A risk monitoring and management method and system for the comprehensive utilization industrial chain of solid waste

Abstract

The present invention discloses a risk monitoring and management method and system for a solid waste comprehensive utilization industrial chain, including obtaining processing nodes and logistics paths in the solid waste comprehensive utilization industrial chain to obtain the basic information of the solid waste comprehensive utilization industrial chain, abstracting it into a weighted graph, and constructing a risk monitoring model for the solid waste industrial chain; in the risk monitoring model for the solid waste industrial chain, considering the risk problems of the logistics path, optimizing the risk problems through the ant colony optimization algorithm to obtain the minimum risk value of the logistics path, and designing a reward function for the risk value to dynamically adjust the ant colony algorithm according to the minimum risk value, and updating the risk monitoring model of the solid waste industrial chain; according to the dynamic update of the risk monitoring model of the solid waste industrial chain, carrying out real-time risk assessment and emergency strategy formulation; the present invention guarantees the stability of the solid waste comprehensive utilization industrial chain through risk monitoring and strategy formulation for the solid waste comprehensive utilization industrial chain, and brings a new direction for the risk management of the future solid waste comprehensive utilization industrial chain.

Description

Technical Field

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

[0002] With the rapid development of the global economy and the continuous advancement of the urbanization process, the generation volume of solid waste shows an explosive growth trend. In recent years, the academic and industrial circles have proposed many solutions on how to efficiently and economically achieve the collection, transportation, treatment, and resource utilization of solid waste, such as waste sorting optimization technology based on big data, intelligent monitoring systems for treatment facilities, and waste flow direction planning technology based on graph theory models. The application of these technologies has improved the efficiency of some links in the industrial chain. However, due to the complex network of multiple nodes and multiple paths involved in the comprehensive utilization industrial chain of solid waste, 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. Against this background, intelligent optimization technology based on risk monitoring has gradually become the focus of attention in the academic field, with the expectation of more comprehensive and accurate management of the complex dynamic operating environment.

[0003] Although the existing technologies have made progress in some aspects, there are still obvious deficiencies in dealing with the dynamic characteristics of the comprehensive utilization industrial chain of solid waste. First, most current path optimization methods pay more attention to the ideal planning under static conditions and lack the ability to respond to dynamic variables, which leads to a deviation between the actual application effect of the planning result and the expectation. Second, the risk assessment of processing nodes in the existing technologies mostly focuses on the measurement of processing capacity, while ignoring the potential impact of the internal dynamic risks of the nodes on the operation of the overall chain. Third, the existing optimization algorithms are not efficient in dealing with complex optimization problems with multiple objectives and multiple variables. Especially in the case of frequent fluctuations in the full-chain operating environment, their optimization results are difficult to meet the actual needs in real time. These deficiencies may lead to a decline in the overall operating efficiency, an exacerbation of local bottlenecks, and an increase in potential environmental risks in the actual application of the comprehensive utilization industrial chain of solid waste. Therefore, further enhancing the dynamic risk monitoring and emergency response capabilities and constructing an industrial chain management technology with the ability to adaptively optimize risks have become the directions that the industry urgently needs to break through. Summary of the Invention

[0004] The purpose of this part is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this part, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this part, the abstract, and the title. However, 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 comprehensive utilization industrial chain of solid waste to solve the problems mentioned in the background technology.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a risk monitoring and management method for the comprehensive utilization industrial chain of solid waste, including:

[0008] Obtain the processing nodes and logistics paths in the comprehensive utilization industrial chain of solid waste, obtain the basic information of the comprehensive utilization industrial chain of solid waste, abstract the basic information of the comprehensive utilization industrial chain of solid waste into a weighted graph, and construct a risk monitoring model for the solid waste industrial chain;

[0009] In the risk monitoring model of the solid waste industrial chain, consider the risk problems of the logistics path, optimize the risk problems through the ant colony optimization algorithm, obtain the minimum risk value of the logistics path, design a reward function for the risk value to dynamically adjust the ant colony algorithm, and update the risk monitoring model of the solid waste industrial chain at the same time;

[0010] According to the dynamic update of the risk monitoring model of the solid waste industrial chain, conduct real-time risk assessment and emergency strategy formulation to form a management method for the risk monitoring model of the solid waste industrial chain.

