Chain drugstore purchasing scheme generation method and system based on big data

Abstract

The invention discloses a chain drugstore purchasing scheme generation method and system based on big data, and relates to the field of purchasing management. The method comprises the following steps: firstly, collecting historical sales data, real-time inventory data and external environment data, and constructing a multi-dimensional database; performing standardization processing on the data, and constructing a medicine classification matrix through a data mining model; identifying key factors influencing drug requirements by using a causal analysis method, and establishing a requirement influence factor weight matrix; based on the weight matrix, calculating purchase parameters such as a safe inventory level, an economic purchase batch, a purchase trigger point and a purchase period; and finally, in combination with a supplier evaluation system, generating a purchasing scheme including a purchasing medicine list, a purchasing quantity, purchasing time and supplier selection. According to the method, through multi-dimensional data analysis and causal relationship mining, scientization and accuracy of purchasing decision are realized, the purchasing efficiency is improved, and the inventory cost is reduced.

Description

Technical Field

[0001] This application relates to the field of procurement management, and particularly to a method and system for generating a procurement plan for chain pharmacies based on big data. Background Art

[0002] The procurement management of chain pharmacies is an important link in the pharmaceutical retail industry, and its core task is to ensure the timeliness of drug supply and the rationality of inventory. Currently, chain pharmacies generally adopt a method of formulating procurement plans based on the analysis of historical sales data. Specifically, procurement personnel predict future demand by analyzing historical sales records and combining empirical judgments, and then determine the procurement quantity and procurement time accordingly. There is a prominent problem in the actual application of this method: due to the failure to fully consider the causal relationship between multi-dimensional data, the accuracy of procurement decisions is insufficient. For example, the demand for certain drugs is affected by multiple factors such as seasonal changes, regional population characteristics, and disease prevalence, and there are often complex causal relationships between these factors. Traditional methods rely only on simple data statistics and empirical judgments, and cannot accurately identify and quantify the impact of these factors on drug demand, thus easily causing inventory backlogs or shortages, affecting the operation efficiency and service quality of pharmacies. Therefore, there is an urgent need for a technical method that can generate accurate procurement plans. Summary of the Invention

[0003] This application provides a method and system for generating a procurement plan for chain pharmacies based on big data, which can improve the accuracy of the procurement plan.

[0004] In a first aspect, this application provides a method for generating a procurement plan for chain pharmacies based on big data, the method comprising: Collecting historical sales data, real-time inventory data, and external environment data of chain pharmacies, and constructing a multi-dimensional database based on the historical sales data, the real-time inventory data, and the external environment data; the external environment data includes season information, regional population characteristics, epidemic data, and medical insurance policy information; Performing standardization processing on the data in the multi-dimensional database to obtain standardized multi-dimensional data, and constructing a drug classification matrix based on the standardized multi-dimensional data through a preset data mining model; Based on the drug classification matrix, using a causal analysis method to identify key factors affecting the demand for various drugs, and establishing a demand impact factor weight matrix according to the key factors; Performing parameter calculation based on the demand impact factor weight matrix to obtain procurement parameters including safety inventory level, economic procurement batch, procurement trigger point, and procurement cycle; According to the procurement parameters and in combination with a preset supplier evaluation system, a procurement plan including a list of purchased drugs, the quantity of purchases, the purchase time, and the selection of suppliers is generated.

[0005] By adopting the above technical solution, the present application constructs a multi-dimensional database by collecting historical sales data, real-time inventory data, and external environment data of chain pharmacies, thereby realizing the comprehensive collection and systematic management of data. The collected external environment data includes seasonal information, regional population characteristics, epidemic data, and medical insurance policy information, ensuring the comprehensiveness and relevance of the data. By standardizing the data in the multi-dimensional database and using a preset data mining model to construct a drug classification matrix, data from different sources and of different types can be analyzed uniformly. Based on the drug classification matrix, a causal analysis method is used to identify the key factors affecting the demand for various drugs, and a demand impact factor weight matrix is established, thereby scientifically quantifying the degree of influence of each factor on drug demand. Further, parameter calculations are performed based on the demand impact factor weight matrix to obtain procurement parameters including safety inventory levels, economic order quantities, procurement trigger points, and procurement cycles, realizing the precision and scientific nature of procurement decisions. Finally, by combining the procurement parameters with a preset supplier evaluation system, a procurement plan including a list of purchased drugs, the quantity of purchases, the purchase time, and the selection of suppliers is generated, thereby realizing the intelligent management of the entire procurement process, improving procurement efficiency, reducing inventory costs, and ensuring the timeliness and accuracy of drug supply. The technical solution of the present invention realizes data-driven and intelligent optimization of procurement decisions by constructing a complete technical chain from data collection to plan generation, significantly improving the operation efficiency and service quality of chain pharmacies.

[0006] In a second aspect of the present application, a system for generating a procurement plan for chain pharmacies based on big data is provided. The system includes: a data collection module, a first data processing module, a second data processing module, a third data processing module, and a procurement plan determination module; The data collection module is used to collect historical sales data, real-time inventory data, and external environment data of chain pharmacies, and construct a multi-dimensional database based on the historical sales data, the real-time inventory data, and the external environment data; the external environment data includes seasonal information, regional population characteristics, epidemic data, and medical insurance policy information; The first data processing module is used to standardize the data in the multi-dimensional database to obtain standardized multi-dimensional data, and construct a drug classification matrix based on the standardized multi-dimensional data through a preset data mining model; The second data processing module is used to identify the key factors affecting the demand for various drugs based on the drug classification matrix by using a causal analysis method, and establish a demand impact factor weight matrix according to the key factors; The third data processing module is configured to perform parameter calculation based on the demand influence factor weight matrix to obtain procurement parameters including safety inventory level, economic procurement lot size, procurement trigger point, and procurement cycle; The procurement plan determination module is configured to generate a procurement plan including a list of drugs to be procured, procurement quantity, procurement time, and supplier selection according to the procurement parameters and in combination with a preset supplier evaluation system.

