Vaccine management method and system based on big data analysis and storage medium

Abstract

The invention provides a vaccine management method and system based on big data analysis, a storage medium and electronic equipment, and the method comprises the steps: generating a unique identification code for each vaccine, and binding the unique identification code with the corresponding vaccine; when the vaccines are stored or distributed, acquiring management information uploaded by a user after scanning the unique identification code of each vaccine; integrating the external data set and the internal data set into a data set, wherein the external data set comprises a disease incidence rate, epidemic disease data and seasonal changes; constructing a vaccine usage amount prediction model and a vaccine demand prediction model according to the data set, predicting a predicted usage amount in a first preset time period in the future according to the usage amount prediction model, and predicting a predicted demand amount in the first preset time period in the future according to the vaccine demand prediction model; and according to the predicted usage amount and the predicted demand amount, the stock amount is acquired, and the optimal order amount is calculated according to the stock amount. According to the invention, fine management of vaccines can be realized.

Description

Technical Field

[0001] The present invention relates to the field of vaccine management technology, and in particular to a vaccine management method, system and storage medium based on big data analysis. Background Art

[0002] There are several major problems in the existing management of nasal enteral vaccine boxes. First, the temperature control and monitoring of vaccine boxes usually rely on manual operation, lacking real-time monitoring and automated adjustment, which causes the vaccine to become ineffective due to inappropriate temperature. Second, the storage and distribution records of vaccines mostly rely on manual records, which are prone to inaccurate or lost records, affecting the tracking and management efficiency of vaccines. Finally, there is a lack of effective data analysis tools to predict vaccine demand and optimize inventory management, resulting in the inability of vaccine supply to meet actual demand in a timely manner or excess.

[0003] One of the existing technologies related to the nasal enteral vaccine box management platform based on the Internet of Things and big data analysis is the traditional vaccine cold chain management system. Such systems usually rely on basic temperature monitoring technology combined with manual recording and management of the circulation and storage process of vaccines.

[0004] Traditional vaccine cold chain management systems mainly rely on refrigeration equipment (such as refrigerators and freezers) to maintain the appropriate temperature of vaccines, and use simple temperature recorders (such as minimum and maximum temperature recorders and digital temperature recorders) to monitor the temperature. Vaccine distribution and storage records often rely on manual operations, including manual recording of vaccine batch numbers, expiration dates, storage conditions and other information.

[0005] 1. Limitations of temperature monitoring: Temperature monitoring in traditional systems is usually not real-time, and data transmission relies on manual reading, which leads to monitoring lags and data inaccuracy. Once equipment failure or power outage occurs, the problem cannot be discovered and solved in time, increasing the risk of vaccine damage.

[0006] 2. Inadequate recording and tracking: Manual recording of vaccine information is prone to recording errors or information omissions, affecting the accuracy and efficiency of vaccine management. In addition, the lack of an effective electronic tracking mechanism makes the transparency and traceability of vaccine circulation insufficient.

[0007] 3. Lack of data analysis and demand forecasting: Traditional cold chain management systems usually do not have integrated big data analysis capabilities, and are unable to analyze and predict vaccine usage patterns and demand trends, resulting in a lack of data support for vaccine procurement and inventory management, and insufficient or oversupply of vaccines.

[0008] 4. Inefficient management: Due to its reliance on a large number of manual operations and paper records, the traditional vaccine management process is inefficient, has a long response time, and is difficult to adapt to rapidly changing vaccine needs, especially in public health emergencies.

[0009] 5. Cost and resource utilization issues: Due to the lack of efficient temperature control and inventory management strategies, traditional systems lead to higher energy consumption and vaccine waste, increasing overall management costs. Summary of the invention

[0010] The purpose of the present invention is to provide a vaccine management method, system and storage medium based on big data analysis, aiming to solve at least one technical problem in the above-mentioned background technology.

