Multi-source data-based hazardous waste full life cycle early warning and management method

Abstract

The invention relates to the technical field of hazardous waste early warning management, and particularly discloses a hazardous waste full-life-cycle early warning and management method based on multi-source data, which constructs a hazardous waste full-life-cycle management system by providing the hazardous waste full-life-cycle early warning and management method based on the multi-source data. Digital identities are given to the hazardous wastes, so that accurate information management from a warehousing source is realized; early recognition and intelligent intervention of temporary storage environment risks are realized through a multi-source sensor data fusion and hierarchical response mechanism; the core of the method is that based on real-time data, single or multiple hazard sources can be intelligently analyzed and positioned, reaction paths and linkage risks are dynamically simulated, and a global knowledge base is continuously updated. The system finally forms a closed loop with autonomous learning and optimization capabilities, the management mode of hazardous wastes is improved from passive disposal to active early warning and scientific decision, and the storage security and the management intelligence level are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of hazardous waste early warning management, in particular to a hazardous waste whole life cycle early warning and management method based on multi-source data. BACKGROUND

[0002] In the field of intelligent management of hazardous waste, the existing technology has initially realized the digital identification and intelligent management of the attributes of hazardous waste. On this basis, the entire system is evolving towards deep perception, intelligent decision-making, and full-chain collaboration. Through the fusion of Internet of Things high-precision sensing, multi-source data fusion analysis, and artificial intelligence prediction models, the management system can not only automatically identify the chemical composition and hazard category of waste, but also can track the dynamic changes in the generation, storage, transfer, and disposal process in real time. Intelligent algorithms cross-verify historical data and real-time monitoring to independently analyze and judge key links such as storage safety thresholds and disposal cycle optimization, and conduct risk early warning, preliminarily establishing a closed-loop control mechanism. Further, the introduction of blockchain technology strengthens the non-tamperability and traceability of hazardous waste flow data, making every link from the waste-producing unit to the disposal terminal form a credible record, effectively eliminating the supervision blind spot. The continuous evolution of this intelligent ecosystem is gradually promoting the transition of hazardous waste management from traditional passive response to forward-looking and precise prevention and control, significantly improving the controllability of environmental risks, and providing data-driven decision support for hazardous waste resource utilization under the background of circular economy, ultimately building a new paradigm for hazardous waste intelligent governance that covers the entire territory, responds in real time, and optimizes adaptively.

[0003] For example, the Chinese invention patent with publication number CN115081957B discloses a hazardous waste management platform for hazardous waste temporary storage and monitoring. The platform divides the collected hazardous waste area image into multiple sub-block regions through a region division module. A region matching module is used to match the sub-block regions on different frames of hazardous waste area images to obtain multiple matching region pairs and unmatched regions. An abnormal region screening module is used to screen out abnormal regions from the multiple matching region pairs and unmatched regions. A first data acquisition module is used to screen out a suspected region sequence and calculate the corresponding change continuity. A second data acquisition module is used to calculate the edge extension of the suspected region sequence. An abnormal alarm module is used to calculate the hazardous waste liquid leakage probability. The abnormal region is alarmed according to the hazardous waste liquid leakage probability.

[0004] For example, the Chinese invention patent with publication number CN118114986A discloses a method for classifying the danger of hazardous waste, comprising the following steps: S1: establishing a hazardous substance component knowledge base, corresponding each component to a danger code, and assigning a danger grouping code to the danger code; S2: establishing a danger level calculation rule, including a weight calculation rule, a pH calculation rule, and a flash point calculation rule; S3: detecting the hazardous waste, combining the content of each component or the pH value or flash point value of the hazardous waste obtained by detection with the danger weight calculation judgment table, and determining the danger level according to the pH value calculation rule, the flash point value calculation rule, and the weight calculation rule; S4: taking the highest level of danger in the same category as the final danger level, and sorting the highest level of danger in different categories, and outputting the highest level of danger first.

[0005] The above technology at least has the following technical problems: In the existing intelligent management system for hazardous waste, although the digital identification and basic information management of the attributes of hazardous waste have been initially realized, in actual operation, how to realize accurate early warning and active intervention of dynamic risks in the whole life cycle still faces severe challenges. Specifically, the existing technology cannot effectively deal with the following problems: First, when different hazardous wastes are temporarily stored, their complex physical and chemical properties cause self-reaction or mutual reaction, leading to safety accidents such as thermal runaway, and the traditional static management cannot evaluate and warn such risks in real time according to the dynamic combination of the stored hazardous waste, secondly, for the temperature anomaly caused by reaction heat in the temporary storage facility, the existing system lacks a closed-loop response mechanism from accurate perception, risk tracing to hierarchical control, and often responds late or handles roughly, and cannot intelligently adjust the power of emergency facilities such as gas purification devices according to the risk level, resulting in either insufficient response or resource waste. SUMMARY

[0006] In view of the deficiencies of the prior art, the present application provides a multi-source data-based hazardous waste whole life cycle early warning and management method, which can effectively solve the problems involved in the background art.

[0007] To achieve the above purpose, the present application realizes the following technical scheme: The present application provides a multi-source data-based hazardous waste whole life cycle early warning and management method, comprising: S1. The weighed and packaged hazardous waste is sequentially stored according to the configuration information, and the temperature upper limit limit value of the temporary storage facility is updated after each hazardous waste is successfully stored, and the storage is continuously updated until there is no hazardous waste that can be stored, and the storage is ended; S2. After the storage is ended, the temperature of the temporary storage facility is continuously monitored, the monitoring data of each temperature sensor is acquired and analyzed, and whether to execute the hazardous waste danger source analysis process is determined based on the analysis result; S3. The hazardous waste danger source condition is obtained based on the hazardous waste danger source analysis result, so as to update the hazardous waste conflict attribute set of the abnormal hazardous waste.

[0008] Compared with the prior art, the embodiments of the present application have at least the following advantages or beneficial effects:

[0009] (1) The present application provides a hazardous waste full life cycle early warning and management method based on multi-source data, constructs a hazardous waste full life cycle management system integrating intelligent sensing, dynamic early warning and traceability, realizes accurate information management from the source of warehousing by giving digital identity to hazardous waste; realizes early identification and intelligent intervention of temporary storage environmental risks through multi-source sensor data fusion and hierarchical response mechanism; the core is to intelligently analyze and locate single or multiple hazards based on real-time data, dynamically simulate reaction path and chain risk, and continuously update global knowledge base. The system finally forms a closed loop with autonomous learning and optimization capability, improves the management mode of hazardous waste from passive disposal to active early warning and scientific decision-making, and significantly improves the storage safety and management intelligence level.

