Dangerous goods logistics park layout optimization method, system, equipment and medium

Abstract

The invention provides a dangerous goods logistics park layout optimization method and system, and belongs to the technical field of park layout optimization, and the method comprises the steps: constructing a dangerous goods logistics park accident risk objective function and a comprehensive relation objective function of all functional intervals of a dangerous goods logistics park; and establishing a dangerous goods logistics park layout optimization model according to the objective function and related constraint conditions, and calculating the dangerous goods logistics park layout optimization model by using a non-inferior stratification genetic algorithm (NSGA) II algorithm and a sorting method of approximate ideal solutions (TOPSIS) to obtain a dangerous goods logistics park layout optimization scheme. The accident risk of the dangerous goods logistics park is reduced to the greatest extent while economic benefits and logistics operation efficiency of the dangerous goods logistics park are considered.

Description

Technical Field

[0001] The present invention belongs to the field of park layout optimization, and specifically relates to a method, system, equipment and medium for optimizing the layout of a dangerous goods logistics park. Background Art

[0002] The layout of dangerous goods logistics parks is related to the safety of people’s lives and property. Reasonable layout of dangerous goods logistics parks can not only reduce the construction and operation costs of the park and improve the economic benefits of the park, but also help prevent accidents and reduce accident losses, which is of great significance.

[0003] Although the current layout of hazardous goods logistics parks takes into account the permissible risk standards, when selecting specific layout plans, most only consider the warehousing and logistics efficiency goals, while ignoring the harmfulness and accident risks of stored goods. It is not comprehensive to control the risks of hazardous goods logistics parks by only considering the permissible risk standards and warehousing and logistics efficiency goals. The elimination rate of accident risk hazards in logistics parks is low, which is not conducive to the safety of logistics parks. Moreover, most hazardous goods logistics parks are located in sensitive areas of cities, which have a high risk factor. Therefore, it is very necessary to comprehensively consider the factors affecting the layout of hazardous goods logistics parks. Summary of the invention

[0004] In order to solve the problem of accident risks in the layout of a hazardous goods logistics park, the present invention provides a method, system, equipment and medium for optimizing the layout of a hazardous goods logistics park.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A method for optimizing the layout of a hazardous goods logistics park comprises the following steps:

[0007] Obtaining logistics park modeling data, wherein the logistics park modeling data includes the number of hazard sources, the number of grids, the number of personnel, and personal risk parameters;

[0008] Constructing an accident risk objective function for a hazardous goods logistics park based on the number of hazard sources, the number of grids, the number of personnel, and individual risk parameters;

[0009] According to the analysis of the logistics relationship, non-logistics relationship and comprehensive relationship of the dangerous goods logistics park, the comprehensive relationship objective function of each functional area of ​​the dangerous goods logistics park is constructed;

[0010] With the goal of minimizing the accident risk, maximizing the economic benefits and maximizing the comprehensive relationship between each functional area in the dangerous goods logistics park, a layout optimization model of the dangerous goods logistics park is established according to the accident risk objective function of the dangerous goods logistics park, the comprehensive relationship objective function of each functional area, the economic benefit objective function and related constraints;

[0011] The hazardous goods logistics park layout optimization model is calculated by a non-inferior hierarchical genetic algorithm and a sorting method that approximates an ideal solution to obtain a sorted park layout plan, and a recommended plan for optimizing the hazardous goods logistics park layout is determined based on the sorted park layout plan.

[0012] Preferably, the accident risk objective function of the hazardous goods logistics park is specifically:

[0013]

[0014] Where PLL is the potential number of deaths; IR ij The personal risk of the jth grid caused by the i-th hazard source; d j The total number of people in the jth grid; M is the number of hazardous sources; N is the number of grids.

