An inventory facility emergency disposal intelligent matching and decision method based on knowledge graph constraint reasoning

Abstract

This invention provides an intelligent matching and decision-making method for emergency response to existing facilities based on knowledge graph constraint reasoning, belonging to the field of intelligent decision-making technology for environmental emergency response. The invention first acquires basic emergency response data and constructs a knowledge graph for emergency response to existing facilities; it maps the scene feature information of the target existing facilities into target scene entities and a set of constraint rules; based on the knowledge graph, it performs constraint reasoning to filter out response technologies that do not meet safety and environmental constraints, obtaining a set of candidate response technologies; then, it combines and filters technologies according to the order of response stages and the connection relationship between response technologies, generating staged response technology combinations and emergency response decision schemes, and updating the knowledge graph or constraint rule set based on feedback from response implementation. This invention can reduce the workload of manually verifying the stage connections after isolated matching of individual technologies.

Description

Technical Field

[0001] This invention relates to the field of intelligent decision-making technology for environmental emergency response, and in particular to an intelligent matching and decision-making method for emergency response of existing facilities based on knowledge graph constrained reasoning. Background Technology

[0002] Hazardous waste and general industrial solid waste storage facilities often originate from historical stockpiles, remnants of production shutdowns, improper temporary storage management, or illegal dumping and landfilling. These facilities typically involve waste residue dumps, sludge storage sites, waste liquid storage ponds, tailings ponds, and informal landfill sites, and are prone to risks such as seepage prevention failures, inadequate rainwater and wastewater separation, leachate overflow, dust dispersion, and pollutant migration. The "Standard for Pollution Control of Hazardous Waste Storage" GB 18597—2023 has already set requirements for the site selection, pollution control, operation management, environmental monitoring, and environmental emergency response of hazardous waste storage facilities; the "Standard for Pollution Control of General Industrial Solid Waste Storage and Landfill" GB 18599—2020 also sets environmental protection requirements for the site selection, construction, operation, closure, land reclamation, and monitoring of general industrial solid waste storage sites and landfills. In their article "Current Status and Suggestions for Hazardous Waste Management in my country" (Journal of Environmental Engineering Technology, Vol. 3, No. 1, 2013), Wang Qi, Huang Qifei, and others pointed out that hazardous waste management requires strengthening source management and improving the capacity for harmless utilization and disposal technologies. The aforementioned regulations and research indicate that the disposal of existing facilities cannot solely focus on pollutant removal results; it must also simultaneously meet requirements for pollution control, on-site operations, environmental monitoring, and emergency management.

[0003] With the increasing informatization of environmental management, the disposal of hazardous waste and general industrial solid waste has begun to organize disposal technology information through methods such as technology databases, case databases, and contingency plan databases. A common practice is to establish structured data based on fields such as facility type, pollutant type, disposal stage, technology name, equipment and materials, personnel requirements, disposal cycle, cost reference, and compliance basis, and then retrieve available disposal technologies based on the scenario characteristics entered on-site. Li Xinru, Zhou Min, et al., in "Application of Smart Environmental Protection System in Environmental Governance" (Journal of Environmental Engineering Technology, Vol. 11, No. 5, 2021), recorded that the environmental emergency information management system can combine data contingency plan systems and decision support systems to propose corresponding emergency plans. The above methods can improve the retrieval efficiency of disposal technology data and reduce the phenomenon of grassroots staff relying entirely on experience for selection.

[0004] However, existing technology or contingency plan databases typically use individual treatment technologies as the basic search object, and the matching results mainly reflect the compatibility between a particular treatment technology and facility type, pollutant type, or technology level. Emergency treatment of existing facilities often involves multiple stages, including initial risk control, rapid risk reduction, cleanup and transportation, end-of-pipe treatment, monitoring and control, and long-term restoration. There are relationships between treatment technologies at different stages, such as sequential order, process integration, material matching, compliance restrictions, and mutual exclusion. Existing matching methods are insufficient in expressing these relationships, easily yielding several individually usable treatment technologies, but making it difficult to directly determine whether these technologies can form a continuously feasible, phased treatment plan. Therefore, after the plan is generated, manual verification of technology integration and equipment / material matching is still required, affecting the efficiency and consistency of emergency response decision-making. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, the purpose of this invention is to provide an intelligent matching and decision-making method for emergency response of existing facilities based on knowledge graph constraint reasoning. Through knowledge graph constraint reasoning, the available response technologies in the target scenario are limited to a combination of phased response technologies that meet safety constraints, environmental constraints, response stage sequence, and technical connection relationships. This reduces the problem of needing manual verification of phase connection after matching individual technologies in isolation.

[0006] To achieve the above objectives, the present invention provides the following solution: A decision-making method for emergency response to hazardous and solid waste storage facilities based on knowledge graph-constrained reasoning includes: Obtain basic data on emergency response to existing hazardous and solid waste facilities, and construct a knowledge graph of emergency response to existing facilities based on the relationships between facility type, risk scenario, pollutant type, treatment stage, treatment technology, equipment and materials, compliance requirements, and case effects. Obtain the scene feature information of the target existing facilities, and map the scene feature information to the target scene entities and constraint rule set in the knowledge graph of emergency response of existing facilities; Based on the target scenario entities and the set of constraint rules, constraint reasoning is performed in the knowledge graph of emergency response to existing facilities to screen out response technologies that do not meet safety and environmental constraints, thus obtaining a set of candidate response technologies. Based on the sequence of treatment stages and the connection between treatment technologies, the candidate treatment technology set is combined and screened to generate a staged treatment technology combination; Emergency response decision-making plans are generated based on a combination of phased response technologies, and the emergency response knowledge graph or set of constraint rules for existing facilities is updated based on feedback from the implementation of the response.

[0007] Preferably, basic data on emergency response to existing hazardous and solid waste facilities are acquired, and a knowledge graph on emergency response to existing facilities is constructed based on the relationships between facility type, risk scenario, pollutant type, treatment stage, treatment technology, equipment and materials, compliance requirements, and case results. This knowledge graph includes: The basic data on emergency response to existing hazardous and solid waste facilities were organized into categories, including facility type, risk scenario, pollutant type, treatment stage, treatment technology, equipment and materials, compliance requirements, and case results. Generate graph nodes based on the following entries: facility type, risk scenario, pollutant type, treatment stage, treatment technology, equipment and materials, compliance requirements, and case effect. By connecting the nodes in the graph based on their relationships, a knowledge graph for emergency response to existing facilities can be obtained.

[0008] Preferably, the relationships between facility type, risk scenario, pollutant type, treatment stage, treatment technology, equipment and materials, compliance requirements, and case results include: The scenario attribution relationship between facility type and risk scenario; The pollution correspondence between risk scenarios and pollutant types; The applicability of technologies between pollutant types and treatment technologies; The stage attribution relationship between the treatment phase and the treatment technology; The material matching relationship between disposal technology and equipment / materials; The compliance constraints between processing technologies and compliance requirements; Case validation relationship between treatment techniques and case results.

