remaining available storage capacity or absolute inventory level) at each future point in time, considering its own cyclical patterns and current initial state. The independent prediction results of all warehouses or warehouse groups are aggregated to form storage balance prediction data. This data collection, which includes individualized predictions for each smart hazardous waste warehouse, clearly demonstrates the dynamic details of inventory distribution throughout the entire hazardous waste storage network, providing precise spatial basis for subsequent scheduling optimization.

[0096] The basic forecast data and warehouse inventory forecast data are fused and analyzed to generate initial inventory forecast data. The fusion analysis process employs a multi-source data collaborative processing method to establish data correlation mapping relationships and align the time-dimensional and spatial-dimensional forecast results spatiotemporally. A feature-weighted fusion algorithm is used, allocating fusion weights based on the confidence index and importance of the forecast data. The time-dimensional forecast emphasizes the accuracy of the overall trend, while the spatial-dimensional forecast emphasizes the rationality of the distribution.

[0097] Consistency checks are performed during the fusion process to detect conflicts between the two types of forecast data. Outliers are adjusted using conflict resolution algorithms to ensure the logical consistency of the fusion result. The final generated initial inventory forecast data adopts a multi-dimensional data structure, including time series forecast values, spatial distribution forecast values, comprehensive confidence indicators, and anomaly marker information, providing a complete forecasting foundation for subsequent rule-based decision-making. The forecast data output format supports direct use and analysis in subsequent processing stages.

[0098] This embodiment effectively extracts the periodic patterns of hazardous waste inventory changes through sequence reconstruction and periodic pattern recognition technologies, providing an accurate data foundation for predictive analysis. Based on the correlation analysis between periodic characteristics and transportation operations, it achieves coordinated prediction of inventory consumption and transportation scheduling, improving the practicality and reliability of the prediction results. Spatial-dimensional inventory prediction data is obtained through warehouse location analysis, fully reflecting the capacity distribution of different warehouse locations and providing a basis for refined inventory management. A multi-source data fusion method is used to organically combine the prediction results from the time and spatial dimensions, generating comprehensive and accurate initial inventory prediction data.

[0099] In one embodiment, the inventory forecast information is compared and mapped to the warning level based on the warning signal generation layer at multiple levels to output hazardous waste warning information, including: The early warning signal generation layer calibrates the early warning threshold of inventory forecast information based on preset hazardous waste warehouse safety capacity parameters, generating multi-level threshold benchmarks. It then invokes preset hazardous waste warehouse safety capacity parameters, which are predefined key performance indicators, typically including the warehouse's maximum design safety capacity, the upper limit of permissible operational capacity, and the buffer capacity that must be maintained based on regulations or safety standards. These parameters provide static capacity boundaries for each warehouse or each type of hazardous waste.

[0100] Inventory forecast information is loaded as input. This information includes the predicted inventory levels of various warehouses and types of hazardous waste over a future period, and is one or more dynamic sequences that change over time. The core operation of the early warning threshold calibration is to correlate the dynamic forecast data with the static safety capacity parameter. This calculation does not simply treat the safety capacity as a fixed threshold point, but rather divides the safety capacity into segments based on the safety management strategy, thereby generating multi-level threshold benchmarks with different warning meanings. For example, the first-level threshold benchmark may be set as a relatively high percentage of the safety capacity (such as 80%), indicating that the inventory level has entered a state requiring attention; the second-level threshold benchmark may be closer to the safety capacity (such as 95%), indicating a high-risk state; and the highest-level threshold benchmark may directly correspond to the safety capacity itself or slightly exceed it, indicating an emergency state that is about to or has already exceeded the limit.

[0101] The calibration process iterates through all future time points in the inventory forecast information. For the forecast inventory at each time point, the aforementioned segmentation rules are applied to calculate a series of specific threshold values ​​corresponding to that time point. These threshold sequences, arranged in chronological order, collectively constitute a multi-level threshold benchmark. This benchmark is a dynamic reference framework, and its values ​​may fluctuate slightly over time because, while its calculation basis—the safety capacity parameter—is fixed, the reference values ​​used for segmented calculations (such as a certain percentage benchmark) may be fine-tuned based on the forecast time span or specific management strategies. The generated multi-level threshold benchmark provides a precise, time-point-to-time quantitative comparison standard for subsequent risk level determination.

[0102] After obtaining the multi-level threshold benchmarks, the core risk identification and classification operations are performed. The inputs are inventory forecast information and the multi-level threshold benchmarks generated in the previous step. Risk interval division is a classification process performed point-in-time. For each specific point in time in the inventory forecast information sequence, the predicted inventory value for that point in time is extracted. This single value is then compared item by item with the corresponding thresholds at each level in the multi-level threshold benchmarks. The comparison operation follows a preset judgment logic, typically starting with the highest-level threshold and comparing downwards.