[0010] (2) The present application integrates weighing, electronic tags, multi-source sensor monitoring and dynamic data analysis to construct a full-process closed-loop management mechanism from warehousing to storage. It realizes real-time tracking and digital management of the physical state and chemical properties of hazardous waste, not only significantly improves the accuracy and efficiency of information recording, but also changes the management mode from traditional manual intervention and passive response to systematic, data-driven active early warning and intelligent decision-making, thereby greatly improving the safety and management refinement level of the hazardous waste temporary storage link as a whole.

[0011] (3) The present application has a deep analysis capability of hazardous sources based on multi-source data fusion. When the monitoring data is abnormal, the system can quickly locate the risk unit and intelligently judge whether it is a self-reaction of a single waste or an interaction between multiple wastes. By simulating the reaction path and evaluating the risk of new products, the system can dynamically update the global hazardous waste conflict attribute set. This enables the entire management system to have the ability of continuous learning and self-optimization, and the early warning model can be continuously accurate with the accumulation of data, thereby being able to predict and prevent more complex chain risks in advance, greatly enhancing the scientificity and foresight of management.

[0012] (4) The present application automatically retrieves and binds complete information including the conflict attribute set through the intelligent terminal, so that each hazardous waste has a unique digital identity. This design not only ensures the standardization and traceability of warehousing operation, but more importantly, lays a solid data foundation for subsequent risk early warning. The system can pre-judge storage compatibility based on the conflict attribute set, thereby avoiding safety accidents caused by improper mixing from the source, realizing early identification and prevention of risks. BRIEF DESCRIPTION OF DRAWINGS

[0013] The application is further described by using the accompanying drawings, but the embodiments in the drawings do not constitute any limitation to the application, and other embodiments can be obtained by the ordinary skilled in the art without creative labor on the basis of the following drawings.

[0014] Figure 1 The method step flowchart of the application is shown in the figure.

[0015] Figure 2 The process flowchart of the hazardous waste storage management and intelligent temperature control of the application is shown in the figure.

[0016] Figure 3 The process flowchart of the hazardous waste storage temperature intelligent monitoring and emergency response of the application is shown in the figure.

[0017] Figure 4 The process flowchart of the hazardous waste storage temperature dynamic regulation and state recovery of the application is shown in the figure.

[0018] Figure 5 The process flowchart of the hazardous waste multi-level hazard source analysis and early warning optimization of the application is shown in the figure. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by the ordinary skilled in the art without creative labor fall within the protection scope of the application.

[0020] Referring to Figure 1 The application provides a hazardous waste whole life cycle early warning and management method step flowchart based on multi-source data, which comprises the following steps: S1. sequentially storing the weighed and packaged hazardous waste in the warehouse according to the configuration information, updating the upper limit value of the temperature of the temporary storage facility when each hazardous waste is successfully stored in the warehouse, continuously storing and updating until there is no hazardous waste to be stored in the warehouse, and ending the storage; S2. continuously monitoring the temperature of the temporary storage facility after the storage is ended, acquiring and analyzing the monitoring data of each temperature sensor, and determining whether to execute the hazardous waste hazard source analysis process based on the analysis result; and S3. acquiring the hazardous waste hazard source condition based on the hazardous waste hazard source analysis result, and updating the hazardous waste conflict attribute set of the abnormal hazardous waste.

[0021] Referring to Figure 2As shown, the figure is a schematic diagram of the hazardous waste storage management and intelligent temperature control process of the present application. In the figure, information matching is first performed through storage scanning: if the matching is successful, the storage is completed and the temporary storage facility conflict attribute table is updated, then the system calculates the individual safety temperature and determines the preliminary control upper limit; if the matching fails, the resource warning mechanism is triggered immediately. In the temperature control stage, the system determines the final set temperature through comparison analysis and updates the temperature control system in real time, realizing the whole-process closed-loop monitoring from storage management to intelligent temperature control.

[0022] Specifically, the weighed and packaged hazardous waste is sequentially stored according to the configuration information, and the specific storage process is as follows: S11. An intelligent management terminal and an industrial scale are deployed at the source of hazardous waste generation, the hazardous waste is weighed, packaged and provided with an electronic tag, the source and weight information of the hazardous waste are registered through the intelligent management terminal, the complete attribute information and the hazardous waste conflict attribute set are automatically retrieved and extracted from the attribute database to form configuration information; S12. The intelligent management terminal generates a unique identification code by combining the source information, weight information, attribute information and conflict attribute set with time data, and scans the electronic tag of the hazardous waste to complete the binding; S13. The hazardous waste after completing the electronic tag binding is scanned for storage, the unique identification code is obtained by scanning the electronic tag, and the weight information, attribute information and conflict attribute set of the hazardous waste are analyzed; S14. The storage is completed after the matching is successful, and the hazardous waste conflict attribute table of the target temporary storage facility is updated synchronously, if the matching fails, the resource warning mechanism is triggered.

[0023] In a specific embodiment, by terminal screen registration source information (including the production of plant "three synthetic line", production line responsible for the number of artificial, waste batch number F-20240527-08) and weight information (gross weight, tare weight, net weight and unit of measurement), the system automatically according to the waste category code from the attribute database retrieval and extraction of complete attribute information (including the chemical name "xylene isomer mixture", the hazard category "flammable liquid 3", the spontaneous combustion temperature 525 ℃, the volatility grade "high") and hazardous waste conflict attribute set (including the forbidden substance "strong oxidizing agent such as nitrate", the forbidden condition "high temperature and open flame", the potential reaction product "carbon dioxide and water"), all these data together constitute the complete configuration information of the batch of hazardous waste, the intelligent management terminal binds all the above information with time data (the accurate time stamp of year, month, day, hour, minute and second of warehousing operation), generates a global unique identification code "HAZ-WASTE-8X7B2T9R" through hash algorithm, and writes the code into the special UHF electronic tag, and then firmly pastes the tag on the designated position of the hazardous waste packaging barrel. The packaging barrel is transported to the central temporary storage, and the fixed reader / writer at the entrance of the warehouse automatically scans the electronic tag. After successfully reading the unique identification code, the system analyzes all the key data embedded in it in real time, including the accurate weight reading, complete chemical attributes and conflict attribute set. The system performs core matching verification: it compares the conflict attribute set parsed with the conflict attribute table of all existing hazardous wastes in the target temporary storage in real time. The matching success conditions are: first, there is available space in the warehouse that meets the storage conditions of the hazardous waste (such as the space of the explosion-proof cabinet); second, through conflict model calculation, it is confirmed that there is no forbidden combination with all existing hazardous wastes in the warehouse (such as waste acid and expired solidifying agent). Once the two conditions are met, the system confirms the warehousing and immediately updates the warehouse conflict attribute table. The matching failure occurs under two circumstances: either there is no available storage space that meets the conditions in the warehouse, or the conflict model identifies that it has a clear reaction risk with some existing hazardous waste in the warehouse (such as detecting that there is a large amount of strong oxidizing agent sodium nitrate in the warehouse). Once the matching fails, the system immediately triggers the resource warning mechanism to notify the administrator to make manual decisions, ensuring that the hazardous waste is guided to other safe temporary storage facilities, thereby establishing a solid safety line at the source of warehousing.