[0011] In a first aspect, the present invention provides a vaccine management method based on big data analysis, the method comprising:

[0012] Generate a unique identification code for each vaccine in each batch, and bind the unique identification code to the corresponding vaccine;

[0013] When the vaccine is stored or distributed, the management information uploaded by the user after scanning the unique identification code of each vaccine is obtained, and the management information includes the storage location, storage time, distribution time, and distribution location;

[0014] Based on big data technology, the historical management information for the whole year is summarized and analyzed, and an internal data set is constructed based on the summary and analysis results, and an external data set is obtained, and the external data set and the internal data set are integrated into a data set, wherein the external data set includes disease incidence, epidemiological data, and seasonal changes;

[0015] Constructing a vaccine usage prediction model and a vaccine demand prediction model according to the data set, and predicting the predicted usage within a first preset time period in the future according to the usage prediction model, and predicting the predicted demand within a first preset time period in the future according to the vaccine demand prediction model;

[0016] The inventory quantity in the first preset time period in the future is obtained based on the predicted usage in the first preset time period in the future and the predicted demand in the first preset time period in the future, and the optimal order quantity for purchasing vaccines in the first preset time period in the future is calculated based on the inventory quantity in the first preset time period in the future.

[0017] Furthermore, the step of constructing a vaccine usage prediction model and a vaccine demand prediction model based on the data set includes:

[0018] The vaccine usage prediction model is constructed according to the following formula:

[0019]

[0020] Among them, Y trepresents the amount of vaccine used in the tth time period, c represents the constant term, p represents the order of the autoregressive term, q represents the order of the moving average term, represents the parameter of the autoregressive term in the i-th time period, θ i The parameter representing the moving average term of the i-th time period, ∈ t represents the error term in the tth time period, Y t-i represents the amount of vaccine used in the ti-th time period, ∈ t-i represents the error term in the ti-th time period.

[0021] Furthermore, the step of constructing a vaccine usage prediction model and a vaccine demand prediction model based on the data set also includes:

[0022] The vaccine demand prediction model is constructed according to the following formula:

[0023] V t =β0+β1X1+β2X2+…+β n X n

[0024] Among them, V t represents the demand for vaccines in the tth time period, β0 represents the intercept of the model, β1, β2, ..., β n Represents the regression coefficient, obtained by training the model, X1, X2, ..., X n Represents external influencing factors.

[0025] Furthermore, the step of obtaining the inventory quantity in the first preset time period in the future according to the predicted usage quantity in the first preset time period in the future and the predicted demand quantity in the first preset time period in the future, and calculating the optimal order quantity for purchasing vaccines in the first preset time period in the future according to the inventory quantity in the first preset time period in the future includes:

[0026] Get the inventory volume in the first preset time period in the future according to the following formula:

[0027] K t =V t -Y t

[0028] The optimal order quantity for purchasing vaccines in the first preset time period in the future is obtained according to the following formula:

[0029]

[0030] Among them, K t represents the inventory in the tth time period, H t represents the cold chain storage cost of the vaccine in stock in the tth time period, S* represents the fixed cost of purchasing the vaccine, It represents the optimal order quantity for purchasing vaccines in the tth time period.

[0031] Furthermore, the method further comprises:

[0032] The actual demand in a first preset time period in the future is obtained, and the vaccine demand prediction model is updated according to the actual demand in the first preset time period in the future.

[0033] Furthermore, the step of obtaining the actual demand in the first preset time period in the future and updating the vaccine demand prediction model according to the actual demand in the first preset time period in the future includes:

[0034] The parameter adjustment of the vaccine demand prediction model is obtained according to the following formula:

[0035] Δβ=α(D t -V t )

[0036] Among them, Δβ represents the parameter adjustment of the vaccine demand prediction model, D t represents the actual demand in the first preset time period in the future, and α represents the learning rate, which is used to control the amplitude of parameter adjustment.

[0037] In a second aspect, the present invention provides a vaccine management system based on big data analysis, the system comprising:

[0038] An identification module, used to generate a unique identification code for each vaccine in each batch, and bind the unique identification code to the corresponding vaccine;

[0039] A management information acquisition module is used to obtain management information uploaded by users after scanning the unique identification code of each vaccine when the vaccine is stored or distributed. The management information includes storage location, storage time, distribution time, and distribution location;

[0040] A data analysis module is used to summarize and analyze historical management information throughout the year based on big data technology, and to construct an internal data set based on the summary analysis results, obtain an external data set, and integrate the external data set and the internal data set into a data set, wherein the external data set includes disease incidence, epidemiological data, and seasonal changes;

[0041] A model building module, used to build a vaccine usage prediction model and a vaccine demand prediction model according to the data set, and predict the predicted usage within a first preset time period in the future according to the usage prediction model, and predict the predicted demand within the first preset time period in the future according to the vaccine demand prediction model;

[0042] The vaccine management module is used to obtain the inventory quantity in the first preset time period in the future based on the predicted usage in the first preset time period in the future and the predicted demand in the first preset time period in the future, and calculate the optimal order quantity for purchasing vaccines in the first preset time period in the future based on the inventory quantity in the first preset time period in the future.