[0103] If the predicted inventory level exceeds or equals the highest-level threshold (e.g., 100% of safety capacity), that point in time is immediately classified as the highest-risk level. If it does not exceed the highest-level threshold but exceeds or equals the second-highest-level threshold, it is classified as the second-highest-risk level; and so on, until the first threshold not exceeded is found, thus determining its risk level. This comparison process ensures that each point in time has a clear risk label. This point-by-point comparison operation traverses all time points in the inventory forecast sequence, and its output is not a simple list of risk levels, but a risk range data tightly coupled to the time axis.

[0104] This data not only records the risk level at each point in time, but more importantly, it implies the continuity of risk status over time. When multiple consecutive points in time are classified as having the same risk level, they actually form a "risk interval" with a start and end point on the timeline. The structure of the risk interval data clearly reveals the duration, start time, and evolution pattern of the risk status. For example, the data may show that starting from time T1 in the future, the inventory forecast value enters a high-risk state and continues until time T2 before falling back to a medium-risk state. This time interval-based expression, compared to isolated point judgments, better reflects the potential cumulative effect and persistent threat of risk, providing a direct data foundation for subsequently identifying persistent early warning segments.

[0105] Input risk interval data, which clearly indicates the risk level for each time point. The persistence rule in the warning triggering rules is invoked. This rule defines the minimum duration of the risk state necessary to trigger a warning. The rule stipulates that a high-risk level state must be maintained continuously for at least N hours (e.g., 8 hours) to be considered a valid warning candidate event, while brief, instantaneous risk spikes followed by a decline are filtered out to avoid generating invalid alarms. The implementation process performs a time-dimensional scan analysis on the risk interval data.

[0106] The scanning operation traverses the entire risk interval data sequence, identifying all consecutive time intervals with the same risk level (especially medium and above). For each identified consecutive risk interval, its duration is calculated. This duration is then compared to the shortest duration threshold set for that risk level in the persistence rules. If the duration of the interval is greater than or equal to the threshold required by the rule, the entire consecutive interval is marked as a candidate interval segment. Conversely, if the duration of a risk interval is shorter than the threshold, it indicates that the risk state is transient and insufficient to constitute a substantial threat, and therefore it is not marked.

[0107] The system focuses on higher-risk intervals, but the rules also apply to lower-risk intervals to achieve tiered early warning. All risk intervals that meet the persistence rules are extracted to form a set of candidate interval segments. This shifts the focus of risk from isolated moments to time periods with significant duration, ensuring that subsequent warning events have sufficient severity and stability, thus providing an important guarantee for the accuracy and effectiveness of early warnings.

[0108] After selecting candidate interval segments based on duration, a second, more decisive round of screening and integration is conducted based on the severity of the risk. The set of candidate interval segments is input. The severity rule from the warning triggering rules is applied here, defining the weight and priority of different risk levels in the warning decision-making process. The severity rule may explicitly stipulate that only candidate interval segments reaching or exceeding a specific level (such as "high risk" or "emergency risk") are eligible to trigger a formal warning, while segments with lower risk levels, even if they meet the persistence requirement, may only be used as monitoring references or may not generate proactive warnings at this stage.

[0109] Each candidate interval segment is iterated over, and its corresponding risk level is checked to see if it meets the minimum warning level threshold set in the severity rule. Candidate interval segments that meet the threshold are directly included in the target data segment set. Segments with risk levels below the threshold are excluded. Furthermore, the severity rule may contain more complex logic, such as handling the merging of segments with different risk levels: if a "high-risk" candidate segment is immediately followed by a "medium-risk" candidate segment (with no zero-risk interval in between), and the total duration of both is long, the severity rule may instruct that these two adjacent segments be merged into a single continuous warning event, using the higher risk level as the criterion, thus more comprehensively reflecting the continuity of risk.

[0110] All fragments that pass the severity screening, as well as those merged according to the rules, are ultimately integrated into a unified target data fragment set. Each fragment in this set simultaneously meets the dual criteria of persistence (having a certain duration) and severity (reaching a certain level), and is a risk event that truly needs to attract attention and trigger subsequent response mechanisms.

[0111] Input the target data segment set. Assigning an early warning intensity value is a quantitative assessment process, typically based on two core dimensions: the inherent risk level of the risk segment (e.g., "high risk" or "emergency risk") and its duration. The assignment logic might be: mapping the risk level to a base intensity coefficient, and simultaneously mapping the duration to a duration-weighted coefficient; the final early warning intensity value is a function of these two coefficients. For example, a segment with an "emergency risk" level and a long duration will yield a significantly higher early warning intensity value than a shorter "high risk" segment.

[0112] This calculation assigns a quantified warning intensity value to each independent risk segment in the target data segment set, which intuitively reflects the severity of the risk. Based on a pre-defined mapping table, this quantified value is categorized into a limited number of warning levels (e.g., Level 1 to Level 4), thus obtaining the segment warning level for each segment. This level combines risk type and duration, making it more accurate and operational than risk levels determined solely based on instantaneous inventory levels. After completing the level classification, an information coding integration operation is performed.

[0113] The target data fragment set (containing identification information such as start and end time, associated warehouse, and hazardous waste category for each fragment) is structurally integrated with the calculated fragment warning level. The integration process assembles the data according to a predefined, machine-readable data format (such as a specific JSON structure, XML schema, or protocol buffer format).

