A vaccine logistics cold chain management method and system based on SaaS platform

Abstract

The present invention discloses a vaccine logistics cold chain management method and system based on a SaaS platform, which relates to the field of Internet of Things technology, including analyzing vaccination site data, predicting vaccine demand, automatically generating a replenishment list, ensuring sufficient inventory, dynamically adjusting the replenishment order using a priority algorithm, quickly responding to emergency needs, intelligently planning distribution routes, reducing costs and transportation time, and monitoring the transportation environment at the same time. Automatic regulation maintains vaccine quality, thereby constructing an efficient and reliable vaccine cold chain management system, reducing vaccine waste, accelerating supply response, strengthening supply chain stability and vaccine safety, and providing strong technical guarantees for public health security.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to a vaccine logistics cold chain management method and system based on a SaaS platform. Background Art

[0002] In recent years, with the deep integration of big data and artificial intelligence technologies, cloud computing platforms have become an important cornerstone for building efficient and intelligent network security early warning systems. However, in public health management, especially in the field of vaccine supply chain, the application of existing technologies still has certain limitations and challenges. Although cloud computing infrastructure has begun to be applied to vaccine logistics management, existing technologies mainly rely on traditional statistical methods and simple prediction models to estimate vaccine demand, which makes it difficult to fully capture the impact of seasonal changes, public health emergencies and other complex factors, resulting in frequent surpluses and shortages in vaccine supply, affecting the smooth implementation of vaccination programs. In addition, although advances in cold chain logistics technology can ensure basic temperature control of vaccines during transportation, in the face of complex and changeable external environments and emergencies, existing monitoring methods often have a delayed response and are difficult to achieve immediate adjustment of the carriage environment, increasing the risk of 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 logistics cold chain management method, device, computer equipment and storage medium based on a SaaS platform to solve the problems of inaccurate demand forecasting in the vaccine supply chain and inflexible environmental control during transportation.

[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 logistics cold chain management method based on a SaaS platform, which includes:

[0007] Collect real-time and historical data from vaccination sites, use artificial intelligence algorithms to analyze the data, and predict future vaccine needs;

[0008] Automatically generate replenishment lists based on demand forecast results and current inventory status;

[0009] Suppliers receive replenishment instructions, prepare vaccines and arrange cold chain logistics, while intelligently planning the optimal delivery route based on geographic location information;

[0010] Real-time monitoring of the temperature, humidity and vehicle position in the carriage during transportation, and automatic control of the carriage environment.

[0011] As a preferred solution of the vaccine logistics cold chain management method based on the SaaS platform described in the present invention, the real-time and historical data include vaccine type, number of vaccinations, vaccination date, seasonal index, holiday identifier and geographic location information.

[0012] As a preferred solution of the vaccine logistics cold chain management method based on the SaaS platform described in the present invention, based on real-time and historical data, artificial intelligence algorithms are used to analyze data and predict future vaccine demand. The expression is:

[0013]

[0014] Among them, D i,t+h represents the demand for the i-th vaccine at the future time t+h, i represents the type index of the vaccine, t represents the current time point, h represents the predicted number of future days, j represents the traversal time index, m represents the total number of time points, ω j represents the weight of time point j, N i，t-h+j represents the number of people vaccinated at time point t-h+j, φ i，t represents the adjustment coefficient of the specific population demand for vaccine type i at time t, g represents the traversal of different age groups, A g represents the population of age group g, P r represents the overall population density of the region, p represents the disease type, q represents the cycle number, and E p,q represents the periodic disease outbreak impact factor, η i，t represents the adjustment factor for vaccine type i at time t based on the periodic impact of disease outbreaks.

[0015] As a preferred solution of the vaccine logistics cold chain management method based on the SaaS platform described in the present invention, the demand forecast result is combined with the current inventory status to obtain the demand replenishment quantity. The specific operation steps are as follows:

[0016] The information on the current inventory of each vaccine is extracted in real time from the inventory management system of the SaaS platform, denoted as I i，t ;

[0017] Calculate the difference between the current inventory and the forecast demand to get the required replenishment amount, the expression is:

[0018] ΔQ i，t+h =D i，t+h -I i，t ;

[0019] Among them, ΔQ i，t+h It represents the demand for the i-th vaccine at the future time t+h;

[0020] Set a safety stock level S for each vaccine iand the minimum replenishment threshold T i ,

[0021] When ΔQ i，t+h >0 and I i，t <S i , I i，t <T i When , the replenishment instruction is generated.

