A method and system for cold chain distribution of vaccines based on big data analysis

Abstract

The present invention discloses a vaccine cold chain distribution method and system based on big data analysis, which relates to the technical field of big data analysis, including integrating information resources of vaccine cold chain, screening and verifying, constructing a multi-objective optimization model, predicting cost, time and carbon emissions, formulating a multi-modal transport plan based on the prediction results, finding the shortest route, and designing a logistics relay plan. During distribution, once an emergency occurs, an alternative route is generated until the distribution is completed. By integrating and verifying vaccine cold chain information, data accuracy is improved, the multi-objective optimization model balances cost, time and environmental factors, and promotes the refined greening of distribution strategies. Multi-modal transport supported by GIS and algorithms ensures efficient management of paths and temperature control, accelerates logistics response, and the emergency alternative route mechanism ensures supply chain resilience, jointly promoting the vaccine supply chain to be efficient, economical, green, and have strong emergency response capabilities, and ensuring that vaccines are distributed safely, quickly and environmentally friendly.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis, and in particular to a vaccine cold chain distribution method and system based on big data analysis. Background Art

[0002] In recent years, with the rapid development of global public health, vaccines, as a key means of preventing and controlling infectious diseases, have become a major challenge in their safe and effective distribution to various places. This process involves precise temperature control equipment, real-time monitoring systems and efficient logistics management, aiming to minimize losses and costs while meeting growing demand and strict regulatory requirements. However, the current vaccine cold chain distribution system still faces many challenges. First, information islands are common, data in various links are scattered, and there is a lack of unified management and efficient utilization, resulting in inflexible resource allocation and slow response. Secondly, traditional distribution strategies focus on a single mode of transportation, fail to fully utilize the synergistic benefits of multimodal transport, often sacrifice cost efficiency or environmental sustainability, and ignore the control of carbon emissions. In addition, in the face of emergencies, there is a lack of dynamic adjustment capabilities and an imperfect emergency response mechanism, which can easily cause delivery delays or even vaccine failure. Summary of the invention

[0003] In view of the above existing problems, the present invention is proposed.

[0004] Therefore, the present invention provides a vaccine cold chain distribution method and system based on big data analysis to solve the problems of information islands, single decision-making and insufficient response capabilities in vaccine cold chain distribution.

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

[0006] In a first aspect, an embodiment of the present invention provides a vaccine cold chain distribution method based on big data analysis, which includes integrating information resources of the vaccine cold chain, and screening and verifying the information resources;

[0007] Build a multi-objective optimization model to predict cost, time and carbon emissions;

[0008] Develop multimodal transport plans based on forecast results;

[0009] For multimodal transport solutions, find the shortest route and design logistics relay plans;

[0010] During delivery, if an emergency occurs, an alternative route is generated until the delivery is completed.

[0011] As a preferred solution of the vaccine cold chain distribution method based on big data analysis described in the present invention, the integrated vaccine cold chain information resources refer to the summary of vaccine demand, storage and transportation capacity information from vaccine suppliers, logistics companies, storage centers, and vaccination sites, as well as real-time road conditions and weather forecast data provided by the transportation department, and the collection of costs, time consumption and carbon emission factors of different transportation modes.

[0012] As a preferred solution of the vaccine cold chain distribution method based on big data analysis described in the present invention, a multi-objective optimization model is constructed to predict cost, time and carbon emissions, including:

[0013] Define the minimum cost as C, the minimum delivery time as T, and the minimum carbon emission as E;

[0014] Design cost function f D (D), expressed as

[0015] f D (D) = aD b +cD+d;

[0016] In the formula, a represents the cost coefficient that has a nonlinear relationship with distance, b represents the exponent that controls the growth rate of distance cost, d represents the fixed cost, D represents the distance, and c represents the fixed cost per unit distance;

[0017] Design speed adjustment function g S (s,t), expressed as,

[0018]

[0019] In the formula, S represents speed, k represents the adjustment coefficient of the maximum possible influence of external conditions on speed, λ represents the decay rate, t0 represents the reference time point, and t represents the time variable;

[0020] Design carbon emission prediction function h E (E,D), expressed as,

[0021] h E (E,D)=mD n +pE q ;

[0022] In the formula, m represents the coefficient related to the distance D, p represents the coefficient directly related to the current carbon emissions E, n represents the index of the impact of the distance D on the growth rate of carbon emissions, and q represents the index of the nonlinear impact of the growth of carbon emissions E itself;

[0023] Combining nonlinear terms and dynamic adjustment mechanisms, a multi-objective optimization model F is constructed, which can be expressed as:

[0024]

[0025] In the formula, e -α·gS(S,t) represents the speed influence factor that changes with time, W C , W T , W E are the weights of cost, time and carbon emission respectively, α represents the adjustment parameter of the speed adjustment function, β represents the adjustment parameter of the nonlinear relationship between time T and carbon emission E, and γ represents the adjustment parameter of the carbon emission prediction function.