[0009] Preferably, the scenario feature information of the target existing facilities is obtained, and the scenario feature information is mapped to the target scenario entities and constraint rule set in the knowledge graph of emergency response to existing facilities, including: Determine the target facility type, target risk scenario, target pollutant type, and target treatment needs from the scenario feature information; Match graph nodes in the knowledge graph of emergency response to existing facilities with the target facility type, target risk scenario, and target pollutant type; The matched graph nodes are identified as entities in the target scene. Generate a set of constraint rules based on the target disposal requirements.

[0010] Preferably, the constraint rule set includes: Safety constraints used to exclude disposal technologies that do not meet disposal safety requirements; Environmental constraints used to exclude treatment technologies that do not meet pollution control requirements; Time constraints used to define the response order during the handling phase; Cost constraints used to limit the scope of resource consumption in disposal; Technical constraints are used to define the compatibility between different treatment technologies.

[0011] Preferably, based on the target scenario entities and the set of constraint rules, constraint reasoning is performed in the knowledge graph of emergency response to existing facilities to filter out response technologies that do not meet safety and environmental constraints, resulting in a set of candidate response technologies, including: Starting with the target scene entity as the inference point, retrieve the disposal technologies that are related to the target scene entity in the knowledge graph of emergency response of existing facilities; The treatment techniques obtained from the search were identified as the treatment techniques to be screened out. Safety screening should be performed on the technology to be screened out in accordance with safety constraints; Environmental screening is conducted on the treatment technologies that remain after safety screening, in accordance with environmental constraints. The treatment technologies that were retained after environmental screening were identified as a set of candidate treatment technologies.

[0012] Preferably, the candidate treatment technology set is combined and screened according to the order of treatment stages and the connection between treatment technologies to generate a staged treatment technology combination, including: The candidate treatment technologies are categorized into stages according to the order of treatment stages. Based on the connection relationship, determine whether the treatment technologies in adjacent treatment stages meet the compatibility relationship; By combining processing technologies that satisfy the before-and-after compatibility relationship, a processing technology combination to be verified is obtained; Perform phased integrity checks on the combination of processing techniques to be verified; The combination of processing technologies that meets the phase integrity verification is identified as the phased processing technology combination.

[0013] Preferably, the connection between the treatment technologies includes: The compatibility between the treatment techniques used in the previous treatment stage and the treatment techniques used in the next treatment stage; Substitutability and compatibility among different treatment technologies in the same treatment phase; The material matching relationship between disposal technology and equipment / materials; The compliance constraints between handling technologies and compliance requirements.

[0014] Preferably, an emergency response decision-making plan is generated based on a combination of phased response technologies, including: The target treatment stage and target treatment technology are determined based on a combination of phased treatment technologies. Determine the corresponding equipment, materials, and compliance requirements based on the target treatment technology; Organize the target disposal technology, equipment, materials, and compliance requirements according to the phase sequence of the target disposal phase; Generate emergency response decision-making plans that include the target response phase, target response technologies, equipment and materials, and compliance requirements.

[0015] Preferably, updating the emergency response knowledge graph or constraint rule set of existing facilities based on feedback from the implementation of the response includes: Obtain feedback on the implementation of emergency response plans after their execution; Extract the disposal results, disposal cycle, resource consumption, and compliance results from the feedback on the disposal implementation; Update the case effectiveness in the emergency response knowledge graph of existing facilities based on the response results; Update the timeliness or cost constraints in the set of constraint rules based on the disposal cycle and resource consumption. Update the environmental constraints in the compliance requirements or constraint rule set of the knowledge graph for emergency response of existing facilities based on the compliance results.

[0016] The present invention discloses the following beneficial effects: (1) This invention constructs a knowledge graph of emergency response for existing facilities, which establishes the relationships between facility type, risk scenario, pollutant type, treatment stage, treatment technology, equipment and materials, compliance requirements, and case effects. This enables the dispersed technical items, case information, and compliance information to form a unified data association structure. After the target scenario enters the decision-making process, the system can determine the range of treatment technologies related to the target scenario along the relationships in the graph, reducing the workload of manually comparing each technical item from the full set. In specific implementation, the difference between the number of all treatment technology items and the number of treatment technologies in the candidate treatment technology set can be used as a statistical indicator for the amount of irrelevant technical items to be screened out.

[0017] (2) This invention maps the scene feature information of the target existing facilities into target scene entities and a set of constraint rules, enabling on-site information such as facility type, risk scenario, and pollutant type to correspond with existing data in the knowledge graph of emergency response for existing facilities. This processing method provides clear data sources and constraints for the selection of response technologies, avoiding the selection of response technologies solely based on staff experience. In specific implementation, the number of graph nodes hit by the target scene entities and the number of constraint items contained in the constraint rule set can be recorded to characterize the completeness of the target scene information entering the graph reasoning process.

[0018] (3) This invention performs constraint reasoning in the knowledge graph of emergency response to existing facilities and filters out treatment technologies that do not meet safety and environmental constraints, so that safety requirements and pollution control requirements are pre-verified before the combination of solutions. The candidate treatment technology set formed after filtering no longer contains treatment technologies that are obviously unsuitable for the safety conditions or environmental requirements of the target scenario, and the technology range for subsequent combination screening is more concentrated. In specific implementation, the number of treatment technologies filtered out by safety constraints and the number of treatment technologies filtered out by environmental constraints can be used to characterize the convergence effect of safety constraints and environmental constraints on the candidate treatment technology set, respectively.

[0019] (4) This invention combines and filters candidate disposal technologies according to the order of disposal stages and the connection relationship between disposal technologies to generate a staged disposal technology combination. This process addresses the problem in the prior art that individual technologies can be matched but the connection between multiple stages is insufficient, expanding the recommended disposal technologies from individual technologies to combinations of technologies with a stage order. In specific implementation, the completeness and continuity of the staged disposal technology combination can be characterized by the number of stages covered and the number of technology connections that satisfy the connection relationship between adjacent disposal stages, thereby reducing the need for manual supplementation of stage gaps after the scheme is generated.

[0020] (5) This invention generates emergency response decision-making schemes based on a combination of phased response technologies, and updates the existing facility emergency response knowledge graph or constraint rule set based on response implementation feedback, enabling data generated by the executed schemes to flow back to the decision-making process of subsequent similar scenarios. In specific implementation, response results, response cycles, resource consumption, and compliance results can be recorded, and the above data can be used as the basis for updating case effects, constraint rule sets, or graph relationships. Through this feedback update method, the screening and combination screening of response technologies for subsequent similar scenarios have traceable data basis, which is conducive to maintaining the consistency of expression of emergency response decision-making schemes in different batches. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A flowchart of the method provided in an embodiment of the present invention; Figure 2 A schematic diagram of a knowledge graph for emergency response to existing facilities provided in an embodiment of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] The purpose of this invention is to provide an intelligent matching and decision-making method for emergency response to existing facilities based on knowledge graph constraint reasoning. By driving the knowledge graph of emergency response to existing facilities through reasoning and screening using target scenario entities and constraint rule sets, a phased combination of response technologies that conforms to safety constraints, environmental constraints and the order of response stages is generated, thereby improving the consistency of technical connections in emergency response decision-making schemes.

