Solid waste comprehensive utilization industry chain risk monitoring management method and system

Abstract

The invention discloses a solid waste comprehensive utilization industry chain risk monitoring management method and system, and the method comprises the steps: obtaining processing nodes and logistics paths in a solid waste comprehensive utilization industry chain, obtaining basic information of the solid waste comprehensive utilization industry chain, abstracting the basic information into a weighted graph, and constructing a solid waste industry chain risk monitoring model; in the solid waste industrial chain risk monitoring model, the risk problem of a logistics path is considered, the risk problem is optimized through an ant colony optimization algorithm, the minimum risk value of the logistics path is obtained, the ant colony algorithm is dynamically adjusted according to the minimum risk value and a reward function of the design risk value, and the solid waste industrial chain risk monitoring model is updated; performing real-time risk assessment and emergency strategy making according to dynamic updating of the solid waste industry chain risk monitoring model; according to the method, risk monitoring and strategy making are carried out on the solid waste comprehensive utilization industry chain, the stability of the solid waste comprehensive utilization industry chain is guaranteed, and a new direction is brought to risk management of the solid waste comprehensive utilization industry chain in the future.

Description

Technical Field

[0001] The present invention relates to the technical field of solid waste logistics monitoring and risk management, and in particular to a solid waste comprehensive utilization industry chain risk monitoring management method and system. Background Art

[0002] With the rapid development of the global economy and the continuous advancement of urbanization, the amount of solid waste generated has shown an explosive growth trend. In recent years, academia and industry have proposed many solutions to efficiently and economically collect, transport, treat and utilize solid waste, such as waste sorting optimization technology based on big data, intelligent monitoring systems for treatment facilities, and waste flow planning technology based on graph theory models. The application of these technologies has improved the efficiency of certain links in the industrial chain, but since the solid waste comprehensive utilization industry chain involves a complex network of multiple nodes and multiple paths, as well as the influence of the dynamic environment during the treatment process (such as weather, traffic, equipment status, etc.), the efficient collaborative management of the entire chain still faces many challenges. In this context, intelligent optimization technology based on risk monitoring has gradually become the focus of academic attention, in order to manage the complex dynamic operating environment more comprehensively and accurately.

[0003] Although the existing technology has made progress in some aspects, it still has obvious shortcomings in dealing with the dynamic characteristics of the solid waste comprehensive utilization industry chain. First, most of the current path optimization methods focus more on ideal planning under static conditions, but lack the ability to respond to dynamic variables, which leads to deviations between the actual application effect of the planning results and expectations; second, the risk assessment of processing nodes in the existing technology is mostly focused on the measurement of processing capacity, while ignoring the potential impact of the dynamic risks within the nodes on the overall chain operation; third, the existing optimization algorithms are not efficient in dealing with complex optimization problems with multiple objectives and multiple variables, especially when the operating environment of the entire chain fluctuates frequently, and its optimization results are difficult to meet actual needs in real time. These deficiencies may lead to a decrease in overall operating efficiency, aggravation of local bottlenecks, and an increase in potential environmental risks in the actual application of the solid waste comprehensive utilization industry chain. Therefore, further improving dynamic risk monitoring and emergency response capabilities, and building an industrial chain management technology with adaptive optimization risk capabilities has become a direction that the industry urgently needs to break through. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed. Therefore, the present invention provides a risk monitoring and management method for the solid waste comprehensive utilization industry chain to solve the problems mentioned in the background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides a risk monitoring and management method for a solid waste comprehensive utilization industry chain, comprising: Obtaining the processing nodes and logistics paths in the solid waste comprehensive utilization industry chain, obtaining basic information of the solid waste comprehensive utilization industry chain, abstracting the basic information of the solid waste comprehensive utilization industry chain into a weighted graph, and constructing a solid waste industry chain risk monitoring model; In the solid waste industry chain risk monitoring model, the risk problem of the logistics path is considered, and the risk problem is optimized through the ant colony optimization algorithm to obtain the minimum risk value of the logistics path. According to the minimum risk value, a reward function of the risk value is designed to dynamically adjust the ant colony algorithm, and the solid waste industry chain risk monitoring model is updated at the same time; Based on the dynamic update of the solid waste industry chain risk monitoring model, real-time risk assessment and emergency strategy formulation are carried out to form a management method for the solid waste industry chain risk monitoring model.

