Vaccine distribution and real-time management system for multiple persons in vaccination unit

Abstract

The invention relates to the technical field of vaccine management, and discloses a multi-person vaccine distribution and real-time management system for a vaccination unit. Comprising a demand prediction and dynamic inventory management module, a cold chain management and temperature control system module, a vaccine distribution and scheduling module, a data synchronization and real-time updating module, a vaccine validity period and batch number management module and a system safety and fault tolerance module. An intelligent scheduling system based on demand prediction and dynamic inventory management, a temperature control system combining a self-adaptive PID control algorithm and an Internet of Things technology, a multi-layer redundant backup and automatic fault recovery mechanism, and a vaccine management system combining batch number tracing and valid period management are adopted. The technical effects of accurate demand prediction, inventory optimization, stable temperature control, high availability, data integrity guarantee, comprehensive vaccine batch tracing and effective period monitoring are achieved, and the problems of inaccurate vaccine distribution, temperature control failure, difficult system failure recovery and incomplete batch tracing in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of vaccine management, and specifically to a multi-dose vaccine distribution and real-time management system for vaccination units. Background Art

[0002] Currently, traditional systems for vaccine distribution and management mostly rely on manual operations and semi-automated means, mainly managing vaccine inventory through manual labor or simple digital tools. Most existing systems rely on fixed inventory models and manual updating of vaccination data. The demand for vaccines is usually estimated from historical vaccination data, but intelligent prediction of future demand is not carried out. These traditional systems lack an efficient demand prediction mechanism and usually cannot adjust inventory in a timely manner according to changes in the peak vaccination period or demand fluctuations during public health emergencies. In addition, the inventory management and scheduling methods of existing systems mostly adopt fixed plans and do not have the ability to flexibly adjust according to real-time data changes. This has led to shortages or surpluses of vaccines during the peak vaccination period, affecting the utilization efficiency of vaccine resources and the smooth progress of the vaccination process.

[0003] Most existing vaccine cold chain management systems use a single temperature control device to control the temperature of the vaccine storage and transportation environment. These systems usually set a fixed temperature range. In some cases, the temperature control device does not have the ability to adapt and cannot adjust the temperature according to changes in the external environment or the specific requirements of the vaccine. For example, in an environment with large temperature fluctuations, traditional temperature control systems may not be able to make timely adjustments, resulting in an unstable vaccine storage environment, thereby affecting the effectiveness and safety of the vaccine. In addition, existing systems usually lack a real-time feedback mechanism for temperature control data. When the temperature control device fails or deviates, the system cannot automatically detect and repair it, resulting in abnormal vaccine temperatures not being discovered in a timely manner.

[0004] In terms of data management, existing technologies often have the problem of information silos. Vaccine inventory, vaccination data, temperature control status, and expiration date information are scattered in different modules or systems, lacking an effective data synchronization and real-time update mechanism. The lag or inconsistency of data between modules may lead to decision-making errors. For example, due to data asynchronization between the vaccine inventory module and the vaccine distribution module, incorrect distribution strategies may be caused, resulting in shortages or surpluses of vaccines.

[0005] In vaccine expiration date management, existing technologies usually only monitor the expiration date of vaccines through manual checks or simple expiration date reminder functions. These systems lack the ability to dynamically monitor the batch number and expiration date of each vaccine and cannot achieve automated vaccine batch traceability and expiration date warning. The use of expired vaccines is still a potential risk because existing management systems do not fully consider the association between vaccine batch numbers and expiration dates and cannot remind or lock vaccines approaching expiration in real time for processing.

[0006] In general, existing technologies have major deficiencies in intelligent management, temperature control system adaptability, data synchronization and update timeliness, and vaccine expiration and batch number management. Due to the lack of intelligent scheduling and real-time monitoring mechanisms, traditional systems cannot fully cope with the challenges brought about by fluctuations in vaccination demand and public health emergencies, resulting in waste of vaccine resources, vaccine failure or untraceability. Therefore, there is an urgent need for a more intelligent, efficient and accurate vaccine management system to address the deficiencies in existing technologies. Summary of the invention

[0007] In view of the shortcomings of the prior art, the present invention provides a multi-dose vaccine distribution and real-time management system for vaccination units, which solves the problems in the prior art of inaccurate vaccine distribution, inventory management, temperature control management and expiration date monitoring, and lack of intelligent scheduling and data synchronization.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: a multi-dose vaccine distribution and real-time management system for vaccination units, comprising:

[0009] Demand forecasting and dynamic inventory management module, which is used to predict future vaccination demand based on historical vaccination data and vaccine demand, and use dynamic inventory optimization algorithms to adjust vaccine inventory to ensure that inventory matches vaccination demand;

[0010] The cold chain management and temperature control system module is used to control the temperature of vaccines during storage and transportation. The PID control algorithm or adaptive PID control algorithm is used to adjust the temperature control equipment to ensure that the temperature of the vaccine storage environment remains within the specified range;

[0011] The vaccine distribution and scheduling module is used to automatically select the appropriate amount of vaccines and optimize the distribution path according to vaccination needs, inventory status and temperature control requirements, and use scheduling algorithms to optimize the vaccine distribution process;

[0012] Data synchronization and real-time update module, used to synchronize data between modules in real time, ensuring the consistency and real-time update of vaccination information, inventory information, temperature control status and other data;

[0013] The vaccine expiration date and batch number management module is used to monitor the expiration date of vaccines, ensure that vaccines that are about to expire are not distributed, and perform vaccine traceability management based on batch numbers;

[0014] The system security and fault-tolerance module is used to encrypt the storage and transmission of data and ensure the high availability of the system through a redundant backup mechanism.

[0015] Preferably, the demand forecasting and dynamic inventory management module includes:

[0016] Process historical vaccination data using time series analysis methods and predict future demand through machine learning algorithms;

[0017] Optimize the allocation of vaccine inventory using a mixed integer linear programming algorithm and adjust the inventory in real time to ensure that vaccination needs are met.

[0018] Preferably, the cold chain management and temperature control system module includes:

[0019] Monitor the temperature using temperature control equipment and adjust the temperature control equipment in real time through a PID control algorithm or an adaptive PID control algorithm to keep the temperature of the vaccine storage environment within the specified range.

[0020] Preferably, the vaccine distribution and scheduling module includes:

[0021] Based on the needs of vaccination sites, inventory data, and temperature control requirements, automatically select an appropriate number of vaccines and optimize the vaccine distribution path using the ant colony algorithm and genetic algorithm to ensure the optimal allocation of resources between vaccination sites.

[0022] Preferably, the data synchronization and real-time update module includes:

[0023] Use message queue technology to synchronize vaccination records, inventory data, temperature control data, etc. in real time, and use the AES encryption and SSL / TLS encryption protocols to ensure the security of data transmission.

