Intelligent level-to-level management system and method for sanitary emergency materials

Abstract

The invention discloses an intelligent level-to-level management system and method for health emergency materials, and relates to the technical field of emergency material management, and the system comprises a material information input module, a warehouse information input module, a material taking data module, a material taking cost analysis module, and a material optimal position analysis module. The material information input module is used for collecting material basic data; the warehouse information input module is used for collecting warehouse layout information and generating warehouse basic data; according to the intelligent graded management system and method for the sanitary emergency materials, a material taking cost function model taken to a warehouse exit when various materials are placed in storage positions of all positions in a warehouse is constructed through the modules, and a target function of the minimum taking cost when various materials are placed in all the storage positions of the warehouse can be constructed in cooperation with material priorities; and the placement positions of various materials at the minimum taking cost are analyzed, so that the overall calling efficiency when the warehouse deals with the emergency event can be improved.

Description

Technical Field

[0001] This invention relates to the field of emergency supplies management technology, specifically to an intelligent hierarchical management system and method for health emergency supplies. Background Technology

[0002] Emergency medical supplies refer to the collective term for medicines, medical devices, equipment, reagents, protective gear, and other supplies necessary for emergency medical rescue, disease control, and ensuring the basic life safety and health of the public during public emergencies (such as natural disasters, accidents, and public health emergencies). The efficiency of the allocation of emergency medical supplies directly impacts the effectiveness of rescue efforts after a public emergency, making their management crucial. Intelligent, tiered management of emergency medical supplies, leveraging the deep integration of technologies such as the Internet of Things and big data, is driving the transformation from traditional, extensive stockpiling to a precise and efficient modern emergency support system.

[0003] The placement of emergency supplies during public emergencies directly impacts their mobilization efficiency. Therefore, planning reasonable placement based on the usage and importance of various emergency supplies in the warehouse to improve mobilization efficiency is a problem that needs to be solved. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent hierarchical management system and method for health emergency supplies, so as to solve the above-mentioned shortcomings in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an intelligent hierarchical management system for health emergency supplies, comprising a supplies information input module, a warehouse information input module, a supplies retrieval data module, a supplies retrieval cost analysis module, and a supplies optimal location analysis module;

[0006] The material information entry module is used to collect basic material data;

[0007] The warehouse information entry module is used to collect warehouse layout information and generate basic warehouse data. Warehouse location information can be the warehouse's geographical coordinates (such as satellite positioning coordinates). Warehouse layout information includes the location coordinates of all shelves and storage locations, storage location height (shelf levels), aisle information, entrance and exit locations, path length from each storage location to the entrance and exit, and different functional areas (such as picking area, storage area, and cold storage area), the storage space size and load capacity of each storage location, and environmental information of the area where each storage location is located, such as aisle width. Data can be extracted from CAD drawings, digital floor plans generated through laser scanning, UAV mapping, etc., by importing the data.

[0008] The material retrieval data module is used to collect historical outbound time, retrieval quantity, retrieval tool, retrieval duration, and corresponding emergency event information for each warehouse and each material, generating material business data. Specifically, the historical outbound time of each warehouse and each material allows for the calculation of the usage frequency of each material in each warehouse. The retrieval quantity and retrieval tool can be used for training and updating the material retrieval cost function for the corresponding tool. Retrieval tools can include manual labor, handcarts, and forklifts, etc. Emergency event information during material retrieval is used to determine whether different categories of material retrieval correspond to an emergency event, which is helpful in determining the correlation between materials.

[0009] The material acquisition cost analysis module is used to perform multinomial regression analysis based on basic material data, basic warehouse data and material business data to generate a material acquisition cost function model.

[0010] The optimal location analysis module for supplies is used to extract and analyze the importance data and usage frequency data of supplies based on basic emergency supplies data and supply business data, and then calculate the priority of supplies. It is also used to construct an objective function based on basic warehouse data and supply retrieval cost function to minimize the sum of the product of the priority of supplies and the retrieval cost of supplies in the warehouse, and optimize the solution to obtain the recommended data of the optimal location of supplies. Optimization algorithms such as genetic algorithm, particle swarm optimization algorithm, and ant colony optimization algorithm can be used in the solution process.

[0011] Furthermore, the material information entry module collects basic material data, including the following steps:

[0012] Collect the unique code, name, volume, weight, and importance level information of the materials to generate basic material data.