[0043] In a third aspect, the present invention provides a storage medium, which stores one or more programs, which, when executed by a processor, implement the above-mentioned vaccine management method based on big data analysis.

[0044] In a fourth aspect, the present invention provides an electronic device, the electronic device comprising a memory and a processor, wherein:

[0045] The memory is used to store computer programs;

[0046] When the processor is used to execute the computer program stored in the memory, the above-mentioned vaccine management method based on big data analysis is implemented.

[0047] Compared with the prior art, the present invention has the following advantages:

[0048] 1. Each vaccine is assigned a unique identification code, and all vaccines are tracked and recorded based on the unique identification code, realizing the whole process tracking of vaccines. Every step from warehousing, storage to distribution is automatically recorded in the internal data set. This electronic and automated recording method not only improves the accuracy and efficiency of the records, but also avoids the frequent occurrence of manual recording errors.

[0049] 2. Using big data analysis technology, the platform can predict vaccine demand and optimize inventory based on historical vaccine usage data and other relevant factors (such as seasonal disease patterns and regional demand differences), and provide an important basis for subsequent inventory and procurement management. This prediction and optimization mechanism makes the supply of vaccines more accurate and efficient, avoiding vaccine shortages or surpluses caused by inaccurate estimates. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a flow chart of a vaccine management method based on big data analysis proposed in one embodiment of the present invention;

[0051] Figure 2 This is a schematic diagram of the structure of a vaccine management system based on big data analysis proposed in one embodiment of the present invention.

[0052] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Unless otherwise defined, the technical terms or scientific terms used herein should be understood by people with general skills in the field to which the present invention belongs. "Including" and similar words used in this article mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0054] It should be noted that the nasal intestinal vaccine box management platform mentioned in the following embodiments includes three parts: an Internet of Things temperature monitoring system, an electronic recording system, and a data analysis and prediction system, among which:

[0055] Regarding the IoT temperature monitoring system, the following processes are mainly involved:

[0056] 1. Temperature sensor deployment: Temperature sensors are deployed in the nasal enteral vaccine box to monitor the temperature inside the box in real time. These sensors can accurately measure the temperature of the vaccine storage environment to ensure that it is within the ideal temperature range to maintain the effectiveness and safety of the vaccine.

[0057] 2. Wireless data transmission: Real-time temperature data is transmitted to the central management platform via wireless networks (such as WiFi, cellular networks, Bluetooth, etc.). This allows remote monitoring of the temperature conditions of the vaccine boxes without physical contact with the vaccine boxes.

[0058] 3. Remote monitoring and automatic adjustment: After receiving the temperature data, the central management platform can automatically adjust the temperature inside the vaccine box according to the set temperature range. If an abnormal temperature is detected, the system can trigger an alarm and automatically adjust the temperature through a control device (such as a cooling system), or notify the manager to intervene manually.

[0059] Regarding the electronic record system, the following processes are mainly involved:

[0060] 1. Unique identification: Use barcodes or RFID tags to provide a unique identification for each vaccine unit. These identifications not only contain basic information about the vaccine, such as production batch number, expiration date, etc., but can also track the entire circulation process of the vaccine.

[0061] 2. Automatic recording: During the storage, distribution and use of vaccines, each operation is automatically recorded in an electronic database through a scanning device (such as a barcode scanner or RFID reader). This process reduces the need for manual recording, reduces the probability of erroneous recording, and ensures the accuracy and completeness of the records.

[0062] Regarding the data analysis and prediction system, the following processes are mainly involved:

[0063] 1. Big data analysis: Big data technology is used to analyze the accumulated vaccine usage data, including the storage, distribution and use records of vaccines. By analyzing this data, the system can identify trends in vaccine use, the occurrence of seasonal diseases, and differences in vaccine demand in different regions.