[0024] Specifically, the monitoring data of each temperature sensor is acquired and analyzed, and the specific analysis process is: the monitoring data of each temperature sensor is acquired in real time, and compared with the upper limit temperature threshold value of the temporary storage facility; if the monitoring data of each temperature sensor acquired in real time is less than the upper limit temperature threshold value of the temporary storage facility, the monitoring data of each temperature sensor is continuously acquired; if the monitoring data of each temperature sensor acquired in real time is greater than or equal to the upper limit temperature threshold value of the temporary storage facility, the temperature sensor whose monitoring data is greater than or equal to the upper limit temperature threshold value of the temporary storage facility is marked as an abnormal temperature sensor, and the monitoring data of the abnormal temperature sensor is further compared with the upper limit temperature threshold value of the stored hazardous waste; if the monitoring data of the abnormal temperature sensor is less than the upper limit temperature threshold value of the stored hazardous waste, the monitoring data of the abnormal temperature sensor is further analyzed; if the monitoring data of the abnormal temperature sensor is greater than or equal to the upper limit temperature threshold value of the stored hazardous waste, the gas purification device is immediately started at the maximum operating power, and the hazardous waste hazard source analysis process is executed.

[0025] In a specific embodiment, first, the readings of the temperature sensors arranged at the key positions, i.e., the monitoring data, are collected in real time, and continuously compared with the preset overall temperature safety threshold value of the temporary storage facility, i.e., the upper limit temperature threshold value of the temporary storage facility; when all the sensor data is lower than the facility threshold value, the system maintains a normal monitoring cycle; once it is found that one or more sensor readings reach or exceed the facility safety threshold value, the sensor is immediately marked as an abnormal temperature sensor, and a hierarchical analysis mechanism is started to compare its monitoring value with the specific temperature threshold value of the stored hazardous waste, i.e., the upper limit temperature threshold value of the stored hazardous waste, for a second time: if the abnormal sensor reading is still lower than the safety threshold value of the hazardous waste, the data trend analysis mode is entered, and the temperature change law of the point is continuously tracked; but when the abnormal sensor reading breaks through the temperature threshold value of the hazardous waste, the system immediately triggers the highest level of response, starts the gas purification device to the maximum operating power, which is the rated maximum power of the gas purification device, and executes the hazardous waste hazard source special analysis process in parallel. Through this hierarchical linkage disposal mechanism, the overall control of the overall operation state of the facility is realized, and precise emergency response can be realized for specific hazardous waste storage units.

[0026] Reference Figure 3As shown, it is a hazardous waste storage temperature intelligent monitoring and emergency response process schematic diagram of the present application. In the figure, the real-time temperature of the storage environment is monitored, and all sensor data are compared and analyzed. When all sensor data are normal, the system maintains continuous monitoring state; once an abnormal temperature sensor is identified, it is immediately marked and abnormal temperature comparison is performed. If the abnormal temperature does not reach the upper limit of the hazardous waste, the system remains normal operation; if it reaches or exceeds the upper limit, the hazard source analysis is immediately performed and the gas purification device is simultaneously started to maximum power, forming a closed-loop management mechanism of hierarchical early warning and rapid emergency response.

[0027] Specifically, the temperature upper limit defining value of the temporary storage facility is updated. The specific updating process is: for the newly stored hazardous waste, the system deducts the safety buffer value calculated comprehensively from the highest allowable storage temperature in its attribute parameters, thereby accounting for the individual safety temperature of the hazardous waste; the individual safety temperature of all hazardous wastes in the current temporary storage facility is retrieved, and the minimum value is selected from them, which is established as the preliminary temperature control upper limit of the temporary storage facility; based on the updated hazardous waste conflict attribute table, all hazardous wastes in the facility are analyzed in combination to identify potential mutual reaction combinations, then according to the mass, quantity and spatial distribution parameters of the conflict substances, the overall conflict intensity of the facility is quantified, and a facility level risk compensation coefficient is calculated; the preliminary temperature control upper limit is subtracted by the risk compensation coefficient to calculate the final set temperature of the temporary storage facility, and the parameters of the temperature control system are updated.

[0028] In a specific embodiment, in a hazardous waste intelligent management system, when a batch of new chemical raw material waste barrels arrives and is planned to be stored in temporary storage warehouse No. B-12, an automatic process for dynamically updating the temperature safety threshold of the warehouse is started. The system first scans the electronic tags of the new batch of waste barrels to obtain their key attributes, and identifies that the main component is epoxy resin, and the maximum allowed storage temperature indicated in the attribute parameters is 50°C. The system does not directly adopt this value, but starts a complex safety buffer calculation: it calls the packaging data of the waste barrel (confirms that it is a sealed steel barrel with certain heat insulation), the total mass of this time, the abnormal event record of this category of substances in the past year (it is found that there was a record of pressure rise in the barrel due to temporary temperature rise), and evaluates the possibility of non-contact interaction with the existing substances in the warehouse (such as a certain acid curing agent) (such as reducing the overall thermal stability when coexisting). After comprehensively considering these factors, the system calculates the applicable safety buffer value at this time through the built-in algorithm, and the individual safety temperature of the batch of epoxy resin waste barrels is 50°C-7°C=43°C; then, the system performs a panoramic scan on all storage units in warehouse B-12, retrieves and lists all current individual safety temperatures, including the newly stored epoxy resin waste barrels (43°C), the waste engine oil stored in the corner of the warehouse (individual safety temperature 48°C), and a small amount of waste activated carbon stored on the other side (individual safety temperature 65°C). By comparison, the system automatically sets the lowest value among them, i.e. the 43°C of the newly stored epoxy resin waste barrels, as the preliminary temperature control upper limit of the temporary storage facility; however, the process does not end. The system then enters the collaborative risk assessment stage, which calls the latest "hazardous waste conflict attribute table" to quickly pair and analyze the combinations of all substances in the warehouse. The model identifies a potential risk: the newly stored epoxy resin and the small amount of isocyanate waste (a curing agent) pre-existing in the warehouse are judged as "potential reaction combination" although they belong to different shelves. The system further calculates the inherent reaction risk of epoxy resin and isocyanate, the real-time mass of both, the 3-meter spatial distance between them, and the environmental ventilation parameters, and calculates the overall conflict intensity index of the facility by weighting the multi-dimensional data through a weighted comprehensive algorithm, thereby quantitatively calculating an overall conflict intensity index of the facility as 3.1 (based on a scale of 0-5). According to the preset conversion formula, this conflict intensity index is mapped to a specific risk compensation coefficient, which is 1.5°C in this case. This compensation coefficient aims to offset the potential synergistic heating risk caused by the coexistence of hazardous waste. Finally, the system performs the core operation: subtracting the risk compensation coefficient 1.5°C from the preliminary temperature control upper limit 43°C, the final set temperature of the B-12 temporary storage warehouse is 41.5°C. This new threshold is immediately issued to all temperature control and monitoring units of the warehouse, replacing the old set value.From this moment, the real-time data of all temperature sensors will be compared with this updated, more accurate safety benchmark of 41.5℃, ensuring that the safety management level of the entire temporary storage facility is always dynamically matched with the real risk corresponding to the hazardous waste composition in the warehouse.