[0007] As a preferred solution of the risk monitoring and management method of the solid waste comprehensive utilization industry chain described in the present invention, the processing nodes and logistics paths in the solid waste comprehensive utilization industry chain are obtained to obtain basic information of the solid waste comprehensive utilization industry chain, including: The geographical locations where the solid waste undergoes physical or chemical treatment, classification or storage during the comprehensive utilization process are regarded as processing nodes, and the solid waste transportation routes from one processing node to another are regarded as logistics paths; The processing nodes and logistics routes are obtained through the geographic information system to form the basic information of the solid waste comprehensive utilization industry chain.

[0008] As a preferred solution of the solid waste comprehensive utilization industry chain risk monitoring management method of the present invention, the basic information of the solid waste comprehensive utilization industry chain is abstracted into a weighted graph to construct a solid waste industry chain risk monitoring model, including: Each processing node is mapped to a node in a weighted graph, and each logistics path is mapped to a directed edge in the weighted graph according to the node position in the weighted graph to form a weighted graph; The weighted graph is used as an input set for constructing a risk monitoring model for the solid waste industry chain.

[0009] As a preferred solution of the solid waste comprehensive utilization industry chain risk monitoring and management method described in the present invention, in the solid waste industry chain risk monitoring model, the risk problem of the logistics path is considered, including: The solid waste industry chain risk monitoring model receives the weighted graph and extracts the attributes of the processing nodes and logistics paths; According to the attributes of the processing nodes and logistics paths, the risk value of each logistics path is calculated, and a dynamic risk matrix is ​​generated.

[0010] As a preferred solution of the risk monitoring and management method for the solid waste comprehensive utilization industry chain described in the present invention, the risk problem is optimized by an ant colony optimization algorithm to obtain the minimum risk value of the logistics path, including: Assigning a corresponding pheromone concentration value to each logistics route, and using the pheromone concentration value as the priority of selecting the logistics route; Create an ant colony, the number of ants in the colony is obtained by the number of logistics paths, and assign each ant in the colony to any processing node in the weighted graph; Let each ant start from the corresponding processing node and select the next processing node according to the risk value of each logistics path, where each selection of the next processing node is based on the pheromone concentration on the path and the heuristic information on the path; If a path loop occurs during path generation by the ants, the path and the processing nodes in the path are taken as alternatives; Repeat the above process until the pheromone concentration value reaches the maximum, output the minimum risk value of the corresponding logistics path and update the dynamic risk matrix.

[0011] As a preferred solution of the solid waste comprehensive utilization industry chain risk monitoring management method of the present invention, wherein: according to the minimum risk value, a risk value reward function is designed to dynamically adjust the ant colony algorithm, and at the same time update the solid waste industry chain risk monitoring model, including: The reward function of the risk value is defined according to the updated dynamic risk matrix, and the smaller the output result of the reward function is, the lower the risk value of the logistics path is; The risk value selection of the ant on each logistics path is judged according to the output result of the reward function. If the output result of the reward function is greater than the risk value of the ant on each logistics path, the pheromone concentration on the path and the heuristic information on the path are reduced; otherwise, the pheromone concentration on the path and the heuristic information on the path are increased.

[0012] As a preferred solution of the solid waste comprehensive utilization industry chain risk monitoring management method of the present invention, wherein: according to the dynamic update of the solid waste industry chain risk monitoring model, real-time risk assessment and emergency strategy formulation are carried out, including: Based on the expert experience method, the risk assessment of the updated solid waste industry chain risk monitoring model is carried out, and the risk assessment score of the expert experience method is used as the threshold for the real-time risk assessment of the model; If the real-time risk assessment score of the model is higher than the risk assessment score of the expert experience method, the emergency strategy formulation is triggered, that is, calling the alternative logistics paths and processing nodes in the model, or deleting the logistics path with the highest risk value, and updating the weighted graph.