[0064] 2. Demand forecasting: Based on historical data and analysis results, the system can predict future vaccine demand. This includes predicting the demand for a certain vaccine in a specific region within a specific time period, thereby guiding the vaccine procurement and distribution strategy.

[0065] 3. Inventory management optimization: Based on demand forecasts, the management platform can formulate and adjust vaccine procurement plans and inventory levels to minimize inventory costs and expiration risks while ensuring sufficient vaccine supply to meet actual demand.

[0066] Through the above working principles, the management platform can achieve precise control and monitoring of the temperature inside the vaccine box to ensure the quality of the vaccine; automatically record the circulation information of the vaccine to improve management efficiency and record accuracy; and use big data analysis to optimize vaccine inventory and distribution strategies, improve resource utilization efficiency, and ultimately achieve more efficient and intelligent vaccine management.

[0067] like Figure 1 As shown, an embodiment of the present invention provides a vaccine management method based on big data analysis, which is applied to a nasal intestinal vaccine box management platform. The method includes steps S101 to S105, wherein:

[0068] Step S101: Generate a unique identification code for each vaccine in each batch, and bind the unique identification code to the corresponding vaccine;

[0069] It should be pointed out that the unique identification code of the management platform can be a barcode or an RFID tag, which contains detailed information of the vaccine, such as batch number, production date, expiration date, etc.

[0070] Step S102: When the vaccine is stored or distributed, the management information uploaded by the user after scanning the unique identification code of each vaccine is obtained, and the management information includes the storage location, storage time, distribution time, and distribution location;

[0071] It should be pointed out that barcode scanners or RFID readers are installed at key nodes of vaccine storage and distribution to identify the unique identification code of the vaccine to obtain information, and the vaccine management information needs to be uploaded so as to know the whereabouts of the vaccine and facilitate subsequent traceability and aggregation.

[0072] Step S103: Summarize and analyze the historical management information for the whole year based on big data technology, and build an internal data set based on the summary and analysis results, obtain an external data set, and integrate the external data set and the internal data set into a data set, wherein the external data set includes disease incidence, epidemiological data, and seasonal changes;

[0073] In this step, the internal data set includes not only the vaccine inventory data and distribution data of the electronic record system, but also the data of the temperature monitoring system. Specifically, a temperature sensor is installed in the nasal enteric vaccine box to monitor the temperature in the vaccine box in real time, and the temperature data is transmitted to the management platform through a wireless network. Furthermore, while monitoring the temperature of the vaccine in real time, the temperature of the vaccine is also adjusted. First, at least one temperature sensor is configured to measure the temperature of the vaccine storage environment (vaccine box)\(T\); then the temperature data of the temperature sensor\(T\) is received, and the required adjustment measures are calculated according to the temperature adjustment algorithm\(f(T)\); wherein the temperature adjustment algorithm is defined as\(f(T)=k(T T_{\text{set}})\),\(k\) is the adjustment coefficient, and\(T_{\text{set}}\) is the set target temperature; finally, the temperature adjustment is performed according to the calculation result of the control unit to maintain the vaccine storage environment within the set temperature range.

[0074] Furthermore, the dataset integration formula is as follows:

[0075] D=D1+D2

[0076] Among them, D represents the data set, D1 represents the internal data set, and D2 represents the external data set.

[0077] Step S104: constructing a vaccine usage prediction model and a vaccine demand prediction model according to the data set, and predicting the predicted usage in a first preset time period in the future according to the usage prediction model, and predicting the predicted demand in a first preset time period in the future according to the vaccine demand prediction model;

[0078] In some embodiments, during the analysis stage, machine learning algorithms are applied to perform pattern recognition on historical data on vaccine demand, especially through time series analysis to reveal trends and seasonal changes in vaccine usage. Specifically, the ARIMA model is used, which is suitable for processing historical vaccine demand data to predict future vaccine usage trends.