[0025] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0026] Figure 1 The method flowchart provided in the embodiments of the present invention is as follows: Figure 1 As shown, this invention provides a decision-making method for emergency response to hazardous and solid waste storage facilities based on knowledge graph-constrained reasoning, including: Step 100: Obtain basic data on emergency response to existing hazardous and solid waste facilities, and construct a knowledge graph of emergency response to existing facilities based on the relationships between facility type, risk scenario, pollutant type, treatment stage, treatment technology, equipment and materials, compliance requirements and case effects; Step 200: Obtain the scene feature information of the target existing facilities, and map the scene feature information to the target scene entities and constraint rule set in the knowledge graph of emergency response of existing facilities; Step 300: Based on the target scenario entities and the set of constraint rules, perform constraint reasoning in the knowledge graph of emergency response to existing facilities, screen out the response technologies that do not meet the safety constraints and environmental protection constraints, and obtain a set of candidate response technologies; Step 400: Based on the order of treatment stages and the connection between treatment technologies, the candidate treatment technology set is combined and screened to generate a staged treatment technology combination; Step 500: Generate an emergency response decision plan based on the combination of phased response technologies, and update the emergency response knowledge graph or constraint rule set of existing facilities based on the feedback from the implementation of the response.

[0027] In this embodiment, step 100 is used to form a knowledge graph for emergency response to existing facilities. The basic data for emergency response to existing hazardous and solid waste facilities includes information on facility types, risk scenarios, pollutant types, treatment stages, treatment technologies, equipment and materials, compliance requirements, and case study results. The facility type information comes from six categories: hazardous waste temporary storage facilities, general industrial solid waste storage facilities, tailings ponds, illegal solid and hazardous waste landfills, wastewater leachate storage facilities, and informal municipal solid waste landfills. The treatment stage information is organized into six stages: initial risk control, rapid risk reduction, clearing and transportation, end-of-pipe treatment, monitoring and control, and long-term remediation. The treatment technology information uses 80 treatment technologies already included in the technology library, and the case study results use 60 emergency response cases of existing facilities. This basic data is used to form the node and relationship sources for the knowledge graph, avoiding the use of only technology names as the basis for matching treatment technologies.

[0028] In this embodiment, the basic data on emergency response to existing hazardous and solid waste facilities is organized into entries. This means breaking down data from the same source into knowledge entries that can independently participate in the representation of the graph. After entry-based organization, the following entries are obtained: facility type, risk scenario, pollutant type, treatment stage, treatment technology, equipment and materials, compliance requirements, and case effect. Facility type entries characterize the category of existing facilities; risk scenario entries characterize risk situations such as leakage, dust, leaching, discharge, volatilization, and landslides; and pollutant type entries characterize pollutants such as heavy metals, acid and alkali waste liquids, highly toxic waste, oil sludge, general solid waste, and leachate. Treatment technology entries include at least 15 pieces of information: technology name, technology level, applicable scenarios, core processes, operational procedures, equipment and materials, personnel requirements, treatment cycle, cost reference, safety precautions, environmental protection requirements, related cases, compliance basis, supporting treatment plans, and technology code. The technology code distinguishes different treatment technology entries, the technology name forms the name of the graph node, and the applicable scenarios and environmental protection requirements are used to establish subsequent relationships.

[0029] In this embodiment, graph nodes are generated based on entries for facility type, risk scenario, pollutant type, treatment stage, treatment technology, equipment and materials, compliance requirements, and case effect. Graph nodes are knowledge representation units in the existing facility emergency response knowledge graph. Each graph node corresponds to one entry and retains the entry's category, name, and necessary attributes. Facility type entries generate facility type nodes, risk scenario entries generate risk scenario nodes, pollutant type entries generate pollutant type nodes, treatment stage entries generate treatment stage nodes, treatment technology entries generate treatment technology nodes, equipment and materials entries generate equipment and materials nodes, compliance requirement entries generate compliance requirement nodes, and case effect entries generate case effect nodes. Entries with the same name, meaning, and applicable objects within the same category are merged into one graph node; entries with the same name but different applicable objects generate separate graph nodes, distinguished by entry category and technical code.

[0030] In this embodiment, connecting graph nodes based on association relationships means establishing directed connections between graph nodes based on the applicable objects, treatment stages, equipment and materials, compliance basis, and case records already recorded between entries. A scenario affiliation relationship is established between facility types and risk scenarios, representing the risk situation corresponding to a certain type of existing facility. A pollution correspondence relationship is established between risk scenarios and pollutant types, representing the pollutant objects involved in a certain risk situation. A technology applicability relationship is established between pollutant types and treatment technologies, representing the pollutant objects targeted by a certain treatment technology. A stage affiliation relationship is established between treatment stages and treatment technologies, representing the treatment stage to which a certain treatment technology belongs. A material matching relationship is established between treatment technologies and equipment and materials, representing the equipment and materials required to execute the treatment technology. A compliance restriction relationship is established between treatment technologies and compliance requirements, representing the pollution control and environmental management requirements that the treatment technology needs to meet. A case verification relationship is established between treatment technologies and case results, representing the treatment results, treatment cycle, resource consumption, and compliance results corresponding to the treatment technology.

[0031] In this embodiment, as Figure 2As shown, after generating the graph nodes and connecting the relationships, a knowledge graph for emergency response to existing facilities is obtained. This knowledge graph includes 8 types of graph nodes and 7 types of relationships. Using the treatment technology node as the intermediate connection object, it connects facility type nodes, risk scenario nodes, pollutant type nodes, treatment stage nodes, equipment and material nodes, compliance requirement nodes, and case effect nodes into a single knowledge structure. This knowledge structure allows a treatment technology to simultaneously possess associated information such as scenario source, pollutant object, treatment stage, supporting materials, compliance basis, and case results. Subsequent steps, using the target scenario entity as the starting point for reasoning, can determine the scope of treatment technologies along scenario affiliation relationships, pollution correspondence relationships, and technology applicability relationships. Furthermore, it provides a data foundation for constrained reasoning and combination screening along stage affiliation relationships, material support relationships, compliance restriction relationships, and case verification relationships.

[0032] In step 200 of this embodiment, the constraint rule set is a set of data rules formed after the scenario feature information of the target existing facility is entered into the knowledge graph of emergency response for existing facilities. It is used to limit the screening scope of response technologies and the combination relationships between them. This embodiment determines the target response needs from the scenario feature information and generates the constraint rule set by combining the response technology entries, equipment and material entries, compliance requirement entries, and case effect entries in the knowledge graph of emergency response for existing facilities. The constraint rule set includes five types of constraints: safety constraints, environmental constraints, timeliness constraints, cost constraints, and technology integration constraints. The safety constraints and environmental constraints are used for the preliminary screening of response technologies in step 300, while the timeliness constraints, cost constraints, and technology integration constraints are used for the formation and verification of the phased combination of response technologies in step 400. The target response needs are response target data extracted from the scenario feature information, including the target facility type, target risk scenario, target pollutant type, target response stage scope, and target resource conditions. The target resource conditions include three resource boundaries: cost boundary, equipment and material boundary, and personnel input boundary.