[0011] As a preferred solution of the risk monitoring and management method for the comprehensive utilization industrial chain of solid waste according to the present invention, wherein: obtaining the processing nodes and logistics paths in the comprehensive utilization industrial chain of solid waste, and obtaining the basic information of the comprehensive utilization industrial chain of solid waste, including:

[0012] Take the geographical location points of physical or chemical treatment, classification or storage that occur during the comprehensive utilization of solid waste as processing nodes, and take the solid waste transportation line between one processing node and another node as the logistics path;

[0013] Obtain the processing nodes and logistics paths through the geographic information system to form the basic information of the comprehensive utilization industrial chain of solid waste.

[0014] As a preferred solution of the risk monitoring and management method for the comprehensive utilization industrial chain of solid waste according to the present invention, wherein: abstracting the basic information of the comprehensive utilization industrial chain of solid waste into a weighted graph and constructing a risk monitoring model for the solid waste industrial chain, including:

[0015] Map each processing node to a node in a weighted graph, and map each logistics path to a directed edge in the weighted graph according to the node positions in the weighted graph to form a weighted graph;

[0016] Use the weighted graph as the input set for constructing the risk monitoring model of the solid waste industrial chain.

[0017] As a preferred solution of the risk monitoring and management method for the solid waste comprehensive utilization industrial chain of the present invention, wherein: in the risk monitoring model of the solid waste industrial chain, the risk problem of the logistics path is considered, including:

[0018] The risk monitoring model of the solid waste industrial chain receives the weighted graph and extracts the attributes of the processing nodes and the logistics path;

[0019] According to the attributes of the processing nodes and the logistics path, calculate the risk value of each logistics path and generate a dynamic risk matrix.

[0020] As a preferred solution of the risk monitoring and management method for the solid waste comprehensive utilization industrial chain of the present invention, wherein: through the ant colony optimization algorithm, optimize the risk problem to obtain the minimum risk value of the logistics path, including:

[0021] Assign a corresponding pheromone concentration value to each logistics path, and use the pheromone concentration value as the priority degree for the selection of this logistics path;

[0022] Create an ant colony, the number of ants in the ant colony is obtained from the number of logistics paths, and each ant in the ant colony is assigned to any processing node in the weighted graph;

[0023] Let each ant start from the corresponding processing node and select the next processing node according to the risk value of each logistics path. Among them, each time the next processing node is selected based on the pheromone concentration on this path and the heuristic information on this path;

[0024] If a path loop appears in the path generation by the ant, then regard this path and the processing nodes in the path as alternatives;

[0025] 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.

[0026] As a preferred solution of the risk monitoring and management method for the solid waste comprehensive utilization industrial chain of the present invention, wherein: according to the minimum risk value, design a reward function for the risk value to dynamically adjust the ant colony algorithm, and at the same time update the risk monitoring model of the solid waste industrial chain, including:

[0027] The reward function of the risk value is defined according to the updated dynamic risk matrix, and the smaller the output result of this reward function, the lower the risk value of this logistics path;

[0028] Based on the output result of the reward function, judge the risk value selection of the ant on each logistics path. If the output result of the reward function is greater than the risk value of the ant on each logistics path, reduce the pheromone concentration and heuristic information on this path. Otherwise, increase the pheromone concentration and heuristic information on this path.

[0029] As a preferred solution of the risk monitoring and management method for the solid waste comprehensive utilization industrial chain of the present invention, wherein: according to the dynamic update of the solid waste industrial chain risk monitoring model, perform real-time risk assessment and emergency strategy formulation, including:

[0030] Based on the expert experience method, conduct a risk assessment on the updated solid waste industrial chain risk monitoring model, and use the risk assessment score of the expert experience method as the threshold for the real-time risk assessment of the model;

[0031] If the real-time risk assessment score of the model is higher than the risk assessment score of the expert experience method, trigger the formulation of an emergency strategy, that is, call the alternative logistics paths and processing nodes in the model, or delete the logistics path with the highest risk value, and update the weighted graph.