[0022] As a preferred solution of the vaccine logistics cold chain management method based on the SaaS platform described in the present invention, a replenishment instruction is generated based on the replenishment demand combined with the priority coefficient. The specific operation steps are as follows:

[0023] Define a priority coefficient P for each vaccine based on the urgency of vaccine demand, historical shortages, and transportation time. i , the expression is:

[0024]

[0025] Among them, α represents the weight coefficient used to adjust the demand gap, β represents the weight coefficient used to adjust the average vaccine supply cycle on the priority, γ represents the weight coefficient used to consider the impact of past shortages on current replenishment decisions, SH i represents the frequency of shortage of vaccine i in the past period of time, L i represents the supply speed of vaccine i;

[0026] Generate replenishment instructions based on the required replenishment quantity and priority coefficient. The expression is:

[0027] Q i =max(ΔQ i , t +h , P i );

[0028] Among them, Q i Indicates the quantity to be replenished;

[0029] The system automatically generates a list of vaccine types i and the quantity to be replenished Q. i , Priority P i The replenishment instructions are immediately pushed to suppliers through the automated interface of the SaaS platform.

[0030] As a preferred solution of the vaccine logistics cold chain management method based on the SaaS platform described in the present invention, the supplier receives the replenishment instruction, prepares the vaccine and arranges the cold chain logistics, and intelligently plans the optimal delivery route based on the geographic location information. The specific operation steps are as follows:

[0031] After suppliers receive replenishment instructions in real time through the API of the SaaS platform, they automatically verify the validity of the instructions;

[0032] Suppliers need to replenish the quantity Q i Prepare vaccines and package them within the specified temperature range according to cold chain standards;

[0033] For each vaccine i, the total amount of vaccine provided by the selected supplier l can at least meet the demand replenishment at the future time t+h. The expression for the demand to meet the constraint condition is:

[0034]

[0035] in, Indicates that any type of vaccine must meet the required replenishment amount, X ij represents the decision variable;

[0036] The total amount of tasks undertaken by the selected supplier does not exceed its maximum processing capacity. The expression of the supplier processing capacity constraint is:

[0037]

[0038] Among them, C l represents the upper limit of the processing capacity of supplier l, Indicates the selection of any supplier l;

[0039] According to the constraints of supplier processing capacity and demand satisfaction, the objective function is designed to minimize the total cost and time. The expression is:

[0040] min∑ i ∑ j X ij (C l +θ il ·TTC il );

[0041] Among them, θ il represents the efficiency score of supplier l in supplying vaccine i to destination i, TTC il represents the estimated time cost for supplier l to transport the i-th vaccine to the designated destination;

[0042] The SaaS platform calculates the optimal delivery route and selects the best cold chain logistics solution based on the supplier location, vaccine destination, and real-time traffic information, combined with minimizing total cost and time.

[0043] As a preferred solution of the vaccine logistics cold chain management method based on the SaaS platform described in the present invention, the temperature, humidity and vehicle position in the carriage during the transportation process are monitored in real time, and the carriage environment is automatically adjusted. The specific operation steps are as follows:

[0044] Set up an IoT sensor array in the cold chain transport vehicle to continuously monitor temperature, humidity and GPS positioning coordinates and set the ideal temperature range [T min , T max ] and humidity range [H min , H max ];

[0045] The temperature, humidity and GPS location coordinates are transmitted to the SaaS platform in real time via wireless network;

[0046] The SaaS platform compares the preset parameter range based on real-time data. If a deviation is found, it automatically triggers control instructions to the intelligent control system on the transport vehicle and records abnormal events at the same time;

[0047] In response to abnormal events, the system automatically initiates the emergency response strategy and adjusts the cabin environment. The expression is:

[0048]

[0049] Among them, E represents the comprehensive evaluation score of cold chain logistics, T i Indicates the real-time monitored temperature, T target represents the target temperature, △T represents the allowable temperature deviation range, W i represents the importance weight of the measurement point, F e Indicates the frequency of abnormal events, D act represents the actual arrival time, D est Indicates the estimated time of arrival, D tol represents the time deviation tolerance, δ represents the adjustment coefficient, Q avl Indicates the number of vaccines currently available.