[0026] As a preferred solution of the vaccine cold chain distribution method based on big data analysis of the present invention, the multi-modal transport plan based on the prediction results includes:

[0027] First, clarify the starting point, destination, total volume, time window, and budget constraints of vaccine transportation, analyze the location of each transportation node based on geographic information system, and evaluate the accessibility of different transportation modes;

[0028] Then, based on the evaluation results, the best mode of transportation for each section is preliminarily determined, and the internal path optimization is performed for each mode of transportation using the Dijkstra algorithm;

[0029] Finally, based on the prediction results and optimized paths, seamless docking design is carried out to generate a multimodal transport solution.

[0030] As a preferred solution of the vaccine cold chain distribution method based on big data analysis described in the present invention, wherein: for the multimodal transport solution, finding the shortest route includes:

[0031] Construct a weighted directed graph G = (V, E), where the vertex set V includes the starting point, the end point, the transfer point and all the transportation mode conversion points, and the edge set E represents the direct transportation connection between two points;

[0032] For each edge e ij , calculate the time cost w(e ij ), expressed as,

[0033]

[0034] In the formula, m represents the transport mode, d ij Indicates distance, v m Indicates speed, represents the waiting time between nodes i and j;

[0035] Starting from the starting point, the nodes are gradually expanded until the end point is found. In each expansion, the path costs of all its unvisited adjacent nodes are checked and updated, and finally the shortest route is found.

[0036] As a preferred solution of the vaccine cold chain distribution method based on big data analysis described in the present invention, the logistics relay plan is designed to include:

[0037] According to the shortest route results, mark all relay points and generate a transportation mode network diagram;

[0038] Design a time window [t s ,t e ], calculate the buffer time t under the temperature control standard b , expressed as,

[0039]

[0040] Where δt represents the average handover time estimated based on historical data, n r Indicates the number of relay points;

[0041] Set the temperature control standard of the relay point θ min to θ max , and monitor the temperature change dT within the specified range, expressed as,

[0042] θ min -∈≤T c +dT≤θ mac +∈;

[0043] Where, T c represents the temperature of the vaccine during the relay handover, and ∈ represents the allowable temperature error range.

[0044] As a preferred solution of the vaccine cold chain distribution method based on big data analysis described in the present invention, in which: during distribution, once an emergency occurs, an alternative route is generated until the distribution is completed, including:

[0045] In actual distribution, if an emergency occurs and the original route is impassable, in the weighted directed graph G = (V, E), the shortest route is re-found with the accident site as the starting point, an alternative route is generated, and a logistics relay plan is designed until the distribution is completed.

[0046] In a second aspect, the present invention provides a vaccine cold chain distribution system based on big data analysis, including an information resource integration and verification module for integrating information resources of the vaccine cold chain and screening and verifying the information resources;

[0047] Multi-objective optimization prediction model module, which is used to build a multi-objective optimization model to predict cost, time and carbon emissions;

[0048] Multimodal transport solution design module, used to formulate multimodal transport solutions based on forecast results;

[0049] Intelligent route planning and logistics relay planning module, used to find the shortest route and design logistics relay plans for multi-modal transport solutions;

[0050] The emergency response and alternative route generation module is used to generate alternative routes in the event of an emergency during delivery until the delivery is completed.

[0051] In the third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the vaccine cold chain distribution method based on big data analysis as described in the first aspect of the present invention is implemented.

[0052] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the vaccine cold chain distribution method based on big data analysis as described in the first aspect of the present invention.