[0013] Furthermore, the material acquisition cost analysis module constructs a material acquisition cost function model, including the following steps;

[0014] The storage height information of each storage location in each warehouse and the path length for retrieving each material from each storage location are obtained from the basic warehouse data.

[0015] The quantity of each material taken from each warehouse, the tools used for taking the material, and the time taken for taking the material from each storage location are obtained from the material business data.

[0016] Obtain the volume and weight of each material taken from the basic material data;

[0017] Collect information on the retrieval time, path length, volume, weight, quantity, and tools used for each material retrieved from each warehouse, and generate sample data; among which, the time information and the quantity retrieved are the time and quantity of a single retrieval.

[0018] The sample data is categorized into multiple tool sample data sets according to the different tools used for data retrieval;

[0019] Multinomial regression analysis is performed on each tool sample data set to generate a tool material retrieval cost function that corresponds one-to-one with the retrieval tool. The retrieval time is used as the dependent variable in the multinomial regression analysis. The independent variables include storage location height information, volume, weight, path length and retrieval quantity, and may also include interaction terms between volume and weight, volume and path, weight and path length, etc. The constant term is the basic retrieval time term.

[0020] Based on the volume, weight, and quantity information of the materials being retrieved, selection rules for the cost functions of each tool and material retrieval are set, and cost function selection rule data is generated. For example, if the volume is less than a set first volume threshold, the weight is less than a set first weight threshold, and the quantity is less than a set first quantity threshold, manual labor is selected; if the volume is less than a set first volume threshold, the weight is less than a set first weight threshold, and the quantity is greater than or equal to a set first quantity threshold, a handcart is selected; or if at least one of the following conditions is met: the volume is greater than or equal to a set first volume threshold but less than a set second volume threshold, and the weight is greater than or equal to a set first weight threshold but less than a set second weight threshold, a handcart is selected; if at least one of the following conditions is met: the volume is greater than a set second volume threshold, and the weight is greater than a set second weight threshold, a forklift is selected.

[0021] Collect the cost function selection rule data and the cost functions for each tool and material acquisition, and generate a material acquisition cost function model.

[0022] Furthermore, the optimal location analysis module for emergency supplies is used to extract and analyze the importance data and usage frequency data of the supplies based on basic emergency supplies data and operational data, and then calculate the priority of the supplies, including the following steps:

[0023] Based on basic emergency supplies data, we obtain the importance data of the supplies; based on the supply business data, we obtain the outbound time of each supply in each warehouse.

[0024] Based on the outbound time of each material in each warehouse, the outbound frequency of each material in each warehouse is calculated and processed to generate material usage frequency data. For example, the number of outbound times of a certain type of material in a warehouse within a set time sliding window (i.e., the number of times the corresponding material business data is generated) can be obtained, and then the outbound times / the duration of the set time sliding window = material usage frequency; or the material usage frequency can be obtained by calculating the proportion of the outbound times of this type of material within the set time sliding window to the outbound times of all types of materials within the set time sliding window.

[0025] The material importance data of each material in each warehouse is normalized to obtain standard material importance data. The material usage frequency data is also normalized to obtain standard material usage frequency data. In one embodiment, the material importance data can be represented by 1, 2, 3, ..., with larger numbers indicating higher importance. When normalizing the material usage frequency data, it is ensured that the higher the material usage frequency, the larger the normalized value, and that it is normalized to the same range as the material importance data.

[0026] Based on the set priority weights, the standard data of material importance and material usage frequency for each material in each warehouse are weighted and summed to obtain the material priority. A higher material priority value corresponds to a higher priority level, which facilitates the analysis of minimizing the product of material priority and material retrieval cost when constructing the objective function. The priority weights include material importance weights and material usage frequency weights; the sum of material importance weights and material usage frequency weights equals 1, and can be adjusted according to actual needs.

[0027] Furthermore, the optimal location analysis module for the materials constructs the objective function, including the following steps:

[0028] For each warehouse, the retrieval cost of each material placed in each storage location is calculated based on the material retrieval cost function model, and weighted by the corresponding material priority to obtain material storage location priority weighted cost data.

[0029] Based on the weighted cost data of material storage location priority, the total weighted cost of material storage location priority when each material is stored in each storage location of the warehouse is calculated and minimized to obtain the first objective function.