[0024] Preferably, the vaccine expiration date and batch number management module includes:

[0025] Trace and manage the batch numbers of vaccines to ensure that each batch of vaccines can be tracked in real time, and when the expiration date of the vaccine is approaching, the system automatically issues a reminder and freezes the expired vaccines.

[0026] Preferably, the system security and fault tolerance module includes:

[0027] Provide data redundancy backup, encrypted storage, and encrypted transmission to ensure automatic switching to the backup system in case of system failure and guarantee the continuity of business.

[0028] Preferably, the demand prediction and dynamic inventory management module further includes:

[0029] Based on historical vaccination records and peak vaccination period data, use time series analysis methods to predict the demand for vaccines periodically and adjust the vaccine inventory using dynamic programming algorithms to achieve optimal inventory control.

[0030] Preferably, the vaccine distribution and scheduling module further includes:

[0031] When the vaccine demand changes at the vaccination site, the distribution strategy of the vaccine is dynamically adjusted through an adaptive scheduling algorithm, optimizing the vaccine distribution process and reducing resource waste.

[0032] Preferably, the cold chain management and temperature control system module further includes:

[0033] The temperature changes during the storage and transportation of vaccines are monitored in real time through an Internet of Things sensor network, and temperature control collaborative optimization is carried out through a cloud platform to ensure the quality of vaccines throughout the supply chain.

[0034] The present invention provides a multi-dose vaccine distribution and real-time management system for vaccination units. It has the following

[0035] Beneficial effects:

[0036] 1. The present invention adopts an intelligent scheduling system based on demand prediction and dynamic inventory management, achieving the technical effects of accurately predicting vaccine demand and optimizing inventory allocation. Compared with the technical solutions of manually adjusting inventory and distribution strategies in the prior art, it solves the problems of large demand fluctuations and inaccurate inventory management. Through intelligent algorithms, the system can automatically adjust inventory and efficiently distribute vaccines according to the needs of vaccination sites, ensuring the maximum utilization of resources.

[0037] 2. The present invention adopts a temperature control system that combines an adaptive PID control algorithm and Internet of Things technology, achieving the technical effect of accurately adjusting the temperature of the vaccine storage and transportation environment in real time. Compared with the prior art solutions of single temperature control devices or static temperature control, it solves the problems of unstable temperature control and difficulty in adapting to environmental changes. The temperature control system can dynamically adjust operating parameters to ensure that vaccines are always within the optimal temperature range, reducing the risk of temperature control failure.

[0038] 3. The present invention achieves the technical effects of ensuring high system availability and data integrity through a multi-layer redundant backup and automatic fault recovery mechanism. Compared with the prior art solutions where a single point of failure causes the system to crash, it solves the problem that the system cannot quickly recover when a failure occurs. Through automatic fault detection and repair, the system can automatically switch to the backup system when a failure occurs, ensuring that the business is not interrupted, greatly improving the stability and reliability of the system.

[0039] 4. The present invention adopts a vaccine management system that combines batch number traceability and expiration date management, achieving the technical effect of comprehensively tracing vaccine batches and monitoring expiration dates. Compared with the simple batch number management or expiration date reminder solutions in the prior art, it solves the deficiency of being unable to comprehensively control vaccine batch information and expiration date status. Through real-time scanning and dynamic updating, the system can ensure that expired vaccines are not used, and the source and usage of each vaccine can be traced, further improving the safety of vaccine management. Description of the Drawings

[0040] Figure 1 This is the module relationship diagram of the multi-dose vaccine distribution and real-time management system for vaccination units of the present invention;

[0041] Figure 2 This is the structural diagram of the demand forecasting and dynamic inventory management module in the present invention;

[0042] Figure 3 This is the structural diagram of the cold chain management and temperature control system module in the present invention;

[0043] Figure 4 This is the structural diagram of the vaccine distribution and scheduling module in the present invention;

[0044] Figure 5 This is the structural diagram of the data synchronization and real-time update module in the present invention;

[0045] Figure 6 This is the structural diagram of the vaccine expiration date and batch number management module in the present invention;

[0046] Figure 7 This is the structural diagram of the system security and fault tolerance module in the present invention. Detailed implementation manners

[0047] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the specification of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0048] Embodiment:

[0049] Please refer to the attached Figure 1 - attached Figure 7 , the embodiment of the present invention provides a multi-dose vaccine distribution and real-time management system for vaccination units, including,

[0050] Please refer to the attached Figure 1 - attached Figure 2 , a demand forecasting and dynamic inventory management module, which is used to predict future vaccination needs based on historical vaccination data and vaccine demand, and use a dynamic inventory optimization algorithm to adjust the vaccine inventory to ensure that the inventory matches the vaccination demand;

[0051] In the present invention, the demand forecasting and dynamic inventory management module is a key component to ensure the efficient operation of the vaccine distribution and management system. The core task of this module is to guarantee the timely supply and stable inventory of vaccines by forecasting vaccination demand and optimizing inventory management, thus avoiding vaccine expiration or waste. The demand forecasting and dynamic inventory management module is closely associated with other modules in the system, such as the vaccine distribution and scheduling module, the cold chain management and temperature control system module, and the data synchronization and real-time update module, to ensure the coordinated operation of the overall system. By forecasting vaccination demand and dynamically adjusting inventory, the system can achieve precise scheduling for each vaccination site, thereby improving the efficiency of vaccine distribution and ensuring the rational utilization of resources at each vaccination site.

[0052] In this embodiment, the demand forecasting and dynamic inventory management module mainly consists of two sub-modules: the demand forecasting sub-module and the inventory optimization sub-module. The demand forecasting sub-module predicts future vaccine demand through historical vaccination data and other influencing factors, and the inventory optimization sub-module, based on the demand forecast and the current inventory situation, uses optimization algorithms to dynamically adjust inventory allocation to ensure sufficient inventory at each vaccination site without waste. Through the collaboration of these two modules, the present invention can maintain the high efficiency of inventory and vaccine distribution even during peak vaccination demand periods.

[0053] Demand forecasting sub-module:

[0054] In the demand forecasting sub-module, first, by analyzing historical vaccination data and combining factors such as vaccination seasonality and epidemiological changes, time series forecasting of vaccination demand is carried out. To achieve this goal, this module adopts time series analysis methods and machine learning models. Specifically, in some embodiments, the system first uses Fourier transform to perform periodic decomposition on historical vaccination data to extract the periodic trends in the data. The Fourier transform formula is as follows:

[0055]

[0056] where X(f) is the frequency domain representation of the demand data, x(t) is the time domain representation of the historical vaccination data, f is the frequency, and j is the imaginary unit. Through Fourier transform, the system can identify the periodic changes in vaccination data, such as the peak demand for influenza vaccines every winter.