[0033] In this embodiment, the safety constraints are jointly formed by the target risk scenario, the safety precautions in the treatment technology entries, and the usage conditions in the equipment and materials entries. The safety constraints include three types of rules: prohibited use conditions, protection matching conditions, and operational conflict conditions. The prohibited use conditions are used to exclude treatment technologies that conflict with the target risk scenario; the protection matching conditions are used to determine whether the protection requirements of the treatment technology are met by the equipment and materials entries; and the operational conflict conditions are used to determine whether there is an operational relationship between different treatment technologies within the same treatment stage that prohibits simultaneous implementation. The environmental constraints are jointly formed by the target pollutant type, the compliance requirement entries, and the environmental requirements in the treatment technology entries. The environmental constraints include three types of rules: pollution control conditions, secondary pollution restriction conditions, and compliance basis matching conditions. The pollution control conditions are used to determine whether the treatment technology corresponds to the target pollutant type; the secondary pollution restriction conditions are used to exclude treatment technologies that introduce additional pollution risks; and the compliance basis matching conditions are used to determine whether the treatment technology has compliance requirement entries corresponding to the target pollutant type. This embodiment uses identifiers to represent the judgment results of individual rules. The identifier value is either 1 or 0; a value of 1 indicates that the corresponding rule is met, and a value of 0 indicates that the corresponding rule is not met. The safety pass identifier is the product of the prohibited use pass identifier, the protection matching pass identifier, and the operation compatibility pass identifier. The environmental protection pass identifier is the product of the pollution control pass identifier, the secondary pollution restriction pass identifier, and the compliance basis matching identifier. The candidate retention identifier is the product of the safety pass identifier and the environmental protection pass identifier. When the candidate retention identifier is 1, the corresponding treatment technology enters the candidate treatment technology set; when the candidate retention identifier is 0, the corresponding treatment technology is filtered out.

[0034] In this embodiment, the timeliness constraint is formed by the target disposal stage range and the disposal cycle in the disposal technology entries. The disposal stage response sequence refers to the order in which disposal technologies are arranged in the six disposal stages: initial risk control, rapid risk reduction, clearing and transportation, end-of-pipe disposal, monitoring and prevention, and long-term repair. In this embodiment, the above six disposal stages are sequentially determined as stages 1 to 6, and the combination of disposal technologies must not violate the sequential relationship of these stages. The cost constraint is formed by the target resource conditions, the equipment and material entries, and the cost reference in the disposal technology entries. The disposal resource consumption range refers to the resource occupation range formed by the staged disposal technology combination within the three resource boundaries: cost boundary, equipment and material boundary, and personnel input boundary. The technology connection constraint is formed by the disposal stage response sequence, the connection relationship between disposal technologies, and the equipment and material entries. The front-to-back adaptation relationship means that the processing result of the disposal technology in the previous disposal stage meets the entry conditions of the disposal technology in the next disposal stage, and there are no operational conflicts or equipment and material conflicts between the disposal technologies in adjacent disposal stages. This embodiment uses a combined retention identifier to characterize the determination result of the disposal technology combination. The combined retention identifier is the product of the stage sequence pass identifier, the resource boundary pass identifier, and the connection pass identifier. The stage sequence pass identifier is determined by the response order of the disposal stages. It is 1 when the disposal technology combination is arranged in the order of stages 1 to 6, and 0 when a later disposal stage is arranged in reverse order to the previous disposal stage. The resource boundary pass identifier is jointly determined by the cost boundary pass identifier, the equipment and material boundary pass identifier, and the personnel input boundary pass identifier. It is 1 when all three resource boundaries meet the target disposal requirements, and 0 when any resource boundary does not meet the target disposal requirements. The connection pass identifier is jointly determined by the processing result adaptation identifier, the operation conflict resolution identifier, and the equipment and material conflict resolution identifier. It is 1 when the processing result of the disposal technology in the previous disposal stage meets the entry conditions of the disposal technology in the next disposal stage, and there are no operation conflicts or equipment and material conflicts. When the combined retention identifier is 1, the corresponding disposal technology combination enters the staged disposal technology combination; when the combined retention identifier is 0, the corresponding disposal technology combination does not enter the staged disposal technology combination.

[0035] Further, in step 300 of this embodiment, performing constraint reasoning in the knowledge graph of emergency response to existing facilities based on the target scenario entity and the constraint rule set means using the target scenario entity as the starting point for reasoning and determining the scope of treatment technologies corresponding to the target existing facility along the association relationships in the knowledge graph of emergency response to existing facilities. The target scenario entity includes three types of nodes: target facility type nodes, target risk scenario nodes, and target pollutant type nodes. The association relationships include four types of relationships used to reach the treatment technology nodes: scenario affiliation relationships, pollution correspondence relationships, technology applicability relationships, and stage affiliation relationships. The target facility type node points to the corresponding risk scenario node through the scenario affiliation relationship; the target risk scenario node points to the corresponding pollutant type node through the pollution correspondence relationship; the target pollutant type node points to the corresponding treatment technology node through the technology applicability relationship; and the treatment technology node corresponds to the treatment stage through the stage affiliation relationship. The treatment technology corresponding to the treatment technology node reached through the above relationships is determined to be a treatment technology with an association relationship with the target scenario entity.

[0036] In this embodiment, the retrieved treatment technologies are identified as treatment technologies to be screened out. These technologies refer to those that have been matched to the target scenario through the map association relationship but have not yet undergone safety and environmental constraint judgment. When performing safety screening on these technologies, this embodiment calls the safety constraints in the constraint rule set and reads the prohibited use pass flag, protection matching pass flag, and operation compatibility pass flag corresponding to the technology to be screened out. When all three pass flags are 1, the safety pass flag is 1; when any pass flag is 0, the safety pass flag is 0. Treatment technologies with a safety pass flag value of 1 enter the environmental screening process, while treatment technologies with a safety pass flag value of 0 are removed from the technologies to be screened out. The prohibited use pass flag originates from the safety precautions in the target risk scenario and treatment technology entries; the protection matching pass flag originates from the equipment and material entries; and the operation compatibility pass flag originates from the operational relationships between treatment technologies within the same treatment stage.