[0007] In a third aspect of the present application, a computer storage medium is provided. The computer storage medium stores multiple instructions, and the instructions are adapted to be loaded and executed by a processor to perform the above method steps.

[0008] In a fourth aspect of the present application, an electronic device is provided, including: a processor and a memory; wherein, the memory stores a computer program, and the computer program is adapted to be loaded and executed by the processor to perform the above method steps.

[0009] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By collecting historical sales data, real-time inventory data, and external environment data of chain drugstores in the present application to construct a multi-dimensional database, the comprehensive collection and systematic management of data are realized. The collected external environment data includes season information, regional population characteristics, epidemic data, and medical insurance policy information, ensuring the comprehensiveness and relevance of the data. By standardizing the data in the multi-dimensional database and using a preset data mining model to construct a drug classification matrix, data from different sources and different types can be analyzed uniformly. Based on the drug classification matrix, a causal analysis method is used to identify the key factors affecting the demand for various drugs, and a demand influence factor weight matrix is established, thereby scientifically quantifying the influence degree of each factor on drug demand.

[0010] 2. Parameter calculation is performed based on the demand influence factor weight matrix in the present application to obtain procurement parameters including safety inventory level, economic procurement lot size, procurement trigger point, and procurement cycle, realizing the precision and scientific nature of procurement decisions. Finally, by combining the procurement parameters with a preset supplier evaluation system, a procurement plan including a list of drugs to be procured, procurement quantity, procurement time, and supplier selection is generated, thereby realizing the intelligent management of the entire procurement process, improving procurement efficiency, reducing inventory costs, and ensuring the timeliness and accuracy of drug supply. Description of the Drawings

[0011] Figure 1 is a schematic flowchart of a method for generating a procurement plan for chain drugstores based on big data provided by an embodiment of the present application; Figure 2It is a schematic diagram of modules of a system for generating a purchasing plan for chain drugstores based on big data provided by an embodiment of the present application; Figure 3 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application.

[0012] Explanation of reference numerals: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. Detailed implementation manners

[0013] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments.

[0014] In the description of the embodiments of the present application, words such as "for example" or "for illustration" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "for example" or "for illustration" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, using words such as "for example" or "for illustration" aims to present relevant concepts in a specific manner.

[0015] In the description of the embodiments of the present application, the meaning of the term "a plurality" refers to two or more. For example, a plurality of systems refers to two or more systems, and a plurality of screen terminals refers to two or more screen terminals. In addition, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the technical features indicated. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The terms "include", "comprise", "have" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0016] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments.

[0017] Please refer to Figure 1, a flow schematic diagram of a method for generating a procurement plan for chain pharmacies based on big data is proposed. This method can be implemented relying on a computer program, can be implemented relying on a single-chip microcomputer, or can run on a system for generating a procurement plan for chain pharmacies based on big data. This computer program can be integrated in a computer device or can run as an independent tool application. Specifically, this method is applied to a non-fixed air blower dehumidifier. Specifically, this method includes steps 10 to 60, and the above steps are as follows: Step 10: Collect historical sales data, real-time inventory data, and external environment data of chain pharmacies, and construct a multi-dimensional database based on the historical sales data, the real-time inventory data, and the external environment data; the external environment data includes season information, regional population characteristics, epidemic data, and medical insurance policy information; Specifically, the historical sales data, real-time inventory data, and external environment data of chain pharmacies are obtained simultaneously through a data collection system. Specifically, the historical sales data contains the daily sales records of each store in the past three years, and the record content includes information such as drug numbers, sales quantities, and sales amounts; the real-time inventory data records the inventory changes of each store in real time through the pharmacy management system, including information such as inventory quantities, inventory amounts, and expiration dates; the external environment data contains the key external factors affecting drug sales. Among them, the season information records meteorological data such as temperature and humidity, the regional population characteristics record demographic statistics such as population density and age structure, the epidemic data records the incidence and transmission trends of various diseases, and the medical insurance policy information records the updates of the medical insurance catalog and changes in reimbursement policies. After collecting these data, data warehouse technology is used to construct a multi-dimensional database, and the data from different sources are organized and stored according to multiple dimensions such as time dimension, space dimension, and product dimension. By constructing a multi-dimensional database, the system can analyze and mine data from multiple perspectives, providing complete data support for subsequent data analysis and decision-making. For example, when it is necessary to analyze the sales rules of a certain drug in a specific season and a specific region, only the corresponding dimensions need to be selected in the multi-dimensional database for data extraction and analysis to obtain accurate analysis results. In addition, the establishment of the multi-dimensional database also realizes the efficient management and rapid retrieval of data, improving the data processing efficiency.

[0018] Step 20: Perform standardization processing on the data in the multi-dimensional database to obtain standardized multi-dimensional data, and construct a drug classification matrix based on the standardized multi-dimensional data through a preset data mining model; Specifically, first, the data in the multi-dimensional database is standardized. Since the multi-dimensional database contains data from different sources and with different measurement units, standardization is required to make the data comparable. Standardization is achieved by converting the data into a unified numerical range. Specifically, the maximum-minimum normalization method is adopted to map various types of data into the interval [0, 1]. For sales data, it is standardized by calculating the average daily sales volume; for inventory data, it is standardized by calculating the inventory turnover rate; for external environment data, it is standardized separately according to the data type. For example, temperature data is converted into standard scores, and population density data is converted into relative density values. After standardization, a drug classification matrix is constructed through a preset data mining model. The preset data mining model includes a hierarchical clustering algorithm to cluster drugs with similar characteristics into the same category. The rows of the drug classification matrix represent different drugs, the columns represent the characteristics of the drugs, and each element of the matrix represents the standardized value of a certain drug in a certain characteristic dimension. In this way, the system establishes a multi-dimensional classification system for drugs, providing a basis for subsequent causal analysis. This classification method based on standardized data eliminates the dimensional difference of the data, improves the accuracy of classification, and at the same time, the construction of the drug classification matrix enables the system to understand and analyze the characteristics of drugs from multiple dimensions, providing a scientific basis for subsequent procurement decisions.