[0114] The generated hazardous waste early warning information data package clearly includes key fields for each risk event, such as "when (time segment)," "where (warehouse)," "what type of hazardous waste," "what is the risk level (segment warning level)," and "what is the warning intensity (quantified value)." This standardized information structure ensures that the early warning information can be seamlessly parsed and utilized by the early warning release module, scheduling system, or other management platforms, thereby driving a series of automated or manual intervention processes, such as subsequent log recording, alarm notification, or disposal plan generation.

[0115] This embodiment achieves precise quantitative determination of risk levels and improves the accuracy of early warnings by constructing a dynamic multi-level threshold benchmark and mapping risk intervals point by point to inventory forecasts. The application of persistence and severity rules for dual screening and integration of risk intervals effectively filters out transient fluctuations, ensuring that all early warning events have persistence and severity, significantly reducing the false alarm rate. By calculating the intensity of the duration and risk level of target risk segments, the generated early warning level can more comprehensively and objectively reflect the actual threat level of the risk. Finally, standardized information coding integration ensures the uniformity and resolvability of the hazardous waste early warning information structure, providing direct and reliable input for subsequent automated response and disposal decisions, and improving the intelligence level and emergency response efficiency of hazardous waste storage management.

[0116] In one embodiment, based on hazardous waste early warning information combined with hazardous waste treatment data and warehouse analysis information, a treatment plan is constructed for each intelligent hazardous waste warehouse to obtain a hazardous waste treatment plan, including: The analysis of hazardous waste early warning information is as follows. The input hazardous waste early warning information includes not only the unique identifier of the smart hazardous waste warehouse that triggered the warning, but also the warning level, the specific hazardous waste category involved, and the duration of the warning. The analysis operation aims to extract key index elements from this information: "where (warehouse identifier)," "what (hazardous waste type)," and "what level of urgency (warning level)." Next, hazardous waste treatment data is retrieved, which includes historical and current inventory records, hazardous waste characteristic parameters, and detailed information on inbound and outbound flows. The association mapping operation is then performed based on the extracted index elements.

[0117] Based on the two key fields of "warehouse identifier" and "hazardous waste type," the hazardous waste treatment data is traversed and matched to locate all inventory records and attribute parameters directly related to the warning event. For example, for a warning identified as "Warehouse A - Waste Solvent - High Risk," the association mapping process will retrieve detailed parameters related to waste solvents in warehouse A from the hazardous waste treatment data, including inventory quantity, current location, calorific value, compatibility, and last transfer time. This process is not a simple data listing, but a data integration and enhancement process.

[0118] The "urgency" semantics inherent in early warning information are bound to the "objective quantity" attributes contained in hazardous waste treatment data. The result of this mapping is a structured set of hazardous waste inventory parameters. This parameter set not only includes the original inventory values ​​but also significantly enhances the feature dimensions related to the warning level. For example, inventory parameters corresponding to high-risk warnings are marked with extremely high treatment priority, or data corresponding to medium-risk warnings are labeled with tags indicating areas requiring attention. The hazardous waste inventory parameter set constitutes a data view oriented towards early warning-driven decision-making, where each data point is directly associated with a specific risk problem to be addressed, ensuring the clarity and high relevance of the objectives of all subsequent analytical operations.

[0119] Warehouse analytics information typically includes the static attributes of each smart hazardous waste warehouse, such as precise geographic location, designed capacity, processing equipment capacity, and, crucially, data on the relationships between warehouses (e.g., feasible transport routes, affiliations, or complementarity of processing capabilities).

[0120] Graph reconstruction is the core operation of this step. The reconstruction process abstracts the entire hazardous waste storage network into a graph model. In this model, each smart hazardous waste storage unit is defined as a node in the graph. A node is not a blank identifier but needs to be assigned rich attributes. At this point, the hazardous waste storage parameter set is used as the attribute set for these nodes.

[0121] Each warehouse node will be injected with its currently associated hazardous waste inventory parameters. This means that each node not only represents a physical warehouse but also carries its real-time inventory status (such as the quantity of various types of hazardous waste), risk level (derived from the warning level), and other key physicochemical parameters. On the other hand, the relationships between warehouses are defined as edges connecting these nodes. Edge attributes can include transportation distance, estimated time, path capacity limits, or cost coefficients. For example, if there is a permitted and regularly used hazardous waste transfer route between warehouse A and warehouse B, an edge connecting node A and node B will be created in the graph, and this edge will be assigned a corresponding weight attribute.

[0122] By combining node attributes (dynamic inventory and risk status) and edge attributes (static network connections and constraints), the resulting warehouse operation status map provides a global, structured view of the network state. This map transcends the isolated perspective of a single warehouse, clearly demonstrating the distribution of risk within the warehouse network under early warning conditions, while also revealing potential resource allocation channels connected by edges. This map serves as a "sandbox" for subsequent resource scheduling simulations; all optimization calculations are based on this network model, ensuring that scheduling schemes consider both the urgency of individual nodes and the physical and logical constraints of the entire network, laying a solid foundation for generating practical solutions.