[0015] Preferably, the comprehensive relationship objective function of each functional area of ​​the hazardous goods logistics park is specifically:

[0016]

[0017] In the formula, l ij is the logistics correlation between units in the layout plan; v ij is the non-logistics correlation between units; e ij is the adjacency between units, i and j represent unit numbers, i,j∈[1,2,...,n]; e ij The actual distance d between the units ij and the maximum possible distance d max Decide.

[0018] Preferably, the layout optimization model of the hazardous goods logistics park is specifically:

[0019]

[0020] Where, f1 is the minimum total accident risk value of the park; f2 is the maximum total economic benefit value of the park; f3 is the maximum comprehensive correlation value of the total functional areas of the park; K is the total number of plots in the park; M is the total number of types of functional areas in the park; i is the number of plots in the park, i = 1, 2, ..., K; m is the number of the type of functional area in the park, m = 1, 2, ..., M; r im is the number of deaths that may be caused when plot i is used for type m; b im is the unit economic benefit when the i plot is used for type m; s i is the area of ​​plot i; u mn is the logistics correlation between the m-type functional area and the n-type functional area; v mn is the non-logistics correlation between the m-type functional area and the n-type functional area; x imThe value is 1 if the i plot is used for type m, otherwise it is 0; y i j mn =x im ·xj n .

[0021] Preferably, the constraint conditions are specifically:

[0022]

[0023] x im ·s i ≥z

[0024] Where z is the minimum area requirement for plots of type m.

[0025] Preferably, the hazardous goods logistics park layout optimization model is calculated by a non-inferior hierarchical genetic algorithm and a ranking method close to an ideal solution to obtain a ranked park layout plan, and a recommended plan for optimizing the layout of the hazardous goods logistics park is determined according to the ranked park layout plan, specifically comprising the following steps:

[0026] The Pareto optimal solution set of the hazardous goods logistics park layout optimization model is obtained by using a non-inferior hierarchical genetic algorithm;

[0027] The weights of the comprehensive relationships among the park's accident risk, the park's economic benefits, and the park's functional areas are determined through the information entropy method;

[0028] The TOPSIS method is used to calculate the distance from the ideal value to the negative ideal value to evaluate the scheme. The schemes are ranked in order of merit from large to small according to the calculation results, and the recommended scheme for park layout optimization is obtained.

[0029] The present invention also provides a dangerous goods logistics park layout optimization system, comprising:

[0030] The data acquisition module is used to acquire logistics park modeling data, wherein the logistics park modeling data includes the number of hazard sources, the number of grids, the number of personnel, and personal risk parameters.

[0031] The accident risk module is used to construct an accident risk objective function for a hazardous goods logistics park based on the number of hazard sources, the number of grids, the number of personnel, and personal risk parameters.

[0032] The functional area relationship module is used to construct the comprehensive relationship objective function of each functional area in the dangerous goods logistics park based on the logistics relationship, non-logistics relationship and comprehensive relationship analysis of the dangerous goods logistics park.

[0033] The park layout module is used to establish a dangerous goods logistics park layout optimization model based on the dangerous goods logistics park accident risk objective function, the comprehensive relationship objective function of each functional interval, the economic benefit objective function and related constraints, with the goals of minimizing the accident risk, maximizing the economic benefits and maximizing the comprehensive relationship between each functional interval.

[0034] The layout optimization module is used to calculate the layout optimization model of the hazardous goods logistics park through a non-inferior hierarchical genetic algorithm and a sorting method that approximates the ideal solution, obtain a sorted park layout plan, and determine a recommended plan for optimizing the layout of the hazardous goods logistics park based on the sorted park layout plan.

[0035] The present invention also provides a computer device including a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps described in the method for optimizing the layout of a hazardous goods logistics park.

[0036] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is loaded by a processor, it can execute the steps described in the method for optimizing the layout of a hazardous goods logistics park.