[0013] In a second aspect, the present invention provides a risk monitoring and management system for the solid waste comprehensive utilization industry chain, which includes: A solid waste industry chain risk monitoring model construction module is configured to obtain processing nodes and logistics paths in the solid waste comprehensive utilization industry chain, obtain basic information of the solid waste comprehensive utilization industry chain, abstract the basic information of the solid waste comprehensive utilization industry chain into a weighted graph, and construct a solid waste industry chain risk monitoring model; The ant colony algorithm optimization module is configured to consider the risk problem of the logistics path in the solid waste industry chain risk monitoring model, optimize the risk problem through the ant colony optimization algorithm, obtain the minimum risk value of the logistics path, and design a reward function of the risk value according to the minimum risk value to dynamically adjust the ant colony algorithm, and update the solid waste industry chain risk monitoring model at the same time; The risk assessment and emergency response module is configured to conduct real-time risk assessment and emergency strategy formulation based on the dynamic update of the solid waste industry chain risk monitoring model, thereby forming a management method for the solid waste industry chain risk monitoring model.

[0014] In a third aspect, the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: the processor implements any step of the above method when executing the computer program.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: the computer program implements any step of the above method when executed by a processor.

[0016] Compared with the prior art, the invention has the following beneficial effects: 1. The present invention uses the ant colony optimization algorithm to transform the risk optimization problem of the solid waste comprehensive utilization industry chain into a path optimization problem, and combines the pheromone concentration to dynamically adjust the path mechanism, thus solving the limitation of low efficiency of traditional algorithms in dealing with multi-objective optimization problems; 2. The reward function is designed using the minimum risk value, and the parameters of the ant colony algorithm are dynamically adjusted to achieve the optimization of the logistics path. In combination with the expert experience risk assessment method, an efficient emergency response mechanism is established. When the model risk assessment score exceeds the threshold, alternative paths can be quickly activated or high-risk nodes can be adjusted to ensure the continuity and stability of the solid waste comprehensive utilization industry chain; 3. By monitoring and optimizing logistics paths and processing nodes, the comprehensive utilization efficiency of solid waste can be maximized, resource waste can be reduced, potential environmental risks can be reduced, and important technical support can be provided for achieving sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them: Figure 1 This is an overall flow chart of a risk monitoring and management method for a solid waste comprehensive utilization industry chain according to an embodiment of the present invention; Figure 2 This is a flow chart of an ant colony optimization algorithm for a risk monitoring and management method for a solid waste comprehensive utilization industry chain according to an embodiment of the present invention; Figure 3 This is a comparison chart of the changes in path risk values ​​over time for the solid waste comprehensive utilization industry chain risk monitoring and management method described in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0019] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0020] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0021] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0022] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0023] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0024] Example 1: Reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a risk monitoring and management method for a solid waste comprehensive utilization industry chain, comprising: S1. Obtain the processing nodes and logistics paths in the solid waste comprehensive utilization industry chain, obtain the basic information of the solid waste comprehensive utilization industry chain, abstract the basic information of the solid waste comprehensive utilization industry chain into a weighted graph, and construct a solid waste industry chain risk monitoring model; It needs to be explained that the solid waste comprehensive utilization industry chain refers to the process of converting solid waste into resources or products through the collaboration of technology, management, market and other aspects around the collection, classification, treatment and reuse of solid waste; Furthermore, the geographical locations where the solid waste undergoes physical or chemical treatment, classification or storage during the comprehensive utilization process are used as processing nodes; Specifically, the processing nodes can be divided into solid waste generation nodes (such as factories, construction sites, residential communities, etc.), solid waste transfer nodes (such as garbage sorting centers, storage facilities, etc.), solid waste processing nodes (such as incineration