[0029] Referring to Figure 4 As shown in the figure, the current temperature deviation value is first calculated, and the corresponding power increase coefficient is matched to adjust the running state of the purification device. The system continuously monitors the temperature drop slope and makes a judgment: if the slope does not meet the preset requirement, switch to the maximum power mode and perform hazardous source analysis; if the slope meets the requirement, enter the buffer monitoring stage. During the buffer period, if the temperature returns to normal, the abnormal flag is removed, otherwise continue to monitor continuously, and finally the system returns to real-time temperature monitoring state, forming a complete intelligent closed-loop management of temperature dynamic regulation, buffer verification and state recovery.

[0030] Further, the monitoring data of the abnormal temperature sensor is further analyzed, and the specific analysis process is: obtaining the temperature deviation value of the abnormal temperature sensor, matching the increase coefficient of the running power of the gas purification device from the attribute database based on the temperature deviation value of the abnormal temperature sensor, multiplying the increase coefficient of the running power of the gas purification device with the original running power of the gas purification device to obtain the running power increase value of the gas purification device, and adding the running power of the original gas purification device to increase the running power of the gas purification device; continuously obtaining the monitoring data of the abnormal temperature sensor after the increase adjustment, obtaining the monitoring value drop slope of the monitoring data of the abnormal temperature sensor, if the monitoring value drop slope is greater than or equal to the preset defined monitoring value drop slope in the attribute database, continue to obtain the monitoring data of the abnormal temperature sensor within the preset buffer period, if the monitoring data of the abnormal temperature sensor is less than the temperature upper limit defined value of the temporary storage facility within the preset buffer period, the abnormal flag of the abnormal temperature sensor is removed, and the monitoring data of each temperature sensor is continued to be obtained; if the monitoring data of the abnormal temperature sensor is not less than the temperature upper limit defined value of the temporary storage facility within the preset buffer period, the gas purification device is immediately started with the maximum running power, and the hazardous waste hazard source analysis process is executed at the same time; if the monitoring value drop slope is less than the preset defined monitoring value drop slope in the attribute database, the gas purification device is immediately started with the maximum running power, and the hazardous waste hazard source analysis process is executed at the same time.

[0031] The above-mentioned preset buffer period refers to the safety time window of the gas purification device preset in the attribute database.

[0032] In one specific embodiment, in the automated monitoring system of a hazardous waste temporary storage facility, when the system identifies that a certain abnormal temperature sensor has its reading exceeding the overall threshold of the temporary storage facility but has not yet reached the specific dangerous critical value of the stored hazardous waste, a set of fine power adjustment and state recovery procedures will be started. The system will first calculate the temperature deviation value of the abnormal sensor (i.e. the temperature deviation value of the temperature upper limit value of the stored hazardous waste minus the monitoring data of the abnormal temperature sensor), and query the attribute database built-in the system according to this deviation value. The database pre-stores the corresponding gas purification device power increase coefficient of different temperature deviation intervals, for example, a smaller deviation value corresponds to a fine adjustment coefficient of 1.2 times, and a deviation value close to the upper limit of the hazardous waste matches a coefficient of 1.5 times or higher. The system multiplies the matched increase coefficient with the current basic operating power of the purification device to obtain the power value that needs to be increased, and then superimposes the original power to realize the accurate and progressive increase of the operating power of the purification device, aiming to control the temperature back to the safe interval with the most appropriate energy consumption. After the power increase adjustment is completed, the system does not stop the judgment, but continues to track the reading changes of the abnormal temperature sensor and calculates the descending slope of its monitoring value in real time. By collecting the reading of the temperature sensor at fixed time intervals (such as every second or every few seconds) and recording the time stamp and temperature value corresponding to each reading, the effectiveness of the control measures is evaluated. The system compares the calculated actual descending slope with the pre-set "defined monitoring value descending slope" safety benchmark in the attribute database: if the actual descending slope is greater than or equal to the pre-set value, it means that the temperature is rapidly falling back to the safe range, and the system immediately starts a pre-set buffer time period (this is a safety time window pre-defined in the database for observing whether the working condition is stable, for example, 10 minutes). During this buffer time, if the system monitors that the data of the sensor is continuously stable below the temperature upper limit value of the temporary storage facility, it is determined that the risk has been eliminated, the abnormal mark of the sensor is automatically removed, and the whole system returns to the regular round-robin monitoring state of all sensors; otherwise, if its reading exceeds or reaches the facility upper limit again within the buffer time, the system will also remove its abnormal mark, but will continue to perform global monitoring. However, if in the first comparison, the calculated actual descending slope is less than the pre-set safety benchmark, it means that the current temperature rising trend has not been effectively contained, and the risk is still increasing, the system will immediately abandon the progressive adjustment strategy, and instead start the gas purification device at the maximum operating power, and simultaneously trigger the highest priority hazardous waste dangerous source analysis process to deal with the emergency situation.

[0033] Reference Figure 5As shown, it is a schematic diagram of the multi-stage hazardous source analysis and early warning optimization process of the hazardous waste of the application. In the figure, the property of the abnormal unit is first determined. When a single unit abnormality is identified, the system performs self-reaction analysis and retrieves attribute information to determine the key factors; when there are multiple unit abnormalities, co-reaction analysis is performed and the reaction product combination simulation verification is performed. Both analysis paths dynamically update the conflict attribute information set, and based on this, the global early warning algorithm is optimized, thereby realizing the precise identification of different hazardous source types, continuous enhancement of analysis modeling and early warning ability, and closed-loop management.