[0053] The beneficial effects of the present invention are as follows: integrating vaccine cold chain information resources and screening and verifying them, improving data accuracy and decision-making efficiency, and strengthening vaccine safety management. The constructed multi-objective optimization model takes into account cost, time, and environmental impacts, and promotes the refinement and green transformation of distribution strategies. With the help of GIS and algorithm optimization, the multimodal transport solution realizes efficient path planning and temperature control management, greatly improving logistics efficiency and response speed, while reducing costs and risks. The alternative route generation mechanism in emergency situations ensures the resilience of the supply chain and the uninterrupted distribution of vaccines, and effectively responds to unknown challenges. In summary, these steps jointly ensure the efficient operation, cost-effectiveness, environmental sustainability, and emergency response capabilities of the vaccine supply chain, laying a solid foundation for the safe, rapid, and environmentally friendly delivery of vaccines. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0055] Figure 1 This is a flow chart of the vaccine cold chain distribution method based on big data analysis in Example 1.

[0056] Figure 2 This is a decision diagram for generating alternative routes when implementing delivery in Example 1. DETAILED DESCRIPTION

[0057] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0058] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0059] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0060] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a vaccine cold chain distribution method based on big data analysis, comprising the following steps:

[0061] S1. Integrate the information resources of the vaccine cold chain, and screen and verify the information resources.

[0062] Integrating information resources for the vaccine cold chain means aggregating information on vaccine demand, storage and transportation capacity from vaccine suppliers, logistics companies, storage centers, and vaccination sites, as well as real-time road conditions and weather forecast data provided by the transportation department, and collecting information on the cost, time, and carbon emission factors of different transportation modes.

[0063] De-duplicate information resources of the vaccine cold chain, identify and delete duplicate records, avoid information redundancy, check integrity, ensure that key fields of each record (such as vaccine batch number, expiration date) are not missing, use algorithms to detect logical consistency between data, use historical data to compare new input data, identify outliers and make corrections, and after cleaning and verification, classify and integrate various types of information into the central database;

[0064] It should be noted that by integrating information resources from all links of the vaccine supply chain, data transparency and centralized management of the entire vaccine circulation chain have been achieved. This step ensures the comprehensiveness and accuracy of the data by eliminating information islands, which helps to improve decision-making efficiency. Deduplication and integrity checks avoid waste of resources and potential decision-making biases, while algorithm detection of data logical consistency and outlier correction further improves data quality. These measures work together to achieve the beneficial effects of reducing management costs, improving response speed and ensuring vaccine safety, providing a solid and reliable data foundation for subsequent optimization models.

[0065] S2. Build a multi-objective optimization model to predict cost, time and carbon emissions.

[0066] Define the minimum cost as C, the minimum delivery time as T, and the minimum carbon emission as E;

[0067] Based on minimizing the cost C, design the cost function f D (D), expressed as

[0068] f D (D) = aD b +cD+d;

[0069] In the formula, a represents the cost coefficient that is nonlinearly related to distance, reflecting the possible economies of scale in long-distance transportation, b represents the index that controls the growth rate of distance cost. If b>1, the cost increases rapidly with increasing distance, and if b<1, the growth rate decreases, d represents the fixed cost, which is not affected by distance, D represents the distance, and c represents the fixed cost per unit distance.

[0070] It should be noted that the design of the cost function is based on the principles of economic cost analysis. The complex impact of distance on cost is comprehensively considered through nonlinear and linear components to ensure that the model is close to the actual operating cost structure.

[0071] Based on minimizing the delivery time T, design the speed adjustment function g S (s,t), expressed as,

[0072]

[0073] Where S represents speed, that is, the maximum speed that the vehicle can reach without external interference, k represents the adjustment coefficient of the maximum possible influence of external conditions on speed. The larger the value, the more significant the influence of external factors. λ represents the attenuation rate, which determines the speed at which the influence of external conditions on speed decreases over time. The larger the λ, the faster the influence decays. t0 represents the reference time point, which is generally the departure time, and is used to calculate the relative time difference of the time variable t relative to the departure time. t represents the time variable.

[0074] It should be noted that the design basis of the speed adjustment function is to integrate traffic dynamics and environmental response theory, simulate the actual impact of external conditions changing dynamically over time on the transportation speed, and enhance the time dynamics and practicality of the model.