plants, landfills, resource processing plants, etc.) and solid waste reuse nodes (such as recycled material processing plants, manufacturing enterprises, etc.); Furthermore, each node in the processing node is labeled with attribute values, namely, the processing capacity of the node (the amount of solid waste that can be processed per unit time), the type of processing process (such as incineration, landfill, sorting, etc.), and the geographical location coordinates (latitude and longitude); Furthermore, the solid waste transportation route from one processing node to another node is used as a logistics path; Furthermore, the following attribute values ​​are assigned to the logistics path, including spatial attributes (path length, geographic coordinates), dynamic attributes (traffic flow, weather conditions), and static attributes (road type, such as highways, urban roads, etc.); Furthermore, the processing nodes and logistics routes are obtained through the geographic information system to form the basic information of the solid waste comprehensive utilization industry chain; It should be noted that according to the attribute values ​​of the processing nodes and logistics paths, the basic information of the solid waste comprehensive utilization industry chain includes: node information: processing node ID, type of processing node (solid waste generation node, solid waste transfer node, solid waste processing node and solid waste recycling node), geographic coordinates, processing capacity; path information: path starting point, end point, distance, time, cost and historical risk; It needs to be explained that, in the solid waste comprehensive utilization industry chain, the logistics paths between the nodes (solid waste generation points, transfer points, treatment points, and reuse points) are complex and changeable, and are affected by a variety of dynamic and static attributes. Therefore, it is necessary to describe them in the form of graphs and nodes in order to intuitively and efficiently show the relationship between the actual solid waste processing nodes and the transportation routes; Specifically, in the solution of the present invention, a weighted graph is used to describe the relationship between solid waste nodes and transportation routes, and the type of graph can also be changed according to actual needs; Furthermore, each processing node is mapped to a node in a weighted graph, and each logistics path is mapped to a directed edge in the weighted graph according to the node position in the weighted graph, so as to form a weighted graph; It should be noted that mapping each logistics route according to the location of the processing node can ensure the uniqueness of the processing of the solid waste comprehensive utilization industry chain; Furthermore, the weighted graph is used as the input set to construct a risk monitoring model for the solid waste industry chain; It should be noted that by creating a risk monitoring model for the solid waste industry chain with a weighted graph as input, on the one hand, the model has a mathematical structure, and on the other hand, it can effectively support the operation of the ant colony optimization algorithm, that is, through the weight update and path probability calculation of the ant colony optimization algorithm, a low-risk and high-efficiency logistics path can be quickly found; S2. In the risk monitoring model of the solid waste industry chain, the risk problem of the logistics path is considered, and the risk problem is optimized through the ant colony optimization algorithm to obtain the minimum risk value of the logistics path. According to the minimum risk value, the reward function of the risk value is designed to dynamically adjust the ant colony algorithm, and the risk monitoring model of the solid waste industry chain is updated at the same time; It needs to be explained that in the solid waste comprehensive utilization industry chain, logistics risk is an important factor affecting the safety, economy and efficiency of the industry chain; in actual application, logistics risk will directly affect the safety of the solid waste comprehensive utilization industry chain, especially for the treatment and transportation efficiency of hazardous solid waste; Furthermore, the solid waste industry chain risk monitoring model receives the weighted graph and extracts the attributes of the processing nodes and logistics paths; Furthermore, according to the attributes of processing nodes and logistics paths, the risk value of each logistics path is calculated, and a dynamic risk matrix is ​​generated. Specifically, the risk value of each logistics path is obtained according to the distance, time, cost and historical risk of the path (the score is 0~20), among which the historical risk is obtained through the attribute value of the logistics path (spatial attribute, dynamic attribute, static attribute), which can be expressed as follows:

[0025] in, represents the risk value R of node i→another node j at time t, → represents the distance between a node and another node, also called the logistics path; Represents the path length L from node i→j, also called path distance; It is represented as the transportation cost C of node i→j; Represents the historical risk of node i→j, such as bad weather, congestion index, etc.; The associated risk N represents the historical risk of node i→j, that is, the load and processing capacity at the node; Specifically, It is expressed as:

[0026] in, and They are respectively represented as the current load of nodes i and j at time t, and are the maximum processing capacity of nodes i and j respectively; It should be noted that if the node load is close to its maximum processing capacity, the associated risk N will increase significantly, causing the risk value R to increase accordingly; Specifically, the dynamic risk matrix Depend on Composition, expressed as an n×n matrix, is expressed as:

[0027] Among them, 0 means there is no self-loop path, which means that the node will not be connected to its own path, that is, there is no directed edge or undirected edge from a node to the same node; It should be explained that Ant Colony Optimization (ACO) is a meta-heuristic algorithm based on swarm intelligence. It simulates the process of ants leaving pheromones on the path and gradually finds the approximate optimal solution to the optimization problem. It should be noted that, since the solid waste comprehensive utilization industry chain was previously abstracted into a complex multi-node, multi-path weighted graph, the problem can be transformed into finding the logistics path with the lowest risk in the weighted graph, and the ant colony optimization algorithm simulates the foraging process of ants, so that in risk monitoring events (such as the transportation of hazardous solid waste, equipment failure in solid waste treatment, etc.), the pheromone distribution can be quickly adjusted to find the logistics path with the lowest risk again, so that the model has robustness and emergency response capabilities; Specifically, refer to Figure 2 , the steps of ant colony optimization algorithm are as follows: S201, assigning a corresponding pheromone concentration value to each logistics route, and using the pheromone concentration value as the priority of the logistics route being selected; It should be explained that the pheromone concentration value is an important parameter for simulating the behavior of ant colonies, and in the scheme of the present invention, it indicates the priority of the path selected; S202, creating an ant colony, the number of ants in the ant colony is obtained by the number of logistics paths (that is, the number of ants is equal to the number of paths), and each ant in the ant colony is assigned to any processing node in the weighted graph; Specifically, the processing node is taken as the starting point of the logistics path, and the last processing node where the ant completes the logistics path is taken as the end point of the logistics path; S203, let each ant start from the corresponding processing node and select the next processing node according to the risk value of each logistics path, wherein each selection of the next processing node is based on the pheromone concentration on the path and the heuristic information on the path; It should be noted that there may be one or more processing nodes in a logistics path; Specifically, the heuristic information is expressed as the inverse of the risk value of each logistics path. When the value of the heuristic information is lower, the selection priority of the path is higher; S204, if a path loop occurs during path generation by the ants, the path and the processing nodes in the path are taken as candidates; It should be noted that when the alternative is enabled, the storage cost and computational cost of the current algorithm are not considered in the solution of the present invention; S205, repeat S201-204 until the pheromone concentration value reaches the maximum, output the minimum risk value of the corresponding logistics path and update the dynamic risk matrix ; It needs to be explained that in order to improve the adaptability of the ant colony optimization algorithm in a real-time environment, it is necessary to design a reward function to dynamically adjust the pheromone concentration and heuristic information on the path through which the nodes are selected in the ant colony optimization algorithm; Furthermore, the reward function of the risk value is defined according to the updated dynamic risk matrix, and the smaller the output result of the reward function is, the lower the risk value of the logistics path is; Specifically, the reward function is expressed as:

[0028] in, represents the reward value F of node i→j, which is used to adjust the pheromone concentration and heuristic information; represents the risk value R from node i to all possible paths p; k is the weight coefficient, which is used to control the impact of the total path risk on the reward value; It should be noted that the first part of the reward function , the lower the risk value, the greater the reward, thus guiding the ants to choose low-risk paths; the second part of the reward function , balancing the global risk value of node i. If the overall risk value of the path of node i is high, the selection priority of the node will be reduced. Through the first and second parts of the reward function, it is ensured that the reward function not only focuses on a single path, but also optimizes the path selection of the entire ant colony algorithm. Furthermore, the risk value selection of the ant on each logistics path is judged according to the output result of the reward function. If the output result of the reward function is greater than the risk value of the ant on each logistics path, the pheromone concentration on the path and the heuristic information on the path are reduced; otherwise, the pheromone concentration on the path and the heuristic information on the path are increased. It should be noted that when the output of the reward function is less than the path risk value, increasing the pheromone concentration and heuristic information can strengthen the attractiveness of the low-risk path and ensure that more ants choose the safe path; and when the output of the reward function is greater than the path risk value, reducing the pheromone concentration can reduce the probability of ants choosing the high-risk path. S3. Conduct real-time risk assessment and emergency strategy formulation based on the dynamic update of the solid waste industry chain risk monitoring model, and form a management method for the solid waste industry chain risk monitoring model; Furthermore, the risk assessment of the updated solid waste industry chain risk monitoring model is conducted based on the expert experience method, and the risk assessment score of the expert experience method is used as the threshold for the real-time risk assessment of the model; It should be explained that the expert experience method is a method that uses the professional knowledge and experience of experts in the field, combined with existing data and actual conditions, to provide guidance and reference for the analysis and resolution of problems. The risk value assessed by the expert experience method has certain reference value and credibility. Furthermore, if the model's real-time risk assessment score is higher than the expert experience method's risk assessment score, it triggers the formulation of an emergency strategy, which is to call the alternative logistics paths and processing nodes in the model, or delete the logistics path with the highest risk value to update the weighted graph; It should be noted that by formulating emergency strategies, it is possible to quickly respond to dynamic risk events caused by solid waste in a complex and changing logistics environment, so as to reduce the impact on the operation of the solid waste comprehensive utilization industry chain.

[0029] Furthermore, this embodiment also provides a risk monitoring and management system for the solid waste comprehensive utilization industry chain, including: A solid waste industry chain risk monitoring model construction module is configured to obtain processing nodes and logistics paths in the solid waste comprehensive utilization industry chain, obtain basic information of the solid waste comprehensive utilization industry chain, abstract the basic information of the solid waste comprehensive utilization industry chain into a weighted graph, and construct a solid waste industry chain risk monitoring model; The ant colony algorithm optimization module is configured to consider the risk problem of the logistics path in the solid waste industry chain risk monitoring model, optimize the risk problem through the ant colony optimization algorithm, obtain the minimum risk value of the logistics path, and design a reward function of the risk value according to the minimum risk value to dynamically adjust the ant colony algorithm, and update the solid waste industry chain risk monitoring model at the same time; The risk assessment and emergency response module is configured to conduct real-time risk assessment and emergency strategy formulation based on the dynamic update of the solid waste industry chain risk monitoring model, thereby forming a management method for the solid waste industry chain risk monitoring model.

[0030] This embodiment also provides a computer device, which is applicable to the risk monitoring and management method of the solid waste comprehensive utilization industry chain, including: A memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a solid waste comprehensive utilization industry chain risk monitoring and management method as proposed in the above embodiment.

[0031] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0032] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, a risk monitoring and management method for the solid waste comprehensive utilization industry chain as proposed in the above embodiment is implemented.