[0057] In addition, the system also uses machine learning algorithms for further demand forecasting. Specifically, in some embodiments, long short-term memory networks (LSTM) or XGBoost models are used, combined with variables such as the time series characteristics of historical data, the number of vaccinated people, and vaccine types, to predict the demand volume. LSTM can better handle non-linear changes and long-term dependencies in time series data by remembering past information, while XGBoost optimizes the prediction results by improving the model accuracy of decision trees.

[0058] Assume that the vaccine demand at the current moment is y t , and its predicted value can be calculated through the following regression model:

[0059]

[0060] where y t : the vaccination demand (predicted value) at the t-th moment;

[0061] β0: the intercept term of the regression equation;

[0062] β i : the regression coefficient of historical vaccination data, representing the influence weight of historical data on the current demand;

[0063] x t-i : the historical vaccination data (independent variable) in the past i periods;

[0064] ∈ t : the error term, representing random factors that cannot be predicted by the model.

[0065] Through the combination of time series analysis and machine learning algorithms, the demand prediction sub-module can provide accurate future vaccine demand.

[0066] Inventory optimization sub-module:

[0067] The main function of the inventory optimization sub-module is to optimize the vaccine inventory allocation based on demand prediction and the existing inventory situation. To efficiently utilize the inventory and avoid vaccine shortages or expiration, the inventory optimization sub-module adopts a mixed-integer linear programming (MILP) algorithm to adjust the inventory. MILP can calculate the optimal inventory allocation plan under the given inventory, demand, and constraint conditions to ensure that the demand at each vaccination site is met.

[0068] Generally, the inventory optimization problem can be expressed as the following objective function and constraint conditions:

[0069] Objective function:

[0070]

[0071] where C i is the unit cost of the i-th vaccine, x i is the inventory level, W i is the loss cost of expired vaccines, and y i is the quantity of expired vaccines. By minimizing this objective function, the system can reduce the waste of expired vaccines while meeting the demand.

[0072] Constraint conditions:

[0073]

[0074] where d i is the demand for the i-th type of vaccine. The constraint ensures that the inventory can at least meet the demand to avoid shortages.

[0075] In addition, the system also introduces a dynamic inventory adjustment strategy. According to the changes in the actual inventory and vaccination demand, the inventory allocation is adjusted in real time. For example, during the peak vaccination period, the system will automatically increase the distribution volume of vaccines through an inventory optimization algorithm; while during the low-demand period, the system will reduce the vaccine distribution to avoid waste due to expiration.

[0076] The demand forecasting and dynamic inventory management module works closely with other modules (such as the vaccine distribution and scheduling module and the cold chain management module). The forecasting data provided by the demand forecasting module serves as the basis for the vaccine distribution and scheduling module, helping it to reasonably allocate resources and avoid over-distribution or inventory shortages. The inventory optimization module adjusts the inventory data in real time to ensure that the distribution and storage of vaccines will not cause waste due to expiration, while meeting the needs of each vaccination site.

[0077] As an option, the system also supports data docking with external data sources (such as public health databases) to further improve the accuracy of demand forecasting. For example, information on upcoming epidemics can be obtained through external data to adjust the vaccine distribution strategy in advance and ensure the effective allocation of vaccine resources in response to sudden diseases.

[0078] In this embodiment, the demand forecasting and dynamic inventory management module can accurately predict future vaccine demand and optimize management in combination with the actual inventory situation, thereby improving the efficiency of vaccine distribution. Through accurate demand forecasting, the inventory optimization algorithm can effectively reduce the risk of vaccine expiration, ensure that the needs of vaccination sites are met in a timely manner, and at the same time reduce inventory backlogs.

[0079] During implementation, through the combination of time series analysis and machine learning algorithms, the system can dynamically adjust inventory and distribution strategies, improve the efficiency of vaccine distribution during peak vaccination periods, and reduce inventory pressure during low-demand periods. The inventory optimization scheme using the MILP algorithm can minimize the waste of expired vaccines while ensuring that vaccination needs are met, further improving resource utilization.

[0080] In addition, the flexibility and adaptability of the system enable this module to handle various complex vaccination scenarios, including changes in vaccine types, demand fluctuations, sudden diseases, etc. Through close cooperation with modules such as vaccine distribution and cold chain management, it ensures the maximization of vaccine resource utilization and the smooth progress of the vaccination process.

[0081] This embodiment details the specific implementation method of the demand forecasting and dynamic inventory management module. Through time series analysis, machine learning, and the MILP algorithm, the system can accurately predict vaccination demand and optimize inventory allocation, avoiding vaccine waste and insufficient inventory. The collaboration of this module with other modules further improves the efficiency and reliability of the vaccine management system, providing effective technical support for the distribution and real-time management of multi-dose vaccines in vaccination units.

[0082] Please refer to the attached Figure 3 , the cold chain management and temperature control system module, which is used to control the temperature of vaccines during storage and transportation, and adopts the PID control algorithm or the adaptive PID control algorithm to adjust the temperature control equipment to ensure that the temperature of the vaccine storage environment remains within the specified range;

[0083] In the overall system of the present invention, the cold chain management and temperature control system module plays a crucial role, especially in ensuring temperature control of vaccines from storage to transportation. This module is closely connected with the demand forecasting and dynamic inventory management module and the vaccine distribution and scheduling module to ensure the stability and effectiveness of vaccines. Through precise adjustment of the temperature control system, the system can avoid the invalidation of vaccines due to temperature fluctuations, thereby enhancing the reliability and safety of the vaccine management process.

[0084] In this embodiment, the cold chain management and temperature control system module adopts the PID control algorithm or the adaptive PID control algorithm to adjust the temperature in the vaccine storage environment in real time to ensure that the vaccines remain within the optimal temperature control range throughout the storage, transportation, and distribution processes. In addition, this module combines Internet of Things technology to monitor and feedback the temperature data of each link in the cold chain in real time through a sensor network, thereby forming a closed-loop control system to ensure the effectiveness of the temperature control adjustment of the system.

[0085] Control principle of the temperature control system: The core of the temperature control system is to adjust the temperature control equipment through the PID control algorithm to ensure that the environmental temperature for vaccine storage always meets the specified requirements. The PID controller adjusts the temperature through three parts: proportional (P), integral (I), and derivative (D). Among them, the proportional part is used to respond to the current temperature error, the integral part is used to eliminate the accumulated error, and the derivative part is used to predict future temperature changes. In this way, the PID controller can quickly and accurately adjust the temperature to reduce excessive fluctuations.

[0086] Specifically, in the cold chain management and temperature control system module, the PID controller adjusts the output of the temperature control equipment according to the temperature error detected in real time, and adjusts the operating state of the system, so that the temperature is stabilized within the predetermined range. The control formula of the PID controller is:

[0087]

[0088] Among them, u(t): the output of the controller, representing the adjustment amount of the temperature control device;

[0089] e(t): the error between the current temperature and the set target temperature, e(t)=T actual (t)-T set (t);

[0090] K p : proportional gain, controlling the speed of response;

[0091] K i : integral gain, eliminating the steady-state error of the system;

[0092] K d : derivative gain, predicting future temperature changes

[0093] t: time variable;

[0094] The integral of the error, representing the accumulation of past temperature errors;

[0095] The derivative of the error, representing the rate of temperature change.

