Fresh commodity replenishment method and system, electronic equipment and storage medium

Abstract

The invention discloses a fresh commodity replenishment method and system, electronic equipment and a storage medium, and relates to the field of commodity management. According to the method, fresh food hot topics are obtained from social media, demand hotspots are determined according to the fresh food hot topics, weather influence factors are determined according to predicted weather data, and holiday influence factors and seasonal influence factors are determined according to the current date; determining sales influence factors according to the second sales data of the competitor and the first sales data of the target store; through a preset prediction model, according to the sales influence factors, the demand hotspots, the weather influence factors, the holiday influence factors and the seasonal influence factors, determining predicted demand quantities of various fresh commodities in a second preset time period after the current moment; and determining the purchase quantity according to the predicted demand quantity and the stock quantity of the target store, and determining a purchase scheme according to the purchase quantity and the supplier information. By implementing the technical scheme provided by the invention, the accuracy and efficiency of replenishment of fresh commodities are improved.

Description

Technical Field

[0001] The present application relates to the technical field of commodity management, and in particular to a method, system, electronic device and storage medium for replenishing fresh commodities. Background Art

[0002] In the field of fresh food supply chain management, ensuring timely replenishment and reasonable inventory is the key to improving competitiveness and reducing waste. With the growing market demand for fresh food, efficient and accurate replenishment strategies have become the focus of retailers. Effective replenishment can not only meet consumer demand, but also improve merchants' sales performance and avoid economic losses caused by out-of-stock or overstock.

[0003] At present, the traditional approach to fresh product replenishment management mainly includes regular inventory checks, estimating future demand based on historical sales data, and adjusting replenishment plans based on the experience of sales staff. However, these conventional methods often ignore the complex interactions of multiple influencing factors, such as weather changes, holiday effects, and social hot events, resulting in insufficient accuracy and timeliness of forecasts, making it difficult for merchants to accurately adjust replenishment strategies to adapt to the rapidly changing market environment.

[0004] Therefore, a more efficient method of replenishing fresh goods is needed. Summary of the invention

[0005] The present application provides a method, system, electronic device and storage medium for replenishing fresh goods, which improve the accuracy and efficiency of replenishing fresh goods.

[0006] In a first aspect of the present application, a method for replenishing fresh food is provided, which is applied to a fresh food management platform, and the method comprises: Acquire hot fresh food topics within a first preset time period before the current moment from social media, determine demand hotspots based on the hot fresh food topics, determine weather influencing factors based on predicted weather data within a second preset time period after the current moment, and determine holiday influencing factors and seasonal influencing factors based on the current date; Obtaining first sales data of a target store and second sales data of a competitor within a preset range of the target store, and determining a sales influencing factor according to the second sales data and the first sales data; Determine the predicted demand for various fresh products in a second preset time period after the current moment according to the sales influencing factors, the demand hot spots, the weather influencing factors, the holiday influencing factors and the seasonal influencing factors through a preset prediction model; The purchase quantity is determined according to the predicted demand and the inventory of the target store, and the purchase plan is determined according to the purchase quantity and supplier information, wherein the supplier information includes the type of goods supplied, the price and the location of the supplier.

[0007] Optionally, acquiring from social media hot topics about fresh food within a first preset time period before the current moment, and determining demand hotspots according to the hot topics about fresh food includes: Use a web crawler to crawl real-time hot topics on Weibo, WeChat public accounts, Xiaohongshu, Douyin, and Kuaishou, and select the first target topic containing the fresh food keyword in the real-time hot topics; Performing a weighted summation on the number of likes, the number of reposts, and the number of comments on the first target topic to obtain a heat value of the first target topic, and screening out hot fresh food topics whose heat values ​​exceed a threshold; Performing sentiment analysis on the fresh food hot topic to identify the emotions in user discussions, where the emotions include positive, negative or neutral; A second target topic with positive sentiment and increasing discussion heat is determined, and the types and quantities of fresh food are determined based on the second target topic.

[0008] Optionally, determining the weather influencing factors according to the forecast weather data within a second preset time period after the current moment, and determining the holiday influencing factors and the seasonal influencing factors according to the current date includes: Obtain weather data within a second preset time period after the current time provided by the meteorological department, analyze the weather data and determine weather factors affecting the sales and preservation of fresh products, the weather data including temperature, humidity, precipitation, wind speed, wind direction, air pressure, etc.; Obtain holiday schedule information, combine holiday types, historical sales data and consumer behavior analysis to determine holiday influencing factors; The current season is determined according to the current date and weather data, the seasonal characteristics corresponding to the current season are determined, and the seasonal influencing factors are determined according to the seasonal characteristics and historical sales data.