[0123] The simulation uses a warehouse operational status map as its computational basis. The essence of resource scheduling simulation is to search for resource reallocation strategies within a constrained network. The simulation begins with nodes in the map marked as having a high warning level, i.e., intelligent hazardous waste warehouses with high inventory pressure and significant risks. For each such source node, the simulation traverses its connected edges in the map, searching for adjacent nodes that are not flagged or have a low warning level and possess remaining receiving capacity as potential target warehouses. The simulation process is not random but follows predefined optimization principles, such as prioritizing nearby scheduling or utilizing warehouses with the largest remaining capacity.

[0124] For each potential scheduling link from the source node to the target node, a proposed hazardous waste quantity is calculated. This calculation must simultaneously consider the overcapacity of the source node (the difference between predicted inventory and safe capacity) and the receiving capacity of the target node (maximum capacity minus current inventory). This process is iteratively calculated across the entire network in the simulation. After the initial allocation, the inventory status of the target node is updated in the simulation environment. The simulator reassesses the network status, checks whether any nodes are still at high risk, and initiates a new round of scheduling exploration for newly discovered risky nodes or source nodes that have not been fully mitigated.

[0125] This iteration continues until the predicted inventory levels of all nodes in the simulation environment fall below the safety threshold, or the preset maximum number of iterations has been reached. Finally, the set of all scheduling instructions generated during this simulation, including which warehouse to remove which type of hazardous waste, to which warehouse, and the quantity transported, is integrated and output as the initial scheduling measure. This measure is a preliminary solution based on a global network optimization perspective, but its feasibility still needs further verification.

[0126] The initial scheduling measures are subjected to bidirectional constraint verification with the hazardous waste early warning information. If the predicted inventory of any of the smart hazardous waste warehouses triggers a high-level early warning again after scheduling, the scheduling amount is adjusted retrospectively to generate optimized scheduling measures. This step introduces a rigorous verification mechanism. The meaning of bidirectional constraint verification is: first, to verify the effectiveness of the initial scheduling measures in resolving the original early warning, that is, whether the inventory of those warehouses that originally triggered the early warning has indeed dropped to a safe level after the scheduling is applied; second, and more importantly, to verify the potential impact of the measures on the receiving warehouses, that is, to verify whether the implementation of the scheduling will cause any originally safe receiving warehouse or its surrounding warehouses to fall into a new high-level early warning state due to this transfer. The verification operation is completed through a simulation.

[0127] The hazardous waste transfer volume, concretized from the initial scheduling measures, is overlaid into the future inventory projection calculation based on current inventory and a predictive model. The projection calculation covers all involved smart hazardous waste warehouses, especially those acting as hazardous waste recipients. After the projection is completed, the predicted inventory value for each warehouse at the new simulated future time point is compared again with the multi-level threshold benchmarks used when generating the hazardous waste early warning information.

[0128] The verification logic focuses on whether a situation triggers a high-level warning. If the verification finds that a receiving warehouse's predicted inventory exceeds the high-risk or emergency risk threshold after receiving hazardous waste, the initial scheduling measures are deemed flawed, triggering the constraint condition. At this point, the system initiates a backtracking adjustment mechanism. Backtracking refers to canceling or partially canceling the scheduling instruction that caused the problem. The adjustment strategy might be to reduce the transport volume of the problematic scheduling route, or to completely cancel the route and try to find other feasible scheduling paths (e.g., transferring some hazardous waste to another warehouse with more receiving capacity). After adjustment, a revised scheduling measure is formed and put back into the above two-way constraint verification process for verification.

[0129] This "verification-backtracking-adjustment" cycle iterates continuously until a scheduling scheme that passes verification and no longer triggers any new warnings is found. This final approved scheme is determined as the optimized scheduling measure. It is the result of stress testing and fine-tuning based on the initial scheme, and has higher reliability and robustness.

[0130] The planning process is conducted independently for each specific hazardous waste transfer task between warehouses defined in the optimized scheduling measures (e.g., transferring 5 tons of waste solvent from warehouse A to warehouse B). For each transfer task, the source warehouse and the target warehouse are defined nodes on the warehouse operation status map. The purpose of route planning is to calculate one or more feasible transportation routes between these two nodes based on the edges of the map (representing actual roads or transportation routes) and their attributes (such as distance, estimated travel time, transportation cost, and road condition restrictions).

[0131] Planning typically aims for optimal cost or shortest time. Calculations consider practical constraints on routes, such as traffic restrictions for hazardous waste transport vehicles and bridge weight limits. Ultimately, a recommended optimal route is assigned to each transport task. All these independent transport task instructions (including source, destination, type and quantity of materials, and recommended route) are integrated and formatted with necessary auxiliary information (such as planned departure time and precautions) to assemble a well-structured and clearly defined hazardous waste treatment plan.