[0034] Specifically, the hazardous waste hazard source analysis process is executed, and the specific execution process is: S21: data fusion and hazardous source spatial positioning based on multi-source sensor data, obtaining the hazardous source confidence score of each storage unit in the temporary storage facility, and preliminarily determining the hazardous source property according to the score distribution mode. If the score of one unit is significantly higher than that of all other units, it is determined that there is a single hazardous waste unit abnormality, and the hazardous waste unit is marked as the main body abnormal hazardous waste unit, and step S22 is entered; if the scores of multiple units are significantly high and close to each other, it is determined that there are multiple hazardous waste unit abnormalities, and step S23 is entered; S22: execute single hazardous waste self-reaction analysis process, identify the main hazardous waste unit, retrieve all attribute information, and integrate other hazardous waste attribute information and real-time monitoring data in the facility, analyze and lock the key factors and other hazardous waste that promote the occurrence of the self-reaction;

[0035] S23: execute multiple hazardous waste co-reaction analysis process, identify potential reaction product combination, simulate and verify reaction path, and analyze new attributes of reaction products and compatibility of other hazardous waste in the facility, identify new risks generated thereby; S24: based on the analysis conclusion of S22 or S23, dynamically update the local and cloud hazardous waste conflict attribute information set to optimize the global early warning model and risk prediction ability.

[0036] In a specific embodiment, after capturing the persistent temperature anomaly and the elevated volatile organic compound concentration in the No. 7 hazardous waste staging area, the hazardous source analysis process is immediately initiated. The system first performs multi-source sensor data fusion: by integrating the gradient distribution data of the twelve temperature sensors, the real-time readings of the four gas concentration monitoring points, and the thermal radiation distribution captured by the infrared thermal imager in this area, the system calculates the hazardous source confidence score for each storage unit. The score uses a weighted algorithm, in which the temperature anomaly weight accounts for 50%, the gas concentration anomaly weight accounts for 30%, and the thermal radiation intensity weight accounts for 20%. After calculation, the nitrocellulose waste unit stored in shelf B-05 obtains a score of 92.3, while the highest score of the adjacent unit is only 41.5. The main unit score (92.3) is more than 2.2 times the score of the second highest unit (41.5), and the absolute score difference exceeds 50 points. Based on this huge gap, the system determines that there is a single hazardous waste unit anomaly, as the score of a single unit is significantly higher than that of all other units. The system determines that this is a single hazardous waste unit anomaly, and determines B-05 unit as the main abnormal hazardous waste unit. The system immediately executes the single hazardous waste self-reaction analysis process and retrieves all attribute parameters of the B-05 unit. The data shows that the self-ignition temperature of the nitrocellulose waste is 42°C, and the packaging sealing level of the nitrocellulose waste is C level (there is a small amount of air permeability). By analyzing the environmental monitoring data in the facility, the system finds that the environmental humidity is continuously higher than 75% on that day, and the waste lubricating oil stored in the C-08 unit adjacent to the B-05 unit is undergoing slow oxidation and heat release. The system establishes a heat conduction model for simulation and confirms that the heat generated by the C-08 unit is conducted to the B-05 unit through the metal shelf, and the environmental humidity accelerates the thermal decomposition process of the nitrocellulose. This comprehensive analysis locks the key factors that promote the occurrence of self-reaction: adjacent heat source, unsuitable temperature conditions and excessive environmental humidity. Based on this analysis conclusion, the system dynamically updates the hazardous waste conflict attribute information set: in the local and cloud databases, the storage condition requirements of nitrocellulose waste are added with the clause "must maintain a safe distance of at least 2 meters from any exothermic substance", and "environmental humidity exceeding 70%" is added as a risk factor that needs special monitoring. These updates will be synchronized in real time to all connected hazardous waste management facilities for optimizing the prediction accuracy and response strategy of the global early warning model. At the same time, the system automatically generates an emergency disposal plan for the No. 7 staging area, including immediate adjustment of storage layout and strengthening of environmental parameter monitoring.

[0037] Specifically, the hazardous source confidence score of each storage unit in the staging facility is obtained, and the specific acquisition process is: obtaining the readings of abnormal temperature sensors and their physical positions, the gas types and concentration readings identified by each combustible gas detector, and the state signals of the leakage sensor.

[0038] By introducing weighted contribution coefficients to quantify the weighted contribution of normalized temperature data, normalized gas concentration data, and leakage indicator factors to the hazard confidence score of each storage unit, the hazard confidence score of each storage unit is obtained by coupling the weighted contribution coefficients. The specific expression is as follows:

[0039]

[0040] Where Score□ is the hazard confidence score for each storage cell, Norm(T□) is the normalized value of the temperature data for the i-th storage cell, Norm(G□) is the normalized value of the gas concentration data for the i-th storage cell, and δ L is the leakage indicator factor (1 for leakage, 0 for no leakage), w□ is the weight contribution coefficient corresponding to the normalized value of temperature data, wG is the weight contribution coefficient corresponding to the normalized value of gas concentration data, wL is the weight coefficient corresponding to the leakage indicator factor, i = 1, 2, 3, ..., N, N is the total number of storage units.

[0041] In a specific embodiment, in the intelligent monitoring system of the hazardous waste temporary storage facility, when the alarm condition is triggered in storage area 7, the system immediately initiates a spatial location analysis of the hazardous source. Taking the nitrocellulose waste storage unit numbered B-05 in this area as an example, the system first collects real-time data from three key dimensions: the readings of the three temperature sensors closest to the unit are 45°C, 43°C, and 47°C, respectively; the combustible gas detector detects that the concentration of volatile organic compounds has reached 85% of the lower explosive limit; and the pressure vessel leakage sensor associated with the unit is in positive condition. The system standardizes these multi-source heterogeneous data: the temperature readings are compared with the facility safety threshold (50°C) to obtain the temperature normalization value Norm(T□) = 0.86; the gas concentration readings are compared with the alarm threshold (100% LEL) to obtain the gas concentration normalization value Norm(G□) = 0.85; and the leakage indication factor δ... L The value is set to 0. Based on the weight allocation scheme obtained from training with historical accident data, the system assigns weight coefficients w□ = 0.6, w to temperature data, gas concentration data, and leakage status, respectively. G =0.3, w L =0.1. Through coupled calculation, the system obtained the hazard source confidence score for unit B-05: Score□=0.6×0.86+0.3×0.85+0.1×0=0.516+0.255+0=0.771. This score is significantly higher than that of adjacent units (all scores are below 0.4). Combined with the obvious unimodal characteristic of the score distribution, the system accurately identified unit B-05 as the main abnormal hazard source, providing a precise decision-making basis for subsequent emergency response. This scoring mechanism based on multi-source data fusion effectively overcomes the judgment errors caused by false alarms from a single sensor.