[0019] Based on the above embodiments, as an alternative embodiment, constructing a drug classification matrix through a preset data mining model based on the standardized multi-dimensional data includes: S110, extracting features from the sales data records in the standardized multi-dimensional data to obtain sales frequency features, sales volatility features, and sales seasonality features; Specifically, the system extracts features from the sales data records in the standardized multi-dimensional data. First, the sales frequency feature is extracted, which is obtained by dividing the number of sales of each drug within a certain time window by the length of the time window. This feature reflects the sales activity of the drug. Then, the sales volatility feature is extracted, which is obtained by calculating the ratio of the standard deviation to the average value of the sales quantity. This feature reflects the stability of the drug sales volume. Finally, the sales seasonality feature is extracted by performing time series decomposition on the sales data to separate the seasonal component. This feature reflects the periodic change law of drug sales. Through the extraction of these three features, the system comprehensively captures the key characteristics of drugs at the sales level, providing accurate feature indicators for subsequent drug classification. For example, cold medications usually exhibit higher sales frequency features, larger sales volatility features, and obvious sales seasonality features, while chronic disease medications exhibit lower sales volatility features and weaker sales seasonality features. This feature extraction method enables the system to accurately identify the sales patterns of different drugs, laying a foundation for constructing a scientific drug classification matrix.

[0020] S120. Extract features from the demand data records in the standardized multi-dimensional data to obtain demand stability features, demand prediction difficulty features, and demand suddenness features. Specifically, the system extracts features from the demand data records in the standardized multi-dimensional data. First, extract the demand stability feature, which is obtained by calculating the coefficient of variation of the demand quantity within a continuous time period. The coefficient of variation is the ratio of the standard deviation to the average value of the demand quantity, and this feature reflects the fluctuation degree of the drug demand. Second, extract the demand prediction difficulty feature, which is obtained by calculating the mean absolute percentage error between the historical predicted value and the actual demand value. This feature reflects the complexity of the drug demand prediction. Finally, extract the demand suddenness feature, which is obtained by calculating the ratio of the peak value to the average value of the demand quantity. This feature reflects the suddenness degree of the drug demand. Through the extraction of these three features, the system accurately depicts the key attributes of drugs at the demand level. For example, commonly used drugs exhibit higher demand stability features and lower demand prediction difficulty features, while emergency drugs exhibit higher demand suddenness features. This multi-dimensional demand feature extraction provides an accurate demand feature description for subsequent drug classification and procurement decisions, improving the accuracy of procurement decisions.

[0021] S130. Extract features from the inventory data records in the standardized multi-dimensional data to obtain storage condition features, shelf life features, and inventory cost features. Specifically, the system extracts features from the inventory data records in the standardized multi-dimensional data. First, extract the storage condition feature, which is obtained by analyzing environmental parameters such as temperature, humidity, and light required for the drug, and quantify the strictness degree of the storage condition as a numerical value. This feature reflects the complexity of drug storage. Second, extract the shelf life feature, which is obtained by calculating the time length from the drug production date to the expiration date. This feature reflects the storage time limit of the drug. Finally, extract the inventory cost feature, which is obtained by calculating the warehousing cost, insurance cost, and capital occupation cost per unit of the drug. This feature reflects the economic cost of drug storage. Through the extraction of these three features, the system completely depicts the key attributes of drugs at the inventory management level. For example, ordinary drugs stored at room temperature have lower storage condition feature values, while drugs that require cold chain storage have higher storage condition feature values and inventory cost feature values. This feature extraction method enables the system to accurately evaluate the inventory management difficulty and cost of different drugs, providing an important basis for formulating reasonable procurement strategies.

[0022] S140. Combine the sales frequency feature, the sales volatility feature, the sales seasonality feature, the sales frequency feature, the sales volatility feature, the sales seasonality feature, the storage condition feature, the shelf life feature, and the inventory cost feature to form a drug feature vector. Specifically, the system combines the extracted features to construct a drug feature vector. First, arrange the sales frequency feature, the sales volatility feature, and the sales seasonality feature at the sales level in sequence. These features represent the sales activity, sales stability, and periodic change law of drugs respectively. Then, add the demand stability feature, the demand prediction difficulty feature, and the demand suddenness feature at the demand level to the feature sequence. These features reflect the fluctuation degree, prediction complexity, and suddenness of demand respectively. Finally, add the storage condition feature, the shelf life feature, and the inventory cost feature at the inventory level to the feature sequence. These features represent the storage requirements, time limit, and economic cost respectively. By combining these nine features in a fixed order, a complete drug feature vector is formed. Each drug corresponds to a feature vector, and each dimension in the vector represents the quantitative value of the drug on a specific feature. Through this way of constructing the feature vector, the system integrates the multi-dimensional attributes of drugs into a unified mathematical expression form, providing a standardized input data format for subsequent drug classification through a data mining model.

[0023] S150. Input the drug feature vector into the preset data mining model to obtain multi-dimensional drug data, and construct the drug classification matrix based on the multi-dimensional drug data.

[0024] Specifically, the preset data mining model includes a clustering analysis model, an association rule mining model, and a pattern recognition model. First, input the drug feature vector into the clustering analysis model. By calculating the distance between sample points in the feature space, cluster drugs with similar features into classes to obtain the initial drug classes. Then, input the drug feature vector into the association rule mining model to mine the association relationships between different features and obtain the drug association relationships. Then, input the drug feature vector into the pattern recognition model to identify the change law of drug demand and obtain the drug demand patterns. The system combines the initial drug classes, the drug association relationships, and the drug demand patterns to form multi-dimensional drug data. Based on the multi-dimensional drug data, the system constructs a drug classification matrix. The rows of this matrix represent different drugs, the columns represent the various features and class attributes of drugs, and the matrix elements represent specific feature values or class identifiers. In this way, the system realizes the multi-dimensional classification of drugs, enabling each class of drugs to have clear feature descriptions and management strategies, providing a scientific classification basis for subsequent procurement decisions.