[0037] The method for optimizing the layout of a hazardous goods logistics park provided by the present invention has the following beneficial effects:

[0038] The present invention optimizes the layout of the dangerous goods logistics park through the modeling data of the logistics park, taking into account the influencing targets such as the accident risk of the dangerous goods logistics park, the economic benefits of the park, and the comprehensive relationship of the dangerous goods logistics park. A dangerous goods logistics park layout optimization model is constructed with the goal of minimizing the accident risk of the dangerous goods logistics park, maximizing the economic benefits, and maximizing the comprehensive relationship between each functional area. The non-inferior hierarchical genetic algorithm (NSGAⅡ) and the top-ranking method close to the ideal solution (TOPSIS) method are used to solve the dangerous goods logistics park layout optimization model, and a recommended plan for the layout of the dangerous goods logistics park is obtained. While considering the accident risk, economic benefits, and logistics operation efficiency of the dangerous goods logistics park, the accident risk of the dangerous goods logistics park can be minimized, which is conducive to improving the safety of the dangerous goods logistics park. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiment of the present invention and its design scheme, the following briefly introduces the drawings required for this embodiment. 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.

[0040] Figure 1The present invention is a flowchart of a method for optimizing the layout of a hazardous goods logistics park in an embodiment of the present invention.

[0041] Figure 2 This is a layout diagram of a dangerous goods logistics park in an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to enable those skilled in the art to better understand the technical solution of the present invention and implement it, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the scope of protection of the present invention.

[0043] In the description of the present invention, it is to be understood that the terms “center”, “longitudinal”, “lateral”, “length”, “width”, “thickness”, “up”, “down”, “front”, “back”, “left”, “right”, “vertical”, “horizontal”, “top”, “bottom”, “inside”, “outside”, “axial”, “radial”, “circumferential”, etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing the technical solutions of the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0044] In addition, the terms "first", "second", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance. In the description of the present invention, it should be noted that, unless otherwise clearly specified or limited, the terms "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances. In the description of the present invention, unless otherwise specified, "plurality" means two or more, which will not be described in detail here.

[0045] Example

[0046] The present invention not only considers economic benefits and logistics efficiency, but also considers the impact of accident risk on the layout of dangerous goods logistics parks, thereby minimizing accident risks and reducing accident losses. Specifically, the present invention provides a method for optimizing the layout of dangerous goods logistics parks, such as Figure 1 As shown, including:

[0047] S1. Obtain logistics park modeling data, which includes the number of hazard sources, the number of grids, the number of personnel, and personal risk parameters.

[0048] S2. Establish the accident risk objective function of the dangerous goods logistics park based on the number of hazard sources, the number of grids, the number of personnel and personal risk parameters. The specific calculation formula is:

[0049]

[0050] Where PLL is the potential number of deaths; IR ij The personal risk of the jth grid caused by the i-th hazard source; d j The total number of people in the jth grid; M is the number of hazardous sources; N is the number of grids.

[0051] Step 3: Based on the analysis of the logistics relationship, non-logistics relationship and comprehensive relationship of the dangerous goods logistics park, the comprehensive relationship objective function of each functional area of ​​the dangerous goods logistics park is constructed. The specific calculation method is as follows:

[0052] (1) Logistics relationship analysis

[0053] Logistics relations are mainly caused by logistics activities such as material movement, processing, transportation, and storage. Through the analysis of the operation process, the logistics volume between each functional area is determined, and the ratio of the logistics volume between each functional area to the total logistics volume of the park is calculated. The logistics intensity is used to represent the logistics relationship intensity between each functional area, and it is divided into five levels as shown in Table 1:

[0054] Table 1 Logistics intensity level ratio table

[0055]

[0056] (2) Analysis of non-logistics relationships

[0057] Non-logistics relationships take into account work procedures, organizational relationships, and environmental safety, including: sharing of facilities and equipment, convenience of supervision and management, frequency of personnel contact, and guarantee of personnel work and life safety. Non-logistics relationships are classified according to the closeness of functional areas, and their levels and reasons are shown in Table 2:

[0058] Table 2 Non-logistics relationship level table

[0059]

[0060] (3) Comprehensive relationship analysis

[0061] Considering the convenience of work, the layout should try to make the functional areas with close relationship adjacent in space. The concept of correlation is introduced to indicate the closeness between functional areas. The specific values ​​are shown in Table 3:

[0062] Table 3. Inter-unit correlation values ​​and unit correlation table

[0063]

[0064] Since the cost of dangerous goods flowing within the park is relatively high, the logistics relationship has a greater impact on the non-logistics relationship. Therefore, the weighted ratio of the two is α:β=3:1(α+β=1), and the comprehensive relationship T of the functional area is:

[0065]

[0066] In the formula, l ij is the logistics correlation between units in the layout plan; v ij is the non-logistics correlation between units; e ij is the adjacency between units, i and j represent unit numbers, i,j∈[1,2,...,n]; e ij The actual distance d between the units ij and the maximum possible distance d max Decision, as shown in Table 4:

[0067] Table 4 Adjacency value table

[0068]

[0069] S4. The goal of dangerous goods logistics park is to minimize the accident risk, maximize the economic benefits and maximize the comprehensive relationship between each functional area. According to the objective function of the accident risk of dangerous goods logistics park, the comprehensive relationship objective function of each functional area, the economic benefit objective function and related constraints, the layout optimization model of dangerous goods logistics park is established as follows:

[0070]

[0071]

[0072] x im ·s i ≥z (7)

[0073] Where, f1 is the minimum total accident risk of the park; f2 is the maximum total economic benefit of the park; f3 is the maximum comprehensive correlation value of the total functional areas of the park; K is the total number of plots in the park; M is the total number of types of functional areas in the park; i is the number of plots in the park, i = 1, 2, ..., K; m is the number of the type of functional area in the park, m = 1, 2, ..., M; r im is the number of deaths that may be caused when plot i is used for type m; b im is the unit economic benefit when the i plot is used for type m; s i is the area of ​​plot i; u mn is the logistics correlation between the m-type functional area and the n-type functional area; v mn is the non-logistics correlation between the m-type functional area and the n-type functional area; x imThe value is 1 if the i plot is used for type m, otherwise it is 0; y i j mn =x im ·xj n .

[0074] S5. Calculate the optimization model of the dangerous goods logistics park layout through the non-inferiority hierarchical genetic algorithm and the sorting method that approximates the ideal solution, and obtain the sorted park layout plan.

[0075] The non-inferior hierarchical genetic algorithm is written in Matlab language to solve the Pareto optimal solution set of the dangerous goods logistics park layout optimization model. The decision matrix is ​​constructed by the Pareto optimal solution set. The decision matrix is ​​sorted by the method close to the ideal solution to obtain the optimization plan of the dangerous goods logistics park layout. The specific steps include:

[0076] (1) The Pareto optimal solution set of the layout optimization model of the hazardous goods logistics park is obtained using the non-inferior hierarchical genetic algorithm (NSGAⅡ). The chromosome encoding adopts the 0-1 encoding method. Each chromosome represents a feasible solution. The gene of the chromosome corresponds to the functional area type of each plot unit and the type relationship between two plots. The fitness is determined by the objective function value. Each chromosome has three fitness values ​​f1, f2 and f3.

[0077] (2) The final solution is obtained by using the ranking method that approximates the ideal solution (TOPSIS). The decision matrix is ​​constructed using the Pareto optimal solution set, and the weights of the three target attributes are determined using the information entropy method. The optimal solution is selected based on the decision maker's preference. The specific calculation process is as follows:

[0078] The first step is to use the vector norm method to obtain the normative decision matrix

[0079] Assume that there is a model with n multi-objective attributes and m decision options. The original decision matrix Y = {y ij}, the normalized decision matrix is ​​B = {b ij},but:

[0080]

[0081] The second step is to determine the attribute index weight based on information entropy

[0082] ① Assume that m plans and n indicators constitute the data matrix T, and standardize the data matrix T to obtain the decision matrix R:

[0083]

[0084] where maxt gj , mint gj , They represent the maximum, minimum and average values ​​of the j-th column of the data matrix T respectively.