[0037] In this embodiment, when performing environmental screening on the treatment technologies retained after safety screening, this embodiment calls the environmental constraints in the constraint rule set and reads the pollution control pass identifier, secondary pollution restriction pass identifier, and compliance basis matching identifier respectively. When all three identifiers are 1, the environmental protection pass identifier is 1; when any identifier is 0, the environmental protection pass identifier is 0. The pollution control pass identifier comes from the applicable scenario in the target pollutant type and treatment technology entry, the secondary pollution restriction pass identifier comes from the environmental requirements in the treatment technology entry, and the compliance basis matching identifier comes from the compliance requirement entry. The candidate retention identifier is the product of the safety pass identifier and the environmental protection pass identifier. Treatment technologies with a candidate retention identifier value of 1 are determined as treatment technologies in the candidate treatment technology set; treatment technologies with a candidate retention identifier value of 0 are not included in the candidate treatment technology set. The candidate treatment technology set formed after environmental screening is used as input data for the combination screening in step 400.

[0038] Furthermore, in step 400 of this embodiment, the disposal technologies in the candidate disposal technology set are grouped into stages according to the order of disposal stages. This means dividing the candidate disposal technology set into six disposal stages based on the disposal stage entry corresponding to each candidate disposal technology: initial risk control, rapid risk reduction, cleanup and transportation, end-of-pipe disposal, monitoring and prevention, and long-term repair. The order of disposal stages is the sequential arrangement of the above six disposal stages. In this embodiment, initial risk control is determined as stage 1, rapid risk reduction as stage 2, cleanup and transportation as stage 3, end-of-pipe disposal as stage 4, monitoring and prevention as stage 5, and long-term repair as stage 6. After stage grouping, the disposal technologies retain corresponding disposal technology entries, equipment and material entries, compliance requirement entries, and case effect entries as data sources for determining whether different disposal stages can be connected.

[0039] In this embodiment, the connection relationships between the treatment technologies include four types: pre- and post-treatment adaptation relationships, substitution adaptation relationships, material matching relationships, and compliance restriction relationships. The pre- and post-treatment adaptation relationship is used to determine whether the treatment technology in the previous treatment stage can be continuously implemented with the treatment technology in the next treatment stage. The determination criteria include the processing result of the previous treatment technology, the entry conditions of the next treatment technology, and the operational restrictions between adjacent treatment stages. The substitution adaptation relationship is used to determine whether different treatment technologies in the same treatment stage are substitutes for the same function. The material matching relationship is used to determine whether the equipment and materials required by the treatment technology have corresponding sources in the equipment and material entries. The compliance restriction relationship is used to determine whether the treatment technology has compliance requirement entries that match the target scenario. In this embodiment, a pre- and post-treatment adaptation identifier is used to represent the connection determination result between adjacent treatment stages. The pre- and post-treatment adaptation identifier takes a value of 1 or 0; when the processing result of the previous treatment stage meets the entry conditions of the next treatment stage and there is no operational restriction conflict, the pre- and post-treatment adaptation identifier takes a value of 1; otherwise, it takes a value of 0.

[0040] In this embodiment, after determining whether the disposal technologies in adjacent disposal stages satisfy the pre- and post-disposal adaptation relationship based on the connection relationship, the disposal technologies with a pre- and post-disposal adaptation flag value of 1 are combined to obtain the disposal technology combination to be verified. For cases where there are more than two candidate disposal technologies within the same disposal stage, this embodiment determines a substitution retention flag based on the substitution adaptation relationship. The substitution retention flag is either 1 or 0; when the disposal technologies within the same disposal stage are in a substitution adaptation relationship and all satisfy the material matching relationship and compliance restriction relationship, one disposal technology corresponding to the target disposal requirement is retained, and the substitution retention flag is 1; when there is no substitution adaptation relationship between the disposal technologies within the same disposal stage, or when there are material matching conflicts or compliance restriction conflicts, the substitution retention flag is 0. The disposal technology combination formed after processing the pre- and post-disposal adaptation relationship and the substitution adaptation relationship is determined as the disposal technology combination to be verified.

[0041] In this embodiment, performing a stage integrity check on the combination of processing technologies to be checked means determining whether the combination of processing technologies to be checked covers the processing stage corresponding to the target processing requirement, and determining whether there is a sequential adaptation relationship between adjacent processing stages. Stage integrity is represented by an identifier used to characterize the check result of the combination of processing technologies to be checked. The stage integrity identifier is either 1 or 0. When the combination of processing technologies to be checked covers the processing stages required for the target processing requirement, and the sequential adaptation identifiers between adjacent processing stages are all 1, the stage integrity identifier is 1. When the combination of processing technologies to be checked lacks any processing stage required for the target processing requirement, or the sequential adaptation identifier between any adjacent processing stages is 0, the stage integrity identifier is 0. In this embodiment, the combination of processing technologies to be checked with a stage integrity identifier value of 1 is identified as a staged processing technology combination, and the combination of processing technologies to be checked with a stage integrity identifier value of 0 is deleted from the combination screening results.

[0042] Furthermore, in this embodiment, when updating the existing facility emergency response knowledge graph or the constraint rule set based on the feedback from the response implementation, a feedback correction value is first calculated based on the response result, response cycle, resource consumption, and compliance results. This feedback correction value characterizes the actual adaptability of the target response technology in the target scenario, and the calculation formula is as follows: In the formula, For the first Feedback correction values ​​for target handling technology; The value is 1 when the disposal is completed, 0.5 when the disposal is partially completed, and 0 when the disposal is not completed. The value is determined as the ratio of the preset treatment cycle to the actual treatment cycle, with an upper limit of 1. The resource consumption adaptation value is determined according to the ratio of preset resource consumption to actual resource consumption, with a maximum value of 1. The compliance result identifier is set to 1 when the compliance requirements are met and 0 when the compliance requirements are not met. The preset handling period is derived from the handling period in the handling technology item, and the actual handling period is derived from the handling implementation feedback; the preset resource consumption is derived from the target resource conditions in the target handling requirements, and the actual resource consumption is derived from the handling implementation feedback. The sum of the weights of the above four indicators is 1, the weight of the handling result identifier is set to 0.40, and the weights of the handling period adaptation value, resource consumption adaptation value, and compliance result identifier are all set to 0.20.

[0043] In this embodiment, when updating the case effects in the knowledge graph of emergency response for existing facilities based on the anti-expensive correction value, the case verification relationship between the target response technology and the case effects is weighted and updated using the following formula: In the formula, Verify the relation weights for the updated cases; Verify the relation weights for the cases before the update; To update the coefficients for feedback; This refers to the number of historical case verification relationships that have been formed under the same target scenario entity using the target handling technology. This is derived from the number of existing connections between target scenario entities, target response technologies, and case effects in the existing facility emergency response knowledge graph. The feedback update coefficient decreases as the number of historical case verification relationships increases, to reduce the excessive influence of single response implementation feedback on the weight of case verification relationships. When a case verification relationship is first formed, The value is 0. The value is 1; when 4 historical case verification relationships have been formed, Values The value is 0.20.