[0030] Establish constraints for the first objective function, including: each material must be placed in a storage location, and the total volume of materials stored in each storage location shall not exceed the maximum storage capacity of the storage location.

[0031] Furthermore, the optimal location analysis module for materials is also used to calculate the retrieval cost of each material when placed in each storage location based on the material retrieval cost function model, and to weight it using the corresponding material priority, including the following steps:

[0032] Based on the volume and weight of the materials, the corresponding tool material retrieval cost function is selected according to the cost function selection rule data;

[0033] Determine whether the number of the selected tool and material acquisition cost function is equal to 1;

[0034] If so, the selected tool material acquisition cost function is multiplied by the material priority corresponding to the material to obtain the material storage location priority weighted cost data of the material in that storage location;

[0035] If not, and greater than 1, then based on the proportion of the number of times each retrieval tool is used to the total number of times the retrieval tool is used in the material business data when the material is retrieved, the tool selection coefficient of each retrieval tool is obtained.

[0036] The cost function for each selected tool and material is weighted and summed using the corresponding tool selection coefficient, and then multiplied by the material priority corresponding to the material to obtain the material storage location priority weighted cost data of the material in that storage location.

[0037] If not, and the value is less than 1, an error occurs, and relevant data needs to be checked, such as whether the volume and weight of the materials are correct, and whether the cost function selection rule data is correct.

[0038] Furthermore, the optimal location analysis module for materials is also used to construct a second objective function, which aims to shorten the storage distance of materials with greater correlation, including the following steps:

[0039] Based on the material business data, the materials corresponding to each emergency event are searched, and emergency event material retrieval data is generated. Each emergency event corresponds to one retrieval, and one retrieval can include one or more categories of materials. For example, for emergency event 1, the material categories retrieved include: material 1, material 2 and material 3. That is, the emergency event material retrieval data for emergency event 1 = (material 1, material 2, material 3).

[0040] All categories of materials are paired up to generate multiple material association combinations;

[0041] For each material association combination, the frequency of simultaneous occurrence of materials of the corresponding category in all emergency event material data is statistically calculated to generate combination support data;

[0042] Calculate the frequency of each category of supplies in all emergency event supplies data to generate supplies support data;

[0043] Calculate the ratio of the support data of each combination to the product of the material support data of the corresponding two types of materials to obtain the combination correlation strength data;

[0044] Based on the warehouse basic data, obtain the location coordinates of the storage locations of the two categories of materials corresponding to each combination support data, and calculate the combination distance;

[0045] The second objective function is constructed by minimizing the sum of the products of all combined association strength data and the corresponding combined distances.

[0046] Furthermore, the optimal location analysis module for materials is also used to perform a weighted summation of the first objective function and the second objective function based on the set objective function weights to obtain the objective function, wherein the sum of the weights of the first objective function and the second objective function is equal to 1. During the algorithm optimization process, the weight of the second objective function can be gradually increased, and the final objective function weight is determined by comparing the quality of the objective function solutions under each weight.

[0047] A method for intelligent hierarchical management of public health emergency supplies includes the following steps:

[0048] S1. The material information entry module collects basic material data;

[0049] S2. The warehouse information entry module collects warehouse layout information and generates basic warehouse data.

[0050] S3, the material retrieval data module collects historical data on the outbound time, retrieval quantity, retrieval tools, retrieval duration, and corresponding emergency events for each warehouse and each material, and generates material business data.

[0051] S4. The Material Acquisition Cost Analysis Module performs multinomial regression analysis based on basic material data, basic warehouse data, and material business data to generate a material acquisition cost function model.

[0052] S5, the optimal location analysis module for supplies is used to extract and analyze the importance data and frequency of use data of supplies based on the basic data of emergency supplies and the business data of supplies, and then calculate the priority of supplies.

[0053] S6. The optimal location analysis module for materials also constructs an objective function based on basic warehouse data and material retrieval cost function to minimize the sum of the product of material priority and retrieval cost in the placement of materials in the warehouse, and optimizes the solution to obtain recommended data for the optimal location of materials.