[0050] In the second aspect, the present invention provides a vaccine logistics cold chain management system based on a SaaS platform, including: a data collection and analysis module: responsible for collecting real-time and historical data of vaccination sites, using artificial intelligence algorithms for in-depth analysis, and predicting future demand for different vaccines;

[0051] Demand forecasting and inventory management module: Based on the predicted vaccine demand results and compared with the current inventory status, a replenishment list of required vaccine types and quantities is automatically generated;

[0052] Intelligent replenishment instruction module: automatically triggers and sends replenishment instructions to suppliers, dynamically adjusts replenishment priorities based on the urgency of the vaccine, historical shortages, and supply chain efficiency;

[0053] Supplier management and cold chain logistics scheduling module: After receiving instructions through the integrated API, suppliers prepare vaccines and utilize cold chain logistics resources, and the system intelligently plans the optimal delivery route based on geographical location;

[0054] Environmental monitoring and automatic control module: During transportation, the temperature, humidity and vehicle position in the car are monitored in real time. Once they deviate from the preset standards, the automatic control mechanism is immediately activated to maintain an ideal cold chain environment.

[0055] In a 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 logistics cold chain management method based on the SaaS platform as described in the first aspect of the present invention is implemented.

[0056] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored, wherein: when the computer program is executed by a processor, it implements any step of the vaccine logistics cold chain management method based on the SaaS platform as described in the first aspect of the present invention.

[0057] The beneficial effects of the present invention are as follows: by analyzing vaccination site data, predicting vaccine demand, automatically generating replenishment lists, ensuring sufficient inventory, dynamically adjusting the replenishment order using a priority algorithm, quickly responding to emergency needs, intelligently planning distribution routes, reducing costs and transportation time, and simultaneously monitoring the transportation environment, and automatically controlling to maintain vaccine quality, an efficient and reliable vaccine cold chain management system is constructed, which reduces vaccine waste, accelerates supply response, strengthens supply chain stability and vaccine safety, and provides strong technical guarantees for public health security. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] 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.

[0059] Figure 1 This is a flow chart of the vaccine logistics cold chain management method based on the SaaS platform in Example 1.

[0060] Figure 2 Generate a graph for the replenishment instruction in Example 1. DETAILED DESCRIPTION

[0061] 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.

[0062] 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.

[0063] 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.

[0064] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a vaccine logistics cold chain management method based on a SaaS platform, comprising the following steps:

[0065] S1. Collect real-time and historical data from vaccination sites, use artificial intelligence algorithms to analyze the data, and predict future vaccine demand.

[0066] S1.1. Real-time and historical data include vaccine type, number of people vaccinated, vaccination date, seasonal index, holiday identifier and geographic location information.

[0067] S1.2. Based on real-time and historical data, use artificial intelligence algorithms to analyze data and predict future vaccine demand. The expression is:

[0068]

[0069] Among them, D i,t+h represents the demand for the i-th vaccine at the future time t+h, i represents the type index of the vaccine, t represents the current time point, h represents the predicted number of future days, j represents the traversal time index, m represents the total number of time points, ω j represents the weight of time point j, reflecting the contribution of different time points in historical data to the prediction, N i，t-h+j represents the number of people vaccinated at time point t-h+j, φ i，t represents the adjustment coefficient of the specific population demand for vaccine type i at time t, g represents the traversal of different age groups, A g represents the population of age group g, P r represents the overall population density of the region, p represents the disease type, q represents the cycle number, and E p，q represents the periodic disease outbreak impact factor, η i，t represents the adjustment factor for vaccine type i at time t based on the periodic impact of disease outbreaks.

[0070] S1.2.1. Integrate the collected facts and historical data to construct a data set;

[0071] Historical vaccination records: including date, vaccine type, number of people vaccinated, etc.;

[0072] Seasonal indicators: such as month codes (1-12), representing changes throughout the year;

[0073] Holiday information: Mark specific dates as holidays, represented by binary variables (0 / 1);

[0074] Geographic location features: such as longitude and latitude coordinates, or area codes obtained through clustering algorithms;

[0075] Use mean and median to fill in numerical data; for categorical data, use mode or specific label (such as "unknown") to fill in, and use Z-score or IQR method to identify and process data points that exceed the threshold;

[0076] Applying Fourier analysis to identify periodic patterns, the expression is:

[0077] η(i, t) = ∑[A k *cos(2π*k*t / T)+B k *sin(2π*k*t / T)];

[0078] Among them, A k and B k are the Fourier coefficients, k is the frequency index, and T is the period length.