[0096] In some embodiments, the PID control algorithm calculates and updates the proportional gain, integral gain, and derivative gain by obtaining temperature data in real time to meet different temperature control requirements and environmental changes. The advantage of this method is that it can automatically adjust parameters in different temperature environments to ensure the stable operation of the system under complex working conditions.

[0097] Adaptive PID control algorithm:

[0098] To further improve the accuracy and stability of the temperature control system, in some embodiments, an adaptive PID control algorithm is adopted. Compared with the traditional PID control, the adaptive PID control can automatically adjust the PID parameters according to changes in environmental conditions to cope with scenarios with large temperature fluctuations.

[0099] The core idea of the adaptive PID control is to dynamically adjust the gain parameters of the controller according to the change of the real-time temperature error to adapt to different operating environments. Specifically, the system will adjust the gain values of proportional, integral, and derivative in real time according to factors such as the magnitude of the current temperature error and the speed of temperature change to ensure that the system makes the most appropriate response to temperature changes.

[0100] In some embodiments, the system uses an adaptive method based on fuzzy logic, combines temperature data and historical temperature control feedback information, and dynamically adjusts the gain value of the PID controller through a fuzzy inference algorithm. This method can further improve the control accuracy and reduce temperature fluctuations, thereby ensuring that the vaccine is always in an ideal temperature control environment during storage.

[0101] Combination of temperature control system and Internet of Things technology:

[0102] In this embodiment, the temperature control system not only relies on the PID control algorithm to adjust the temperature, but also combines with the Internet of Things technology to form a comprehensive temperature control monitoring system. The Internet of Things technology collects temperature data in real time through sensors and uploads these data to the cloud platform or local data center. The temperature control device can make timely adjustments by obtaining these data to ensure that the temperature of the vaccine storage environment is always maintained within the specified range.

[0103] Through the Internet of Things technology, the system can achieve real-time monitoring of each link in the cold chain, including cold storage, transport vehicles, vaccination stations, etc. The temperature sensors installed on these devices will regularly transmit temperature data to the cloud platform, and the cloud platform processes and analyzes these data and provides real-time feedback on the adjustment requirements of the temperature control device to form a closed-loop control system.

[0104] Specifically, the operation process of the temperature control system is as follows: The sensor collects the temperature data in the storage device in real time, and the data is transmitted to the control system through a wireless network. The control system calculates the temperature control parameters that need to be adjusted by comparing the error between the current temperature and the target temperature, and sends instructions to adjust the operating state of the temperature control device. In this way, the system can achieve precise temperature control throughout the entire cold chain process.

[0105] Multi-layer temperature control coordination in cold chain management:

[0106] The cold chain management module provided in this embodiment does not rely solely on a single device for temperature control, but adopts a multi-layer temperature control coordination scheme to ensure that the temperature can be strictly controlled at each link in the entire vaccine supply chain, from vaccine storage to transportation and then to vaccination.

[0107] The multi-layer temperature control system ensures that the temperature of each link is fully monitored and adjusted through the coordinated operation of multiple temperature control nodes and temperature control devices. For example, the temperature control devices in cold storage, the temperature control systems in transport vehicles, and the temperature control devices at vaccination stations can all work independently, but synchronize information in real time through the cloud platform. The cloud platform can coordinate the operation of the temperature control devices at each link according to the real-time data received, thereby achieving comprehensive and stable temperature management.

[0108] In some embodiments, the system analyzes the historical operation data of the temperature control device through a big data analysis platform to predict the temperature change trend in advance and make pre-adjustments. In this way, in the cold chain, the temperature control device can respond more quickly to environmental changes, avoid excessive temperature fluctuations, and ensure a high degree of stability of the temperature control system during the storage and transportation of vaccines.

[0109] By combining the PID control algorithm and Internet of Things technology, the cold chain management and temperature control system module can efficiently and accurately adjust the temperature of the vaccine storage environment, avoiding the impact of temperature fluctuations on the quality of vaccines. The adaptive PID control algorithm further improves the stability of the system in complex environments. Especially when dealing with drastic temperature changes, it can automatically adjust the control parameters to ensure the efficient operation of the system.

[0110] In addition, the combination of the temperature control system and Internet of Things technology provides real-time temperature data feedback, enabling the temperature control system to adjust its working state at any time. Through the temperature monitoring of the entire cold chain process, the system can ensure that each link of the vaccine from storage, transportation to vaccination meets the temperature control requirements, maximizing the effectiveness and safety of the vaccine.

[0111] This embodiment details the technical implementation of the cold chain management and temperature control system module. Through the combination of the PID control algorithm, adaptive PID control, and Internet of Things technology, the system can achieve precise temperature control, ensuring that the temperature control requirements during the storage and transportation of vaccines are met. The multi-layer temperature control cooperation scheme further improves the stability and response ability of the system, ensuring the effective guarantee of the quality and safety of vaccines.

[0112] Please refer to the appendix Figure 4 , the vaccine distribution and scheduling module, which is used to automatically select an appropriate number of vaccines according to the vaccination demand, inventory status, and temperature control requirements, and optimize the distribution path, and adopts a scheduling algorithm to optimize the vaccine distribution process;

[0113] The vaccine distribution and scheduling module is a core component to ensure the accurate and efficient distribution of vaccines. The function of this module is to automatically select an appropriate number of vaccines and optimize their distribution paths according to the demand data provided by the aforementioned demand prediction and dynamic inventory management module and the temperature control status of the cold chain management and temperature control system module. The system uses an intelligent scheduling algorithm to ensure the on-demand distribution of vaccines, avoiding resource waste and insufficient resources at vaccination points.

[0114] In this embodiment, the vaccine distribution and scheduling module adopts advanced optimization algorithms, such as the ant colony algorithm and the genetic algorithm, and calculates the optimal vaccine distribution path by combining various factors such as demand, inventory, temperature control requirements, and the geographical location of vaccination sites. This module not only supports scheduled distribution but also can dynamically adjust the distribution plan according to the actual vaccination demand, ensuring timely supply of vaccines during the peak vaccination period and avoiding over-distribution during low demand.

[0115] Vaccine Distribution Strategy and Scheduling Algorithm:

[0116] In this embodiment, the vaccine distribution and scheduling module realizes intelligent scheduling through the ant colony algorithm and the genetic algorithm. The ant colony algorithm mimics the foraging behavior of ants in nature and dynamically optimizes the distribution path according to factors such as the demand, inventory, and temperature control status of vaccination sites; the genetic algorithm searches for the optimal solution by simulating the mechanisms of natural selection and gene mutation.