[0132] This embodiment achieves a precise depiction of the global state of the warehouse network by mapping hazardous waste early warning information with inventory processing data and constructing a warehouse operation status map, providing a reliable data foundation for subsequent optimization decisions. Resource scheduling simulation based on the map, combined with a two-way constraint verification mechanism, ensures that the generated scheduling measures effectively mitigate existing inventory risks without triggering new early warnings, significantly improving the feasibility and reliability of the treatment plan. By combining the optimized scheduling measures with the warehouse network topology for disposal path planning, the final hazardous waste treatment plan not only includes resource allocation strategies but also refines executable logistics paths, enhancing the overall integrity and operability of the plan. This method realizes intelligent decision-making throughout the entire process from risk early warning to disposal plan generation, effectively improving the emergency response speed and resource utilization efficiency of hazardous waste storage management.

[0133] In one embodiment, resource scheduling simulation is performed on each intelligent hazardous waste warehouse based on the warehouse operation status map to obtain initial scheduling measures, including: Analysis and key data extraction of the warehouse operation status map. Each node in the map represents a smart hazardous waste warehouse, and the node attributes encapsulate various status parameters of the warehouse. The extraction operation is performed synchronously on all nodes in the map, focusing on three core parameters most directly related to resource scheduling decisions: remaining storage capacity, hazardous waste inventory, and processing capacity. The remaining storage capacity parameter indicates the maximum physical space or weight of hazardous waste that the warehouse can currently receive. The hazardous waste inventory parameter describes the quantity or volume of various types of hazardous waste currently actually stored in the warehouse.

[0134] The processing capacity parameter defines the inherent technical capability of a warehouse to process (e.g., solidify, incinerate, package) a specific type of hazardous waste per unit of time. The extraction operation yields a list of raw parameters for all warehouses. The classification and integration operation then structures and organizes this raw data. Integration is not a simple aggregation, but rather a categorization based on scheduling logic requirements. A typical integration method is vertical classification by hazardous waste type, grouping the inventory, required / acceptable quantities, and corresponding processing capacity parameters of all warehouses belonging to the same hazardous waste type (e.g., waste acid, waste solvent) together to form a global scheduling view for that type of waste.

[0135] Another approach is to group warehouses horizontally by role or region. The output of this process is a set of scheduling elements, an organized, structured data volume that clearly expresses the following elements: all transshipment demands currently existing in the network (typically stemming from warehouses with inventory exceeding safety thresholds and their excess capacity), available receiving resources in the network (represented by warehouses with sufficient remaining capacity and their capacity information), and processing capacity resources distributed across nodes. This set of scheduling elements clarifies and quantifies the implicit "demands" and "resources" in the graph, providing precise input for the next stage of matching and planning.

[0136] After clarifying the scheduling elements (demand and resources), the core matching and route planning functions are executed. The goal is to find a feasible receiver and determine a transportation route for each volume of hazardous waste that needs to be transferred, thus forming the basic framework for scheduling. The input elements include a set of parameters and a pre-defined hazardous waste transfer network model.

[0137] The preset hazardous waste transfer network is an independently defined network model that includes resource attributes. Its nodes are also intelligent hazardous waste warehouses, but the attributes of the edges (i.e., transportation paths) are more detailed, such as the distance of the path, the estimated transportation time, the cost coefficient, road traffic restrictions, and the requirements for hazardous waste transportation qualifications.

[0138] The matching operation first establishes a connection between demand and resources. For each specific transfer demand identified in the set of scheduling elements (such as "Warehouse A needs to transfer 5 tons of waste solvent"), this operation searches for all potential target smart hazardous waste warehouses that meet the resource attributes in the preset hazardous waste transfer network.

[0139] Matching criteria include: the target warehouse must have sufficient spare storage capacity to receive the hazardous waste; the target warehouse must have the qualifications and capabilities to process this type of hazardous waste (processing capacity compatibility); in addition, strategies such as relative geographical proximity may also need to be considered. The matching process will filter out multiple candidate target warehouses for a single requirement.

[0140] For each "demand-target" pair, a transfer route is planned. Route planning is based on the topology and edge attributes of a pre-defined hazardous waste transfer network, calculating one or more feasible routes between the source warehouse and the selected target warehouse that comply with hazardous waste transportation regulations and practical constraints. Planning typically aims to minimize cost or time. For each transfer demand, a preferred target smart hazardous waste warehouse and a preferred transfer route are determined. All such assignment decisions for "source warehouse - hazardous waste type and quantity - target warehouse - transfer route" are aggregated to form a scheduling and configuration scheme.

[0141] The process takes a scheduling configuration scheme and a set of scheduling elements as input. The scheduling configuration scheme specifies the source, destination, and route of each batch of hazardous waste to be transferred, but does not yet specify the specific execution order, batch division, or resource occupation time arrangement. The load allocation operation first handles the timing planning of the overall load. Based on the processing capacity of each node in the scheduling element set, the availability of transportation resources (such as the number of vehicles and the shift schedule), and possible operation time windows (such as daytime operation only), it assigns a reasonable execution time interval or sequence to each transfer task in the scheduling configuration scheme. It ensures that the same operation node (such as the same transport vehicle or the same set of processing equipment) is not assigned multiple conflicting tasks at the same time.