[0079] S2. Automatically generate a replenishment list based on demand forecast results and current inventory status.

[0080] S2.1. Combine the demand forecast results with the current inventory status to obtain the required replenishment quantity. The specific steps are as follows:

[0081] The information on the current inventory of each vaccine is extracted in real time from the inventory management system of the SaaS platform, denoted as I i，t ;

[0082] Calculate the difference between the current inventory and the forecast demand to get the required replenishment amount, the expression is:

[0083] ΔQ i，t+h =D i，t+h -I i，t ;

[0084] Among them, ΔQ i，t+h It represents the demand for the i-th vaccine at the future time t+h;

[0085] Set a safety stock level S for each vaccine i and the minimum replenishment threshold T i ,

[0086] When ΔQ i，t+h >0 and I i，t <S i , I i，t <T i When , the replenishment instruction is generated.

[0087] S2.2. Generate replenishment instructions based on replenishment demand and priority coefficient. The specific steps are as follows:

[0088] Define a priority coefficient P for each vaccine based on the urgency of vaccine demand, historical shortages, and transportation time. i , the expression is:

[0089]

[0090] Among them, α represents the weight coefficient used to adjust the demand gap, β represents the weight coefficient used to adjust the average vaccine supply cycle on the priority, γ represents the weight coefficient used to consider the impact of past shortages on current replenishment decisions, SH i represents the frequency of shortage of vaccine i in the past period of time, L i represents the supply speed of vaccine i;

[0091] Generate replenishment instructions based on the required replenishment quantity and priority coefficient. The expression is:

[0092] Q i =max(ΔQ i，t+h , P i );

[0093] Among them, Q i Indicates the quantity to be replenished;

[0094] The system automatically generates a list of vaccine types i and the quantity to be replenished Q. i , Priority P i The replenishment instructions are immediately pushed to suppliers through the automated interface of the SaaS platform.

[0095] S2.2.1. Summarize basic vaccine information, historical sales, disease epidemic data, traffic conditions, etc.;

[0096] Using the expert scoring method, input features include vaccine characteristics (effectiveness period, targeted population), epidemiological data (incidence rate, growth rate), supply chain status, etc., and output urgency score;

[0097] Calculate the average transport time, adjust the transport time based on real-time traffic data forecasts, and adjust the priority coefficient accordingly;

[0098] Predict demand changes at future points based on historical data;

[0099] Through a multi-objective optimization model, the replenishment quantity of each vaccine is determined by comprehensively considering the demand replenishment quantity and priority.

[0100] S3. Suppliers receive replenishment instructions, prepare vaccines and arrange cold chain logistics, while intelligently planning the optimal delivery route based on geographic location information.

[0101] S3.1. After receiving the replenishment instruction in real time through the API of the SaaS platform, the supplier automatically verifies the validity of the instruction;

[0102] Suppliers need to replenish the quantity Q i Prepare vaccines and package them within the specified temperature range according to cold chain standards;

[0103] For each vaccine i, the total amount of vaccine provided by the selected supplier l can at least meet the demand replenishment at the future time t+h. The expression for the demand to meet the constraint condition is:

[0104]

[0105] in, Indicates that any type of vaccine must meet the required replenishment amount, X ij represents the decision variable;

[0106] The total amount of tasks undertaken by the selected supplier does not exceed its maximum processing capacity. The expression of the supplier processing capacity constraint is:

[0107]

[0108] Among them, C l represents the upper limit of the processing capacity of supplier l, Indicates the selection of any supplier l;

[0109] According to the constraints of supplier processing capacity and demand satisfaction, the objective function is designed to minimize the total cost and time. The expression is:

[0110] min∑ i ∑ j X ij (C l +θ il ·TTC il );

[0111] Among them, θ ilrepresents the efficiency score of supplier l in supplying vaccine i to destination i, TTC il represents the estimated time cost for supplier l to transport the i-th vaccine to the designated destination;

[0112] The SaaS platform calculates the optimal delivery route and selects the best cold chain logistics solution based on the supplier location, vaccine destination, and real-time traffic information, combined with minimizing total cost and time.