[0044] In this embodiment, when updating the timeliness constraints or cost constraints in the constraint rule set according to the disposal cycle and the resource consumption, the constraint boundary update formula is adopted: In the formula, This is the updated processing cycle boundary. This refers to the processing cycle boundary before the update. This refers to the actual processing time in the feedback process. For the updated resource consumption boundary, This represents the resource consumption boundary before the update. To address the actual resource consumption during the implementation feedback process. The handling cycle boundary is used to update the timeliness constraint, and the resource consumption boundary is used to update the cost constraint. The feedback update coefficient... The feedback update coefficient is consistent with the value of the update of the case verification relationship weight, so that the case effect, timeliness constraint and cost constraint are synchronously corrected based on the same treatment implementation feedback.

[0045] When updating the compliance requirements in the knowledge graph of emergency response for existing facilities or the environmental constraints in the set of constraint rules based on the compliance results, this embodiment uses a compliance maintenance flag for determination. The compliance maintenance flag has a value of 1 or 0; it is 1 when the compliance result meets the compliance requirements and 0 when the compliance result does not meet the compliance requirements. When the compliance maintenance flag is 1, the compliance restriction relationship between the target treatment technology and the compliance requirements is retained, and the compliance result is written into the case effect; when the compliance maintenance flag is 0, the environmental constraints corresponding to the target treatment technology are marked as pending updates, and the feedback content of not meeting the compliance requirements is written into the data item corresponding to the compliance requirements.

[0046] In one implementation, the basic data for emergency response to hazardous and solid waste storage facilities is formed by combining treatment technology evaluation data, standardized technology library data, and case effect data. The treatment technology evaluation data is used to determine the technical level and attribute information of treatment technology items. The standardized technology library data is used to form the graph nodes and relationships in step 100. The case effect data is used to form the case effect items in step 100 and serves as the historical data basis for the treatment implementation feedback update in step 500. All of the above data serve as basic data, constraint data, or feedback data in steps 100 to 500, without changing the main processing line of this embodiment, which performs knowledge graph constraint reasoning based on target scenario entities and constraint rule sets.

[0047] As the data source for constructing the knowledge graph of emergency response to existing facilities in step 100, this embodiment first evaluates the practicality of emergency response technologies for existing hazardous and solid waste facilities. The evaluation objects include six types of existing facilities: hazardous waste temporary storage or stockpiling facilities, general industrial solid waste stockpiling facilities, tailings or industrial slag storage facilities, illegal solid or hazardous waste landfills, waste liquid or leachate storage facilities, and informal municipal solid waste landfills. The evaluation objects also cover six disposal stages: initial risk control, rapid risk reduction, clearing and transportation, end-of-pipe treatment, monitoring and control, and long-term remediation. This embodiment uses the 80 Class A and Class B disposal technologies ultimately included in the technology library as the main source of disposal technology entries, and uses Class C and Class D disposal technologies identified during the evaluation process as reference objects for screening out those subject to safety or environmental constraints. The evaluation results of the above six types of existing facilities, six disposal stages, and disposal technologies are used to form facility type entries, disposal stage entries, disposal technology entries, and case effect entries, respectively.

[0048] As attribute data for the disposal technology entries in step 100, this embodiment uses the analytic hierarchy process (AHP) to evaluate the practicality of disposal technologies on a 100-point scale. The evaluation dimensions include six aspects: emergency response timeliness, technical operability, environmental compliance, cost-effectiveness, scenario adaptability, and safety and reliability. Emergency response timeliness, with a weight of 25%, characterizes the disposal technology's responsiveness to risk spread, pollution containment period, and rapid risk control capabilities. Technical operability, with a weight of 20%, characterizes the operational difficulty, equipment and material requirements, and personnel requirements of the disposal technology. Environmental compliance, with a weight of 20%, characterizes the degree to which the disposal technology meets pollution control and compliance requirements. Cost-effectiveness, with a weight of 15%, characterizes the consumption of materials, equipment, labor, and maintenance resources corresponding to the disposal technology. Scenario adaptability, with a weight of 10%, characterizes the types of facilities and risk scenarios to which the disposal technology is applicable. Safety and reliability, with a weight of 10%, characterizes the operational safety conditions and accident prevention capabilities during the implementation of the disposal technology. These six evaluation dimensions, after being incorporated into the disposal technology entries, serve as the data source for generating the constraint rule set in subsequent step 200.

[0049] As a further limitation to the generation of the constraint rule set in step 200, the emergency timeliness, cost-effectiveness, environmental compliance, and safety reliability correspond to the data sources for timeliness constraints, cost constraints, environmental constraints, and safety constraints, respectively. The technical operability and scenario adaptability serve as auxiliary attributes for determining whether a treatment technology is suitable for the target scenario entity, and do not replace the preliminary screening of safety and environmental constraints. Therefore, the practicality evaluation result of the treatment technology is not the sole output of the emergency response decision-making scheme, but rather as attribute data for the treatment technology item. After the target scenario entity enters the existing facility emergency response knowledge graph, it participates in constraint reasoning along with facility type, risk scenario, pollutant type, treatment stage, equipment and materials, compliance requirements, and case effects.

[0050] As the source of the grade for the disposal technology items in step 100, this embodiment grades the disposal technologies according to the evaluation results on a percentage scale. A disposal technology with a score of 90 or above is graded as A; a score of 70 to 89 is graded as B; a score of 60 to 69 is graded as C; and a score below 60 is graded as D. This embodiment uses grade A and grade B technologies as the source of disposal technology nodes for entry into the emergency disposal knowledge graph of existing facilities, with 32 grade A technologies and 48 grade B technologies. This embodiment uses 12 grade C and D disposal technologies as reference data for constraint screening and does not use them as a recommended source of disposal technology nodes. The above grade labeling is used to characterize the initial attributes of the disposal technology and does not directly determine whether a disposal technology enters the candidate disposal technology set. The candidate disposal technology set is still determined by the safety and environmental constraints in step 300.

[0051] As a further refinement of the standardized technology library data in step 100, this embodiment organizes each treatment technology into a technology entry. Each technology entry includes 15 pieces of information: technology code, technology name, technology level, applicable scenario, core process, practical steps, equipment and materials, personnel requirements, treatment cycle, cost reference, safety precautions, environmental protection requirements, associated case number, compliance basis, and supporting treatment plan. The technology code is formed according to the combination rules of facility category, treatment stage, and sequence number, and is used to distinguish different treatment technology entries. The technology name is used to form a treatment technology node, the technology level is used to characterize the practicality evaluation result of the treatment technology, the applicable scenario is used to establish the technical applicability relationship between the treatment technology and the risk scenario, the equipment and materials are used to establish the material matching relationship between the treatment technology and the equipment and materials, the compliance basis is used to establish the compliance restriction relationship between the treatment technology and the compliance requirements, and the associated case number is used to establish the case verification relationship between the treatment technology and the case effect.