[0085] ② The entropy value of each evaluation index can be calculated according to the following formula:

[0086]

[0087] ③Calculate the weight of evaluation index:

[0088]

[0089] The third step is to construct a weighted normative matrix

[0090] Let the weight vector of multiple attributes w = {w 1 ,w 2 ,...,w n} T ,but:

[0091] x ij =w ij × ij i=1,2,...,mj=1,2,...,n (12)

[0092] Step 4: Find the ideal solution x + and negative ideal solution x -

[0093] Assume that the ideal solution x + The j-th attribute value of Negative ideal solution x - The j-th attribute value of but:

[0094] The ideal solution is: (j is a benefit attribute) or (j is a cost attribute)

[0095] The negative ideal solution is: (j is a cost attribute) or (j is a benefit attribute)

[0096] Step 5: Calculate the distance from each alternative to the ideal point and the negative ideal point

[0097] Distance from alternative to ideal point for:

[0098]

[0099] Distance from alternative to negative ideal point for:

[0100]

[0101] Step 6: Queue indicator value of each solution (comprehensive evaluation index)

[0102]

[0103] Step 7: C i The values ​​​​arrange the solutions in order of merit from large to small.

[0104] S6. According to the order of merit of the solutions, a recommended solution for optimizing the layout of the hazardous goods logistics park is obtained.

[0105] Taking a logistics park as an example, the layout is optimized through the above method:

[0106] Step 1: The layout of a dangerous goods logistics park is as follows: Figure 2 The relevant data are shown in Tables (5) to (11). One functional area is arranged for each of the nine plots, of which the area of ​​the storage area and the operation area shall not be less than 50,000 m 2 .

[0107] Table 5 Area data of each plot

[0108]

[0109] Table 6 Related parameters of different functional area types

[0110]

[0111] Table 7 The average individual risk value r caused by type k in plot i to the entire park ik

[0112]

[0113] Table 8 Data on logistics correlation of different types of functional areas

[0114]

[0115]

[0116] Table 9 Non-logistics correlation data of different types of functional areas

[0117]

[0118] Table 10 Distance data between plots (unit: m)

[0119]

[0120] Note: d max =696.4

[0121] Table 11 Adjacency data table

[0122]

[0123] Step 2: Use Matlab language to write the NSGAⅡ algorithm, take the initial population size as 200, the crossover probability as 0.6, the mutation probability as 0.02, the maximum evolutionary generation as 500, and obtain a total of 21 Pareto solutions.

[0124] Step 3: Use these solutions to construct a 21×3 decision matrix. First, the weight vector of accident risk value, economic benefit, and comprehensive relationship attribute of functional area is calculated by information entropy method: w=[0.2452, 0.4194, 0.3354]. Then, the TOPSIS method is used to sort the 21 Pareto solutions. The solution ranked first is a satisfactory solution. This solution is used as the recommended solution for the park layout. The solution is shown in Table 12.

[0125] Table 12 Recommended solutions

[0126]

[0127] The present invention also provides a dangerous goods logistics park layout optimization system, comprising:

[0128] The data acquisition module is used to obtain logistics park modeling data, which includes the number of hazardous sources, the number of grids, the number of personnel and personal risk parameters.

[0129] The accident risk module is used to construct the accident risk objective function of the hazardous goods logistics park based on the number of hazard sources, the number of grids, the number of personnel, and personal risk parameters.

[0130] The functional area relationship module is used to construct the comprehensive relationship objective function of each functional area in the dangerous goods logistics park based on the logistics relationship, non-logistics relationship and comprehensive relationship analysis of the dangerous goods logistics park.