[0075] Based on minimizing carbon emissions E, design a carbon emissions prediction function h E (E,D), expressed as,

[0076] h E (E,D)=mD n +pE q ;

[0077] In the formula, m represents the coefficient related to the distance D, reflecting the direct impact of the transportation distance on carbon emissions; p represents the coefficient directly related to the current carbon emissions E, reflecting the promotion of the existing carbon emissions level to further emissions; n represents the index of the impact of the distance D on the growth rate of carbon emissions, n>1 means that the impact of distance growth on carbon emissions is accelerating; q represents the index of the nonlinear impact of the growth of carbon emissions E itself, q>1 indicates that as the emissions increase, its growth rate is also accelerating;

[0078] It should be noted that the design basis of the carbon emission prediction function is to draw on the emission model in environmental science, combine the characteristics of logistics activities, quantify the comprehensive effect of distance and cumulative emissions on carbon footprint, and support green logistics decision-making.

[0079] Combining nonlinear terms and dynamic adjustment mechanisms, a multi-objective optimization model F is constructed, which can be expressed as:

[0080]

[0081] In the formula, e -α·gS(S,t) represents the speed influence factor that changes with time, W C , W T , W E are the weights of cost, time and carbon emissions respectively, which are used to balance the importance of different objectives in multi-objective optimization. α represents the adjustment parameter of the speed adjustment function. When α is larger, the adjustment effect of time factor on cost is more obvious. β represents the adjustment parameter of the nonlinear relationship between time T and carbon emissions E, which controls the nonlinear relationship between carbon emissions and time saving. A high β means that more time is willing to be sacrificed in order to reduce carbon emissions. γ represents the adjustment parameter of the carbon emission prediction function, which increases the influence of the carbon emission prediction function on the total objective function. When γ is large, more attention is paid to the reduction of carbon emissions.

[0082] It should be noted that by designing a multi-objective optimization model that includes a cost function, a speed adjustment function, and a carbon emission prediction function, a detailed planning of the vaccine cold chain distribution strategy is achieved. The introduction of the cost function takes into account the complex relationship between transportation distance and cost, and aims to find the most cost-effective solution through the guidance of economic principles. The speed adjustment function is dynamically adjusted according to external conditions to ensure the accuracy of the delivery time prediction, enhancing the practicality and adaptability of the model. The support of the carbon emission prediction function shows that the model attaches importance to environmental protection and promotes green logistics practices. The optimization model constructed by combining these functions has achieved the beneficial effect of reducing costs, shortening delivery time, and reducing environmental impact, reflecting the concept of sustainable development.

[0083] S3. Develop a multimodal transport plan based on the forecast results.

[0084] First, clarify the starting point, destination, total volume, time window, and budget constraints of vaccine transportation. Use geographic information system technology to accurately mark and analyze the geographical locations of key nodes such as the starting point, transfer station, and destination, and evaluate factors such as the direct distance, terrain challenges, and climate conditions of different transportation routes to provide basic data for the subsequent selection of transportation modes. Based on GIS data, evaluate the accessibility and timeliness of different transportation modes such as roads, railways, aviation, and waterways;

[0085] Then, based on the evaluation results, the most suitable transportation mode is preliminarily assigned to different sections of the entire transportation route. For each selected transportation mode, the Dijkstra algorithm is used to find the shortest path from the starting point to the end point in the network graph of the mode. This classic algorithm can effectively handle graphs with weights. The weights here can be distance, time or cost, ensuring that the optimal internal path is found;

[0086] Finally, based on the prediction results and optimized paths, a seamless connection plan is designed between different transport modes, including rapid loading and unloading of goods, temperature control management, safety inspection and document delivery, to ensure the integrity and effectiveness of the vaccine during transportation, identify potential risk points in each link, and formulate corresponding emergency plans to generate multimodal transport plans. Based on the predicted transport volume, time window and budget constraints, a comprehensive cost-benefit analysis of the multimodal transport plan is conducted to ensure that the plan is both economical and efficient.

[0087] It should be noted that through the application of GIS technology, this step realizes the precise planning of vaccine transportation routes, ensuring the efficiency and feasibility of transportation. Based on the evaluation of different transportation modes and the application of Dijkstra algorithm, the customized multimodal transport plan not only optimizes the route selection, but also takes into account key links such as temperature control management, effectively ensuring the integrity and effectiveness of the vaccine. This process reduces unnecessary transfers and delays, improves logistics efficiency, and ensures the economy and environmental friendliness of the plan through cost-benefit analysis.

[0088] S4. For multimodal transport solutions, find the shortest route and design a logistics relay plan.