[0117] Specifically, in some embodiments, the ant colony algorithm optimizes the vaccine distribution path in the following way:

[0118] First, set the distance matrix between vaccination sites and allocate a certain initial number of ants to each vaccination site. Ants move on the path through "pheromone", and the intensity of the pheromone existing on the path is directly related to the quality of the path. Ants randomly select paths and adjust the path selection according to the pheromone concentration, ultimately making the vaccine distribution path tend to be optimal. The objective function of the ant colony algorithm can be expressed as:

[0119]

[0120] where d ij is the distance from vaccination site i to vaccination site j, and x ij is the path selection variable, indicating whether to select the path from vaccination site i to vaccination site j.

[0121] The ant colony algorithm finds the shortest path that meets the requirements through continuous iteration, thereby reducing unnecessary vaccine transportation time and temperature control risks.

[0122] In another embodiment, the genetic algorithm is used to further optimize the distribution strategy. The genetic algorithm evaluates multiple distribution paths by simulating the natural selection process of organisms and selects the optimal path. Each generation of paths is selected, crossed, and mutated according to fitness until convergence to the optimal solution. The fitness function of the genetic algorithm can be expressed by the following formula:

[0123]

[0124] where f(x) is the fitness function, representing the fitness of the path. The larger the value, the better the path;

[0125] d ij is the distance from vaccination site i to vaccination site j;

[0126] x ij is a path selection variable, indicating whether to select the path from vaccination site i to vaccination site j;

[0127] n is the total number of vaccination sites.

[0128] By maximizing the fitness function, the genetic algorithm can find the optimal vaccine distribution path.

[0129] Dynamic scheduling and demand changes:

[0130] Generally, the demand at vaccination sites changes over time and may be affected by various factors such as vaccination peaks and epidemic fluctuations. Therefore, the vaccine distribution and scheduling module needs to have the ability to dynamically adjust. When the vaccination demand changes, the system can automatically adjust the distribution strategy based on real-time data.

[0131] As an option, the vaccine distribution and scheduling module in this embodiment uses an adaptive scheduling algorithm to adjust the distribution plan in real time. These algorithms can optimize the distribution strategy based on multiple factors such as the real-time demand, inventory level, and temperature control status of vaccination sites. For example, when the demand at a certain vaccination site increases significantly, the system will give priority to distributing vaccines to this site and adjust the distribution plans of other vaccination sites.

[0132] In addition, the dynamic scheduling algorithm also considers the status of the temperature control system. At some vaccination sites, if the operation of the temperature control equipment is unstable or the temperature is close to the critical value, the system will give priority to selecting other vaccination sites with better temperature control conditions for vaccine distribution, thus reducing the temperature control risk.

[0133] Combination of distribution path and inventory management:

[0134] During the operation of the vaccine distribution and scheduling module, the vaccine inventory management module provides key input data. The inventory management module will regularly update the inventory information and transmit it to the distribution and scheduling module. The system optimizes the distribution path according to the demand and inventory status of vaccination sites to ensure that the inventory will not be insufficient or excessive.

[0135] In specific implementation, the vaccine distribution and scheduling module works closely with the inventory optimization module. When the inventory is close to being insufficient, the distribution system will adjust the distribution plan according to the demand forecast to reduce the dependence on inventory. On the contrary, when the inventory is excessive, the system will avoid vaccine waste by adjusting the distribution volume and ensure the priority satisfaction of demand.

[0136] In this embodiment, the vaccine distribution and scheduling module can efficiently calculate the optimal vaccine distribution path by using advanced optimization methods such as ant colony algorithm and genetic algorithm, reduce unnecessary resource waste, and ensure that the vaccine can reach the vaccination site on time. The dynamic scheduling function enables the system to have a high degree of flexibility and adaptability, capable of coping with sudden demand changes and complex vaccine distribution tasks. In addition, combined with the temperature control management system, it avoids the invalidation of vaccines due to temperature control problems, further improving the reliability and safety of the system.

[0137] Through the close cooperation of the inventory management, demand forecasting, temperature control management, and vaccine distribution modules, this embodiment effectively improves the accuracy and efficiency of vaccine distribution. The system can dynamically adjust the distribution quantity and path of vaccines according to the fluctuations in the demand of vaccination sites, avoiding the problems of over-distribution of vaccines and insufficient inventory, while maintaining the stable operation of the temperature control system.

[0138] This embodiment details the technical implementation of the vaccine distribution and scheduling module. By adopting optimization methods such as ant colony algorithm and genetic algorithm, combined with dynamic scheduling and inventory management, the system can achieve precise and efficient vaccine distribution. The flexibility and adaptability of the system enable it to cope with various complex vaccination scenarios, ensuring the smooth and safe distribution of vaccines.

[0139] Please refer to the appendix Figure 5 , the data synchronization and real-time update module, which is used to synchronize the data between each module in real time to ensure the consistency and real-time update of data such as vaccination information, inventory information, and temperature control status;

[0140] In the overall system of the present invention, the data synchronization and real-time update module plays a key role in ensuring the consistency and real-time nature of data between various modules in the system. This module is closely connected with the demand forecasting and dynamic inventory management module, the vaccine distribution and scheduling module, the cold chain management and temperature control system module, and the vaccine expiration date and batch number management module. By real-time updating and synchronizing the data of each module, the system can accurately reflect the needs of vaccination units, inventory status, temperature control environment, and vaccine expiration date information, thereby improving the efficiency and safety of vaccine distribution.

[0141] In this embodiment, the data synchronization and real-time update module realizes the real-time transmission, update, and protection of data for each module through message queue technology and encryption protocols. Each module inside the system transmits real-time data through the data synchronization module to ensure the security of data during transmission and enable each module to obtain the latest status information in a timely manner. Message queue technology makes data transmission efficient, reliable, and scalable, while encryption technology guarantees the confidentiality and integrity of data during transmission.

[0142] Data Synchronization and Message Queue Technology:

[0143] In general, the interaction of multiple modules within the system depends on accurate data information, and this information needs to be transmitted between modules in a reliable manner. In this embodiment, data synchronization adopts a data transmission method based on message queue (such as Kafka or RabbitMQ) technology. A message queue is a middleware that transmits data between producers and consumers. By sending data messages to the queue, it ensures that data can flow orderly and stably to the downstream system.

[0144] In specific implementation, the data generated by each module of the system (such as vaccination records, inventory data, temperature control status, vaccine expiration information, etc.) is pushed as messages to the message queue. The data synchronization module delivers these messages to the modules that need to use this data according to priority and requirements. For example, the inventory management module obtains inventory data in real time according to vaccination requirements, and the scheduling module obtains vaccine distribution information according to distribution requirements, etc.