[0009] Optionally, the acquiring first sales data of a target store and second sales data of a competitor within a preset range of the target store, and determining a sales influencing factor according to the second sales data and the first sales data includes: Using a web crawler to crawl the competitor's price list, comparing the selling prices of the same fresh products in the competitor and the target store, and calculating the price difference rate; Assessing the risk level of a potential price war based on the price difference rate, wherein the risk levels include low risk, medium risk and high risk; The sales influencing factors are determined according to the risk level, the sales growth rate of the competitor, and the sales growth rate of the target store. The sales influencing factors include price, market demand, and brand image.

[0010] Optionally, determining the purchase quantity according to the predicted demand quantity and the inventory quantity of the target store includes: Calculate the difference between the predicted demand and the inventory, set the safety stock coefficient of the target fresh product according to the shelf life of the target fresh product, determine the safety stock quantity according to the safety stock coefficient, and add the difference to the safety stock quantity to obtain the purchase quantity.

[0011] Optionally, determining a procurement plan according to the procurement volume and supplier information includes: Determine a supply distance score according to the distance between the target store and the supplier to be selected, determine a product quality score according to the product quality of the supplier to be selected, and determine a product cost score according to the product price of the supplier to be selected; The supply distance score, product quality score, product cost score of the candidate supplier are weightedly summed with the corresponding weights to obtain the comprehensive score of the candidate supplier, and procurement is carried out from the candidate supplier with the highest comprehensive score.

[0012] Optionally, the method further includes: Obtaining GPS track data of the transport vehicle to determine the current location of the transport vehicle, and determining a potential transport route of the transport vehicle based on the current location and the destination location of the transport vehicle; Based on historical traffic data and real-time traffic information, analyze and calculate the busyness and congestion probability of each potential transportation route; An optimal route is selected from the potential transportation routes according to the busyness and congestion probability.

[0013] In a second aspect of the present application, a system for replenishing fresh goods is provided, including a first influencing factor module, a second influencing factor module, a prediction module, and a purchasing module, wherein: A first influencing factor module is configured to obtain from social media hot topics about fresh food within a first preset time period before the current moment, determine demand hotspots based on the hot topics about fresh food, determine weather influencing factors based on predicted weather data within a second preset time period after the current moment, and determine holiday influencing factors and seasonal influencing factors based on the current date; A second influencing factor module is configured to obtain first sales data of a target store and second sales data of a competitor within a preset range of the target store, and determine a sales influencing factor according to the second sales data and the first sales data; A prediction module configured to determine the predicted demand for various fresh commodities within a second preset time period after the current moment according to the sales influencing factors, the demand hot spots, the weather influencing factors, the holiday influencing factors and the seasonal influencing factors through a preset prediction model; The purchasing module is configured to determine the purchasing quantity according to the predicted demand and the inventory of the target store, and determine the purchasing plan according to the purchasing quantity and supplier information, wherein the supplier information includes the type of supplied goods, price and supplier location.

[0014] In the third aspect of the present application, an electronic device is provided, including 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 both 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 any one of the methods described above.

[0015] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, any of the methods described above is executed.

[0016] 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 combining the hot topics of fresh food on social media to determine the hot spots of demand, consumers' interests and preferences can be captured more accurately. At the same time, combined with the forecast of weather data, holidays and seasonal factors, various external conditions that affect the demand for fresh food can be fully considered, so as to more accurately predict the demand for fresh food in the future; 2. Based on accurate forecast demand, the purchase volume of target stores can be determined more reasonably. This helps to avoid inventory backlogs or out-of-stock situations, optimize inventory turnover, and reduce waste and losses; 3. By obtaining and analyzing competitors' sales data, you can understand market dynamics and competition situations, and provide reference for formulating procurement plans. This helps the target store maintain its advantage in the competition and increase its market share; 4. Use the preset forecasting model to forecast demand, and realize the intelligent and automated replenishment decision-making. This not only improves decision-making efficiency, but also reduces the risk of decision-making errors caused by human factors; 5. Formulate procurement plans based on forecasted demand and supplier information to integrate supply chain resources more efficiently. By optimizing supplier selection, price negotiation, and logistics distribution, procurement costs can be reduced and the overall efficiency of the supply chain can be improved; 6. Accurate replenishment and inventory management can ensure the quality and supply stability of fresh products, thereby improving customer satisfaction and loyalty. This is crucial for retailers such as fresh food e-commerce or supermarkets, and helps to establish a good brand image and reputation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of the method for replenishing fresh goods disclosed in the embodiment of the present application; Figure 2 It is a module schematic diagram of a system for replenishing fresh goods disclosed in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device disclosed in an embodiment of the present application.