[0032] In a second aspect, the present invention provides a risk monitoring and management system for a solid waste comprehensive utilization industrial chain, which includes:

[0033] A solid waste industrial chain risk monitoring model construction module, configured to obtain the processing nodes and logistics paths in the solid waste comprehensive utilization industrial chain, obtain the basic information of the solid waste comprehensive utilization industrial chain, abstract the basic information of the solid waste comprehensive utilization industrial chain into a weighted graph, and construct a solid waste industrial chain risk monitoring model;

[0034] An ant colony algorithm optimization module, configured to consider the risk problem of the logistics path in the solid waste industrial chain risk monitoring model, optimize the risk problem through the ant colony optimization algorithm, obtain the minimum risk value of the logistics path, design a reward function for the risk value according to the minimum risk value to dynamically adjust the ant colony algorithm, and at the same time update the solid waste industrial chain risk monitoring model;

[0035] A risk assessment and emergency response module, configured to perform real-time risk assessment and emergency strategy formulation according to the dynamic update of the solid waste industrial chain risk monitoring model, and form a management method for the solid waste industrial chain risk monitoring model.

[0036] In a third aspect, the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and wherein: when the processor executes the computer program, any step of the above method is implemented.

[0037] Fourthly, the present invention provides a computer-readable storage medium, on which a computer program is stored, wherein: when the computer program is executed by a processor, any step of the above method is implemented.

[0038] Compared with the prior art, the beneficial effects of the invention are as follows:

[0039] 1. By means of the ant colony optimization algorithm, the present invention transforms the risk optimization problem of the comprehensive utilization industrial chain of solid waste into a path optimization problem, and combines the mechanism of dynamically adjusting the path according to the pheromone concentration, thus solving the limitation of the traditional algorithm in dealing with multi-objective optimization problems with low efficiency.

[0040] 2. A reward function is designed using the minimum risk value, and by dynamically adjusting the parameters of the ant colony algorithm, the optimization of the logistics path is realized. Combining with the expert experience risk assessment method, an efficient emergency response mechanism is established, which can quickly enable alternative paths or adjust high-risk nodes when the risk assessment score of the model exceeds the threshold, so as to ensure the continuity and stability of the comprehensive utilization industrial chain of solid waste; 3. By monitoring and optimizing the logistics path and processing nodes, the comprehensive utilization efficiency of solid waste can be maximally improved, resource waste can be reduced, potential environmental risks can be lowered, and important technical support is provided for realizing sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings according to these drawings without creative efforts. Among them:

[0042] Figure 1 It is the overall flowchart of the risk monitoring and management method for the comprehensive utilization industrial chain of solid waste according to an embodiment of the present invention;

[0043] Figure 2 It is the flowchart of the ant colony optimization algorithm of the risk monitoring and management method for the comprehensive utilization industrial chain of solid waste according to an embodiment of the present invention;

[0044] Figure 3 It is the comparison diagram of the change of the path risk value over time of the risk monitoring and management method for the comprehensive utilization industrial chain of solid waste according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] To make the above objects, features, and advantages of the present invention more apparent and understandable, the following provides a detailed description of the specific embodiments of the present invention in conjunction with the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0046] In the following description, numerous specific details are set forth to facilitate a thorough understanding of the present invention. However, the present invention may be practiced in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the spirit of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0047] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation manner of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that mutually excludes other embodiments.

[0048] The present invention is described in detail in conjunction with schematic diagrams. When detailing the embodiments of the present invention, for ease of explanation, the cross-sectional views showing the device structure will be enlarged locally out of the general scale, and the schematic diagrams are only examples and should not limit the scope of protection of the present invention herein. In addition, in actual production, three-dimensional spatial dimensions including length, width, and depth should be included.