[0145] Whenever the data of a module changes, the corresponding message will be pushed to the message queue to notify other modules to perform synchronous updates. The message queue mechanism can ensure the consistency of data updates among modules and avoid data inconsistency problems caused by delays or errors.

[0146] In some embodiments, in order to further ensure the scalability and processing capacity of the system, a distributed message queue solution is adopted. By deploying message queues on multiple servers, the system can handle a large number of concurrent data requests and ensure the efficiency and stability of data transmission.

[0147] Data Encryption and Security:

[0148] When transmitting sensitive data, the security of the data is of utmost importance. To ensure the confidentiality and integrity of data during transmission, the AES encryption algorithm and SSL / TLS encryption protocol are adopted in this embodiment to protect the data.

[0149] Specifically, the AES encryption algorithm is used to encrypt the transmitted data to prevent the data from being stolen or tampered with during transmission. The AES encryption algorithm is a symmetric encryption algorithm, and its encryption process can be expressed as:

[0150] C = E(K, P)

[0151] where C is the encrypted data;

[0152] E is the encryption function, indicating that the plaintext data is encrypted into ciphertext through the key;

[0153] K is the key;

[0154] P is the original data.

[0155] Through this encryption process, it is ensured that only the recipient with the key can decrypt and read the data.

[0156] The SSL / TLS encryption protocol is used to ensure the security of data during network transmission, ensuring that the data is not tampered with or leaked during network transmission. Through the SSL / TLS protocol, the system can establish a secure communication channel, ensuring the privacy and integrity of the data during transmission.

[0157] In some embodiments, the data synchronization module can select different encryption methods according to the sensitivity of the data, ensuring that the system can flexibly handle the security of the data according to the actual situation.

[0158] Data Consistency and Real-time Update:

[0159] The data synchronization and real-time update module is not only responsible for data transmission but also for ensuring data consistency. In the present invention, data consistency is ensured by the orderliness of the message queue. When the data of a certain module changes, the corresponding message will be pushed to the message queue, and other modules will update according to the data in the message after receiving these messages.

[0160] In some embodiments, in order to further ensure data consistency, a transaction mechanism is adopted to guarantee data synchronization operations. Whenever the data changes, the system will perform transaction management on each operation, ensuring that the data update operation is either completely successful or completely failed, thus avoiding the situation of partial updates in the system, which may lead to data inconsistency.

[0161] Through the implementation of the data synchronization and real-time update module, the system can maintain data real-time and consistency among multiple modules. The message queue technology ensures the efficient and reliable transmission of data, avoiding errors caused by data transmission delay or loss. In addition, the system protects the data through data encryption and the SSL / TLS protocol, ensuring the confidentiality and integrity of the data during transmission.

[0162] In some embodiments, by introducing a distributed message queue solution, the system can still maintain the stability and efficiency of data transmission in a high-concurrency environment. This enables the system to support multiple vaccination units to run simultaneously and dynamically adjust the vaccine distribution plan according to the actual needs of each vaccination unit, ensuring the optimal utilization of vaccine resources.

[0163] This embodiment details the technical implementation of the data synchronization and real-time update module. By adopting message queue technology, AES encryption, and SSL / TLS encryption protocols, the system can efficiently and securely transmit and synchronize data among various modules. By ensuring data consistency and real-time performance, the system can avoid vaccine distribution problems caused by data latency or errors, improving the stability and reliability of the overall system. In addition, the use of a distributed message queue enables the system to have high scalability and can meet the requirements of multiple vaccination sites and high concurrency.

[0164] Please refer to the attached Figure 6 , the vaccine expiration date and batch number management module, which is used to monitor the expiration date of vaccines, ensure that vaccines approaching expiration are not distributed, and conduct traceability management of vaccines based on batch numbers;

[0165] In the overall system of the present invention, the vaccine expiration date and batch number management module plays a crucial role in ensuring that each vaccine is correctly used within its expiration date. This module is closely connected to the aforementioned demand forecasting and dynamic inventory management module, vaccine distribution and scheduling module, and data synchronization and real-time update module, ensuring the effectiveness and safety of vaccines. By precisely managing the expiration date and batch number of vaccines, the use of expired vaccines is avoided, and at the same time, the origin and usage of each vaccine can be traced.

[0166] In this embodiment, the vaccine expiration date and batch number management module realizes the full life cycle management of vaccines by integrating a batch number traceability system, QR code or barcode scanning technology, and combining with the inventory management and distribution scheduling modules in the system. This module ensures that vaccination units do not use vaccines approaching expiration or already expired by monitoring the expiration status of vaccines in real time, thereby avoiding the loss of vaccine efficacy. Combining the batch number management function, the system can ensure that the batch information of each vaccine can be accurately traced, improving the transparency and safety of vaccine management.

[0167] Batch number management and traceability system:

[0168] Generally, the vaccine batch number is the unique identifier for each batch of vaccines, and batch number management plays an important role in ensuring vaccine quality and traceability. In this embodiment, batch number management realizes the accurate recording and traceability of vaccine batch numbers through barcode or QR code technology.

[0169] Specifically, the vaccine batch number is associated with vaccine inventory information, vaccination records, and vaccine distribution status through barcodes or QR codes. By scanning the QR code or barcode of each vaccine, the system can obtain its relevant batch number information, production date, expiration date, storage conditions, etc. These information are stored in the system database and are synchronized with vaccination records and distribution plans in real time.

[0170] In some embodiments, the system monitors the expiration date of each batch of vaccines to ensure that expired vaccines are not used during the vaccine distribution process. If a batch of vaccines is close to expiration, the system will automatically mark the batch of vaccines as "near expiration" and remove it from the available inventory. Furthermore, when the vaccination unit scans the vaccine barcode or QR code, the system will double-check the expiration date of the vaccine to avoid the mistaken use of expired vaccines.

[0171] Expiration date management and automatic warning:

[0172] Vaccine expiration management is an important part of ensuring the safe use of vaccines. In this embodiment, the system tracks the expiration status of each batch of vaccines in real time by establishing a vaccine expiration monitoring mechanism. Once the expiration date of a vaccine approaches a preset threshold, the system will automatically issue an early warning and take corresponding management measures.

[0173] Specifically, the system will record the production date and expiration date of each batch of vaccines in the database and set a reminder mechanism based on the expiration date. When the vaccine that is close to expiration reaches the warning threshold, the system will display the expiration status of the batch of vaccines through the interface and prompt the administrator to take corresponding measures, such as adjusting the distribution strategy or suspending the distribution of the batch of vaccines.

[0174] In some embodiments, the system automatically optimizes the vaccine distribution plan through dynamic scheduling based on the expiration date of the vaccine, combined with inventory data and vaccination needs. For vaccines that are about to expire, the system will give priority to distributing them to vaccination points with higher demand to reduce expiration waste.