[0042] In one specific embodiment, in the daily monitoring of the No. 9 hazardous waste temporary storage library, the system triggered both high temperature and flammable gas alarms. After the hazard source analysis process was started, the system integrated the multi-source sensor data of this library area: the temperature sensor network showed that the temperatures of two storage units, F-15 and G-07, reached 78°C and 75°C, respectively, which were much higher than those of other units (all below 45°C); the four flammable gas detectors deployed on the upper part of the warehouse all detected a sharp rise in isobutylene concentration, and formed overlapping high-concentration areas near the two units; the infrared thermal imager also captured that both units showed a significant bright red thermal spot. The system used the pre-set weighted algorithm (temperature weight 50%, gas concentration weight 30%, and thermal radiation intensity weight 20%) to calculate the hazard source confidence score for each unit. The calculation result showed that the F-15 unit (storing waste tert-butyllithium solution) scored 86.5 points, and the G-07 unit (storing waste phosphorus oxychloride) scored 84.2 points, while all other units in the library area scored less than 35 points. The scores of these two high-score units were close to each other (the absolute score difference was only 2.3 points, and both were significantly higher than the background value), fully meeting the determination condition of “multiple hazardous waste unit abnormalities”, and the system immediately entered the co-reaction analysis process (S23).

[0043] Further, the single hazardous waste self-reaction analysis process is executed, and the specific analysis process is as follows: the self-reaction characteristics of the main abnormal hazardous waste unit, the required catalyst of the main abnormal hazardous waste unit, and the reaction conditions of the main abnormal hazardous waste unit are obtained from the attribute information of the main abnormal hazardous waste unit; combined with the real-time environmental data in the facility, the environmental factors triggering or accelerating the self-reaction are analyzed and confirmed, the conflict attribute set of hazardous waste is retrieved, and it is investigated whether there is other hazardous waste in the facility that can provide the above catalyst or reaction conditions, so as to lock the external factors that aggravate the self-reaction.

[0044] In a specific embodiment, in the hazardous waste intelligent management system, when the system locks the B-09 storage unit as the "main abnormal hazardous waste unit" through the hazardous source confidence score, the deep self-reaction analysis process for the unit is immediately started. The system first extracts all the attribute information of the unit from its electronic tag, the key data including: the waste is "diisopropyl benzene peroxide", its self-reaction characteristic is "thermal decomposition", it will decompose violently and release heat when the temperature exceeds 65°C; the catalyst required for the decomposition reaction is "acid and metal ion"; the conditions for accelerating the reaction include "heat" and "friction", and the system immediately retrieves the real-time environmental data flow around the B-09 unit for analysis. The temperature sensor data shows that the unit has been in a high temperature environment of 58-61°C for the past hour, although it has not reached its decomposition threshold of 65°C, but it has constituted a serious heat condition. By comparing the heat source distribution map in the facility, the system finds that C-12 area, which is only one wall away from B-09 unit, is a small power distribution room, and the heat dissipation of which has caused the temperature background value of the adjacent shelf to rise by about 15°C, thus confirming that "continuous heating" is the primary environmental factor that triggers the intensification of its self-reaction tendency. Then, the system initiates a query to the central database to retrieve the "hazardous waste conflict attribute set" to check whether there are other hazardous wastes in the facility that can provide acid or metal ion catalysts. The search result returns quickly: D-03 unit, which is only 5 meters away from B-09 unit, stores "waste iron-containing chloride catalyst", whose attribute is clearly marked as "release iron ions when wet"; at the same time, E-07 unit on the same shelf stores "waste acid solution". The system immediately marks the two as high-risk associated units. The analysis model points out that once the acid mist of "waste acid solution" or the dust of "iron-containing chloride" comes into contact with the peroxide of B-09 unit due to accidental circumstances (such as package damage), it will greatly reduce the activation energy of the decomposition reaction, and the actual trigger temperature of the violent decomposition will be greatly reduced from 65°C to the current environmental temperature range, thus locking these two units as the key external factors that intensify the self-reaction risk this time. Based on this conclusion, the system generates an emergency disposal plan: immediately transfer B-09 unit to a low-temperature, isolated area, and conduct an emergency check on the sealing condition of D-03 and E-07 units.

[0045] Specifically, multiple hazardous waste co-reaction analysis processes are performed, and the specific analysis process is: according to the hazardous waste conflict attribute set and the real-time sensor data, all the interacting hazardous waste combinations are identified; the physical and chemical properties of the reaction products are analyzed, and the compatibility of these new properties with all other hazardous wastes in the facility is evaluated, and the triggered chain risk is predicted.

[0046] In a specific embodiment, when multiple storage units simultaneously exhibit abnormalities in the central monitoring system of the hazardous waste temporary storage facility, the system automatically executes a co-reaction analysis process. Taking the actual monitoring of the simultaneous temperature abnormalities and rising gas concentration in the F-07 unit (acidic waste liquid, pH <1) and the G-12 unit (cyanide-containing waste) as an example, the system first performs matching retrieval from the hazardous waste conflict attribute set and quickly identifies that the combination belongs to the "strong acid and cyanide" high-risk compatibility. Contact between the two will immediately react to generate highly toxic hydrogen cyanide gas. The system then calls the chemical substance database to analyze the key attributes of the reaction product: hydrogen cyanide gas has high volatility, high toxicity (IDLH concentration is only 50 ppm), and its vapor density is lower than air, making it easy to accumulate in the upper space. Based on these new attributes, the system starts the whole facility compatibility assessment: it is found that the fire sprinkler system installed at the top of the current storage area, if started, will generate hydrogen cyanide acid by combining with the water flow, exacerbating equipment corrosion; at the same time, monitoring data shows that the generated hydrogen cyanide is spreading throughout the facility through the air duct, and the D-05 unit located downwind stores strong oxidizing agent "waste sodium hypochlorite". The system predicts through reaction path simulation that if hydrogen cyanide and sodium hypochlorite mix in the ventilation duct, an oxidation reaction will occur, causing a fire or even an explosion. This series of analysis enables the system to accurately predict the "toxic gas diffusion → encounter oxidizing agent to trigger fire" chain risk and immediately start targeted emergency response, including closing the ventilation system and isolating the relevant area.