[0049] Meanwhile, in the description of the present invention, it should be noted that the orientation or positional relationships indicated by terms such as "upper, lower, inner, and outer" are based on the orientation or positional relationships shown in the accompanying drawings. This is 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 orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention. In addition, the terms "first, second, or third" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0050] Unless otherwise clearly defined and limited in the present invention, the terms "mounted, connected, and coupled" 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, and can also be indirectly connected through an intermediate medium, or can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0051] Embodiment 1: Refer to Figure 1 and Figure 2, which is the first embodiment of the present invention. This embodiment provides a risk monitoring and management method for the integrated solid waste utilization industrial chain, including:

[0052] S1. Obtain the processing nodes and logistics paths in the integrated solid waste utilization industrial chain, obtain the basic information of the integrated solid waste utilization industrial chain, abstract the basic information of the integrated solid waste utilization industrial chain into a weighted graph, and construct a risk monitoring model for the solid waste industrial chain;

[0053] It should be explained that the integrated solid waste utilization industrial chain refers to the processing process of converting solid waste into resources or products through the coordination of multiple aspects such as technology, management, and market around the links of solid waste collection, classification, treatment, and reuse;

[0054] Furthermore, the geographical location points of physical or chemical treatment, classification, or storage that occur during the integrated utilization of solid waste are used as processing nodes;

[0055] 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, warehousing facilities, etc.), solid waste processing nodes (such as incineration plants, landfills, resource treatment plants, etc.), and solid waste reuse nodes (such as recycled material processing plants, manufacturing enterprises, etc.);

[0056] Even further, each node in the processing nodes is marked with attribute values, that is, the processing capacity of the node (the amount of solid waste that can be processed per unit time), the type of processing technology (such as incineration, landfill, sorting, etc.), and the geographical location coordinates (latitude and longitude);

[0057] Furthermore, the solid waste transportation line between one processing node and another is used as the logistics path;

[0058] Even further, the following attribute values are assigned to the logistics path, including spatial attributes (the length of the path, geographical coordinates), dynamic attributes (traffic flow, weather conditions), and static attributes (road type, such as highway, urban road, etc.);

[0059] Even further, the processing nodes and logistics paths are obtained through a geographic information system to form the basic information of the integrated solid waste utilization industrial chain;

[0060] It should be noted that according to the attribute values of the processing nodes and logistics paths, the basic information of the comprehensive utilization industrial chain of solid waste 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 reuse node), geographical coordinates, and processing capacity; path information: path start point, end point, distance, time, cost, and historical risk;

[0061] It should be explained that in the comprehensive utilization industrial chain of solid waste, since the logistics paths between various nodes (solid waste generation points, transfer points, processing points, and reuse points) are complex and changeable, and are affected by various dynamic and static attributes at the same time, it is necessary to describe them in the form of graphs and nodes in order to intuitively and efficiently display the relationship between the actual solid waste processing nodes and transportation lines;

[0062] Specifically, in the solution of the present invention, a weighted graph is used to describe the relationship between solid waste nodes and transportation lines, and the type of graph can also be replaced according to actual needs;

[0063] 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 positions in the weighted graph to form a weighted graph;

[0064] It should be noted that mapping each logistics path according to the position of the processing node can ensure the uniqueness of the processing of the comprehensive utilization industrial chain of solid waste;

[0065] Even further, the weighted graph is used as the input set for constructing the risk monitoring model of the solid waste industrial chain;

[0066] It should be noted that by creating a risk monitoring model of the solid waste industrial chain with a weighted graph as the 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;

[0067] S2. In the risk monitoring model of the solid waste industrial chain, considering the risk problem of the logistics path, through the ant colony optimization algorithm, the risk problem is optimized 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 at the same time, the risk monitoring model of the solid waste industrial chain is updated;

[0068] It should be noted that in the industrial chain of solid waste comprehensive utilization, logistics risk is an important factor affecting the safety, economy and efficiency of the industrial chain; in practical applications, logistics risk will directly affect the safety of the industrial chain of solid waste comprehensive utilization, especially for the treatment and transportation efficiency of hazardous solid waste;

[0069] Furthermore, the risk monitoring model of the solid waste industrial chain receives the weighted graph and extracts the attributes of the processing nodes and logistics paths;

[0070] Even further, according to the attributes of the processing nodes and logistics paths, calculate the risk value of each logistics path and generate a dynamic risk matrix; specifically, the risk value of each logistics path is obtained based on the distance, time, cost and historical risk of the path (the score is 0-20), where the historical risk is obtained through the attribute values of the logistics path (spatial attribute, dynamic attribute, static attribute), which can be expressed by the formula:

[0071] Among them, represents the risk value R of node i → another node j at time t, → represents the distance between the node and another node, also called the logistics path; represents the path length L of node i → j, also called the path distance; represents the transportation cost C of node i → j; represents the historical risk of node i → j, such as bad weather, congestion index, etc.; represents the associated risk N of the historical risk of node i → j, that is, the load and processing capacity at the node;

[0072] Specifically, is expressed as:

[0073]

[0074] Among them, and respectively represent the current load of nodes i and j at time t, and are the maximum processing capacities of nodes i and j respectively; it should be noted that when the node load is close to its maximum processing capacity, the associated risk N will increase significantly, resulting in an increase in the risk value R;

[0075] Specifically, the dynamic risk matrix is composed of and is expressed as an n×n matrix, which is expressed as:

[0076]

[0077] Among them, a value of 0 indicates no self-loop path, meaning that a node will not be connected to its own path, that is, there is no directed or undirected edge from a certain node to the same node;

[0078] It should be explained that the Ant Colony Optimization (ACO) is a meta-heuristic algorithm based on swarm intelligence. By simulating the process of ants leaving pheromones on paths, it gradually finds an approximate optimal solution to the optimization problem;

[0079] It should be noted that since the solid waste comprehensive utilization industrial chain was abstracted as a weighted graph with complex multi-nodes and multi-paths before, the problem can be transformed into finding the logistics path with the lowest risk in the weighted graph. The ant colony optimization algorithm simulates the foraging process of ants, enabling quick adjustment of the pheromone distribution and re-finding the logistics path with the lowest risk in risk monitoring events (such as the transportation of hazardous solid waste, equipment failures in solid waste treatment, etc.), making the model have robustness and emergency response capabilities;

[0080] Specifically, referring to Figure 2 , the steps of the ant colony optimization algorithm are as follows:

[0081] S201. Assign corresponding pheromone concentration values to each logistics path, and use the pheromone concentration value as the priority of being selected for this logistics path;

[0082] It should be explained that the pheromone concentration value is an important parameter simulating the behavior of the ant colony, representing the priority of being selected for the path in the solution of the present invention;

[0083] S202. Create an ant colony. The number of ants in the ant colony is obtained from the number of logistics paths (that is, there are as many ants as there are paths), and each ant in the ant colony is assigned to any processing node in the weighted graph;

[0084] Specifically, take this processing node as the starting point of the logistics path, and take the last processing node where the ant finishes walking the logistics path as the end point of the logistics path;

[0085] 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. Among them, each time the next processing node is selected based on the pheromone concentration on this path and the heuristic information on this path;

[0086] It should be noted that there will be one or more processing nodes in a logistics path;

[0087] Specifically, the heuristic information is represented as the reciprocal of the risk value of each logistics path. When the value of the heuristic information is lower, the selection priority of this path is higher;

[0088] S204. If a path loop occurs during path generation by the ant, then this path and the processing nodes in the path are taken as alternatives;

[0089] It should be noted that when alternatives are enabled, the storage cost and calculation 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 ;

[0090] It should 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 the heuristic information of the path passed by the selected node in the ant colony optimization algorithm;

[0091] Furthermore, the reward function of the risk value is defined according to the updated dynamic risk matrix, and the smaller the output result of this reward function, the lower the risk value of this logistics path;

[0092] Specifically, the reward function is expressed as:

[0093]

[0094] Among them, represents the reward value F of node i→j, which is used to adjust the pheromone concentration and the heuristic information; represents the risk value R of node i to all possible paths p; k is a weight coefficient, which is used to control the influence of the total path risk on the reward value;

[0095] It should be noted that the first part of the reward function , the lower the risk value, the greater the reward, thus guiding the ant to choose a low - risk path; the second part of the reward function , balances the global risk value of node i. If the overall risk value of the path of node i is relatively high, then the selection priority of this node will be reduced; through the first part and the second part of the reward function, it is ensured that the reward function not only focuses on a single path but also can optimize the path selection of the entire ant colony algorithm;