[0089] Construct a weighted directed graph G = (V, E), where the vertex set V includes the starting point, the end point, the transfer point and all the transportation mode conversion points, and the edge set E represents the direct transportation connection between two points;

[0090] For each edge e ij , calculate the time cost w(e ij ), expressed as,

[0091]

[0092] In the formula, m represents the transport mode, d ij Indicates distance, v m Indicates speed, represents the waiting time between nodes i and j;

[0093] Starting from the starting point, the nodes are gradually expanded until the end point is found. In each expansion, the path costs of all its unvisited adjacent nodes are checked and updated to ensure that the path with the lowest total cost is always followed, and finally the shortest route is found.

[0094] According to the shortest route results, determine the points where logistics relay is required and generate a transportation mode network diagram;

[0095] Design a time window [t s ,t e ], ensure that logistics activities are completed within this period to reduce waiting and delays, and calculate the buffer time t under the temperature control standard b , expressed as,

[0096]

[0097] Where δt represents the average handover time estimated based on historical data, n r Indicates the number of relay points;

[0098] Set the temperature control standard of the relay point θ min to θ max , vaccines need to be stored within a specific temperature range to maintain their effectiveness. These two values ​​define the minimum and maximum temperature limits for vaccine storage and transportation, and monitor the temperature change dT within the specified range, expressed as,

[0099] θ min -∈≤T c +dT≤θ max +∈;

[0100] Where, T c represents the temperature of the vaccine during the relay handover, and ∈ represents the allowable temperature error range.

[0101] It should be noted that by constructing a weighted directed graph and using the shortest path algorithm, this step achieves the goal of quickly identifying the shortest route in multimodal transport, ensuring the rapid delivery of vaccines. The meticulous design of the logistics relay plan, including the setting of time windows and strict compliance with temperature control standards, not only ensures the quality control of vaccines, but also reduces time loss and temperature fluctuation risks through refined management, achieving the beneficial effects of increasing delivery speed, ensuring vaccine quality and reducing temperature control risks.

[0102] S5. During delivery, if an emergency occurs, generate an alternative route until the delivery is completed.

[0103] In actual distribution, GPS positioning systems and real-time traffic information platforms (such as Google Maps API) are used to monitor road conditions. Once unforeseen events such as traffic accidents, natural disasters, road closures, etc. are found on the original route, the emergency response mechanism is immediately triggered. In the weighted directed graph G = (V, E), the current location where the emergency occurs is marked as the new starting point S', and the Dijkstra algorithm is applied again to find a new path with the lowest time cost, generate alternative routes, re-mark the logistics relay points based on the alternative routes, and adjust the original relay plan until the distribution is completed.

[0104] It should be noted that this step ensures the flexibility and resilience of vaccine distribution through real-time monitoring and emergency response mechanisms. In the face of emergencies, routes can be quickly adjusted and relay plans replanned to minimize the impact of unexpected events on vaccine distribution. This measure effectively improves the risk resistance of the vaccine supply chain and ensures that vaccines can be delivered on time and safely even under adverse conditions, achieving the beneficial effects of improving emergency response capabilities, ensuring distribution continuity, and enhancing supply chain stability.

[0105] This embodiment also provides a vaccine cold chain distribution system based on big data analysis, including an information resource integration and verification module for integrating information resources of the vaccine cold chain and screening and verifying the information resources;

[0106] Multi-objective optimization prediction model module, which is used to build a multi-objective optimization model to predict cost, time and carbon emissions;

[0107] Multimodal transport solution design module, used to formulate multimodal transport solutions based on forecast results;

[0108] Intelligent route planning and logistics relay planning module, used to find the shortest route and design logistics relay plans for multi-modal transport solutions;

[0109] The emergency response and alternative route generation module is used to generate alternative routes in the event of an emergency during delivery until the delivery is completed.

[0110] This embodiment also provides a computer device, which is suitable for the case of a vaccine cold chain distribution method based on big data analysis, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the vaccine cold chain distribution method based on big data analysis proposed in the above embodiment.

[0111] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0112] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by the processor, the vaccine cold chain distribution method based on big data analysis as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, disk or optical disk.

[0113] In summary, the beneficial effect of the present invention is to integrate vaccine cold chain information resources and perform screening and verification, improve data accuracy and decision-making efficiency, and strengthen vaccine safety management. The constructed multi-objective optimization model takes into account cost, time and environmental impacts, and promotes the refinement and green transformation of distribution strategies. With the help of GIS and algorithm optimization, the multimodal transport solution realizes efficient path planning and temperature control management, greatly improves logistics efficiency and response speed, and reduces costs and risks. The alternative route generation mechanism in emergency situations ensures the resilience of the supply chain and the uninterrupted distribution of vaccines, and effectively responds to unknown challenges. In summary, these steps jointly ensure the efficient operation, cost-effectiveness, environmental sustainability and emergency response capabilities of the vaccine supply chain, laying a solid foundation for the safe, rapid and environmentally friendly delivery of vaccines.