[0175] To ensure the accuracy of expiration date management, the system can set multiple expiration date warning levels. For example, the system can set the "expiring soon" level. When the number of days left before the vaccine expires is lower than a certain threshold, the system automatically marks the vaccine as about to expire and restricts its distribution.

[0176] Association management between vaccine expiration date and batch number:

[0177] In this embodiment, there is a close relationship between the expiration date and batch number of the vaccine. The batch number of each batch of vaccine not only includes the production date and expiration date, but also records the production factory, transportation conditions, vaccination records and other information of the batch of vaccine. Through the association of this information, the system can track and manage the quality and status of the vaccine in real time.

[0178] Specifically, the system will automatically associate the production information, expiration date information and vaccination records of each batch of vaccines according to the batch number. When the vaccination unit needs to return the vaccine, the system will automatically identify the effectiveness of the batch of vaccines based on the batch number information and make corresponding processing according to the expiration date management rules. If a batch of vaccines has expired, the system will mark it as "unavailable" to prevent it from being distributed.

[0179] Data synchronization and batch number and expiration date update:

[0180] In this embodiment, the vaccine expiration date and batch number management module closely collaborates with the aforementioned data synchronization and real-time update module to ensure that the expiration date and batch number data of each vaccine can be updated in real time and synchronized to each module of the system.

[0181] In actual operation, when the expiration date or batch number information of the vaccine changes, the system will immediately transmit the updated information to other modules through message queue technology. For example, the inventory management module and the vaccine distribution and scheduling module will adjust the inventory allocation and distribution strategy according to the latest expiration date and batch number information. In this way, the system can avoid using expired or unqualified vaccines and ensure the safety of vaccine distribution.

[0182] In addition, in order to improve the accuracy and reliability of the data, the system can use distributed database technology to ensure that there are no delays or errors in data synchronization between modules. Whenever new batch number and expiration date data enter the system, the database will automatically update and transmit the latest information to relevant modules to ensure that all data remains consistent.

[0183] The vaccine expiration date and batch number management module in this embodiment effectively improves the safety of vaccine use and management efficiency through batch number traceability, expiration date monitoring, and automatic warning functions. Through two-dimensional code or bar code technology, the system can accurately record the batch number information of each vaccine and track its expiration date status in real time to avoid the use of expired vaccines. At the same time, the system can dynamically adjust the vaccine distribution plan according to the expiration date warning and inventory status, reduce vaccine waste, and improve resource utilization.

[0184] Through close cooperation with the data synchronization and real-time update module, the batch number and expiration date information of the vaccine can be updated in real time and shared throughout the system, ensuring data consistency between modules. The automatic warning function and the associated management of batch number and expiration date of the system further improve the accuracy and efficiency of vaccine management and reduce the possibility of human error.

[0185] This embodiment details the specific implementation method of the vaccine expiration date and batch number management module. Through the batch number traceability system, two-dimensional code or bar code scanning, expiration date monitoring and warning mechanism, and cooperation with the data synchronization and real-time update module, this system can ensure the effectiveness and safety of the vaccine. The implementation of this module greatly improves the accuracy and transparency of vaccine management and provides strong technical support for the vaccine management of vaccination units.

[0186] Please refer to the attached Figure 7 , the system security and fault tolerance module, which is used for encrypting the storage and transmission of data and ensuring the high availability of the system through a redundant backup mechanism.

[0187] In the overall system of the present invention, the system security and fault tolerance module is responsible for ensuring the high availability of the vaccine management system, the security of data, and the automatic recovery function in case of failures. This module closely cooperates with the aforementioned data synchronization and real-time update module, vaccine distribution and scheduling module, cold chain management and temperature control system module, etc., to ensure that the system can operate stably under various operating conditions and provide necessary security guarantees. Through redundant backup, encryption technology, and fault tolerance mechanisms, the system can recover in time in case of failures and ensure that data is not lost, thereby enhancing the reliability of the overall system.

[0188] In this embodiment, the system security and fault tolerance module adopts technical means such as redundant backup, encrypted storage and transmission, and fault recovery mechanisms to ensure the stability of the system, the security of data, and the fast recovery ability in case of failures. This module guarantees the high availability and data integrity of the vaccine management system in the face of various complex environments by integrating multiple security technologies.

[0189] Redundant backup and high availability design:

[0190] Generally, in order to ensure that the system can still operate in case of failures, the system needs to adopt a redundant backup scheme. Redundant backup ensures the integrity of data and reduces the impact of single points of failure on the system operation. In this embodiment, redundant backup is achieved by deploying key components on multiple physical nodes or servers. Whenever the data of the system is updated, these updates will be automatically synchronized to the backup server to ensure that the system can switch to the backup server and continue to operate when the primary server fails.

[0191] Specifically, the key modules of the system, such as the database, message queue, cache layer, etc., are redundantly deployed on multiple nodes. These redundant nodes ensure the consistency and integrity of data through real-time synchronization technologies (such as distributed database replication technology). The primary node and slave nodes of the database ensure the consistency of data backup through a timed data synchronization mechanism, avoiding irrecoverable problems caused by data loss or failures.

[0192] In some embodiments, a dual-active data center solution is adopted, that is, the primary and standby nodes of the system are deployed in two data centers at different geographical locations. Through a fully automatic fault switching mechanism, once a primary node failure is detected, the system can automatically switch to the standby data center, maintain the high availability of the system, and reduce the service interruption time.

[0193] Encrypted storage and transmission:

[0194] With the increasing prominence of data security issues, ensuring the security of data transmission and storage has become an important part of system design. In this embodiment, sensitive data in all systems (such as vaccination records, inventory information, vaccine expiration date data, etc.) is protected using encryption technology.

[0195] Specifically, the AES encryption algorithm is used to perform symmetric encryption on the data to protect the security of the data during storage. The encryption process of the AES algorithm is carried out through the following formula:

[0196] C = E(K, P)

[0197] Where C is the encrypted data, E is the encryption function, K is the encryption key, and P is the original data. Whenever vaccine-related information is stored or updated, the system uses the AES encryption algorithm to encrypt the data to ensure that only authorized users or systems can access this sensitive data.

[0198] For communication and data transmission between systems, the SSL / TLS encryption protocol is adopted to ensure the security of data during transmission. During the transmission process, the SSL / TLS protocol prevents data from being stolen or tampered with by a third party by establishing an encrypted channel between the sender and the receiver.

[0199] In some embodiments, the system can adopt different encryption strategies according to different data sensitivity levels. For example, for some temporary data or non-sensitive data, a lightweight encryption method can be adopted to reduce the system burden; while for key data such as vaccination records and inventory management, the system uses a stronger encryption method for protection.

[0200] Fault recovery mechanism:

[0201] To ensure that the system can be quickly restored in case of a failure, the system security and fault tolerance module in this embodiment is equipped with a comprehensive fault recovery mechanism. This mechanism not only ensures the recoverability of the system after a failure but also ensures the integrity and consistency of the data.