[0079] Specifically, the model is defined as follows:

[0080]

[0081] Among them, Y t represents the amount of vaccine used in the tth time period, c represents the constant term, p represents the order of the autoregressive term, q represents the order of the moving average term, represents the parameter of the autoregressive term in the i-th time period, θ i The parameter representing the moving average term of the i-th time period, ∈ t represents the error term in the tth time period, Y t-i represents the amount of vaccine used in the ti-th time period, ∈ t-i Represents the error term of the ti-th time period. The vaccine usage forecasting model constructed using the ARIMA model can accurately predict future changes in vaccine demand by fitting the cyclical and trend line characteristics in historical vaccine demand data.

[0082] In addition, in some embodiments, in order to construct a vaccine demand prediction model, a demand prediction model based on historical data is constructed based on the collected data, and linear regression is applied in combination with external factors to predict vaccine demand. Specifically, different influencing factors (such as season, disease outbreak rate, etc.) are used as independent variables to predict vaccine demand through multivariate regression analysis. Furthermore, a vaccine demand prediction model is constructed according to the following formula:

[0083] V t =β0+β1X1+β2X2+…+β n X n

[0084] Among them, V t represents the demand for vaccines in the tth time period, β0 represents the intercept of the model, β1, β2, ..., β n Represents the regression coefficient, obtained by training the model, X1, X2, ..., X n Represents external influencing factors, such as disease incidence, seasonal changes, etc. The vaccine demand forecasting model constructed by the above algorithm can accurately predict future vaccine demand and provide a scientific basis for inventory and procurement decisions.

[0085] Step S105: Obtain the inventory within the first preset time period in the future based on the predicted usage within the first preset time period in the future and the predicted demand within the first preset time period in the future, and calculate the optimal order quantity for purchasing vaccines within the first preset time period in the future based on the inventory within the first preset time period in the future.

[0086] Specifically, in this step, the default inventory cost of the vaccine to be used in the first preset time period is low and can be ignored, and then the inventory in the first preset time period in the future is obtained according to the following formula:

[0087] K t =V t -Y t

[0088] Then, the optimal order quantity for purchasing vaccines in the first preset time period in the future is obtained based on the following formula:

[0089]

[0090] Among them, K t represents the inventory in the tth time period, H t represents the cold chain storage cost of the vaccine in stock in the tth time period, S * represents the fixed cost of purchasing vaccines, It represents the optimal order quantity for purchasing vaccines in the tth time period.

[0091] It should be pointed out that since the vaccines used in actual situations will more or less involve cold chain storage, the first preset time period should not be too long, otherwise the error will be high. By way of example, the first preset time period can be 10 days, 20 days or 30 days, etc., and should not exceed one month.

[0092] In addition, in some embodiments, the actual demand in the first preset time period in the future is also obtained, and the vaccine demand prediction model is updated according to the actual demand in the first preset time period in the future, so as to continuously iterate the demand prediction model. Specifically, the parameter adjustment amount of the vaccine demand prediction model is obtained according to the following formula:

[0093] Δβ=α(D t -V t )

[0094] Among them, Δβ represents the parameter adjustment of the vaccine demand prediction model, D t represents the actual demand in the first preset time period in the future, and α represents the learning rate, which is used to control the amplitude of parameter adjustment. By establishing this feedback mechanism, the prediction model can be continuously optimized according to the fluctuation of actual demand, so that it can maintain efficient prediction capabilities over a long period of time, thereby better guiding vaccine management decisions.

[0095] In summary, the above-mentioned vaccine management method based on big data analysis has the following advantages:

[0096] 1. Real-time temperature monitoring and automatic adjustment functions achieved through IoT technology provide a storage environment for vaccines that is always within the ideal temperature range. Compared with traditional manual monitoring or fixed temperature settings, this dynamic adjustment mechanism can automatically optimize temperature settings according to actual conditions, greatly reducing the risk of vaccine failure due to abnormal temperature.

[0097] 2. The electronic recording system using barcode or RFID technology realizes the whole process tracking of vaccines, and every step from warehousing, storage to distribution is automatically recorded in the electronic database. This electronic and automated recording method not only improves the accuracy and efficiency of the records, but also greatly reduces the possibility of human errors. Compared with the traditional manual recording method, this is a huge improvement.