[0054] 1. Compared with existing technologies, the present invention provides an intelligent hierarchical management system and method for health emergency supplies. By setting up a material information input module, a warehouse information input module, a material retrieval data module, a material retrieval cost analysis module, and a material optimal location analysis module, it constructs a material retrieval cost function model for retrieving various materials from the warehouse exit when placing them in storage locations. This model can be combined with material priorities to construct an objective function for minimizing the retrieval cost when placing various materials in storage locations in the warehouse. It analyzes the placement position of various materials at the minimum retrieval cost, thereby improving the overall mobilization efficiency of the warehouse when handling emergency events.

[0055] 2. Compared with the prior art, the present invention provides an intelligent hierarchical management system and method for health emergency supplies. By comprehensively calculating the priority of supplies based on their importance and usage frequency, the system achieves the goal of placing supplies with high importance and high usage frequency in storage locations with low retrieval cost when analyzing the placement of various supplies at the lowest retrieval cost. This facilitates the quick retrieval of supplies with high importance and high usage frequency.

[0056] 3. Compared with the prior art, the intelligent hierarchical management system and method for health emergency supplies provided by the present invention, by constructing a second objective function and summing it after weighting when constructing the objective function of minimizing retrieval cost, achieves the effect of allocating highly correlated supplies to nearby storage locations when analyzing the placement of various supplies at the minimum retrieval cost, so that supplies needed for various emergency events are placed nearby. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0058] Figure 1 A system structure block diagram provided for embodiments of the present invention;

[0059] Figure 2 The diagram illustrates the method steps provided in this embodiment of the invention. Detailed Implementation

[0060] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0061] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Exemplary embodiments will be described more fully below with reference to the accompanying drawings; however, these exemplary embodiments may be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will enable those skilled in the art to fully understand the scope of this disclosure.

[0062] Where there is no conflict, the various embodiments of this disclosure and the features thereof in the embodiments may be combined with each other.

[0063] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0064] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. As used herein, the singular forms “a” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded.

[0065] The embodiments described herein can be described with reference to plan views and / or cross-sectional views using the ideal schematic diagrams of this disclosure. Therefore, the exemplary illustrations can be modified according to manufacturing techniques and / or tolerances. Therefore, the embodiments are not limited to those shown in the drawings, but include modifications to configurations formed based on manufacturing processes. Therefore, the areas illustrated in the drawings are schematic in nature, and the shapes of the areas shown in the figures illustrate specific shapes of areas of an element, but are not intended to be limiting. Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art.

[0066] Please see Figure 1 A smart hierarchical management system for health emergency supplies includes a supplies information entry module, a warehouse information entry module, a supplies retrieval data module, a supplies retrieval cost analysis module, and a supplies optimal location analysis module.

[0067] The materials information entry module is used to collect basic materials data, including: collecting the materials' unique code, name, volume, weight, and importance level information to generate basic materials data; among which, multiple importance levels can be set as needed, and then the importance levels are converted into structured data for storage, which facilitates data processing and analysis. For example, importance levels can be represented by numbers, with larger numbers indicating higher importance; furthermore, the collected materials information can also include category (such as food, medical, rescue tools), production date, shelf life, storage requirements (such as room temperature, refrigeration, moisture protection), etc.

[0068] The warehouse information entry module is used to collect warehouse layout information and generate basic warehouse data. This layout information includes the location coordinates of all shelves and storage locations, storage location height (shelf level), aisle information, entrance and exit locations, path length from each storage location to the entrance / exit, and different functional areas (such as picking area, storage area, and cold storage area), the storage space size and load capacity of each storage location, and environmental information of the area where each storage location is located, such as aisle width. Data can be extracted from CAD drawings, digital floor plans generated through laser scanning, UAV mapping, etc., by importing the data from the CAD drawings.

[0069] The material retrieval data module is used to collect historical data on the outbound time, retrieval quantity, retrieval tools, retrieval duration, and corresponding emergency events for each warehouse and each material, generating material business data. Specifically, the historical outbound time of each warehouse and each material allows for the calculation of the usage frequency of each material in each warehouse. The retrieval quantity and retrieval tools can be used for training and updating the material retrieval cost function for the corresponding tools. Retrieval tools can include manual labor, handcarts, and forklifts, etc. Emergency event information during material retrieval is used to determine whether different categories of material retrieval correspond to an emergency event, which is helpful in identifying the correlation between materials.