[0047] Specifically, the hazardous waste conflict attribute set of the abnormal hazardous waste is updated, and the specific updating process is: comparing the newly added conflict relationship with the existing knowledge base, identifying and resolving logical conflicts; performing data integrity verification to ensure that all necessary attribute parameters are complete and accurate; for key or doubtful new knowledge, submit to the cloud for review to standardize the incremental knowledge; update the standardized incremental knowledge to the local attribute database and the cloud central knowledge base, and establish an independent version identifier for each update to realize complete tracing and version control of the knowledge evolution process; recalibrate the key parameters in the global early warning algorithm, including adjusting the weight coefficients in the hazardous source confidence scoring algorithm, and optimizing the conflict judgment rules of the storage matching module.

[0048] In a specific embodiment, in the intelligent management system of the hazardous waste temporary storage facility, when the system first identifies the risk of exothermic reaction between "ammonium nitrate" and "metallic zinc powder" in a high-humidity environment through co-reaction analysis, the dynamic updating process of the conflict attribute set is immediately started. The system first compares the newly added conflict relationship with the existing knowledge base and finds that the original rule base only marks the dangerous combination of ammonium nitrate and some strong reducing agents, but does not explicitly include the interaction with metallic zinc powder. Through logical conflict detection, it is confirmed that this is valid new knowledge rather than redundant or contradictory information. Then the system performs strict data integrity verification and automatically checks the necessary parameters of the conflict relationship, including reaction type (redox), trigger condition (environmental humidity > 80%), reaction product (nitrogen oxides, ammonia gas) and risk level (high risk) and other key fields, to ensure that all information is complete and meets the data specification. Since this combination has a high risk and is recorded for the first time, the system uploads it as incremental knowledge to be reviewed to the cloud expert review platform, and the domain experts confirm the reaction mechanism and standardize the description method. After review, this set of standardized incremental knowledge is updated to the local database and the cloud central knowledge base, and an independent version identifier "conflict set_v3.2.1" is established for this update, complete recording of update time, new content and reviewer, realizing the whole process traceability of knowledge evolution. Based on the updated knowledge base, the system automatically recalibrates the global early warning algorithm: the weight coefficient w□ of the humidity parameter is increased from 0.2 to 0.3, and the judgment rule "ammonium nitrate and active metal powder need to be stored in separate zones" is added in the storage matching module, so that the system can more accurately warn similar risk combinations in the future, completing the closed-loop optimization from a single abnormal event to the improvement of the system's defense capability.

[0049] The attribute database is used for storing parameters of the hazardous waste full life cycle early warning and management method based on multi-source data.

[0050] The above is only an example and description of the structure of the present application. Those skilled in the art can make various modifications or supplements to the described specific embodiments or use similar ways to replace, as long as they do not deviate from the structure of the invention or exceed the scope defined by the invention, which should belong to the protection scope of the present application.

Claims

1. A method for early warning and management of hazardous waste throughout its entire life cycle based on multi-source data, characterized in that: include: S1. Weigh and package the hazardous waste into the warehouse in sequence according to the configuration information. When each piece of hazardous waste is successfully put into the warehouse, update the upper limit value of the temperature limit of the temporary storage facility. Continue to put into the warehouse and update until there is no more hazardous waste to be put into the warehouse, and the storage ends. S2. After the storage is completed, the temperature of the temporary storage facility is continuously monitored, the monitoring data of each temperature sensor is acquired and analyzed, and the hazardous waste hazard source analysis process is determined based on the analysis results. S3. Obtain the status of hazardous waste sources based on the hazardous waste hazard source analysis results, and then update the hazardous waste conflict attribute set of abnormal hazardous waste.

2. The method for early warning and management of hazardous waste based on multi-source data throughout its entire life cycle, as described in claim 1, is characterized in that: The weighed and packaged hazardous waste is sequentially stored in the warehouse according to the configuration information. The specific storage process is as follows: S11. Deploy intelligent management terminals and industrial scales at hazardous waste generation sources to weigh, package, and attach electronic tags to hazardous waste. Register the source and weight information of hazardous waste through the intelligent management terminal, and automatically retrieve and extract complete attribute information and hazardous waste conflict attribute sets from the attribute database to form configuration information. S12. The intelligent management terminal combines source information, weight information, attribute information, and conflict attribute set with time data to generate a unique identification code, and scans the electronic tag of hazardous waste to complete the binding; S13. After the electronic tag binding is completed, the hazardous waste is scanned and put into the database. The electronic tag is scanned to obtain the unique identification code, and the weight information, attribute information and conflict attribute set of the hazardous waste are parsed. S14. After a successful match, the data is entered into the database, and the hazardous waste conflict attribute table of the target temporary storage facility is updated synchronously. If the match fails, a resource warning mechanism is triggered.

3. The method for early warning and management of hazardous waste throughout its entire life cycle based on multi-source data according to claim 1, characterized in that: The specific update process for the temperature upper limit of the temporary storage facility is as follows: For newly received hazardous waste, the system calculates the individual safe temperature of the hazardous waste by deducting the safety buffer value calculated comprehensively from the maximum allowable storage temperature in its attribute parameters. Retrieve the individual safe temperatures of all hazardous wastes in the current temporary storage facility, select the minimum value, and establish this minimum value as the initial upper limit for temperature control of the temporary storage facility; Based on the updated hazardous waste conflict attribute table, a combination analysis of all hazardous wastes in the facility is conducted to identify potential combinations of mutual reactions. Subsequently, based on the mass, quantity and spatial distribution parameters of the conflicting materials, the overall conflict intensity of the facility is quantified, and a risk compensation coefficient at the facility level is calculated. Subtract the risk compensation coefficient from the initial temperature control upper limit to calculate the final set temperature of the temporary storage facility, and update the parameters of the temperature control system.