[0096] Even further, according to the output result of the reward function, the risk value selection of the ant on each logistics path is judged. If the output result of the reward function is greater than the risk value of the ant on each logistics path, then the pheromone concentration and the heuristic information on this path are reduced, otherwise, the pheromone concentration and the heuristic information on this path are increased;

[0097] It should be noted that when the output result of the reward function is less than the path risk value, increasing the pheromone concentration and heuristic information can strengthen the attraction of low-risk paths and ensure that more ants choose safe paths; while when the output result of the reward function is greater than the path risk value, reducing the pheromone concentration can reduce the probability of ants choosing high-risk paths;

[0098] S3. According to the dynamic update of the solid waste industrial chain risk monitoring model, perform real-time risk assessment and emergency strategy formulation to form a management method for the solid waste industrial chain risk monitoring model;

[0099] Furthermore, based on the expert experience method, conduct a risk assessment on the updated solid waste industrial chain risk monitoring model, and use the risk assessment score of the expert experience method as the threshold for the real-time risk assessment of the model;

[0100] It should be explained that the expert experience method is a method that provides guidance and reference for the analysis and solution of problems through the professional knowledge and experience of experts in the field, combined with existing data and actual situations. The risk value evaluated by the expert experience method has certain reference value and credibility;

[0101] Even further, if the real-time risk assessment score of the model is higher than the risk assessment score of the expert experience method, trigger the formulation of an emergency strategy, that is, 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;

[0102] It should be noted that by formulating an emergency strategy, it is possible to quickly respond to dynamic risk events brought by solid waste in a complex and changeable logistics environment, so as to reduce the impact on the operation of the solid waste comprehensive utilization industrial chain.

[0103] Furthermore, this embodiment also provides a risk monitoring and management system for the solid waste comprehensive utilization industrial chain, including:

[0104] A solid waste industrial chain risk monitoring model construction module, configured to obtain the processing nodes and logistics paths in the solid waste comprehensive utilization industrial chain, obtain the basic information of the solid waste comprehensive utilization industrial chain, abstract the basic information of the solid waste comprehensive utilization industrial chain into a weighted graph, and construct a solid waste industrial chain risk monitoring model;

[0105] An ant colony algorithm optimization module, configured to consider the risk problem of the logistics path in the solid waste industrial chain risk monitoring model, optimize the risk problem through the ant colony optimization algorithm to obtain the minimum risk value of the logistics path, design a reward function for the risk value according to the minimum risk value to dynamically adjust the ant colony algorithm, and at the same time update the solid waste industrial chain risk monitoring model;

[0106] A risk assessment and emergency response module is configured to perform real-time risk assessment and emergency strategy formulation according to the dynamic update of the solid waste industrial chain risk monitoring model, and form a management method for the solid waste industrial chain risk monitoring model.

[0107] This embodiment also provides a computer device applicable to the situation of the risk monitoring and management method for the comprehensive utilization industrial chain of solid waste, including:

[0108] A memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement a risk monitoring and management method for the comprehensive utilization industrial chain of solid waste as proposed in the above embodiment.

[0109] This computer device can be a terminal, and this computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of this computer device is used to provide computing and control capabilities. The memory of this 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 this computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of this computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of this computer device can be a touch layer covered on the display screen, or a button, a trackball, or a touchpad set on the shell of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0110] This embodiment also provides a storage medium, on which a computer program is stored, and when this program is executed by a processor, it implements a risk monitoring and management method for the comprehensive utilization industrial chain of solid waste as proposed in the above embodiment.

[0111] 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 described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0112] Example 2: Refer to Figure 3 , which is the second embodiment of the present invention. This embodiment provides a risk monitoring and management method for the comprehensive utilization industrial chain of solid waste, including: In order to verify the superiority of the present invention's solution compared with the prior art, the following experiments were carried out; the experiment simulated the comprehensive utilization industrial chain of solid waste, including the links of waste generation, transportation, transfer, treatment, and reuse, constructed a dynamic risk monitoring model, and introduced expert experience and the ant colony optimization algorithm for path optimization; the experimental settings are as follows:

[0113] Experimental environment:

[0114] Hardware environment: Processing server: Dell PowerEdge R940, 64-core Intel Xeon Platinum 8268, 256GB RAM; Data acquisition device: Industrial-grade sensor network (IoT), including traffic monitoring devices and meteorological station data collectors;

[0115] 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 a simulated weighted graph;

[0116] Experimental data collection:

[0117] The number of processing nodes is 15, including waste generation nodes (5), including industrial parks A and B, urban residential areas C and D, and medical waste treatment points E; Sorting centers (5), including waste 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,

[0118] Resource treatment plants N and O, and the logistics paths are 80 in total, with specific distributions as follows: Waste generation nodes → sorting centers: 25, Sorting centers → treatment nodes: 40, Paths between treatment nodes: 15, all generated based on the geographic information system; Dynamic data sources: Weather, traffic flow, and historical accident rates; Static data: Path attributes: length, type (highway / urban / rural road);

[0119] Experimental results: The data of some nodes and paths obtained are shown below, referring to Table 1 and Table 2;

[0120] Table 1 Self-attributes of some nodes

[0121]

[0122] Table 2 Relationship data between some nodes and paths

[0123]

[0124] Conduct a fusion experiment based on the dynamic (real-time) risk factors and historical risk factors in Table 1 and Table 2, and the experimental results of the path risk values are for reference Figure 3 , through Figure 3It can be seen that for the experimental graphs presented by the traditional method, the path risk values remain at a relatively high level within time steps 0 to 100, and the risk value of the average path reaches approximately 11.37. While for the experimental graphs presented by the method of the present invention, with the same time steps, the risk value of the average path is maintained at approximately 9.44. It can be obtained that the present invention has improved the avoidance of path risk values by approximately 16.97% compared to the traditional method, which is sufficient to show that the method of the present invention can minimize the potential environmental risks of solid waste during transportation compared to the traditional method, ensuring the safety and economy of the solid waste comprehensive utilization industrial chain and achieving the management goal of the solid waste comprehensive utilization industrial chain.

[0125] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can take 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.) that contain computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages. For example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0126] This application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of this application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0127] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0128] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, thereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one process or more processes and / or blocks Figure 1 one process or more processes and / or blocks Figure 1 one block or more blocks.

[0129] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications falling within the scope of the present application. Obviously, those skilled in the art can make various changes and variations 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 equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. A risk monitoring and management method for the comprehensive utilization industrial chain of solid waste, characterized in that, Including: Obtain the processing nodes and logistics paths in the comprehensive utilization industrial chain of solid waste to get the basic information of the comprehensive utilization industrial chain of solid waste, abstract the basic information of the comprehensive utilization industrial chain of solid waste into a weighted graph, and construct a risk monitoring model for the solid waste industrial chain; In the risk monitoring model of the solid waste industrial chain, consider the risk problem of the logistics path, optimize the risk problem through the ant colony optimization algorithm to obtain the minimum risk value of the logistics path, design a reward function for the risk value according to the minimum risk value to dynamically adjust the ant colony algorithm, and update the risk monitoring model of the solid waste industrial chain at the same time; In the risk monitoring model of the solid waste industrial chain, considering the risk problem of the logistics path, including: The risk monitoring model of the solid waste industrial chain receives the weighted graph and extracts the attributes of the processing nodes and logistics paths; Calculate the risk value of each logistics path according to the attributes of the processing nodes and logistics paths, and generate a dynamic risk matrix; The optimization of the risk problem through the ant colony optimization algorithm to obtain the minimum risk value of the logistics path includes: Assign a corresponding pheromone concentration value to each logistics path, and use the pheromone concentration value as the priority degree for this logistics path to be selected; Create an ant colony, the number of ants in the ant colony is obtained from the number of logistics paths, and each ant in the ant colony is assigned 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. Among them, each time the next processing node is selected based on the pheromone concentration on this path and the heuristic information on this path; If a path cycle occurs in the path generation by the ant, then regard this path and the processing nodes in the path as alternatives; Repeat the process of the ant colony optimization algorithm until the pheromone concentration value reaches the maximum, output the minimum risk value of the corresponding logistics path and update the dynamic risk matrix; Specifically, the risk value of each logistics path is obtained according to the distance, time, cost and historical risk of the path. Among them, the historical risk is obtained through the attribute value of the logistics path, which is expressed as: Among them, represents the risk value R of node i → another node j at time t, and → represents the distance between the node and another node; represents the path length L of node i → j; represents the transportation cost C for node i → j; represents the historical risk of node i → j; represents the associated risk N of the historical risk of node i → j, that is, the load and processing capacity at the node; Specifically, It is expressed as: Among them, and respectively represent the current load of nodes i and j at time t, and are respectively the maximum processing capabilities of nodes i and j; Design a reward function for the risk value according to the minimum risk value to dynamically adjust the ant colony algorithm and update the risk monitoring model of the solid waste industrial chain at the same time, 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, the lower the risk value of this logistics path; Judge the risk value selection of ants on each logistics path according to the output result of the reward function. If the output result of the reward function is greater than the risk value of ants on each logistics path, then reduce the pheromone concentration and the heuristic information on this path, otherwise, increase the pheromone concentration and the heuristic information on this path; According to the dynamic update of the risk monitoring model of the solid waste industrial chain, conduct real-time risk assessment and emergency strategy formulation to form a management method for the risk monitoring model of the solid waste industrial chain.