[0202] In terms of specific implementation, the system monitors the status of each key module through an automatic fault detection and repair function. When the system detects a failure in a certain module, it will immediately start the standby module to ensure the business continuity of the system. Specifically, the system can automatically switch to a backup server, a backup database, or other redundant devices according to the type of failure to ensure that the business is not interrupted.

[0203] For example, when the primary database node fails, the system automatically forwards database access requests to the backup node and updates the database status to ensure data consistency. For the message queue system, if it is found that the message queue service fails, the system automatically switches to the standby queue service to ensure that messages are not lost and subsequent tasks can continue to be processed.

[0204] In a possible implementation, the system periodically saves the data status of the system through backup and logging functions. When a major system failure occurs, it can be restored to the state before the failure by restoring the most recent backup data or applying log files, minimizing data loss to the greatest extent.

[0205] Fault tolerance and system consistency:

[0206] The fault tolerance mechanism in this embodiment not only ensures the high availability of the system in case of failures, but also guarantees data consistency among multiple nodes and services in the system. The system uses distributed transaction management technology to ensure that no data loss or inconsistency occurs when data is exchanged between various modules.

[0207] Generally, when multiple modules exchange data, the system ensures that all data operations are either completely successful or completely failed through the transaction mechanism, avoiding data inconsistency caused by partial operation failures. Specifically, the system uses technologies such as the two-phase commit protocol or compensating transactions when executing operations to ensure data integrity and consistency.

[0208] The system security and fault tolerance module in this embodiment ensures the stability and data security of the system by adopting technical means such as redundant backup, encrypted storage and transmission, and fault recovery mechanisms. Through redundant deployment and automatic failover mechanisms, the system can automatically recover in case of failures, avoiding service interruptions. Encryption technology ensures the confidentiality and integrity of sensitive data during storage and transmission, preventing data from being stolen or tampered with.

[0209] In addition, the fault tolerance mechanism ensures the consistency and reliability of data among multiple modules through technologies such as distributed transactions and the two-phase commit protocol. Through the effective combination of these technical means, the system can maintain efficient and stable operation in the face of various complex environments and fault scenarios, ensuring the security and high availability of the vaccine management system.

[0210] This embodiment details the technical implementation of the system security and fault tolerance module. Through technologies such as redundant backup, encrypted storage and transmission, fault recovery mechanisms, and distributed transactions, the system can ensure quick recovery in case of failures and guarantee data security and consistency. The implementation of this module improves the reliability and security of the overall system, providing a solid technical guarantee for the vaccine management system.

[0211] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-dose vaccine distribution and real-time management system for vaccination units, characterized in that: include, Demand forecasting and dynamic inventory management module, which is used to predict future vaccination demand based on historical vaccination data and vaccine demand, and use dynamic inventory optimization algorithms to adjust vaccine inventory to ensure that inventory matches vaccination demand; The cold chain management and temperature control system module is used to control the temperature of vaccines during storage and transportation. The PID control algorithm or adaptive PID control algorithm is used to adjust the temperature control equipment to ensure that the temperature of the vaccine storage environment remains within the specified range; The vaccine distribution and scheduling module is used to automatically select the appropriate amount of vaccines and optimize the distribution path according to vaccination needs, inventory status and temperature control requirements, and use scheduling algorithms to optimize the vaccine distribution process; Data synchronization and real-time update module, used to synchronize data between modules in real time, ensuring the consistency and real-time update of vaccination information, inventory information, temperature control status and other data; The vaccine expiration date and batch number management module is used to monitor the expiration date of vaccines, ensure that vaccines that are about to expire are not distributed, and perform vaccine traceability management based on batch numbers; The system security and fault-tolerance module is used to encrypt the storage and transmission of data and ensure the high availability of the system through a redundant backup mechanism.

2. The vaccination unit multi-dose vaccine distribution and real-time management system according to claim 1, characterized in that: The demand forecasting and dynamic inventory management module includes: Use time series analysis methods to process historical vaccination data and use machine learning algorithms to predict future demand; A mixed integer linear programming algorithm is used to optimize the allocation of vaccine inventory, and the inventory is adjusted in real time to ensure that vaccination needs are met.

3. The vaccination unit multi-dose vaccine distribution and real-time management system according to claim 1, characterized in that: The cold chain management and temperature control system module includes: Use temperature control equipment to monitor temperature, and adjust the temperature control equipment in real time through PID control algorithm or adaptive PID control algorithm to keep the storage environment temperature of the vaccine within the specified range.

4. The vaccination unit multi-dose vaccine distribution and real-time management system according to claim 1, characterized in that: The vaccine distribution and scheduling module includes: Based on vaccination site needs, inventory data, and temperature control requirements, the appropriate number of vaccines is automatically selected, and the ant colony algorithm and genetic algorithm are used to optimize the vaccine distribution path to ensure optimal resource allocation between vaccination sites.

5. The vaccination unit multi-dose vaccine distribution and real-time management system according to claim 1, characterized in that: The data synchronization and real-time update module includes: Message queue technology is used to synchronize vaccination records, inventory data, temperature control data, etc. in real time, and AES encryption and SSL / TLS encryption protocols are used to ensure the security of data transmission.

6. The vaccination unit multi-dose vaccine distribution and real-time management system according to claim 1, characterized in that: The vaccine validity period and batch number management module includes: Traceability management is carried out on vaccine batch numbers to ensure that each batch of vaccines can be tracked in real time. When the vaccine expiration date is approaching, the system automatically issues reminders and freezes expired vaccines.

7. The vaccination unit multi-dose vaccine distribution and real-time management system according to claim 1, characterized in that: The system safety and fault tolerance module includes: Provide data redundant backup, encrypted storage and encrypted transmission to ensure automatic switching to the backup system in the event of a system failure, thereby ensuring business continuity.

8. The vaccination unit multi-dose vaccine distribution and real-time management system according to claim 1, characterized in that: The demand forecasting and dynamic inventory management module further includes: Based on historical vaccination records and peak vaccination data, time series analysis methods are used to make periodic forecasts of vaccine demand, and dynamic programming algorithms are used to adjust vaccine inventory to achieve optimal inventory control.

9. The vaccination unit multi-dose vaccine distribution and real-time management system according to claim 1, characterized in that: The vaccine distribution and scheduling module further includes: When vaccine demand at vaccination sites changes, the vaccine distribution strategy is dynamically adjusted through an adaptive scheduling algorithm to optimize the vaccine distribution process and reduce resource waste.

10. The vaccination unit multi-dose vaccine distribution and real-time management system according to claim 1, characterized in that: The cold chain management and temperature control system module further includes: The temperature changes during vaccine storage and transportation are monitored in real time through the IoT sensor network, and temperature control is collaboratively optimized through the cloud platform to ensure the quality of vaccines throughout the entire supply chain.

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