[0070] The material retrieval cost analysis module is used to perform multinomial regression analysis based on basic material data, basic warehouse data, and material business data to generate a material retrieval cost function model, including the following steps:

[0071] (1) Obtain the storage height information of each storage location in each warehouse from the basic warehouse data, as well as the path length for retrieving each material from each storage location;

[0072] (2) Obtain the quantity of each material taken from each warehouse, the tools used for taking the materials, and the time taken from each storage location from the material business data;

[0073] (3) Obtain the volume and weight of each material from the basic material data;

[0074] (4) Collect information on the time, path length, volume, weight, quantity and tools used for taking each material from each warehouse, and generate sample data; among which, the time information and quantity taken are the time and quantity of a single take-out.

[0075] (5) Classify the sample data into multiple tool sample data sets according to the different tools used;

[0076] (6) Perform multinomial regression analysis on each tool sample data set to generate a tool material retrieval cost function that corresponds one-to-one with the retrieval tool; when performing multinomial regression analysis, the retrieval time is used as the dependent variable; the independent variables include storage height information, volume, weight, path length and retrieval quantity, and may also include volume and weight interaction terms, volume and path interaction terms, weight and path length interaction terms, etc.; the constant term is the basic retrieval time term;

[0077] (7) Based on the volume, weight and quantity information of the materials to be taken, set the selection rules of the cost function for each tool and material to be taken, and generate cost function selection rule data; for example, when the volume is less than the first volume threshold, the weight is less than the first weight threshold, and the quantity is less than the first quantity threshold, manual labor is selected; when the volume is less than the first volume threshold, the weight is less than the first weight threshold, and the quantity is greater than or equal to the first quantity threshold, a handcart is selected; or when the volume is greater than or equal to the first volume threshold and less than the second volume threshold, or the weight is greater than or equal to the first weight threshold and less than the second weight threshold, a handcart is selected; when the volume is greater than the second volume threshold and the weight is greater than the second weight threshold, a forklift is selected.

[0078] (8) Collect cost function selection rule data and cost functions for each tool and material use, and generate a material use cost function model.

[0079] In other embodiments, to improve the accuracy of the cost function for tool and material retrieval, independent variable terms can be added according to the type of tool used during multinomial regression analysis. For example, for manual labor: add operation complexity terms, path complexity terms, operation proficiency terms, etc.; for handcarts: add loading efficiency terms, turning penalty terms, aisle width impact terms, etc.; for forklifts: add lifting height time impact terms, operating space impact terms, etc.

[0080] The optimal location analysis module for supplies is used to extract and analyze the importance data and usage frequency data of supplies based on basic emergency supplies data and supply business data, and then calculate the priority of supplies. It is also used to construct an objective function based on basic warehouse data and supply retrieval cost function to minimize the sum of the product of the priority of supplies and the retrieval cost of the placement of supplies in the warehouse, and optimize the solution to obtain the recommended data of the optimal location of supplies. Optimization algorithms such as genetic algorithm, particle swarm optimization algorithm, and ant colony optimization algorithm can be used in the solution process.

[0081] The calculation of material priority includes the following steps:

[0082] (1) Obtain material importance data based on basic emergency material data, and obtain the outbound time of each material in each warehouse based on material business data;

[0083] (2) Based on the outbound time of each material in each warehouse, calculate the outbound frequency of each material in each warehouse to generate material usage frequency data; for example, the outbound frequency of a certain type of material in a warehouse within a set time sliding window (i.e., the number of times the corresponding material business data is generated) can be obtained, and then the outbound frequency / the duration of the set time sliding window = material usage frequency; or the material usage frequency can be obtained by calculating the proportion of the outbound frequency of this type of material within the set time sliding window to the outbound frequency of all types of materials within the set time sliding window.

[0084] (3) Normalize the material importance data of each material in each warehouse to obtain material importance standard data, and normalize the material usage frequency data to obtain material usage frequency standard data; in one embodiment, the material importance data can be represented by 1, 2, 3, ..., the larger the number, the higher the importance; when normalizing the material usage frequency data, ensure that the higher the material usage frequency, the larger the normalized value, and normalize it to the same range as the material importance data.

[0085] (4) Based on the set priority weights, the material importance standard data and material usage frequency standard data of each material in each warehouse are weighted and summed to obtain the material priority. The larger the material priority value, the higher the priority level, which is convenient for analyzing and minimizing the product of material priority and material acquisition cost when constructing the objective function later. The priority weights include material importance weight and material usage frequency weight. Material importance weight + material usage frequency weight = 1, which can be adjusted according to actual needs.