[0098] 3. Using big data analysis technology, the platform can predict vaccine demand and optimize inventory based on historical vaccine usage data and other relevant factors (such as seasonal disease patterns and regional demand differences). This prediction and optimization mechanism makes the supply of vaccines more accurate and efficient, avoiding vaccine shortages or surpluses caused by inaccurate estimates, which is unmatched by traditional estimates based on experience.

[0099] 4. The platform improves the transparency and traceability of vaccine management by integrating various technologies. Whether it is the temperature conditions, storage history or distribution records of vaccines, they can be queried and monitored through the platform. This is of great significance for enhancing public confidence in the safety of vaccines, as well as tracking problems and determining responsibility when necessary.

[0100] 5. Through real-time monitoring, data analysis, and optimized inventory, the platform is able to respond quickly in public health emergencies, ensuring that vaccines are distributed to where they are needed in a timely and effective manner. This emergency response capability is critical to controlling disease outbreaks and protecting public health.

[0101] like Figure 2 As shown, an embodiment of the present invention further provides a vaccine management system based on big data analysis, the system comprising:

[0102] The identification module 10 is used to generate a unique identification code for each vaccine in each batch and bind the unique identification code to the corresponding vaccine;

[0103] The management information acquisition module 20 is used to acquire the management information uploaded by the user after scanning the unique identification code of each vaccine when the vaccine is stored or distributed. The management information includes the storage location, storage time, distribution time, and distribution location;

[0104] The data analysis module 30 is used to summarize and analyze the historical management information throughout the year based on big data technology, and to construct an internal data set based on the summary analysis results, obtain an external data set, and integrate the external data set and the internal data set into a data set, wherein the external data set includes disease incidence, epidemiological data, and seasonal changes;

[0105] A model building module 40 is used to build a vaccine usage prediction model and a vaccine demand prediction model according to the data set, and predict the predicted usage within a first preset time period in the future according to the usage prediction model, and predict the predicted demand within a first preset time period in the future according to the vaccine demand prediction model;

[0106] The vaccine management module 50 is used to obtain the inventory within the first preset time period in the future based on the predicted usage within the first preset time period in the future and the predicted demand within the first preset time period in the future, and calculate the optimal order quantity for purchasing vaccines within the first preset time period in the future based on the inventory within the first preset time period in the future.

[0107] On the other hand, the present invention further proposes a storage medium on which one or more programs are stored, which, when executed by a processor, implement the above-mentioned vaccine management method based on big data analysis.

[0108] On the other hand, the present invention also proposes an electronic device, including a memory and a processor, wherein the memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory to implement the above-mentioned vaccine management method based on big data analysis.

[0109] Those skilled in the art will appreciate that the logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For purposes of this specification, "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0110] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0111] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0112] Although the embodiments of the present invention are described in detail above, it is obvious to those skilled in the art that various modifications and variations can be made to these embodiments. However, it should be understood that such modifications and variations are within the scope and spirit of the present invention as described in the claims. Moreover, the present invention described herein may have other embodiments and may be implemented or realized in a variety of ways.

Claims

1. A vaccine management method based on big data analysis, applied to a nasal intestinal vaccine box management platform, characterized in that: The method comprises: Generate a unique identification code for each vaccine in each batch, and bind the unique identification code to the corresponding vaccine; When the vaccine is stored or distributed, the management information uploaded by the user after scanning the unique identification code of each vaccine is obtained, and the management information includes the storage location, storage time, distribution time, and distribution location; Based on big data technology, the historical management information for the whole year is summarized and analyzed, and an internal data set is constructed based on the summary and analysis results, and an external data set is obtained, and the external data set and the internal data set are integrated into a data set, wherein the external data set includes disease incidence, epidemiological data, and seasonal changes; Constructing a vaccine usage prediction model and a vaccine demand prediction model according to the data set, and predicting the predicted usage within a first preset time period in the future according to the usage prediction model, and predicting the predicted demand within a first preset time period in the future according to the vaccine demand prediction model; The inventory quantity in the first preset time period in the future is obtained based on the predicted usage in the first preset time period in the future and the predicted demand in the first preset time period in the future, and the optimal order quantity for purchasing vaccines in the first preset time period in the future is calculated based on the inventory quantity in the first preset time period in the future.