[0052] As a further refinement of the association construction in step 100, this embodiment stratifies the disposal technology entries according to facility type, disposal stage, pollutant type, and technology name. Facility type distinguishes between hazardous waste facilities, general solid waste facilities, tailings slag ponds, illegal landfill facilities, waste liquid facilities, and municipal solid waste facilities. Disposal stage distinguishes between initial risk control, rapid risk reduction, cleanup and transportation, end-of-pipe treatment, monitoring and control, and long-term remediation. Pollutant type distinguishes between heavy metals, acid and alkali waste liquids, highly toxic waste, oil sludge, general solid waste, and leachate. Technology name refers to a specific disposal technology. The above stratified arrangement results are used to form graph nodes and association paths in the knowledge graph of emergency disposal of existing facilities, enabling the target scenario entity in step 200 to progressively match the disposal technology related to the target scenario along the facility type, risk scenario, pollutant type, and disposal stage.

[0053] As a further limitation on the formation of the candidate disposal technology set in step 300, the technology level in the disposal technology entry is used to assist in determining the processing order of the disposal technologies to be screened out. Level A and Level B technologies are included in the judgment scope of safety constraints and environmental constraints, while Level C and Level D technologies serve as reference objects when screening out due to safety constraints or environmental constraints. The technology level does not directly determine whether a disposal technology enters the candidate disposal technology set. For disposal technologies that hit the target scene entity, this embodiment still performs preliminary screening according to safety constraints and environmental constraints, and the disposal technologies retained after safety and environmental screening are determined as the candidate disposal technology set. Thus, a data support relationship is formed between the disposal technology evaluation level and the knowledge graph constraint reasoning, but it does not replace the constraint reasoning process.

[0054] As a further refinement of the generation of phased disposal technology combinations in step 400, the disposal stage, equipment and materials, compliance requirements, and supporting disposal plans in the disposal technology entries are used to determine the connection relationship between disposal technologies. This connection relationship is not solely based on the evaluation score of a single disposal technology, but is determined jointly by the order of disposal stages, their compatibility, material support, and compliance restrictions. Under the same target scenario, the candidate disposal technology set is first grouped into six disposal stages, and then combined and screened based on the compatibility between adjacent disposal stages. Disposal technology combinations that satisfy the order of disposal stages, material support, and compliance restrictions are identified as phased disposal technology combinations. This process addresses the issue that while a single disposal technology can match the target scenario, multi-stage disposal technologies struggle to form a continuous solution.

[0055] As the data source for feedback updates in step 500, this embodiment uses 60 existing facility emergency response cases as the initial sample data for case effect entries. Each case effect entry records at least the facility type, risk scenario, pollutant type, treatment technology used, treatment result, treatment cycle, resource consumption, and compliance result. The treatment result is used to update the case effect, the treatment cycle is used to update the timeliness constraint, the resource consumption is used to update the cost constraint, and the compliance result is used to update the compliance requirement or environmental constraint. The case effect entries do not directly replace the constraint reasoning result, but rather serve as the historical data basis for treatment implementation feedback in step 500, used to revise the existing facility emergency response knowledge graph or constraint rule set.

[0056] In one implementation, the target existing facility is a historical legacy chromium-containing sludge storage site with a recorded stockpile of 260 tons. The risk scenario is identified as leaching leakage, and the pollutant type is identified as heavy metals. After the target scenario entity is entered into the emergency response knowledge graph of the existing facility, treatment technologies are retrieved along the scenarios, pollution correspondences, and technology applicability relationships. Incompatible technologies are then screened out based on safety and environmental constraints. The screened treatment technologies are combined and screened according to the order of treatment stages to form a phased treatment technology combination that includes seepage control, heavy metal stabilization, and closed-loop removal. This phased treatment technology combination is used to generate an emergency response decision plan. The treatment cycle, resource consumption, and compliance results after the plan is implemented are recorded in the case effect entry. In this case effect entry, the traditional experience-based treatment method is recorded as 14 days and 182,000 yuan, while the corresponding plan in this embodiment is recorded as 4 days and 125,000 yuan. The above data are only used as feedback data for the case effect entry and do not represent the effect limitation of this invention in all target scenarios.

[0057] In one implementation, the target existing facility is a tailings dam with excessive leachate discharge. The facility type is defined as a tailings slag dam, the risk scenario is leachate discharge, and the pollutant type is heavy metals or leachate. After the target scenario entity is entered into the knowledge graph of emergency response for existing facilities, treatment technologies that do not meet pollution control requirements are first eliminated through environmental constraints. Then, the compatibility of treatment technologies in adjacent treatment stages is determined through technology connection constraints. After combination and screening, a phased treatment technology combination including flood interception and drainage, flocculation and sedimentation, and heavy metal capture is formed. The treatment cycle, resource consumption, and compliance results generated after the implementation of the plan are used to update the timeliness constraints, cost constraints, and environmental constraints, respectively. In the effect item of this case, the traditional experience treatment method is recorded as 7 days and 96,000 yuan, while the corresponding plan in this embodiment is recorded as 2 days and 68,000 yuan; the downstream river water quality recovery is recorded as meeting the standard within 24 hours. The above data are recorded as the effect item of this case and are not intended to limit the effect of this invention to all tailings slag dam scenarios.

[0058] In one implementation, this embodiment statistically analyzes 60 case effect items to form a total sample record for feedback updates. The statistics include solution generation time, disposal cycle, resource consumption, compliance results, and on-site execution records. The solution generation time characterizes the time consumed in forming an emergency response decision plan after the target scenario entity enters the knowledge graph for constrained reasoning. The disposal cycle is used to update timeliness constraints, resource consumption is used to update cost constraints, and compliance results are used to update compliance requirements or environmental constraints. The statistical records for the 60 case effect items include an average solution generation time of 30 seconds, a 72% reduction in disposal cycle compared to traditional experience-based methods, a 31% reduction in disposal cost compared to traditional experience-based methods, and a 100% compliance rate for soil, water, or air environmental indicators after disposal. These statistical records serve only as sample results for the 60 case effect items, illustrating the data format of the case effect items and disposal implementation feedback, and do not constitute a limitation of this invention that all target scenarios achieve the same numerical results.

[0059] In one embodiment, this embodiment forms a decision-making function data unit corresponding to the above-mentioned data. The decision-making function data unit includes a scenario information input data unit, a technology library data unit, a constraint reasoning data unit, a solution generation data unit, and a feedback update data unit. The scenario information input data unit is used to form the scenario feature information in step 200; the technology library data unit is used to form the knowledge graph for emergency response to existing facilities in step 100; the constraint reasoning data unit is used to perform safety screening and environmental screening in step 300; the solution generation data unit is used to perform emergency response decision-making solution generation in step 500; and the feedback update data unit is used to perform updating the knowledge graph for emergency response to existing facilities or the constraint rule set in step 500. The above-mentioned decision-making function data unit is only used to illustrate the data processing division of this embodiment and does not change the execution order and data transmission relationship of the method steps in the claims.