[0113] S3.1.1 The basis for determining the supplier's processing capacity includes the following:

[0114] Summarize the actual supply of suppliers over the past period of time, calculate the average daily / weekly supply, and analyze historical supply records;

[0115] Verify the maximum production capacity statement provided by the supplier, taking into account factors such as production line efficiency, number of employees, working hours, etc.

[0116] Refer to the average level or best practices of suppliers in the same industry and set a reasonable upper limit;

[0117] Record key indicators such as on-time delivery rate, damage rate, etc. for each order shipped from supplier l to the destination;

[0118] Set a scoring range for each indicator, such as 100 for on-time delivery rate and 0 for damage rate (the lower the better);

[0119] The final efficiency score is obtained through weighted average or other comprehensive scoring methods.

[0120] S3.1.2 The specific steps for estimating the transportation time cost are as follows:

[0121] Calculate the historical average transportation time from supplier l to the destination;

[0122] Get current and predicted traffic congestion, including factors such as road closures, accidents, peak hours, etc.

[0123] Apply the shortest path algorithm combined with real-time traffic data to estimate the shortest time path and estimated transportation time;

[0124] Combine the historical average and real-time calculated transportation time, consider the safety buffer time, and comprehensively estimate the transportation time cost.

[0125] S4. Real-time monitoring of the temperature, humidity and vehicle position in the carriage during transportation, and automatic control of the carriage environment.

[0126] S4.1. Set up an IoT sensor array in the cold chain transport vehicle to continuously monitor temperature, humidity and GPS positioning coordinates and set the ideal temperature range [Tmin , T max ] and humidity range [H min , H max ];

[0127] The temperature, humidity and GPS location coordinates are transmitted to the SaaS platform in real time via wireless network;

[0128] The SaaS platform compares the preset parameter range based on real-time data. If a deviation is found, it automatically triggers control instructions to the intelligent control system on the transport vehicle and records abnormal events at the same time;

[0129] In response to abnormal events, the system automatically initiates the emergency response strategy and adjusts the cabin environment. The expression is:

[0130]

[0131] Among them, E represents the comprehensive evaluation score of cold chain logistics, T i Indicates the real-time monitored temperature, T target represents the target temperature, △T represents the allowable temperature deviation range, W i represents the importance weight of the measurement point, F e Indicates the frequency of abnormal events, D act represents the actual arrival time, D est Indicates the estimated time of arrival, D tol represents the time deviation tolerance, δ represents the adjustment coefficient, Q avl Indicates the number of vaccines currently available.

[0132] S4.1.1. Specific implementation and control measures include:

[0133] Automatic control: Adjust the cabin environment control system, such as adjusting the working mode of the cooling / heating equipment to restore to the ideal temperature and humidity range;

[0134] Route optimization: re-planning transportation routes to reduce delays based on real-time traffic conditions;

[0135] Emergency measures: In extreme cases, consider replacing the transport vehicle or urgently notifying the recipient to take alternative measures.

[0136] This embodiment also provides a vaccine logistics cold chain management system based on a SaaS platform, including:

[0137] Data collection and analysis module: responsible for collecting real-time and historical data from vaccination sites, using artificial intelligence algorithms for in-depth analysis, and predicting future demand for different vaccines;

[0138] Demand forecasting and inventory management module: Based on the predicted vaccine demand results and compared with the current inventory status, a replenishment list of required vaccine types and quantities is automatically generated;

[0139] Intelligent replenishment instruction module: automatically triggers and sends replenishment instructions to suppliers, dynamically adjusts replenishment priorities based on the urgency of the vaccine, historical shortages, and supply chain efficiency;

[0140] Supplier management and cold chain logistics scheduling module: After receiving instructions through the integrated API, suppliers prepare vaccines and utilize cold chain logistics resources, and the system intelligently plans the optimal delivery route based on geographical location;

[0141] Environmental monitoring and automatic control module: During transportation, the temperature, humidity and vehicle position in the car are monitored in real time. Once they deviate from the preset standards, the automatic control mechanism is immediately activated to maintain an ideal cold chain environment.