[0142] After completing the timeline planning, job configuration is performed. For each transfer task with an assigned time window, it is further broken down into finer-grained unit tasks. For example, a large-scale transfer task may be broken down into multiple unit tasks carried by standard transport containers to facilitate flexible scheduling and phased execution.

[0143] Each unit task is clearly defined, including but not limited to: the type and precise quantity of hazardous waste involved, the designated means of transport or type of transport, the planned departure and arrival time windows, and the specific operational nodes responsible for performing the unit task. Operational nodes here refer to specific, actionable physical or logical entities, such as a specific transport vehicle number, the identification of the team responsible for loading and unloading, or the designated entrance to the processing facility.

[0144] All these decomposed unit tasks and their assignment relationships with job nodes are systematically integrated into a unit task allocation scheme. This scheme not only includes "what to do," but also precisely specifies "who does it," "how much to do," and "when to do it," laying the foundation for the scheme's executability and subsequent verification.

[0145] A constraint-based verification and iterative adjustment mechanism is introduced. The verification operation is carried out on the unit task allocation scheme. This operation simulates the load borne by each work node when the scheme is executed. For work nodes of the transportation vehicle type, the load may be the total transportation mileage or number of transportations within a specific time period (such as within a day); for work nodes of the treatment facility type, the load may be the total amount of hazardous waste that needs to be treated per unit time.

[0146] The overall load level of each job node during the execution of the plan is calculated and compared with a predefined processing capacity threshold for each node. The verification logic aims to identify job nodes whose load exceeds their processing capacity threshold, i.e., overloaded nodes. Once any overloaded node is detected, the current plan is deemed infeasible, and a reallocation and adjustment process is initiated. The adjustment strategy targets the task parameters that caused the overload. For example, if a transport vehicle is assigned too many tasks, the adjustment might include: reallocating some of its unit tasks to other available vehicles with lighter loads within the same time period; or, if time permits, postponing the execution time of some unit tasks to a later time period when the vehicle has a lighter load.

[0147] The adjustment process is essentially a rebalancing of task resources. After one round of adjustment, a new, revised unit task allocation scheme is formed and immediately put back into the verification process described above. This cyclical process of "verification - detection of limits - reallocation and adjustment - re-verification" continues until the generated scheme can pass verification, that is, the simulated load of all job nodes in the scheme is within their respective processing capacity thresholds. This scheme is considered feasible under the current conditions and meets all resource constraints, and is finally output as the initial scheduling measure. This measure is the result of fine-tuning and lays a reliable foundation for subsequent global optimization.

[0148] This embodiment extracts and categorizes key parameters from the warehouse operation status map to form a structured set of scheduling elements, providing a clear and comprehensive data foundation for subsequent precise matching and planning, thus improving the accuracy of decision-making. Matching transfer demands with preset network resource attributes and planning routes generates a scheduling configuration scheme, achieving efficient connection between demand and resources and significantly improving the rationality and executability of scheduling planning. Based on the scheduling configuration, load allocation and job configuration are performed, decomposing the macro-plan into specific unit tasks, ensuring that each task has clear operation nodes and execution parameters, enhancing the operability of the scheme. Through continuous verification and iterative adjustment of the load on job nodes, local resource overload is effectively avoided, ensuring that the final output initial scheduling measures meet all actual constraints, improving the feasibility and reliability of the scheme. This method achieves refined and automated generation of hazardous waste scheduling schemes, effectively improving the utilization efficiency of warehousing resources and the speed of scheduling response.

[0149] Reference Figure 2 As shown, the present invention also provides an intelligent analysis system for hazardous waste treatment data, applicable to the intelligent analysis method for hazardous waste treatment data of any of the above-mentioned methods, comprising: The identification module is used to collect hazardous waste data from the intelligent hazardous waste warehouse and associate it with the warehouse identification information of the intelligent hazardous waste warehouse to generate hazardous waste treatment data. The analysis module is used to perform warehouse capacity change analysis and compliance detection on intelligent hazardous waste warehouses based on hazardous waste treatment data, and output warehouse analysis information. The processing module is used to perform periodic inventory forecasting based on warehouse analysis information and hazardous waste treatment data, and generate hazardous waste early warning information. The construction module is used to construct treatment plans for each intelligent hazardous waste warehouse based on hazardous waste early warning information, hazardous waste treatment data, and warehouse analysis information, thereby obtaining hazardous waste treatment plans.