[0114] Example 2

[0115] Referring to Table 1, which is the second embodiment of the present invention, in order to further verify the advancement of the present invention, experimental simulation data of a vaccine cold chain distribution method based on big data analysis is provided.

[0116] In order to verify the effectiveness of the present invention, a simulated vaccine cold chain distribution experiment was designed, covering the whole process from vaccine suppliers to vaccination sites. First, diversified information resources including vaccine demand, storage and transportation capacity information, as well as real-time road conditions, weather forecasts, etc. were collected and integrated. The specific data involved 5 vaccine suppliers, 3 storage centers and 10 vaccination sites. A total of daily demand data (unit: dose), storage capacity (unit: cubic meters) and transport vehicle load (unit: tons) were collected in the past year. At the same time, the traffic congestion index and weather forecast data for three consecutive months provided by the Ministry of Transport were obtained to ensure the timeliness and accuracy of the information.

[0117] Then, the proposed method is used to construct a multi-objective optimization model. According to historical data and current policies, the weight ratio of cost, time, and carbon emissions is set as W. C :W T :W E =2:1:1.5, and adjusted the coefficient values ​​in the cost function, speed adjustment function and carbon emission prediction function based on the actual parameters. For example, a=0.05, b=1.2, c=100, d=500, k=0.9, λ=0.05, m=0.01, n=1.5, p=0.001, q=2.5, α=0.1, β=0.005, γ=0.8. Through iterative calculation, the optimal distribution strategy is obtained.

[0118] Based on this, a Geographic Information System (GIS) analysis was used to determine the best multimodal transport solution, including air transport, land transport and refrigerated truck relay. For each mode, the Dijkstra algorithm was used to optimize the internal path, reducing unnecessary detours and waiting time. In the simulation, a total of 6 different intermodal routes were planned, each of which included at least two transport modes, covering a total distance of about 5,000 kilometers.

[0119] The details are shown in the following table:

[0120] Table 1 Experimental record table

[0121]

[0122] Comparative analysis shows that although route D performs best in delivery time (22.3 hours), its cost (26,000 yuan) and carbon emissions (1,450kg CO2) are higher. Although route C has the lowest cost (22,800 yuan) and less carbon emissions (1,100kg CO2), it takes longer to deliver (26.1 hours). Taking all factors into consideration, route A achieves a good balance between cost (23,500 yuan), delivery time (24.7 hours) and carbon emissions (1,250kg CO2), reflecting the effectiveness of the multi-objective optimization model.

[0123] In addition, the generation mechanism of alternative routes in emergency situations was simulated. When route B was impassable due to an accident, an alternative route was quickly found, which only increased the delivery time by 1.2 hours, increased the cost by 5%, and increased carbon emissions by 3%, ensuring the continuity and reliability of vaccine distribution. This result shows that the emergency response mechanism of the present invention enhances the resilience of the supply chain.

[0124] Through the buffer time calculation under the temperature control standard and the relay point temperature control monitoring, the temperature of the vaccine is ensured to be stable at θ min -∈ to θ max +∈ range, effectively avoiding the risk of vaccine failure caused by temperature fluctuations, further verifying the practicality of the logistics relay plan.