[0142] This embodiment also provides a computer device, which is suitable for the vaccine logistics cold chain management method based on the SaaS platform, 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 logistics cold chain management method based on the SaaS platform as proposed in the above embodiment.

[0143] 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.

[0144] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by the processor, it implements the vaccine logistics cold chain management method based on the SaaS platform as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination of them, 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.

[0145] In summary, the present invention analyzes vaccination site data, predicts vaccine demand, automatically generates replenishment lists, ensures sufficient inventory, dynamically adjusts the replenishment order using a priority algorithm, quickly responds to emergency needs, intelligently plans distribution routes, reduces costs and transportation time, and simultaneously monitors the transportation environment and automatically controls to maintain vaccine quality, thereby building an efficient and reliable vaccine cold chain management system, reducing vaccine waste, accelerating supply response, strengthening supply chain stability and vaccine safety, and providing strong technical guarantees for public health security.

[0146] Example 2

[0147] 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 the vaccine logistics cold chain management method based on the SaaS platform is provided.

[0148] This example aims to verify an innovative vaccine logistics cold chain management method, which integrates real-time and historical data, uses artificial intelligence algorithms to predict vaccine demand, and ensures efficient and safe vaccine distribution through intelligent supply chain management. In order to prove the innovation and advantages of this method, we designed a series of experiments covering data collection, demand forecasting, inventory management, replenishment instruction generation, distribution route planning, and real-time monitoring and environmental control of the transportation process.

[0149] First, we simulated a virtual community with multiple vaccine types and a population distribution of different age groups. We collected 12 months of vaccination data, including vaccine type, number of vaccinations, vaccination date, seasonality index (reflecting the prevalence of seasonal diseases), holiday identifier (marking changes in demand before and after holidays), and geographic location information.

[0150] We then applied a forecasting model to analyze the data using an AI algorithm. Taking influenza B vaccine as an example, the above expression was used in the demand forecast for the next 30 days, where the weights were dynamically adjusted based on historical vaccination trends, and parameters such as the demand adjustment coefficient φ for specific populations, the population size of age groups, and the disease outbreak impact factor E were calculated based on real data. The forecast results showed that the surge in demand for influenza B vaccine during the winter peak was accurately predicted, with an accuracy improvement of 20% compared to traditional time series forecasting methods.

[0151] In the inventory management phase, we extracted the vaccine inventory I in real time, compared it with the predicted demand D, and calculated the demand replenishment ΔQ. We set the safety inventory level and minimum replenishment threshold for each vaccine. When the inventory of influenza B vaccine is below the safety line and the demand replenishment is positive, the replenishment order is triggered immediately. This mechanism effectively avoids vaccine shortages and maintains a healthy level of inventory.

[0152] Next, we generated replenishment instructions based on the replenishment demand and priority coefficient. By comprehensively considering the urgency of the vaccine, historical shortages, and supply cycles, we assigned a priority to each vaccine. Experiments have shown that the priority sorting system allows high-risk vaccines to be replenished first, reducing public health risks caused by shortages and improving response efficiency by about 35%.

[0153] In the distribution route planning stage, we simulated a series of operations after the supplier received the replenishment order, including verifying the order, preparing the vaccine, packaging, and planning the optimal route. By minimizing the objective function of total cost and time, combined with real-time traffic information, we successfully found the most cost-effective distribution plan, saving 15% of the overall logistics cost compared to manual planning, while ensuring the safe delivery of the vaccine within the specified time.

[0154] Finally, during the transportation process, IoT sensors continuously monitored the compartment environment and automatically adjusted it through the SaaS platform to ensure that the vaccine was within the ideal temperature and humidity range throughout the entire process. Through the emergency response strategy, all abnormal events were properly handled within 5 minutes, ensuring that the quality of the vaccine was not damaged and improving the reliability of cold chain logistics.

[0155] The details are shown in the following table:

[0156] Table 1 Experimental record table

[0157]

[0158]

[0159] By comparing the experimental data, we can clearly see that the vaccine logistics cold chain management method based on the SaaS platform significantly improves the accuracy of demand forecasting. The actual demand for influenza B vaccine differs from the predicted demand by only 40 units, and the forecast error rate is only 2.86%, which is much lower than the industry average.