[0025] Based on the above embodiments, as an alternative embodiment, the preset data mining model includes a preset clustering analysis model, a preset association rule mining model, and a preset pattern recognition model; inputting the drug feature vector into the preset data mining model to obtain multi-dimensional drug data, including: S151. Input the drug feature vector into the preset clustering analysis model to obtain the initial drug categories; Specifically, the system inputs the drug feature vector into the preset clustering analysis model for classification. The preset clustering analysis model uses the K-means clustering algorithm to measure the similarity between drugs by calculating the Euclidean distance of the drug feature vector in the feature space. First, the system sets the number of clustering centers K value to 5, representing that the drugs are divided into 5 initial categories. Then, randomly select K sample points as the initial clustering centers, calculate the distance from each drug feature vector to these K clustering centers, and divide each drug feature vector into the category of the nearest clustering center. Next, recalculate the center point of each category, that is, the mean value of all drug feature vectors in this category, as the new clustering center. The system repeats the process of dividing sample points and updating clustering centers until the clustering centers no longer change or reach the preset number of iterations, and finally obtains the initial drug categories. This classification method based on clustering analysis automatically classifies drugs with similar characteristics into the same category, forming a scientific and reasonable initial classification system, providing a classification basis for the subsequent formulation of procurement strategies.

[0026] S152. Input the drug feature vector into the preset association rule mining model to obtain the drug association relationships; Specifically, the system inputs the drug feature vector into the preset association rule mining model for association analysis. The preset association rule mining model uses the Apriori algorithm to discover the association patterns between drugs by analyzing the association relationships between the features in the drug feature vector. First, the system sets the minimum support threshold to 0.1 and the minimum confidence threshold to 0.6 for screening strong association rules. Then, discretize each feature in the drug feature vector, dividing the continuous feature values into discrete intervals. Next, the system scans all drug feature vectors, calculates the frequency of occurrence of each feature combination, and generates frequent item sets. Based on the frequent item sets, the system calculates the conditional probability between feature combinations, and when the support and confidence simultaneously meet the threshold requirements, it is determined as a drug association relationship. The finally obtained drug association relationships reflect the association strength between different features, such as the association rule between the sales frequency feature and the demand stability feature. This analysis method based on association rules reveals the internal connections between drug features and provides a basis for formulating joint procurement strategies.

[0027] S153. Input the drug feature vector into the preset pattern recognition model to obtain the drug demand pattern; Specifically, the system inputs the drug feature vector into the preset pattern recognition model for demand pattern analysis. The preset pattern recognition model uses the decision tree algorithm to identify the demand change law of drugs by analyzing the feature combinations in the drug feature vector. First, the system takes the demand-related features in the drug feature vector, including demand stability feature, demand prediction difficulty feature, and demand suddenness feature, as the main analysis objects. Then, the system calculates the information gain of each feature and selects the feature with the largest information gain as the splitting node of the decision tree. Next, the system divides the samples into different sub-nodes according to the distribution of feature values and repeats the feature selection and splitting process on each sub-node until the termination condition is reached. Through each path of the decision tree, the system identifies the demand change laws corresponding to different feature combinations and forms the drug demand pattern. This pattern recognition-based analysis method transforms the demand characteristics of drugs into clear demand patterns, providing a basis for demand prediction for procurement decisions.

[0028] S154. Use the initial drug category, the drug association relationship, and the drug demand pattern as the multi-dimensional drug data.

[0029] Specifically, the system integrates the analysis results obtained by different models to form multi-dimensional drug data. First, the system takes the initial drug category obtained by the clustering analysis model as the first-dimensional data, which contains the basic classification information and category characteristics of drugs. Then, the system takes the drug association relationship obtained by the association rule mining model as the second-dimensional data, which reflects the association strength and rules between drug characteristics. Next, the system takes the drug demand pattern obtained by the pattern recognition model as the third-dimensional data, which describes the demand change law and prediction characteristics of drugs. The system combines the data of these three dimensions to form the complete multi-dimensional drug data, where each drug contains descriptions in three aspects: category identification, association rules, and demand patterns. This integration method of multi-dimensional data enables the system to comprehensively describe and understand the characteristics of drugs from multiple perspectives, providing a complete data basis for constructing the drug classification matrix in the future.

[0030] Based on the above embodiments, as an alternative embodiment, constructing the drug classification matrix based on the multi-dimensional drug data includes: S155. Establish a drug category dimension based on the initial drug category; Specifically, the system establishes a drug category dimension based on the initial drug categories. First, the system groups the initial drug categories obtained through cluster analysis according to category characteristics to form a category hierarchy. Then, the system analyzes the characteristic centers of each category to determine the core attributes of the category, including the typical values of sales characteristics, demand characteristics, and inventory characteristics. Next, the system assigns a unique identification code to each category and establishes the hierarchical relationship between categories to form a complete drug category dimension. This dimension integrates the category attributes, characteristic centers, and hierarchical relationships of drugs to constitute the first dimension of the drug classification matrix. This method of establishing a dimension based on initial drug categories enables the system to clearly describe the main characteristics and category attributes of each type of drug, providing a categorized management basis for subsequent procurement decisions.

[0031] S156. Establish a demand characteristic dimension based on the drug association relationship; Specifically, a demand characteristic dimension is established based on the drug association relationship. First, the system analyzes the strength of the drug association relationships obtained through association rule mining and calculates the association strength values between each pair of associated characteristics. Then, the system filters the association strength values according to a preset threshold and retains the characteristic pairs with significant association relationships. Next, based on these significant association relationships, the system constructs a demand characteristic network, where the nodes in the network represent drug characteristics and the connections represent the association relationships between characteristics. The system transforms this characteristic network into a demand characteristic dimension, which describes the mutual influence relationships between drug demand characteristics. By establishing the demand characteristic dimension in this way, the system can accurately grasp the correlations between drug demand characteristics, providing a basis for demand association in procurement decisions.