2. The vaccine management method based on big data analysis according to claim 1, characterized in that: The step of constructing a vaccine usage prediction model and a vaccine demand prediction model according to the data set comprises: The vaccine usage prediction model is constructed according to the following formula: Among them, Y t represents the amount of vaccine used in the tth time period, c represents the constant term, p represents the order of the autoregressive term, q represents the order of the moving average term, represents the parameter of the autoregressive term in the i-th time period, θ i The parameter representing the moving average term of the i-th time period, ∈ t represents the error term in the tth time period, Y t-i represents the amount of vaccine used in the ti-th time period, ∈ t-i represents the error term in the ti-th time period.

3. The vaccine management method based on big data analysis according to claim 2, characterized in that: The step of constructing a vaccine usage prediction model and a vaccine demand prediction model according to the data set also includes: The vaccine demand prediction model is constructed according to the following formula: V t =β0+β1X1+β2X2+…+β n X n Among them, V t represents the demand for vaccines in the tth time period, β0 represents the intercept of the model, β1, β2, ..., β n Represents the regression coefficient, obtained by training the model, X1, X2, ..., X n Represents external influencing factors.

4. The vaccine management method based on big data analysis according to claim 3, characterized in that: The steps of obtaining the inventory in the first preset time period in the future according to the predicted usage in the first preset time period in the future and the predicted demand in the first preset time period in the future, and calculating the optimal order quantity for purchasing vaccines in the first preset time period in the future according to the inventory in the first preset time period in the future include: Get the inventory volume in the first preset time period in the future according to the following formula: K t =V t -Y t The optimal order quantity for purchasing vaccines in the first preset time period in the future is obtained according to the following formula: Among them, K t represents the inventory in the t-th time period, Ht represents the cold chain storage cost of the vaccine in the t-th time period, S* represents the fixed cost of purchasing vaccines, It represents the optimal order quantity for purchasing vaccines in the tth time period.

5. The vaccine management method based on big data analysis according to any one of claims 1 to 4, characterized in that: The method further comprises: The actual demand in a first preset time period in the future is obtained, and the vaccine demand prediction model is updated according to the actual demand in the first preset time period in the future.

6. The vaccine management method based on big data analysis according to claim 5, characterized in that: The step of obtaining the actual demand in the first preset time period in the future and updating the vaccine demand prediction model according to the actual demand in the first preset time period in the future includes: The parameter adjustment of the vaccine demand prediction model is obtained according to the following formula: Δβ=α(D t -V t ) Among them, Δβ represents the parameter adjustment of the vaccine demand prediction model, D t represents the actual demand in the first preset time period in the future, and α represents the learning rate, which is used to control the amplitude of parameter adjustment.

7. A vaccine management system based on big data analysis, characterized in that: The system comprises: An identification module, used to generate a unique identification code for each vaccine in each batch, and bind the unique identification code to the corresponding vaccine; A management information acquisition module is used to obtain management information uploaded by users after scanning the unique identification code of each vaccine when the vaccine is stored or distributed. The management information includes storage location, storage time, distribution time, and distribution location; A data analysis module is used to summarize and analyze historical management information throughout the year based on big data technology, and to construct an internal data set based on the summary analysis results, obtain an external data set, and integrate the external data set and the internal data set into a data set, wherein the external data set includes disease incidence, epidemiological data, and seasonal changes; A model building module, used to build a vaccine usage prediction model and a vaccine demand prediction model according to the data set, and predict the predicted usage within a first preset time period in the future according to the usage prediction model, and predict the predicted demand within the first preset time period in the future according to the vaccine demand prediction model; The vaccine management module is used to obtain the inventory quantity in the first preset time period in the future based on the predicted usage in the first preset time period in the future and the predicted demand in the first preset time period in the future, and calculate the optimal order quantity for purchasing vaccines in the first preset time period in the future based on the inventory quantity in the first preset time period in the future.

8. A storage medium, characterized in that: The storage medium stores one or more programs, which, when executed by the processor, implement the vaccine management method based on big data analysis as described in any one of claims 1-6.

9. An electronic device, comprising a memory and a processor, wherein: The memory is used to store computer programs; When the processor is used to execute the computer program stored in the memory, it implements the vaccine management method based on big data analysis as described in any one of claims 1-6.

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