[0150] This invention provides an intelligent analysis system for hazardous waste treatment data. By collecting multi-source hazardous waste data from intelligent hazardous waste warehouses and integrating it with warehouse identification information, a unified hazardous waste treatment data system is constructed. This system achieves end-to-end data integration and traceability management of hazardous waste from warehousing to disposal, providing a complete and reliable data foundation for subsequent intelligent analysis. Based on hazardous waste treatment data, warehouse capacity change analysis and compliance detection can dynamically identify inventory change patterns and compliance risks, enabling real-time monitoring and anomaly warning of hazardous waste warehouse operation, significantly improving the timeliness and accuracy of supervision. By inputting warehouse analysis information and hazardous waste treatment data into a hazardous waste storage model for periodic inventory prediction, forward-looking hazardous waste early warning information is generated, enabling the system to identify inventory bottlenecks and processing pressures in advance, providing a scientific basis for scheduling decisions.

[0151] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the system and each module described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0152] In this embodiment, the processor and memory can be connected via a bus or other means. The memory may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, hard disk, or solid-state drive. The processor may be a general-purpose processor, such as a central processing unit, digital signal processor, application-specific integrated circuit, or one or more integrated circuits configured to implement embodiments of the present invention.

[0153] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. An intelligent analysis method for hazardous waste treatment data, characterized in that, include: Hazardous waste treatment data is generated by collecting hazardous waste data from intelligent hazardous waste storage facilities and associating it with the warehouse identification information of the intelligent hazardous waste storage facilities. Based on the hazardous waste treatment data, the intelligent hazardous waste warehouse is analyzed for changes in warehouse capacity and compliance is checked, and warehouse analysis information is output. Periodic inventory forecasting is performed on the warehouse analysis information and the hazardous waste treatment data to generate hazardous waste early warning information; Based on the hazardous waste early warning information, combined with the hazardous waste treatment data and the warehouse analysis information, a treatment plan is constructed for each of the intelligent hazardous waste warehouses to obtain a hazardous waste treatment plan.

2. The intelligent analysis method for hazardous waste treatment data according to claim 1, characterized in that, The process of collecting hazardous waste data from intelligent hazardous waste storage facilities and associating it with the warehouse identification information of these facilities to generate hazardous waste treatment data includes: The data collected by multiple data acquisition terminals in the intelligent hazardous waste storage facility are standardized in format to form the hazardous waste data; Based on the warehouse identification information, the hazardous waste data is associated and bound with the smart hazardous waste warehouse in terms of storage location to generate hazardous waste warehouse location association data; Perform hazardous waste classification verification and hazardous waste parameter threshold calibration on the hazardous waste storage location associated data to obtain a compliant hazardous waste list; Based on the compliant hazardous waste list, an association mapping relationship is established between hazardous waste items and warehouse location identifiers, thereby generating hazardous waste treatment data.

3. The intelligent analysis method for hazardous waste treatment data according to claim 1, characterized in that, The process involves analyzing warehouse capacity changes and conducting compliance checks on the intelligent hazardous waste warehouse based on the hazardous waste treatment data, outputting warehouse analysis information, including: Time series analysis was performed on the hazardous waste treatment data to obtain the hazardous waste stock trend component and the hazardous waste storage cycle component; Based on the hazardous waste inventory trend component and the hazardous waste inbound cycle component, the warehouse capacity change trend of the intelligent hazardous waste warehouse is predicted, and a warehouse capacity change prediction curve is generated. The warehouse capacity change prediction curve is dynamically matched based on a preset compliance threshold range to obtain a threshold deviation sequence. Based on the predicted capacity change curve and the threshold deviation sequence, abnormal operations of the intelligent hazardous waste warehouse are traced, and an event list report is generated. The status of the intelligent hazardous waste warehouse is assessed based on the warehouse capacity change prediction curve and the event list report to obtain warehouse analysis information.

4. The intelligent analysis method for hazardous waste treatment data according to claim 3, characterized in that, The step of tracing abnormal operations of the intelligent hazardous waste warehouse based on the warehouse capacity change prediction curve and the threshold deviation sequence, and generating an event list report, includes: Based on a preset deviation threshold parameter, the deviation intervals of the threshold deviation sequence are extracted to obtain a set of deviation intervals; Based on the set of deviation intervals, operation records are extracted from the hazardous waste treatment data to obtain a candidate set of operation events; Identify the violation events in the candidate set of operational events and perform a violation deviation analysis with the warehouse capacity change prediction curve to obtain the impact data of the violation events; Based on the impact data of the aforementioned violations, a violation list is constructed, and an event list report is generated.

5. The intelligent analysis method for hazardous waste treatment data according to claim 1, characterized in that, The process of performing periodic inventory forecasting based on the warehouse analysis information and the hazardous waste treatment data to generate hazardous waste early warning information includes: The warehouse analysis information and the hazardous waste treatment data are input into a preset hazardous waste storage model, and the warehouse analysis information and the hazardous waste treatment data are integrated through the hazardous waste data feature fusion layer of the hazardous waste storage model to obtain integrated hazardous waste information. The core layer of inventory forecasting analyzes the inventory consumption patterns of the integrated hazardous waste information to obtain initial inventory forecast data. The initial inventory forecast data is subjected to compliance constraints and business rule corrections by the rule decision layer to obtain inventory forecast information; Based on the early warning signal generation layer, the inventory forecast information is compared at multiple levels and mapped to the early warning level to output the hazardous waste early warning information.