[0160] In terms of inventory management and replenishment decisions, all experimental subjects triggered replenishment instructions in time before the demand surged, ensuring that inventory levels remained within a safe range. In Experiment 1, although demand suddenly increased, potential shortages were avoided through timely replenishment, which shows that the system's automatic response mechanism is efficient.

[0161] The setting of replenishment priorities optimized resource allocation. For example, HPV vaccine (Experiment 3) was given the highest priority due to its high urgency of demand and frequent historical shortages, and was thus replenished first, ensuring the vaccination needs of high-risk groups.

[0162] In the distribution route planning and transportation monitoring part, all vaccines were safely delivered within the specified time, and the cost and time optimization effect was significant. Through intelligent control, even in the emergency situations of extreme weather and traffic congestion simulated in the experiment, no vaccine damage occurred, which demonstrated the high reliability of the cold chain logistics management system and its ability to respond to emergencies.

[0163] To sum up, this embodiment fully demonstrates that the vaccine logistics cold chain management method based on the SaaS platform is not only innovative in theory, but also shows significant advantages in practice, including but not limited to: highly accurate demand forecasting, efficient and flexible inventory and supply chain management, cost and time optimization, and excellent cold chain logistics monitoring capabilities, all of which directly promote the successful implementation of the vaccination program and have a positive impact on public health.

[0164] 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 logistics cold chain management method based on a SaaS platform, characterized by: include, Collect real-time and historical data from vaccination sites, use artificial intelligence algorithms to analyze the data, and predict future vaccine needs; Automatically generate replenishment lists based on demand forecast results and current inventory status; Suppliers receive replenishment instructions, prepare vaccines and arrange cold chain logistics, while intelligently planning the optimal delivery route based on geographic location information; Real-time monitoring of the temperature, humidity and vehicle position in the carriage during transportation, and automatic control of the carriage environment; The real-time and historical data include vaccine type, number of people vaccinated, vaccination date, seasonality index, holiday identifiers, and geographic location information; Based on real-time and historical data, artificial intelligence algorithms are used to analyze data and predict future vaccine demand. The expression is: Among them, D i,t+h represents the demand for the i-th vaccine at the future time t+h, i represents the type index of the vaccine, t represents the current time point, h represents the predicted number of future days, j represents the traversal time index, m represents the total number of time points, ω j Represents the weight of time point t-h+j, N j represents the number of people vaccinated at time point j, φ i,t represents the adjustment coefficient of the specific population demand for vaccine type i at time t, g represents the traversal of different age groups, A g represents the population of age group g, P r represents the overall population density of the region, p represents the disease type, q represents the cycle number, E p,q represents the periodic disease outbreak impact factor, η i,t represents the adjustment factor for vaccine type i at time t based on the periodicity of disease outbreaks; Suppliers receive replenishment instructions, prepare vaccines and arrange cold chain logistics, and intelligently plan the optimal delivery route based on geographic location information. The specific steps are as follows: After suppliers receive replenishment instructions in real time through the API of the SaaS platform, they automatically verify the validity of the instructions; Suppliers need to replenish the quantity Q i Prepare vaccines and package them within the specified temperature range according to cold chain standards; For each vaccine i, the total amount of vaccine provided by the selected supplier j can at least meet the demand replenishment at the future time t+h. The expression of the demand satisfying constraint condition is: in, Indicates that any type of vaccine must meet the required replenishment amount, X ij represents the decision variable; The total amount of tasks undertaken by the selected supplier does not exceed its maximum processing capacity. The expression of the supplier processing capacity constraint is: Among them, C j represents the upper limit of supplier j’s processing capacity, represents the selection of any supplier j; According to the constraints of supplier processing capacity and demand satisfaction, the objective function is designed to minimize the total cost and time. The expression is: min∑ i ∑ j X ij (C j +θ ij ·TTC ij ); Among them, θ ij represents the efficiency score of supplier j in supplying vaccine i to destination i, TTC ij represents the estimated time cost for supplier j to transport the i-th vaccine to the designated destination; The SaaS platform calculates the most optimized delivery route and selects the best cold chain logistics solution based on the supplier’s location, vaccine destination, and real-time traffic information, combined with minimizing total cost and time; Real-time monitoring of the temperature, humidity and vehicle position in the carriage during transportation, and automatic control of the carriage environment. The specific operation steps are as follows: Set up an IoT sensor array in the cold chain transport vehicle to continuously monitor temperature, humidity and GPS positioning coordinates and set the ideal temperature range [T min ,T max ] and humidity range [H min ,H max ]; The temperature, humidity and GPS location coordinates are transmitted to the SaaS platform in real time via wireless network; The SaaS platform compares the preset parameter range based on real-time data. If a deviation is found, it automatically triggers control instructions to the intelligent control system on the transport vehicle and records abnormal events at the same time; In response to abnormal events, the system automatically initiates the emergency response strategy and adjusts the cabin environment. The expression is: Among them, E represents the comprehensive evaluation score of cold chain logistics, T i Indicates the real-time monitored temperature, T target represents the target temperature, ΔT represents the allowable temperature deviation range, W i represents the importance weight of the measurement point, F e Indicates the frequency of abnormal events, D act Indicates the actual arrival time, D est Indicates the estimated time of arrival, D tol represents the time deviation tolerance, δ represents the adjustment coefficient, Q avl Indicates the number of vaccines currently available.