2. The risk monitoring and management method for the solid waste comprehensive utilization industrial chain according to claim 1, wherein Obtain the processing nodes and logistics paths in the comprehensive utilization industrial chain of solid waste, and obtain the basic information of the comprehensive utilization industrial chain of solid waste, including: Regarding the geographical location points where physical or chemical treatment, classification, or storage occurs during the comprehensive utilization of solid waste as processing nodes, and regarding the solid waste transportation line between one processing node and another as a logistics path; Obtain the processing nodes and logistics paths through a geographic information system to form the basic information of the comprehensive utilization industrial chain of solid waste.

3. The risk monitoring and management method for the comprehensive utilization industrial chain of solid waste as described in claim 2, characterized in that, Abstract the basic information of the comprehensive utilization industrial chain of solid waste into a weighted graph, and construct a risk monitoring model for the solid waste industrial chain, including: Map each processing node to a node in a weighted graph, and map each logistics path to a directed edge in the weighted graph according to the node positions in the weighted graph to form a weighted graph; Use the weighted graph as the input set for constructing the risk monitoring model of the solid waste industrial chain.

4. The risk monitoring and management method for the comprehensive utilization industrial chain of solid waste as described in claim 1, characterized in that According to the dynamic update of the risk monitoring model of the solid waste industrial chain, conduct real-time risk assessment and emergency strategy formulation, including: Conduct a risk assessment on the updated risk monitoring model of the solid waste industrial chain based on the expert experience method, and use the risk assessment score of the expert experience method 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, trigger the formulation of an emergency strategy, that is, call the alternative logistics paths and processing nodes in the model, or delete the logistics path with the highest risk value and update the weighted graph.

5. A risk monitoring and management system for the comprehensive utilization industrial chain of solid waste, based on the risk monitoring and management method of the comprehensive utilization industrial chain of solid waste described in any one of claims 1 to 4, characterized in that, Including: A risk monitoring model construction module for the solid waste industrial chain, configured to obtain the processing nodes and logistics paths in the comprehensive utilization industrial chain of solid waste, obtain the basic information of the comprehensive utilization industrial chain of solid waste, abstract the basic information of the comprehensive utilization industrial chain of solid waste into a weighted graph, and construct a risk monitoring model for the solid waste industrial chain; An ant colony algorithm optimization module, configured to consider the risk problem of the logistics path in the risk monitoring model of the solid waste industrial chain, optimize the risk problem through the ant colony optimization algorithm to obtain the minimum risk value of the logistics path, design a reward function for the risk value based on the minimum risk value to dynamically adjust the ant colony algorithm, and update the risk monitoring model of the solid waste industrial chain at the same time; A risk assessment and emergency response module, configured to conduct real-time risk assessment and emergency strategy formulation according to the dynamic update of the risk monitoring model of the solid waste industrial chain, and form a management method for the risk monitoring model of the solid waste industrial chain.

6. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 4.

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