[0086] Constructing the objective function includes the following steps:

[0087] (1) For each warehouse, the retrieval cost of each material placed in each storage location is calculated based on the material retrieval cost function model, and the corresponding material priority is used for weighting to obtain the material storage location priority weighted cost data;

[0088] (2) Based on the material storage location priority weighted cost data, the total material storage location priority weighted cost when each material is stored in each storage location of the warehouse is calculated and minimized to obtain the first objective function, as shown in the following formula:

[0089]

[0090] in, The total priority weighted cost of material storage locations, where n is the quantity of material types and m is the number of storage locations in the warehouse. This indicates the priority of the i-th type of material. This represents the cost of retrieving items from the j-th storage location to the warehouse exit. For binary decision variables, satisfying the formula: .

[0091] (3) Establish the constraints of the first objective function, which include: each material must be placed in one storage location, and the total volume of materials stored in each storage location shall not exceed the maximum storage capacity of the storage location; wherein, each material must be placed in one storage location, as shown in the following formula:

[0092]

[0093] The total volume of materials stored in each storage location shall not exceed the maximum storage capacity of the storage location, as shown in the following formula:

[0094]

[0095] in, This represents the volume of the i-th type of material. This represents the maximum storage capacity of the j-th storage location.

[0096] Furthermore, the optimal location analysis module for materials is also used to construct a second objective function, which aims to shorten the storage distance of materials with greater correlation, including the following steps:

[0097] (1) Search for the materials corresponding to each emergency event based on the material business data and generate emergency event material data; each emergency event corresponds to one retrieval, and one retrieval may include one or more categories of materials. For example, for emergency event 1, the material categories retrieved include: material 1, material 2 and material 3, that is, the emergency event material data for emergency event 1 = (material 1, material 2, material 3).

[0098] (2) Combine all categories of materials in pairs to generate multiple material association combinations;

[0099] (3) For each material association combination, the frequency of the corresponding category of materials appearing simultaneously in all emergency event material data is statistically calculated to generate combination support data; for example, for material association combination (A, B), in all 50 emergency event material data, there are 15 emergency event material data that simultaneously contain material A and material B, then the combination support data of material association combination (A, B) = 15 / 50 = 0.3;

[0100] (4) Calculate the frequency of each category of materials in all emergency event material data and generate material support data; among all 50 emergency event material data, there are 20 emergency event material data containing material A and 20 emergency event material data containing material B, so the material support data for material A and material B are 0.4 and 0.4 respectively.

[0101] (5) Calculate the ratio of the support data of each combination to the product of the material support data of the two corresponding materials to obtain the combination association strength data; the combination association strength data of material association combination (A, B) = 0.3 / (0.4*0.4) = 1.875;

[0102] (6) Based on the warehouse basic data, obtain the location coordinates of the storage locations of the two categories of materials corresponding to each combination support data, and calculate the combination distance;

[0103] (7) Minimize the sum of the products of all combined association strength data and corresponding combined distances to construct a second objective function, as shown in the following formula:

[0104]

[0105] in, This represents the combined association strength data between the i-th type of material and the k-th type of material. Represents storage location j and storage location The distance between them Place the binary decision variable, material i, in the storage location. If it is true, it equals 1; otherwise, it equals 0.

[0106] (8) Based on the set objective function weights, the first objective function and the second objective function are weighted and summed to obtain the objective function, as shown in the following formula:

[0107]

[0108] The sum of the weights of the first objective function and the weights of the second objective function is equal to 1, that is... During algorithm optimization, the weight of the second objective function can be gradually increased. By comparing the quality of the objective function solutions under each weight, the final objective function weight is determined. The objective function Z shares the same constraints as the first objective function.

[0109] Furthermore, the optimal location analysis module is also used to calculate the retrieval cost of each material when placed in each storage location based on the material retrieval cost function model, and to weight it using the corresponding material priority, including the following steps:

[0110] (1) Based on the volume and weight of the materials, select the corresponding tool material retrieval cost function according to the cost function selection rule data;

[0111] (1) Determine whether the number of the selected tool and material cost function is equal to 1;

[0112] (2) If so, the selected tool material acquisition cost function is multiplied by the material priority corresponding to the material to obtain the material storage location priority weighted cost data of the material in the storage location.