4. The method for early warning and management of hazardous waste based on multi-source data throughout its entire life cycle, as described in claim 1, is characterized in that: The specific analysis process for acquiring and analyzing the monitoring data from each temperature sensor is as follows: Real-time monitoring data from each temperature sensor is acquired and compared with the upper temperature limit of the temporary storage facility. If the real-time monitoring data from each temperature sensor is lower than the upper limit of the temporary storage facility's temperature, then the monitoring data from each temperature sensor will continue to be acquired. If the real-time monitoring data of each temperature sensor is greater than or equal to the upper limit of the temperature limit of the temporary storage facility, the temperature sensor whose monitoring data is greater than or equal to the upper limit of the temperature limit of the temporary storage facility is marked as an abnormal temperature sensor, and the monitoring data of the abnormal temperature sensor is further compared with the upper limit of the temperature limit for storing hazardous waste. If the monitoring data of the abnormal temperature sensor is less than the upper limit of the temperature for storing hazardous waste, it is determined that the hazardous waste hazard source analysis process will not be executed, and the monitoring data of the abnormal temperature sensor will be further analyzed. If the monitoring data from the abnormal temperature sensor is greater than or equal to the upper limit of the temperature for storing hazardous waste, then the hazardous waste hazard source analysis process is initiated, and the gas purification device is immediately activated at maximum operating power.

5. The method for early warning and management of hazardous waste based on multi-source data throughout its entire life cycle, as described in claim 4, is characterized in that: The further analysis of the abnormal temperature sensor's monitoring data involves the following specific analysis process: The temperature deviation value of the abnormal temperature sensor is obtained. Based on the temperature deviation value of the abnormal temperature sensor, the increase coefficient of the operating power of the gas purification device is matched from the attribute database. The increase coefficient of the operating power of the gas purification device is multiplied by the original operating power of the gas purification device to obtain the increase value of the operating power of the gas purification device. This value is then added to the original operating power of the gas purification device to increase and adjust the operating power of the gas purification device. After increasing the adjustment, the monitoring data of the abnormal temperature sensor is continuously acquired. The slope of the decrease of the monitoring value of the abnormal temperature sensor is acquired. If the slope of the decrease of the monitoring value is greater than or equal to the preset threshold slope of the monitoring value in the attribute database, the monitoring data of the abnormal temperature sensor is continuously acquired within the preset buffer time period. If the monitoring data of the abnormal temperature sensor is continuously less than the upper limit threshold value of the temporary storage facility within the preset buffer time period, the abnormality mark of the abnormal temperature sensor is removed, and the monitoring data of each temperature sensor is continuously acquired. If the monitoring data of the abnormal temperature sensor does not remain below the upper limit of the temperature of the temporary storage facility within the preset buffer period, the gas purification device will be activated immediately at maximum operating power, and the hazardous waste hazard source analysis process will be executed simultaneously. If the slope of the monitoring value decrease is less than the preset threshold slope of the monitoring value decrease in the attribute database, the gas purification device will be activated immediately at maximum operating power, and the hazardous waste hazard source analysis process will be executed simultaneously. The preset buffer time period refers to the safe time window preset in the attribute database for observing the gas purification device.

6. The method for early warning and management of hazardous waste based on multi-source data throughout its entire life cycle, as described in claim 4, is characterized in that: The specific execution process for the hazardous waste hazard source analysis procedure is as follows: S21: Based on multi-source sensor data, perform data fusion and spatial positioning of hazardous sources, obtain the hazardous source confidence score of each storage unit in the temporary storage facility, and preliminarily determine the nature of the hazardous source according to the score distribution pattern. If the score of one unit is significantly higher than that of all other units in the score distribution, it is determined that a single hazardous waste unit is abnormal, and the hazardous waste unit is marked as the main abnormal hazardous waste unit, and proceed to step S22. If the scores of multiple units are significantly higher and close to each other, it is determined that multiple hazardous waste units are abnormal, and proceed to step S23. S22: Perform a single hazardous waste self-reaction analysis process, identify the main hazardous waste unit, retrieve all its attribute information, and combine the attribute information of other hazardous wastes in the facility with real-time monitoring data to analyze and identify the key factors that promote the self-reaction and other hazardous wastes. S23: Perform co-reaction analysis of multiple hazardous wastes, identify potential reactant combinations, simulate and verify reaction pathways, and analyze the new properties of reaction products and their compatibility with other hazardous wastes in the facility, and identify new risks arising therefrom; S24: Based on the analysis conclusions of S22 or S23, dynamically update the local and cloud-based hazardous waste conflict attribute information sets to optimize the global early warning model and risk prediction capabilities.

7. The method for early warning and management of hazardous waste based on multi-source data throughout its entire life cycle, as described in claim 6, is characterized in that: The specific process for obtaining the hazard source confidence score of each storage unit within the temporary storage facility is as follows: Acquire the readings and physical location of the abnormal temperature sensor, the types and concentrations of gases identified by each combustible gas detector, and the status signals of the leak sensor; By introducing weighted contribution coefficients to quantify the weighted contribution of normalized temperature data, normalized gas concentration data, and leakage indicator factors to the hazard confidence score of each storage unit, the weighted contribution levels are coupled to obtain the hazard confidence score of each storage unit.

8. The method for early warning and management of hazardous waste based on multi-source data throughout its entire life cycle, as described in claim 6, is characterized in that: The specific analysis process for performing a single hazardous waste self-reaction analysis procedure is as follows: The self-reaction characteristics, required catalysts, and reaction conditions of the main abnormal hazardous waste unit are obtained from the attribute information of the main abnormal hazardous waste unit. By combining real-time environmental data within the facility, we can analyze and identify the environmental factors that trigger or accelerate the self-reaction, retrieve the hazardous waste conflict attribute set, and investigate whether there are other hazardous wastes within the facility that can provide the aforementioned catalysts or reaction conditions, thereby identifying external factors that exacerbate the self-reaction.

9. The method for early warning and management of hazardous waste throughout its entire life cycle based on multi-source data according to claim 6, characterized in that: The process of performing co-reaction analysis of multiple hazardous wastes is as follows: Based on the hazardous waste conflict attribute set and real-time sensor data, all interacting hazardous waste combinations were identified; Analyze the physicochemical properties of the reaction products and assess their compatibility with all other hazardous wastes in the facility, predicting any cascading risks that may result.

10. The method for early warning and management of hazardous waste based on multi-source data throughout its entire life cycle, as described in claim 1, is characterized in that: The specific update process for the hazardous waste conflict attribute set of the updated abnormal hazardous waste is as follows: The newly added conflicting relationships are compared with the existing knowledge base to identify and resolve logical conflicts. Data integrity is verified to ensure that all necessary attribute parameters are complete and accurate. For critical or questionable new knowledge, it is submitted to the cloud for review to standardize incremental knowledge. Standardized incremental knowledge is synchronously updated to the local attribute database and the cloud central knowledge base, and an independent version identifier is established for each update, so as to achieve complete traceability and version control of the knowledge evolution process; Key parameters in the global early warning algorithm were recalibrated, including adjusting the weight coefficients in the hazard source confidence scoring algorithm and optimizing the conflict determination rules of the database matching module.

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