[0032] S157. Establish a management strategy dimension based on the drug demand pattern; Specifically, a management strategy dimension is established based on the drug demand pattern. First, the system classifies the drug demand patterns obtained through the pattern recognition model and groups the patterns with similar demand change rules into one group. Then, the system sets corresponding management strategy parameters for each demand pattern, including safety stock coefficients, procurement cycles, and order quantities, etc. Next, the system organizes these management strategy parameters into a management strategy dimension, which describes the specific management strategies under different demand patterns. By using this method of establishing a management strategy dimension based on the demand pattern, the system achieves an accurate correspondence between the demand pattern and the management strategy, providing specific strategy guidance for the procurement management of different types of drugs.

[0033] S158. Combine the drug category dimension, demand characteristic dimension, and management strategy dimension to form a drug classification matrix.

[0034] Specifically, the system establishes a management strategy dimension based on the drug demand pattern. First, the system classifies the drug demand patterns obtained through the pattern recognition model, and groups the patterns with similar demand change rules into one group. Then, the system sets corresponding management strategy parameters for each demand pattern, including safety stock coefficient, procurement cycle, order quantity, etc. Next, the system organizes these management strategy parameters into a management strategy dimension, which describes the specific management strategies under different demand patterns. Through this method of establishing the management strategy dimension based on the demand pattern, the system achieves an accurate correspondence between the demand pattern and the management strategy, providing specific strategic guidance for the procurement management of different types of drugs.

[0035] Step 30: Based on the drug classification matrix, use the causal analysis method to identify the key factors affecting the demand for various drugs, and establish a demand impact factor weight matrix according to the key factors; Specifically, conduct a causal analysis based on the drug classification matrix to identify the key factors affecting drug demand. Specifically, first obtain the historical sales data corresponding to various drugs in the drug classification matrix, perform time series matching on the historical sales data and external environment data, and establish an initial causal relationship data set. Based on the initial causal relationship data set, use the conditional independence test method to verify the causal relationship between variables. The conditional independence test is implemented by calculating the conditional mutual information value between variables. When the mutual information value is greater than the preset significance threshold, it is confirmed that there is a causal relationship between the two variables. In this way, the system finds out the key factors that actually affect drug demand. After obtaining the key factors, calculate the influence degree of each key factor on drug demand, and establish a demand impact factor weight matrix. Both the rows and columns of this weight matrix are key factors, and the element values in the matrix represent the influence intensity of the row factor on the column factor. For example, for cold drugs, through causal analysis, it is found that temperature change and influenza incidence rate are the two main key factors, and their weight values in the weight matrix are 0.6 and 0.4 respectively. By establishing the demand impact factor weight matrix, the system clearly quantifies the influence relationship between various factors, provides a scientific basis for subsequent parameter calculation, and improves the accuracy of procurement decisions.

[0036] Based on the above embodiments, as an alternative embodiment, the step of using the causal analysis method to identify the key factors affecting the demand for various drugs based on the drug classification matrix includes: S310, obtain the historical sales data corresponding to various drugs in the drug classification matrix, and perform time series matching on the historical sales data and external environment data to establish an initial causal relationship data set; Specifically, the system first extracts the identification information of various drugs from the drug classification matrix, and then obtains the corresponding historical sales data based on this identification information. For each type of drug, the system extracts the daily sales records for the past three years, including information such as the quantity sold and the sales amount. At the same time, the system obtains external environmental data, including seasonal information, regional population characteristics, epidemic data, and medical insurance policy information. Then, the system matches the historical sales data with the external environmental data according to the time dimension, and combines the sales data and environmental data at the same time point. The system adopts the method of sliding time window to pair the data at each time point, ensuring the corresponding relationship between the sales data and the environmental data in time. Through this time series matching, the system establishes an initial causal relationship data set, and each record in this data set contains the sales data at a certain time point and the corresponding environmental data. This data matching method provides a complete data basis for subsequent causal relationship analysis, enabling the system to accurately analyze the impact relationship of external environmental factors on drug sales.

[0037] S320. Conduct a conditional independence test on the initial causal relationship data set to obtain target variable pairs that pass the conditional independence test, and calculate the causal strength coefficient of the target variable pairs. Specifically, the system conducts a conditional independence test on the variable pairs in the initial causal relationship data set. First, the system calculates the conditional mutual information value for each pair of variables, and measures their independence by calculating the degree of mutual dependence between two variables under the condition of given other variables. Then, the system sets the significance level to 0.05. When the p-value corresponding to the mutual information value is less than the significance level, it is determined that there is a significant causal relationship between the variable pair, and it is marked as a target variable pair that passes the conditional independence test. Then, the system calculates the causal strength coefficient for the target variable pairs that pass the test. This coefficient is calculated through a combination of conditional probability and time series correlation. For example, for a certain type of cold medicine, the system finds that the causal strength coefficient between its sales volume and temperature change is 0.72, and the causal strength coefficient between its sales volume and the incidence of influenza is 0.85. Through this conditional independence test and causal strength calculation, the system screens out the variable pairs that truly have a causal relationship and quantifies the degree of influence between them, providing a scientific basis for subsequent determination of key factors.

[0038] S330. Screen the causal strength coefficients according to a preset coefficient threshold to obtain key factors.

[0039] Specifically, the system screens the calculated causal strength coefficients to determine the true key factors. First, the system sets the threshold of the causal strength coefficient to 0.6, which is determined based on historical data analysis and expert experience. Then, the system compares the causal strength coefficient of each target variable pair with the preset coefficient threshold. When the causal strength coefficient is greater than the preset threshold, the influencing factor in the variable pair is determined as a key factor. Through this screening method, the system finally obtains the key factors affecting drug demand. For example, for cold medicines, through the screening of causal strength coefficients, the system determines that the temperature change (causal strength coefficient 0.72) and the influenza incidence rate (causal strength coefficient 0.85) are key factors, while the relative humidity (causal strength coefficient 0.45) is excluded because the coefficient is lower than the threshold. This coefficient screening method based on the threshold ensures that the system only retains the factors with significant influence, providing a reliable factor basis for the subsequent establishment of the demand influencing factor weight matrix.