[0113] (3) If not, and greater than 1, then based on the material business data, the proportion of the number of times each tool is used to the total number of times the selected tool is used according to the material cost function, the tool selection coefficient of each tool is obtained; if not, and less than 1, then an error has occurred and the relevant data needs to be checked, such as checking whether the volume and weight of the material are correct, and whether the cost function selection rule data is correct, etc.

[0114] (4) The cost function of each selected tool material is weighted and summed using the corresponding tool selection coefficient, and then multiplied by the material priority corresponding to the material to obtain the material storage location priority weighted cost data of the material in the storage location.

[0115] refer to Figure 2 The present invention also provides a method for intelligent hierarchical management of health emergency supplies, comprising the following steps:

[0116] S1. The material information entry module collects basic material data;

[0117] S2. The warehouse information entry module collects warehouse layout information and generates basic warehouse data.

[0118] S3, the material retrieval data module collects historical data on the outbound time, retrieval quantity, retrieval tools, retrieval duration, and corresponding emergency events for each warehouse and each material, and generates material business data.

[0119] S4. The Material Acquisition Cost Analysis Module performs multinomial regression analysis based on basic material data, basic warehouse data, and material business data to generate a material acquisition cost function model.

[0120] S5, the optimal location analysis module for supplies is used to extract and analyze the importance data and frequency of use data of supplies based on the basic data of emergency supplies and the business data of supplies, and then calculate the priority of supplies.

[0121] S6. The optimal location analysis module for materials is based on basic warehouse data and material retrieval cost function. It constructs an objective function to minimize the sum of the product of material priority and retrieval cost in the placement of materials in the warehouse, and optimizes the solution to obtain recommended data for the optimal location of materials.

[0122] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A smart hierarchical management system for emergency medical supplies, characterized in that, It includes a material information entry module, a warehouse information entry module, a material retrieval data module, a material retrieval cost analysis module, and a material optimal location analysis module; The material information entry module is used to collect basic material data; The warehouse information entry module is used to collect warehouse layout information and generate basic warehouse data. The material retrieval data module is used to collect information on the historical outbound time, retrieval quantity, retrieval tools, retrieval duration, and corresponding emergency events of each warehouse and each material, and generate material business data. The material acquisition cost analysis module is used to perform multinomial regression analysis based on basic material data, basic warehouse data and material business data to generate a material acquisition cost function model. The optimal location analysis module for supplies is used to extract and analyze the importance data and usage frequency data of supplies based on basic emergency supplies data and supply business data, and then calculate the priority of supplies; it is also used to construct an objective function based on basic warehouse data and supply retrieval cost function to minimize the sum of the product of the priority of supplies and the retrieval cost of the supply placement location in the warehouse, and optimize the solution to obtain the recommended data of the optimal location of supplies.

2. The intelligent hierarchical management system for emergency medical supplies according to claim 1, characterized in that, The material information entry module collects basic material data, including the following steps: Collect the unique code, name, volume, weight, and importance level information of the materials to generate basic material data.

3. The intelligent hierarchical management system for emergency medical supplies according to claim 1, characterized in that, The material acquisition cost analysis module constructs a material acquisition cost function model, including the following steps; The storage height information of each storage location in each warehouse and the path length for retrieving each material from each storage location are obtained from the basic warehouse data. The quantity of each material taken from each warehouse, the tools used for taking the material, and the time taken for taking the material from each storage location are obtained from the material business data. Obtain the volume and weight of each material taken from the basic material data; Collect information on the retrieval time, path length, volume, weight, quantity retrieved, and retrieval tools of each material in each warehouse, and generate sample data; The sample data is categorized into multiple tool sample data sets according to the different tools used for data retrieval; Multinomial regression analysis was performed on each tool sample dataset to generate a tool material acquisition cost function that corresponds one-to-one with the acquired tool. Based on the volume, weight, and quantity information of the materials to be used, set the selection rules for the cost function of each tool and material to be used, and generate cost function selection rule data; Collect the cost function selection rule data and the cost functions for each tool and material acquisition, and generate a material acquisition cost function model.