[0060] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0061] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A hazardous waste and solid waste inventory facility emergency disposal decision-making method based on knowledge graph constraint reasoning, characterized in that, include: Obtain basic data on emergency response to existing hazardous and solid waste facilities, and construct a knowledge graph of emergency response to existing facilities based on the relationships between facility type, risk scenario, pollutant type, treatment stage, treatment technology, equipment and materials, compliance requirements, and case effects. Obtain the scene feature information of the target existing facilities, and map the scene feature information to the target scene entity and constraint rule set in the knowledge graph of emergency response of the existing facilities; Based on the target scenario entities and the set of constraint rules, constraint reasoning is performed in the knowledge graph of emergency response to existing facilities to filter out response technologies that do not meet safety and environmental constraints, thereby obtaining a set of candidate response technologies. The candidate treatment technology set is combined and screened according to the order of treatment stages and the connection between treatment technologies to generate a staged treatment technology combination; An emergency response decision-making scheme is generated based on the combination of phased response technologies, and the emergency response knowledge graph of existing facilities or the set of constraint rules is updated based on the feedback from the implementation of the response.

2. The emergency response decision-making method for hazardous and solid waste storage facilities based on knowledge graph constrained reasoning according to claim 1, characterized in that, The acquisition of basic data on emergency response to existing hazardous and solid waste facilities, based on the relationships between facility type, risk scenario, pollutant type, treatment stage, treatment technology, equipment and materials, compliance requirements, and case results, constructs a knowledge graph for emergency response to existing facilities, including: The basic data on emergency response to existing hazardous and solid waste facilities are organized into categories, including facility type, risk scenario, pollutant type, treatment stage, treatment technology, equipment and materials, compliance requirements, and case effect. Generate graph nodes based on the facility type entry, risk scenario entry, pollutant type entry, treatment stage entry, treatment technology entry, equipment and materials entry, compliance requirement entry, and case effect entry; By connecting the graph nodes according to the aforementioned relationships, the knowledge graph for emergency response to existing facilities is obtained.

3. The emergency response decision-making method for hazardous and solid waste storage facilities based on knowledge graph constrained reasoning according to claim 1, characterized in that, The relationships between facility types, risk scenarios, pollutant types, treatment stages, treatment technologies, equipment and materials, compliance requirements, and case outcomes include: The scenario attribution relationship between facility type and risk scenario; The pollution correspondence between risk scenarios and pollutant types; The applicability of technologies between pollutant types and treatment technologies; The stage attribution relationship between the treatment phase and the treatment technology; The material matching relationship between disposal technology and equipment / materials; The compliance constraints between processing technologies and compliance requirements; Case validation relationship between treatment techniques and case results.

4. The emergency response decision-making method for hazardous and solid waste storage facilities based on knowledge graph constrained reasoning according to claim 1, characterized in that, The step of acquiring the scene feature information of the target existing facilities and mapping the scene feature information to the target scene entities and constraint rule set in the emergency response knowledge graph of the existing facilities includes: The target facility type, target risk scenario, target pollutant type, and target treatment requirements are determined from the scenario feature information. Match graph nodes in the existing facility emergency response knowledge graph that correspond to the target facility type, the target risk scenario, and the target pollutant type; The matched graph nodes are identified as the target scene entities; The set of constraint rules is generated based on the target disposal requirements.

5. The emergency response decision-making method for hazardous and solid waste storage facilities based on knowledge graph constrained reasoning according to claim 1, characterized in that, The set of constraint rules includes: Safety constraints used to exclude disposal technologies that do not meet disposal safety requirements; Environmental constraints used to exclude treatment technologies that do not meet pollution control requirements; Time constraints used to define the response order during the handling phase; Cost constraints used to limit the scope of resource consumption in disposal; Technical constraints are used to define the compatibility between different treatment technologies.

6. The emergency response decision-making method for hazardous and solid waste storage facilities based on knowledge graph constrained reasoning according to claim 1, characterized in that, Based on the target scenario entities and the constraint rule set, constraint reasoning is performed in the existing facility emergency response knowledge graph to filter out response technologies that do not meet safety and environmental constraints, resulting in a candidate response technology set, including: Using the target scene entity as the starting point for reasoning, retrieve the disposal technologies that are related to the target scene entity in the knowledge graph of emergency response to existing facilities; The treatment techniques obtained from the search were identified as the treatment techniques to be screened out. The proposed removal treatment technology is safely screened out according to the aforementioned safety constraints; Environmental screening is performed on the treatment technologies that remain after safety screening, in accordance with the aforementioned environmental constraints; The treatment technologies that are retained after environmental screening are identified as the candidate treatment technology set.

7. The emergency response decision-making method for hazardous and solid waste storage facilities based on knowledge graph constrained reasoning according to claim 1, characterized in that, The step of combining and screening the candidate treatment technology set according to the order of treatment stages and the connection between treatment technologies to generate a staged treatment technology combination includes: According to the order of the disposal stages, the disposal technologies in the candidate disposal technology set are categorized by stage; Based on the aforementioned connection relationship, it is determined whether the treatment techniques in adjacent treatment stages satisfy the sequential adaptation relationship; The processing techniques that satisfy the aforementioned before-and-after adaptation relationship are combined to obtain a combination of processing techniques to be verified. Perform a phased integrity check on the combination of processing technologies to be checked; The combination of processing techniques that meets the stage integrity check is defined as the staged processing technique combination.

8. The emergency response decision-making method for hazardous and solid waste storage facilities based on knowledge graph constrained reasoning according to claim 1, characterized in that, The connection between the aforementioned processing technologies includes: The compatibility between the treatment techniques used in the previous treatment stage and the treatment techniques used in the next treatment stage; Substitutability and compatibility among different treatment technologies in the same treatment phase; The material matching relationship between disposal technology and equipment / materials; The compliance constraints between handling technologies and compliance requirements.

9. The emergency response decision-making method for hazardous and solid waste storage facilities based on knowledge graph constrained reasoning according to claim 1, characterized in that, The step of generating an emergency response decision plan based on the combination of phased response technologies includes: The target treatment stage and target treatment technology are determined based on the combination of the phased treatment technologies. The corresponding equipment, materials, and compliance requirements are determined based on the target treatment technology. The target disposal technology, equipment and materials, and compliance requirements are organized in the order of the target disposal phases. Generate an emergency response decision plan that includes the target response stage, the target response technology, the equipment and materials, and the compliance requirements.

10. The emergency response decision-making method for hazardous and solid waste storage facilities based on knowledge graph constrained reasoning according to claim 1, characterized in that, The process of updating the emergency response knowledge graph of existing facilities or the set of constraint rules based on feedback from the implementation of the response includes: Obtain the implementation feedback generated after the emergency response decision-making plan is executed; Extract the handling results, handling cycle, resource consumption, and compliance results from the feedback on the handling implementation; Update the case effects in the emergency response knowledge graph of existing facilities based on the handling results; Update the timeliness constraints or cost constraints in the constraint rule set according to the processing cycle and the resource consumption. Update the compliance requirements in the knowledge graph of emergency response for existing facilities or the environmental constraints in the set of constraint rules based on the compliance results.

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