[0033] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0034] Example 2: Reference Figure 3 , which is the second embodiment of the present invention, and this embodiment provides a risk monitoring and management method for the solid waste comprehensive utilization industry chain, including: in order to verify the superiority of the scheme of the present invention over the prior art, the following experiments were conducted; the experiment simulated the solid waste comprehensive utilization industry chain, including the generation, transportation, transfer, treatment and reuse of waste, constructed a dynamic risk monitoring model, and introduced expert experience and ant colony optimization algorithm to perform path optimization; the experimental settings are as follows: Experimental environment: Hardware environment: Processing server: Dell PowerEdge R940, 64-core Intel Xeon Platinum8268, 256GB RAM; Data collection equipment: industrial-grade sensor network (IoT), including traffic monitoring equipment and weather station data collector; Software environment: Model running platform: Python 3.9 (NumPy, SciPy, NetworkX); GIS data processing: ArcGIS Pro 10.9; Simulation platform: AnyLogic 8.8, used to build simulation weighted graphs; Experimental data collection: There are 15 processing nodes, including waste generation nodes (5), including industrial parks A and B, urban residential areas C and D, and medical waste treatment point E; sorting centers (5), including garbage sorting centers F, G, and H, and hazardous waste storage stations I and J; treatment plants (5), including incineration plants K and L, landfills M and Resource processing plants N and O, with 80 logistics paths, specifically distributed as follows: 25 paths from waste generation node to sorting center, 40 paths from sorting center to processing node, and 15 paths between processing nodes, all generated based on geographic information systems; dynamic data sources: weather, traffic flow, and historical accident rates; static data: path attributes: length, type (highway / urban / rural road); Experimental results: The data of some nodes and paths obtained are shown as follows, refer to Table 1 and Table 2; Table 1 Properties of some nodes

[0035] Table 2 Relationship data between some nodes and paths

[0036] Based on the dynamic (real-time) risk factors and historical risk factors in Tables 1 and 2, a fusion experiment was conducted to obtain the experimental results of the path risk value. Figure 3 ,pass Figure 3 It can be seen that the experimental graph displayed by the traditional method maintains a high level of path risk values ​​within the time step of 0-100, and the average path risk value reaches ≈11.37, while the experimental graph displayed by the method of the present invention maintains an average path risk value of ≈9.44 under the same time step; it can be obtained that the present invention improves the avoidance of path risk values ​​by ≈16.97% compared with the traditional method, which is sufficient to show that the method of the present invention can minimize the potential environmental risks of solid waste in the transportation process compared with the traditional method, so as to ensure the safety and economy of the solid waste comprehensive utilization industry chain and achieve the management goal of the solid waste comprehensive utilization industry chain.

[0037] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present application may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0038] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0039] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0040] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0041] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they are aware of the basic creative concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application. Obviously, those skilled in the art may make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A risk monitoring and management method for the solid waste comprehensive utilization industry chain, characterized in that: include: Obtaining the processing nodes and logistics paths in the solid waste comprehensive utilization industry chain, obtaining basic information of the solid waste comprehensive utilization industry chain, abstracting the basic information of the solid waste comprehensive utilization industry chain into a weighted graph, and constructing a solid waste industry chain risk monitoring model; In the solid waste industry chain risk monitoring model, the risk problem of the logistics path is considered, and the risk problem is optimized through the ant colony optimization algorithm to obtain the minimum risk value of the logistics path. According to the minimum risk value, a reward function of the risk value is designed to dynamically adjust the ant colony algorithm, and the solid waste industry chain risk monitoring model is updated at the same time; Based on the dynamic update of the solid waste industry chain risk monitoring model, real-time risk assessment and emergency strategy formulation are carried out to form a management method for the solid waste industry chain risk monitoring model.

2. The solid waste comprehensive utilization industry chain risk monitoring and management method according to claim 1, characterized in that: Obtain the processing nodes and logistics paths in the solid waste comprehensive utilization industry chain, and obtain the basic information of the solid waste comprehensive utilization industry chain, including: The geographical locations where the solid waste undergoes physical or chemical treatment, classification or storage during the comprehensive utilization process are regarded as processing nodes, and the solid waste transportation routes from one processing node to another are regarded as logistics paths; The processing nodes and logistics routes are obtained through the geographic information system to form the basic information of the solid waste comprehensive utilization industry chain.

3. The solid waste comprehensive utilization industry chain risk monitoring and management method according to claim 2, characterized in that: The basic information of the solid waste comprehensive utilization industry chain is abstracted into a weighted graph, and a solid waste industry chain risk monitoring model is constructed, including: Each processing node is mapped to a node in a weighted graph, and each logistics path is mapped to a directed edge in the weighted graph according to the node position in the weighted graph to form a weighted graph; The weighted graph is used as an input set for constructing a risk monitoring model for the solid waste industry chain.