[0131] The park layout module is used to establish a layout optimization model for the dangerous goods logistics park based on the accident risk objective function of the dangerous goods logistics park, the comprehensive relationship objective function of each functional interval, the economic benefit objective function and related constraints, with the goals of minimizing the accident risk, maximizing the economic benefits and maximizing the comprehensive relationship between each functional interval.

[0132] The layout optimization module is used to calculate the layout optimization model of the dangerous goods logistics park through a non-inferior hierarchical genetic algorithm and a sorting method that approximates the ideal solution, obtain a sorted park layout plan, and determine a recommended plan for optimizing the layout of the dangerous goods logistics park based on the sorted park layout plan.

[0133] Each module in the above-mentioned dangerous goods logistics park layout optimization system can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0134] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps in an embodiment of a method for optimizing the layout of a dangerous goods logistics park. The specific implementation method can be found in the method embodiment, which will not be repeated here.

[0135] Furthermore, the present invention also provides a non-temporary computer-readable storage medium containing instructions, and a computer program is stored on the storage medium. For example, a memory containing instructions, the above instructions can be executed by a processor of a computer device to complete the above method. For example, a non-temporary computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device. When the computer program is executed by the processor, it can implement the steps in an embodiment of a method for optimizing the layout of a dangerous goods logistics park. The specific implementation method can be found in the method embodiment, which will not be repeated here.

[0136] It will be appreciated by those skilled in the art that embodiments of the present invention may provide methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present invention may 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.) containing computer-usable program code.

[0137] The present invention is described with reference to flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as a combination of processes 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 a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce 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.

[0138] 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 including 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.

[0139] 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. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0140] It should be pointed out that the specific implementation methods described above can enable those skilled in the art to understand the invention more comprehensively, but do not limit the invention in any way. Therefore, although the invention has been described in detail in this specification and embodiments, those skilled in the art should understand that the invention can still be modified or replaced by equivalents; and all technical solutions and improvements that do not deviate from the spirit and scope of the invention are included in the protection scope of the patent for the invention. Any figure mark in the claims should not be regarded as limiting the claims involved. Any simple change or equivalent replacement of the technical solution that can be obviously obtained by any technician familiar with the field within the technical scope disclosed in the present invention belongs to the protection scope of the present invention.

Claims

1. A method for optimizing the layout of a dangerous goods logistics park, characterized in that: The following steps are involved: Obtaining logistics park modeling data, wherein the logistics park modeling data includes the number of hazard sources, the number of grids, the number of personnel, and personal risk parameters; Constructing an accident risk objective function for a hazardous goods logistics park based on the number of hazard sources, the number of grids, the number of personnel, and individual risk parameters; According to the analysis of the logistics relationship, non-logistics relationship and comprehensive relationship of the dangerous goods logistics park, the comprehensive relationship objective function of each functional area of ​​the dangerous goods logistics park is constructed; With the goal of minimizing the accident risk, maximizing the economic benefits and maximizing the comprehensive relationship between each functional area in the dangerous goods logistics park, a layout optimization model of the dangerous goods logistics park is established according to the accident risk objective function of the dangerous goods logistics park, the comprehensive relationship objective function of each functional area, the economic benefit objective function and related constraints; The hazardous goods logistics park layout optimization model is calculated by a non-inferior hierarchical genetic algorithm and a sorting method that approximates an ideal solution to obtain a sorted park layout plan, and a recommended plan for optimizing the hazardous goods logistics park layout is determined based on the sorted park layout plan.

2. A method for optimizing the layout of a hazardous goods logistics park according to claim 1, characterized in that: The accident risk objective function of the hazardous goods logistics park is specifically: Where PLL is the potential number of deaths; IR ij The personal risk of the jth grid caused by the i-th hazard source; d j The total number of people in the jth grid; M is the number of hazardous sources; N is the number of grids.