[0040] Based on the above embodiments, as an alternative embodiment, the establishing a demand influencing factor weight matrix according to the key factors includes: S340, calculating the direct influence weight and the interaction influence weight of each of the key factors; Specifically, the system calculates two types of influence weights for the screened key factors. First, the system calculates the direct influence weight, which is obtained by analyzing the degree of influence of each key factor acting alone on drug demand. This weight is calculated through the partial correlation coefficient between the key factor and the demand quantity. Then, the system calculates the interaction influence weight, which is obtained by analyzing the degree of influence of two key factors acting simultaneously on drug demand. This weight is calculated through the interaction coefficient between the factors. For example, for cold medicines, the direct influence weight of the temperature change is 0.6, the direct influence weight of the influenza incidence rate is 0.7, and their interaction influence weight is 0.8, indicating that the combined effect of these two factors has a greater impact on demand. By calculating these two weights, the system not only quantifies the degree of influence of a single factor but also captures the synergy effect between factors, providing accurate weight data for constructing a complete weight matrix.

[0041] S350, constructing a demand influencing factor weight matrix based on the direct influence weight and the interaction influence weight.

[0042] Specifically, the system constructs a demand impact factor weight matrix based on the calculated direct impact weights and interaction impact weights. First, the system creates an n×n matrix framework, where n is the number of key factors, and the row and column identifiers of the matrix are both key factors. Then, the system fills the direct impact weights of each key factor into the diagonal positions of the matrix, and these weights reflect the independent impact degree of a single factor on demand. Next, the system fills the interaction impact weights into the non-diagonal positions of the matrix, and these weights represent the interaction strength between two factors. For example, in the demand impact factor weight matrix, the direct impact weight of 0.6 for temperature change is filled into the diagonal position, and the interaction impact weight of 0.8 between temperature change and the influenza incidence rate is filled into the corresponding non-diagonal position. The weight matrix constructed in this way completely records the impact relationships of each key factor and provides a weight basis for subsequent parameter calculations.

[0043] Step 40: Calculate parameters based on the demand impact factor weight matrix to obtain procurement parameters including safety stock level, economic order quantity, procurement trigger point, and procurement cycle. Specifically, in this embodiment, the system calculates procurement parameters based on the demand impact factor weight matrix. First, the weighted average daily demand μ is calculated using the demand impact factor weight matrix, and this value is obtained by the weighted average of the impact weights of each key factor on demand and historical demand data. The demand standard deviation σ is calculated based on the weighted average daily demand μ to measure the degree of demand fluctuation. Then, the system obtains the preset service level target value P, replenishment lead time L, unit procurement cost A, and unit inventory holding cost h, which are set by the pharmacy according to the actual operation situation. Next, the safety factor k is calculated through the formula k = √(-2ln(1 - P)), and the safety factor k, demand standard deviation σ, and replenishment lead time L are substituted into the formula SS = k×σ×√L to obtain the safety stock level SS. Based on the weighted average daily demand μ, the annual demand D = 365×μ is calculated, and the annual demand D, unit procurement cost A, and unit inventory holding cost h are substituted into the formula EOQ = √(2AD / h) to obtain the economic order quantity EOQ. Subsequently, the weighted average daily demand μ, replenishment lead time L, and safety stock level SS are substituted into the formula ROP = μL + SS to calculate the procurement trigger point ROP. Finally, based on the economic order quantity EOQ and weighted average daily demand μ, the procurement cycle T is calculated through the formula T = EOQ / μ. Through this series of parameter calculations, the system converts the qualitative causal analysis results into quantitative procurement parameters, providing specific numerical bases for procurement decisions and improving the scientificity and accuracy of procurement decisions.

[0044] Based on the above embodiments, as an alternative embodiment, calculating parameters based on the demand influencing factor weight matrix to obtain procurement parameters including safety inventory level, economic order quantity, reorder point, and procurement cycle, includes: The weighted average daily demand μ calculated based on the demand influencing factor weight matrix, and the demand standard deviation σ calculated based on the weighted average daily demand μ; Calculating the safety factor k based on the preset service level target value P and the preset safety factor calculation formula, and determining the safety inventory level SS based on the safety factor k, demand standard deviation σ, and the preset replenishment lead time L; Determining the annual demand D based on the weighted average daily demand μ, and determining the economic order quantity EOQ based on the annual demand D, the preset unit procurement cost A, and the preset unit inventory holding cost h; Determining the reorder point ROP based on the weighted average daily demand μ, the preset replenishment lead time L, and the safety inventory level SS; Determining the procurement cycle T based on the economic order quantity EOQ and the weighted average daily demand μ.

[0045] Specifically, the system calculates procurement parameters based on the demand influencing factor weight matrix. First, the system calculates the weighted average daily demand μ through the demand influencing factor weight matrix, multiplies the historical daily demand corresponding to each key factor by its weight and then sums them. For example, the weighted average daily demand μ calculated for a certain drug is 100 pieces. Based on the weighted average daily demand μ, the system calculates the square root of the sum of the squared deviations of the demand from the mean to obtain the demand standard deviation σ, which is used to measure the degree of demand fluctuation.

[0046] Next, the system calculates the safety factor k based on the preset service level target value P (such as 0.95) using the formula k = √(-2ln(1 - P)). Substitute the safety factor k, demand standard deviation σ, and the preset replenishment lead time L (such as 7 days) into the formula SS = k × σ × √L to calculate the safety inventory level SS.

[0047] Then, the system calculates the annual demand D based on the weighted average daily demand μ, that is, D = 365 × μ. Substitute the annual demand D, the preset unit procurement cost A (such as 100 yuan), and the preset unit inventory holding cost h (such as 10 yuan) into the formula EOQ = √(2AD / h) to calculate the economic order quantity EOQ.