[0125] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A vaccine cold chain distribution method based on big data analysis, characterized in that: include, Integrate vaccine cold chain information resources, and screen and verify the information resources; Build a multi-objective optimization model to predict cost, time and carbon emissions; Develop multimodal transport plans based on forecast results; For multimodal transport solutions, find the shortest route and design logistics relay plans; During delivery, if an emergency occurs, an alternative route is generated until the delivery is completed; The method of finding the shortest route for the multimodal transport solution includes: Construct a weighted directed graph G = (V, E), where the vertex set V includes the starting point, the end point, the transfer point and all the transportation mode conversion points, and the edge set E represents the direct transportation connection between two points; For each edge e ij , calculate the time cost w(e ij ), expressed as, In the formula, m represents the transport mode, d ij Indicates distance, v m Indicates speed, represents the waiting time between nodes i and j; Starting from the starting point, gradually expand the nodes until the end point is found. In each expansion, check and update the path costs of all its unvisited adjacent nodes, and finally find the shortest route; Designing a logistics relay plan includes: According to the shortest route results, mark all relay points and generate a transportation mode network diagram; Design a time window [t s ,t e ], calculate the buffer time t under the temperature control standard b , expressed as, Where δt represents the average handover time estimated based on historical data, n r Indicates the number of relay points; Set the temperature control standard of the relay point θ min to θ max , and monitor the temperature change dT within the specified range, expressed as, i min -∈≤T c +dT≤θ max +∈; Where, T c represents the temperature of the vaccine during the relay handover, ∈ represents the allowable temperature error range; During delivery, if an emergency occurs, generate alternative routes until the delivery is completed, including: In actual distribution, if an emergency occurs and the original route is impassable, in the weighted directed graph G = (V, E), the shortest route is re-found with the accident site as the starting point, an alternative route is generated, and a logistics relay plan is designed until the distribution is completed.

2. The vaccine cold chain distribution method based on big data analysis according to claim 1, characterized in that: The integrated vaccine cold chain information resources refer to the information resources collected from vaccine suppliers, logistics companies, storage centers, and vaccination sites. It collects information on demand, storage and transportation capacity, as well as real-time road conditions and weather forecast data provided by the transportation department, and collects information on the cost, time and carbon emission factors of different transportation modes.

3. The vaccine cold chain distribution method based on big data analysis as claimed in claim 2, characterized in that: Construct a multi-objective optimization model to predict cost, time and carbon emissions, including: Define the minimum cost as C, the minimum delivery time as T, and the minimum carbon emission as E; Design cost function f D (D), expressed as f D (D)=aD b +cD+d; In the formula, a represents the cost coefficient that has a nonlinear relationship with distance, b represents the exponent that controls the growth rate of distance cost, d represents the fixed cost, D represents the distance, and c represents the fixed cost per unit distance; Design speed adjustment function g S (s,t), expressed as, In the formula, S represents speed, k represents the adjustment coefficient of the maximum possible influence of external conditions on speed, λ represents the decay rate, t0 represents the reference time point, and t represents the time variable; Design carbon emission prediction function h E (E,D), expressed as, In the formula, m represents the coefficient related to the distance D, p represents the coefficient directly related to the current carbon emissions E, n represents the index of the impact of the distance D on the growth rate of carbon emissions, and q represents the index of the nonlinear impact of the growth of carbon emissions E itself; Combining nonlinear terms and dynamic adjustment mechanisms, a multi-objective optimization model E is constructed, which is expressed as: In the formula, e -α·gS(S,t) represents the speed influence factor that changes with time, W C , W T , W E are the weights of cost, time and carbon emission respectively, α represents the adjustment parameter of the speed adjustment function, β represents the adjustment parameter of the nonlinear relationship between time T and carbon emission E, and γ represents the adjustment parameter of the carbon emission prediction function.

4. The vaccine cold chain distribution method based on big data analysis as claimed in claim 3, characterized in that: The multi-modal transport plan based on the forecast results includes: First, clarify the starting point, destination, total volume, time window, and budget constraints of vaccine transportation, analyze the location of each transportation node based on geographic information system, and evaluate the accessibility of different transportation modes; Then, based on the evaluation results, the best mode of transportation for each section is preliminarily determined, and the internal path optimization is performed for each mode of transportation using the Dijkstra algorithm; Finally, based on the prediction results and optimized paths, seamless docking design is carried out to generate a multimodal transport solution.

5. A vaccine cold chain distribution system based on big data analysis, based on the vaccine cold chain distribution method based on big data analysis according to any one of claims 1 to 4, characterized in that: include, Information resource integration and verification module, used to integrate vaccine cold chain information resources and screen and verify the information resources; Multi-objective optimization prediction model module, which is used to build a multi-objective optimization model to predict cost, time and carbon emissions; Multimodal transport solution design module, used to formulate multimodal transport solutions based on forecast results; Intelligent route planning and logistics relay planning module, used to find the shortest route and design logistics relay plans for multi-modal transport solutions; The emergency response and alternative route generation module is used to generate alternative routes in the event of an emergency during delivery until the delivery is completed.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the vaccine cold chain distribution method based on big data analysis as described in any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the vaccine cold chain distribution method based on big data analysis described in any one of claims 1 to 4 are implemented.

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