2. The vaccine logistics cold chain management method based on the SaaS platform as claimed in claim 1, characterized in that: Combine the demand forecast results with the current inventory status to get the required replenishment quantity. The specific steps are as follows: The information on the current inventory of each vaccine is extracted in real time from the inventory management system of the SaaS platform, denoted as I i,t ; Calculate the difference between the current inventory and the forecast demand to get the required replenishment amount, the expression is: ΔQ i,t+h =D i,t+h -I i,t ; Among them, ΔQ i,t+h It represents the demand for the i-th vaccine at the future time t+h; Set a safety stock level S for each vaccine i and the minimum replenishment threshold T i , When ΔQ i,t+h >0 and I i,t i , I i,t <T i When , the replenishment instruction is generated.​ 3. The vaccine logistics cold chain management method based on the SaaS platform as claimed in claim 2, characterized in that: Generate replenishment instructions based on replenishment demand and priority coefficient. The specific steps are as follows: Define a priority coefficient P for each vaccine based on the urgency of vaccine demand, historical shortages, and transportation time. i , the expression is: Among them, α represents the weight coefficient used to adjust the demand gap, β represents the weight coefficient used to adjust the average vaccine supply cycle on the priority, γ represents the weight coefficient used to consider the impact of past shortages on current replenishment decisions, SH i represents the frequency of shortage of vaccine i in the past period of time, L i represents the supply speed of vaccine i; Generate replenishment instructions based on the required replenishment quantity and priority coefficient. The expression is: Q i =max(ΔQ i,t+h ,P i ); Among them, Q i Indicates the quantity to be replenished; The system automatically generates a list of vaccine types i and the quantity to be replenished Q. i , Priority P i The replenishment instructions are immediately pushed to suppliers through the automated interface of the SaaS platform.

4. A vaccine logistics cold chain management system based on a SaaS platform, based on the vaccine logistics cold chain management method based on a SaaS platform according to any one of claims 1 to 3, characterized in that: include, Data collection and analysis module: responsible for collecting real-time and historical data from vaccination sites, using artificial intelligence algorithms for in-depth analysis, and predicting future demand for different vaccines; Demand forecasting and inventory management module: Based on the predicted vaccine demand results and compared with the current inventory status, a replenishment list of required vaccine types and quantities is automatically generated; Intelligent replenishment instruction module: automatically triggers and sends replenishment instructions to suppliers, dynamically adjusts replenishment priorities based on the urgency of the vaccine, historical shortages, and supply chain efficiency; Supplier management and cold chain logistics scheduling module: After receiving instructions through the integrated API, suppliers prepare vaccines and utilize cold chain logistics resources, and the system intelligently plans the optimal delivery route based on geographical location; Environmental monitoring and automatic control module: During transportation, the temperature, humidity and vehicle position in the car are monitored in real time. Once they deviate from the preset standards, the automatic control mechanism is immediately activated to maintain an ideal cold chain environment.

5. 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 logistics cold chain management method based on the SaaS platform as described in any one of claims 1 to 3 are implemented.

6. 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 logistics cold chain management method based on the SaaS platform as described in any one of claims 1 to 3 are implemented.

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