[0048] Subsequently, the system substitutes the weighted average daily demand μ, the preset replenishment lead time L, and the safety stock level SS into the formula ROP = μL + SS to calculate the procurement trigger point ROP. Finally, based on the economic order quantity EOQ and the weighted average daily demand μ, the system calculates the procurement cycle T through the formula T = EOQ / μ.

[0049] Step 50: According to the procurement parameters and in combination with a preset supplier evaluation system, generate a procurement plan including a list of drugs to be procured, the procurement quantity, the procurement time, and the supplier selection.

[0050] Specifically, generate a final procurement plan based on the procurement parameters. First, the system extracts the drugs with the current inventory level lower than the procurement trigger point ROP to form a list of drugs to be procured. For each drug in the list, the system determines the procurement quantity according to the economic order quantity EOQ and adjusts it considering the safety stock level SS to ensure that the inventory level after procurement is not lower than the safety stock level SS. When determining the procurement time, the system sets the specific procurement execution time point based on the procurement cycle T. Then, the system selects a suitable supplier in combination with the preset supplier evaluation system, which includes four dimensions: the on-time delivery rate, price competitiveness, quality assurance ability, and emergency response ability. The on-time delivery rate is calculated by dividing the number of on-time arrival batches by the total number of arrival batches, the price competitiveness is calculated by dividing the difference between the highest quote and the supplier's quote by the difference between the highest quote and the lowest quote, the quality assurance ability is calculated by dividing the number of qualified arrival batches by the total number of arrival batches, and the emergency response ability is calculated by dividing the number of successful emergency supply times by the number of emergency demand times. The system calculates the comprehensive score of the supplier based on the scoring results of these four dimensions and selects the supplier with the highest score as the procurement object. Finally, the system generates a complete procurement plan, including a detailed list of drugs to be procured, the specific procurement quantity, a clear procurement time arrangement, and the optimal supplier selection result. In this way, the system realizes the scientific and automated procurement decision-making, improves the procurement efficiency, and reduces the procurement cost.

[0051] Please refer to Figure 2 FIG. 1, which is a schematic diagram of the modules of a system for generating a procurement plan for a chain pharmacy based on big data provided by an embodiment of the present application. The system for generating a procurement plan for a chain pharmacy based on big data may include: a data collection module 1, a first data processing module 2, a second data processing module 3, a third data processing module 4, and a procurement plan determination module 5; The data collection module 1 is used to collect the historical sales data, real-time inventory data, and external environment data of the chain pharmacy and construct a multi-dimensional database based on the historical sales data, the real-time inventory data, and the external environment data; the external environment data includes season information, regional population characteristics, epidemic data, and medical insurance policy information; The first data processing module 2 is configured to perform normalization processing on the data in the multi-dimensional database, obtain the multi-dimensional data after the normalization processing, and based on the multi-dimensional data after the normalization processing, construct a drug classification matrix through a preset data mining model; The second data processing module 3 is configured to, based on the drug classification matrix, use a causal analysis method to identify the key factors affecting the demand for various drugs, and establish a demand influence factor weight matrix according to the key factors; The third data processing module 4 is configured to perform parameter calculation based on the demand influence factor weight matrix to obtain procurement parameters including safety inventory level, economic procurement quantity, procurement trigger point, and procurement cycle; The procurement plan determination module 5 is configured to generate a procurement plan including a procurement drug list, procurement quantity, procurement time, and supplier selection according to the procurement parameters and in combination with a preset supplier evaluation system.

[0052] It should be noted that when the system provided in the above embodiment realizes its functions, only the division of the above functional modules is used for illustration. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be elaborated here.

[0053] The embodiment of the present application also provides a computer storage medium, which can store multiple instructions. The instructions are suitable for being loaded and executed by a processor to execute a method for generating a procurement plan for a chain pharmacy based on big data in the above embodiment. The specific execution process can refer to the specific description in the above embodiment and will not be elaborated here.

[0054] Please refer to Figure 3 The present application also discloses an electronic device. Figure 3 It is a schematic structural diagram of an electronic device disclosed in the embodiment of the present application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0055] Among them, the communication bus 302 is used to realize the connection and communication between these components.

[0056] Among them, the user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may further include a standard wired interface and a wireless interface.

[0057] Among them, the network interface 304 may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface).

[0058] Among them, the processor 301 may include one or more processing cores. The processor 301 connects various parts within the entire server through various interfaces and lines. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by invoking data stored in the memory 305, it performs various functions of the server and processes data. Optionally, the processor 301 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 301 may integrate a combination of one or several of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above modem may not be integrated into the processor 301 and may be implemented separately through a single chip.

[0059] Among them, the memory 305 may include random access memory (RAM) and may also include read-only memory. Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments. Optionally, the memory 305 may also be at least one storage device located far from the aforementioned processor 301. Refer to Figure 3 , as a computer storage medium, the memory 305 may include an operating system, a network communication module, a user interface module, and an application program for a method of generating a procurement plan for chain drugstores based on big data.

[0060] InFigure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an interface for the user to input and obtain the data input by the user; and the processor 301 can be used to call the application program stored in the memory 305 for a method of generating a procurement plan for chain drugstores based on big data. When executed by one or more processors 301, the electronic device 300 is caused to execute one or more of the methods as described in the foregoing embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present application.

[0061] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0062] In several implementation manners provided by the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some service interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0063] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0064] In addition, the functional units in each embodiment of the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0065] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present application. The aforementioned memory includes various media that can store program codes, such as USB flash drives, mobile hard disks, magnetic disks, or optical discs.

[0066] The above are only exemplary embodiments of the present disclosure, and the scope of the present disclosure cannot be limited thereby. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure still fall within the scope covered by the present disclosure. After considering the specification and the disclosure of the practical truth, those skilled in the art will easily think of other implementation schemes of the present disclosure.