4. The solid waste comprehensive utilization industry chain risk monitoring and management method according to claim 3, characterized in that: In the solid waste industry chain risk monitoring model, the risk issues of logistics paths are considered, including: The solid waste industry chain risk monitoring model receives the weighted graph and extracts the attributes of the processing nodes and logistics paths; According to the attributes of the processing nodes and logistics paths, the risk value of each logistics path is calculated, and a dynamic risk matrix is ​​generated.

5. The solid waste comprehensive utilization industry chain risk monitoring and management method according to claim 4, characterized in that: The risk problem is optimized by using the ant colony optimization algorithm to obtain the minimum risk value of the logistics path, including: Assigning a corresponding pheromone concentration value to each logistics route, and using the pheromone concentration value as the priority of selecting the logistics route; Create an ant colony, the number of ants in the colony is obtained by the number of logistics paths, and assign each ant in the colony to any processing node in the weighted graph; Let each ant start from the corresponding processing node and select the next processing node according to the risk value of each logistics path, where each selection of the next processing node is based on the pheromone concentration on the path and the heuristic information on the path; If a path loop occurs during path generation by the ants, the path and the processing nodes in the path are taken as alternatives; Repeat the above process until the pheromone concentration value reaches the maximum, output the minimum risk value of the corresponding logistics path and update the dynamic risk matrix.

6. The solid waste comprehensive utilization industry chain risk monitoring and management method according to claim 4 or 5, characterized in that: According to the minimum risk value, a risk value reward function is designed to dynamically adjust the ant colony algorithm, and the solid waste industry chain risk monitoring model is updated, including: The reward function of the risk value is defined according to the updated dynamic risk matrix, and the smaller the output result of the reward function is, the lower the risk value of the logistics path is; The risk value selection of the ant on each logistics path is judged according to the output result of the reward function. If the output result of the reward function is greater than the risk value of the ant on each logistics path, the pheromone concentration on the path and the heuristic information on the path are reduced; otherwise, the pheromone concentration on the path and the heuristic information on the path are increased.

7. The solid waste comprehensive utilization industry chain risk monitoring and management method according to claim 6, characterized in that: Based on the dynamic update of the solid waste industry chain risk monitoring model, real-time risk assessment and emergency strategy formulation are carried out, including: Based on the expert experience method, the risk assessment of the updated solid waste industry chain risk monitoring model is carried out, and the risk assessment score of the expert experience method is used as the threshold for the real-time risk assessment of the model; If the real-time risk assessment score of the model is higher than the risk assessment score of the expert experience method, the emergency strategy formulation is triggered, that is, calling the alternative logistics paths and processing nodes in the model, or deleting the logistics path with the highest risk value, and updating the weighted graph.

8. A risk monitoring and management system for the solid waste comprehensive utilization industry chain, based on the solid waste comprehensive utilization industry chain risk monitoring and management method according to any one of claims 1 to 7, characterized in that: include: A solid waste industry chain risk monitoring model construction module is configured to obtain processing nodes and logistics paths in the solid waste comprehensive utilization industry chain, obtain basic information of the solid waste comprehensive utilization industry chain, abstract the basic information of the solid waste comprehensive utilization industry chain into a weighted graph, and construct a solid waste industry chain risk monitoring model; The ant colony algorithm optimization module is configured to consider the risk problem of the logistics path in the solid waste industry chain risk monitoring model, optimize the risk problem through the ant colony optimization algorithm, obtain the minimum risk value of the logistics path, and design a reward function of the risk value according to the minimum risk value to dynamically adjust the ant colony algorithm, and update the solid waste industry chain risk monitoring model at the same time; The risk assessment and emergency response module is configured to conduct real-time risk assessment and emergency strategy formulation based on the dynamic update of the solid waste industry chain risk monitoring model, thereby forming a management method for the solid waste industry chain risk monitoring model.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of any one of the methods of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of any method described in claims 1 to 7 are implemented.

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