3. A method for optimizing the layout of a hazardous goods logistics park according to claim 1, characterized in that: The comprehensive relationship objective function of each functional area of ​​the hazardous goods logistics park is specifically: In the formula, l ij is the logistics correlation between units in the layout plan; v ij is the non-logistics correlation between units; e ij is the adjacency between units, i and j represent unit numbers, i,j∈[1,2,...,n]; e ij The actual distance d between the units ij and the maximum possible distance d max Decide.

4. A method for optimizing the layout of a hazardous goods logistics park according to claim 1, characterized in that: The layout optimization model of the hazardous goods logistics park is specifically as follows: Where, f1 is the minimum total accident risk value of the park; f2 is the maximum total economic benefit value of the park; f3 is the maximum comprehensive correlation value of the total functional areas of the park; K is the total number of plots in the park; M is the total number of types of functional areas in the park; i is the number of plots in the park, i = 1, 2, ..., K; m is the number of the type of functional area in the park, m = 1, 2, ..., M; r im is the number of deaths that may be caused when plot i is used for type m; b im is the unit economic benefit when the i plot is used for type m; s i is the area of ​​plot i; u mn is the logistics correlation between the m-type functional area and the n-type functional area; v mn is the non-logistics correlation between the m-type functional area and the n-type functional area; x im The value is 1 if the i plot is used for type m, otherwise it is 0; y ijmn =x im ·x jn .

5. The method for optimizing the layout of a hazardous goods logistics park according to claim 1, characterized in that: The constraints are specifically: x im ·s i ≥z Where z is the minimum area requirement for plots of type m.

6. A method for optimizing the layout of a hazardous goods logistics park according to claim 1, characterized in that: The method of calculating the optimization model of the layout of the hazardous goods logistics park by using a non-inferior hierarchical genetic algorithm and a sorting method close to an ideal solution to obtain a sorted park layout plan, and determining a recommended plan for optimizing the layout of the hazardous goods logistics park according to the sorted park layout plan, specifically includes the following steps: The Pareto optimal solution set of the hazardous goods logistics park layout optimization model is obtained by using a non-inferior hierarchical genetic algorithm; The weights of the comprehensive relationships among the park's accident risk, the park's economic benefits, and the park's functional areas are determined through the information entropy method; The TOPSIS method is used to calculate the distance from the ideal value to the negative ideal value to evaluate the scheme. The schemes are ranked in order of merit from large to small according to the calculation results, and the recommended scheme for park layout optimization is obtained.

7. A dangerous goods logistics park layout optimization system, characterized in that: include: A data acquisition module, used to acquire logistics park modeling data, wherein the logistics park modeling data includes the number of hazard sources, the number of grids, the number of personnel, and personal risk parameters; An accident risk module is used to construct an accident risk objective function for a dangerous goods logistics park according to the number of hazard sources, the number of grids, the number of personnel, and personal risk parameters; The functional area relationship module is used to construct the comprehensive relationship objective function of each functional area of ​​the dangerous goods logistics park based on the logistics relationship, non-logistics relationship and comprehensive relationship analysis of the dangerous goods logistics park; The park layout module is used to establish a layout optimization model for the dangerous goods logistics park based on the dangerous goods logistics park accident risk objective function, the comprehensive relationship objective function of each functional interval, the economic benefit objective function and related constraints, with the goal of minimizing the accident risk of the dangerous goods logistics park, maximizing the economic benefit and maximizing the comprehensive relationship of each functional interval; The layout optimization module is used to calculate the layout optimization model of the hazardous goods logistics park through a non-inferior hierarchical genetic algorithm and a sorting method that approximates the ideal solution, obtain a sorted park layout plan, and determine a recommended plan for optimizing the layout of the hazardous goods logistics park based on the sorted park layout plan.

8. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is loaded into a processor, it can execute the steps of the method according to any one of claims 1 to 6.

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