4. The intelligent hierarchical management system for emergency medical supplies according to claim 1, characterized in that, The optimal location analysis module for the materials calculates the material priority, including the following steps: Based on basic emergency supplies data, we obtain the importance data of the supplies; based on the supply business data, we obtain the outbound time of each supply in each warehouse. Based on the outbound time of each material in each warehouse, the outbound frequency of each material in each warehouse is calculated and processed to generate material usage frequency data. The material importance data of each material in each warehouse is normalized to obtain material importance standard data, and the material usage frequency data is normalized to obtain material usage frequency standard data. Based on the set priority weights, the material importance standard data and material usage frequency standard data of each material in each warehouse are weighted and summed to obtain the material priority.

5. The intelligent hierarchical management system for emergency medical supplies according to claim 1, characterized in that, The optimal location analysis module for the materials constructs the objective function, including the following steps: For each warehouse, the retrieval cost of each material placed in each storage location is calculated based on the material retrieval cost function model, and weighted by the corresponding material priority to obtain material storage location priority weighted cost data. Based on the material storage location priority weighted cost data, the total material storage location priority weighted cost when each material is stored in each storage location of the warehouse is calculated and minimized to obtain the first objective function; Establish constraints for the first objective function, including: each material must be placed in a storage location, and the total volume of materials stored in each storage location shall not exceed the maximum storage capacity of the storage location.

6. The intelligent hierarchical management system for emergency medical supplies according to claim 3, characterized in that, The optimal location analysis module for materials is also used to calculate the retrieval cost of each material when placed in each storage location based on the material retrieval cost function model, and to weight the costs using the corresponding material priorities, including the following steps: Based on the volume and weight of the materials, the corresponding tool material retrieval cost function is selected according to the cost function selection rule data; Determine whether the number of the selected tool and material acquisition cost function is equal to 1; If so, the selected tool material acquisition cost function is multiplied by the material priority corresponding to the material to obtain the material storage location priority weighted cost data of the material in that storage location; If not, and greater than 1, then based on the proportion of the number of times each retrieval tool is used to the total number of times the retrieval tool is used in the material business data when the material is retrieved, the tool selection coefficient of each retrieval tool is obtained. The cost function for each selected tool and material is weighted and summed using the corresponding tool selection coefficient, and then multiplied by the material priority corresponding to the material to obtain the material storage location priority weighted cost data of the material in that storage location.

7. The intelligent hierarchical management system for emergency medical supplies according to claim 5, characterized in that, The optimal location analysis module for the materials is also used to construct a second objective function, which aims to make the storage distance of materials with greater correlation closer, including the following steps: Based on the material business data, search for the materials corresponding to each emergency event and generate emergency event material retrieval data; All categories of materials are paired up to generate multiple material association combinations; For each material association combination, the frequency of simultaneous occurrence of materials of the corresponding category in all emergency event material data is statistically calculated to generate combination support data; Calculate the frequency of each category of supplies in all emergency event supplies data to generate supplies support data; Calculate the ratio of the support data of each combination to the product of the material support data of the corresponding two types of materials to obtain the combination correlation strength data; Based on the warehouse basic data, obtain the location coordinates of the storage locations of the two categories of materials corresponding to each combination support data, and calculate the combination distance; The second objective function is constructed by minimizing the sum of the products of all combined association strength data and the corresponding combined distances.

8. The intelligent hierarchical management system for emergency medical supplies according to claim 7, characterized in that, The optimal location analysis module for materials is also used to perform a weighted summation of the first objective function and the second objective function based on the set objective function weights to obtain the objective function.

9. A method for intelligent hierarchical management of emergency medical supplies, applicable to the intelligent hierarchical management system for emergency medical supplies as described in any one of claims 1-8, characterized in that, Includes the following steps: S1. The material information entry module collects basic material data; S2. The warehouse information entry module collects warehouse layout information and generates basic warehouse data. S3, the material retrieval data module collects historical data on the outbound time, retrieval quantity, retrieval tools, retrieval duration, and corresponding emergency events for each warehouse and each material, and generates material business data. S4. The Material Acquisition Cost Analysis Module performs multinomial regression analysis based on basic material data, basic warehouse data, and material business data to generate a material acquisition cost function model. S5, the optimal location analysis module for supplies is used to extract and analyze the importance data and frequency of use data of supplies based on the basic data of emergency supplies and the business data of supplies, and then calculate the priority of supplies. S6. The optimal location analysis module for materials also constructs an objective function based on basic warehouse data and material retrieval cost function to minimize the sum of the product of material priority and retrieval cost in the placement of materials in the warehouse, and optimizes the solution to obtain recommended data for the optimal location of materials.

Images