6. The intelligent analysis method for hazardous waste treatment data according to claim 5, characterized in that, The initial inventory forecast data is obtained by analyzing the inventory consumption patterns of the integrated hazardous waste information through the core layer of inventory forecasting, including: The core layer of inventory forecasting performs sequence reconstruction and periodic pattern recognition on the integrated hazardous waste information to generate a periodic analysis dataset. The inventory consumption rate in the cycle analysis dataset is matched with a predefined transportation cycle schedule to establish a quantitative correlation between consumption and transportation batches, thereby estimating basic forecast data for future periods. Based on the geographical location and storage capacity attributes in the warehouse analysis information, the periodic analysis dataset is grouped into warehouses, and the independent inventory surplus change trend of each of the intelligent hazardous waste warehouses under the periodic model is calculated to obtain the storage surplus prediction data. The basic forecast data and the warehouse inventory forecast data are analyzed to generate initial inventory forecast data.

7. The intelligent analysis method for hazardous waste treatment data according to claim 5, characterized in that, The method of performing multi-level comparison and early warning level mapping on the inventory forecast information based on the early warning signal generation layer, and outputting the hazardous waste early warning information includes: The warning signal generation layer calibrates the warning threshold of the inventory forecast information based on the preset hazardous waste warehouse safety capacity parameters, and generates a multi-level threshold benchmark. Based on the multi-level threshold benchmark, the inventory forecast information is divided into risk intervals. The hazardous waste inventory value is compared with the threshold benchmark item by item to determine the risk level and obtain the risk interval data. By applying the persistence rule in the preset early warning triggering rules, candidate interval segments in the risk interval data that continuously trigger the persistence rule are identified; The data that triggers the rules in the candidate interval data are filtered according to the severity rules in the warning triggering rules, and then integrated into a target data fragment set; The warning intensity is assigned to the target data fragment set to obtain the warning level of each fragment. The target data fragment set and the fragment warning levels are then integrated by information encoding to generate the hazardous waste warning information.

8. The intelligent analysis method for hazardous waste treatment data according to claim 1, characterized in that, The process involves constructing treatment plans for each of the intelligent hazardous waste warehouses based on the hazardous waste early warning information, the hazardous waste treatment data, and the warehouse analysis information, resulting in a hazardous waste treatment plan, including: The hazardous waste early warning information and the hazardous waste treatment data are correlated and mapped to obtain a set of hazardous waste inventory parameters; Using the hazardous waste inventory parameter set as node attributes, the warehouse analysis information is reconstructed into a graph structure to generate a warehouse operation status map; Based on the warehouse operation status map, resource scheduling simulation is performed on each of the intelligent hazardous waste warehouses to obtain initial scheduling measures; The initial scheduling measures are subjected to bidirectional constraint verification with the hazardous waste early warning information. If the predicted inventory of any of the smart hazardous waste warehouses triggers a high-level early warning again after scheduling, the scheduling amount is adjusted retrospectively to generate optimized scheduling measures. Based on the optimized scheduling measures and the warehouse operation status map, the disposal path is planned to obtain the hazardous waste treatment plan.

9. The intelligent analysis method for hazardous waste treatment data according to claim 8, characterized in that, The initial scheduling measures are obtained by simulating resource scheduling for each of the intelligent hazardous waste warehouses based on the warehouse operation status map, including: The remaining storage capacity, hazardous waste stock, and processing capacity parameters of each intelligent hazardous waste warehouse are extracted from the warehouse operation status map, classified and integrated to form a set of scheduling elements; The transfer requirements in the set of scheduling elements are matched with the resource attributes in the preset hazardous waste transfer network. The intelligent hazardous waste warehouse is assigned to each transfer requirement and a transfer route is planned to generate a scheduling configuration scheme. Based on the scheduling configuration scheme and the set of scheduling elements, load allocation and job configuration are performed to obtain the unit task allocation scheme and job nodes. Verify whether the load of each job node under the unit task allocation scheme exceeds its processing capacity threshold, reallocate and adjust the task parameters of the job nodes that exceed the limit until all job nodes meet the load constraints, and output the initial scheduling measures.

10. An intelligent analysis system for hazardous waste treatment data, characterized in that, The intelligent analysis method for hazardous waste treatment data applied to any one of claims 1-9 includes: The identification module is used to collect hazardous waste data from the intelligent hazardous waste warehouse and associate it with the warehouse identification information of the intelligent hazardous waste warehouse to generate hazardous waste treatment data. The analysis module is used to perform warehouse capacity change analysis and compliance detection on the intelligent hazardous waste warehouse based on the hazardous waste treatment data, and output warehouse analysis information; The processing module is used to perform periodic inventory forecasting on the warehouse analysis information and the hazardous waste treatment data, and generate hazardous waste early warning information; The construction module is used to construct a treatment plan for each of the intelligent hazardous waste warehouses based on the hazardous waste early warning information, the hazardous waste treatment data, and the warehouse analysis information, thereby obtaining a hazardous waste treatment plan.

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