[0067] The present application aims to cover any variations, uses, or adaptive changes of the present disclosure. These variations, uses, or adaptive changes follow the general principles of the present disclosure and include common general knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and the embodiments are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for generating a chain drugstore procurement plan based on big data, characterized in that: The following steps are involved: Collect historical sales data, real-time inventory data and external environment data of chain drug stores, and build a multidimensional database based on the historical sales data, the real-time inventory data and the external environment data; the external environment data includes seasonal information, regional population characteristics, epidemiological data and medical insurance policy information; Standardizing the data in the multidimensional database to obtain standardized multidimensional data and constructing a drug classification matrix based on the standardized multidimensional data through a preset data mining model; Based on the drug classification matrix, a cause-effect analysis method is used to identify key factors affecting the demand for each type of drug, and a demand influencing factor weight matrix is ​​established based on the key factors; Calculate parameters based on the demand influencing factor weight matrix to obtain procurement parameters including safety inventory level, economic procurement batch, procurement trigger point and procurement cycle; According to the procurement parameters and in combination with the preset supplier evaluation system, a procurement plan is generated including a list of purchased drugs, procurement quantity, procurement time and supplier selection.

2. The method according to claim 1, characterized in that: The method of constructing a drug classification matrix based on the standardized multidimensional data through a preset data mining model includes: Extracting features from the sales data records in the standardized multidimensional data to obtain sales frequency features, sales fluctuation features, and sales seasonality features; Extracting features from the demand data records in the standardized multidimensional data to obtain demand stability features, demand forecasting difficulty features, and demand suddenness features; Extracting features from the inventory data records in the standardized multidimensional data to obtain storage condition features, shelf life features, and inventory cost features; The sales frequency feature, the sales fluctuation feature, the sales seasonality feature, the sales frequency feature, the sales fluctuation feature, the sales seasonality feature, the storage condition feature, the shelf life feature and the inventory cost feature are combined to form a drug feature vector; The drug feature vector is input into the preset data mining model to obtain drug multi-dimensional data, and the drug classification matrix is ​​constructed based on the drug multi-dimensional data.

3. The method according to claim 2, characterized in that The preset data mining model includes a preset cluster analysis model, a preset association rule mining model and a preset pattern recognition model; The drug feature vector is input into the preset data mining model to obtain multi-dimensional drug data, including: Inputting the drug feature vector into the preset cluster analysis model to obtain an initial drug category; Inputting the drug feature vector into the preset association rule mining model to obtain drug association relationships; Inputting the drug feature vector into the preset pattern recognition model to obtain a drug demand pattern; The initial drug category, the drug association relationship and the drug demand pattern are used as the drug multi-dimensional data.

4. The method according to claim 3, characterized in that The constructing the drug classification matrix based on the drug multi-dimensional data includes: Establishing a drug category dimension based on the initial drug category; Establishing demand characteristic dimensions based on the drug association relationship; Establishing management strategy dimensions based on the drug demand pattern; The drug category dimension, demand characteristic dimension and management strategy dimension are combined to form a drug classification matrix.

5. The method according to claim 1, characterized in that Based on the drug classification matrix, the causal analysis method is used to identify the key factors affecting the demand for various types of drugs, including: Obtaining historical sales data corresponding to each type of drug in the drug classification matrix, and performing time series matching between the historical sales data and external environment data to establish an initial causal relationship data set; Performing a conditional independence test on the initial causal relationship data set to obtain a target variable pair that passes the conditional independence test, and calculating a causal strength coefficient of the target variable pair; The causal strength coefficients are screened according to a preset coefficient threshold to obtain key factors.

6. The method according to claim 1, characterized in that The step of establishing a demand influencing factor weight matrix according to the key factors includes: Calculate the direct impact weight and interactive impact weight of each of the key factors; A demand influencing factor weight matrix is ​​constructed based on the direct influencing weights and the interactive influencing weights.

7. The method according to claim 1, characterized in that The parameter calculation based on the demand influencing factor weight matrix is ​​performed to obtain procurement parameters including safety inventory level, economic procurement batch, procurement trigger point and procurement cycle, including: A weighted average daily demand μ calculated based on the demand influencing factor weight matrix, and a demand standard deviation σ calculated based on the weighted average daily demand μ; Calculate the safety factor k based on the preset service level target value P and the preset safety factor calculation formula, and determine the safety stock level SS based on the safety factor k, the demand standard deviation σ and the preset replenishment lead time L; Based on the weighted average daily demand μ, the annual demand D is determined, and based on the annual demand D, the preset unit purchase cost A and the preset unit inventory holding cost h, the economic purchase quantity EOQ is determined; Determine a purchase trigger point ROP based on the weighted average daily demand μ, the preset replenishment lead time L and the safety stock level SS; The procurement cycle T is determined based on the economic procurement quantity EOQ and the weighted average daily demand μ.

8. A chain drugstore purchasing plan generation system based on big data, characterized in that: The system comprises: a data acquisition module, a first data processing module, a second data processing module, a third data processing module and a procurement plan determination module; The data collection module is used to collect historical sales data, real-time inventory data and external environment data of chain drug stores, and build a multidimensional database based on the historical sales data, the real-time inventory data and the external environment data; the external environment data includes seasonal information, regional population characteristics, epidemiological data and medical insurance policy information; The first data processing module is used to perform standardization processing on the data in the multidimensional database to obtain the standardized multidimensional data and to construct a drug classification matrix based on the standardized multidimensional data through a preset data mining model; The second data processing module is used to identify key factors affecting the demand for various types of drugs using a cause-and-effect analysis method based on the drug classification matrix, and to establish a demand influencing factor weight matrix based on the key factors; The third data processing module is used to perform parameter calculation based on the demand influencing factor weight matrix to obtain procurement parameters including safety stock level, economic procurement batch, procurement trigger point and procurement cycle; The procurement plan determination module is used to generate a procurement plan including a procurement drug list, procurement quantity, procurement time and supplier selection according to the procurement parameters in combination with a preset supplier evaluation system.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-7.

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