[0018] Explanation of the reference numerals: 201, first influencing factor module; 202, second influencing factor module; 203, prediction module; 204, procurement module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0019] In order to enable technicians in this field 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 in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0020] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0021] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0022] This embodiment discloses a method for replenishing fresh food, which is applied to a fresh food management platform. Figure 1is a flow chart of the method for replenishing fresh goods disclosed in the embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps: S110, obtaining from social media hot topics about fresh food within a first preset time period before the current moment, determining demand hotspots according to the hot topics about fresh food, determining weather influencing factors according to predicted weather data within a second preset time period after the current moment, and determining holiday influencing factors and seasonal influencing factors according to the current date; Social Media: This usually refers to popular social media platforms such as Weibo, WeChat, Douyin, Xiaohongshu, etc. The first preset time period before the current moment: This time period can be the past day, week, or month, depending on the purpose of the analysis and the availability of data. Fresh Hot Topics: These topics are hot discussions or trends related to fresh products such as fruits, vegetables, meat, seafood, etc. Through natural language processing (NLP) technology, these topics can be extracted from posts, comments, and shares on social media. Through the analysis of hot topics, fresh products that consumers have a higher interest or demand for can be identified. For example, if the discussion volume about durian on social media surges, it can be predicted that the demand for durian will also increase. Forecasted Weather Data: This usually comes from professional weather forecasting agencies or service providers. The second preset time period after the current moment: This time period can be the next few days, a week, or longer, depending on the needs of the analysis. Weather Influence Factors: Weather has a great impact on the demand for fresh products. For example, hot weather may increase the demand for cold drinks and fruits, while cold weather may increase the demand for hot food and root vegetables. Holiday Influence Factors: Holidays often bring specific fresh product demand. For example, during the Spring Festival, people may buy more meat, seafood, and specialty foods. Seasonal factors: Seasonal changes can also affect the demand for fresh products. For example, summer is the peak season for watermelons and strawberries, while winter is the peak season for citrus fruits.

[0023] Optionally, acquiring from social media hot topics about fresh food within a first preset time period before the current moment, and determining demand hotspots according to the hot topics about fresh food includes: Use a web crawler to crawl real-time hot topics on Weibo, WeChat public accounts, Xiaohongshu, Douyin, and Kuaishou, and select the first target topic containing the fresh food keyword in the real-time hot topics; Performing a weighted summation on the number of likes, the number of reposts, and the number of comments on the first target topic to obtain a heat value of the first target topic, and screening out hot fresh food topics whose heat values ​​exceed a threshold; Performing sentiment analysis on the fresh food hot topic to identify the emotions in user discussions, where the emotions include positive, negative or neutral; A second target topic with positive sentiment and increasing discussion heat is determined, and the types and quantities of fresh food are determined based on the second target topic.

[0024] Web crawlers are used to extract data from web pages. In this scenario, web crawlers are used to crawl real-time hot topics on social media platforms. Popular social media platforms such as Weibo, WeChat official accounts, Xiaohongshu, Douyin, and Kuaishou were selected because these platforms have a large amount of user-generated content that can reflect current hot topics and trends. Real-time hot topics are usually the most discussed and most concerned topics by users on the platform. Real-time hot topics can be determined through real-time rankings on social media. Fresh keywords: These keywords include words related to fresh products, such as fruits, vegetables, meat, seafood, etc. Topics containing fresh keywords are screened out from real-time hot topics through text matching or natural language processing technology. Heat value: This is a comprehensive indicator used to measure the heat of a topic. In this scenario, the heat value is obtained by weighted summing the number of likes, reposts, and comments on the topic. Weighted sum: Different social media platforms may have different weight assignments, depending on the characteristics of the platform and user behavior. Threshold: This is a preset value used to screen out topics with a heat value high enough to be considered a hot topic. Sentiment analysis: This is a natural language processing technology used to identify sentiment in text. In this scenario, it is used to analyze the sentiment of users in their discussion of the hot topic of fresh produce. Sentiment classification: Sentiments are classified into three categories: positive, negative, or neutral. Positive sentiment indicates that users have a positive attitude towards the topic, negative sentiment indicates a negative attitude, and neutral sentiment indicates no obvious sentiment. Positive sentiment and rising discussion heat: This is the criterion for screening the second target topic. Only those topics that are both positively evaluated by users and continue to receive attention will be selected. Through in-depth analysis of the second target topic, the types of fresh produce discussed by users and the possible purchase quantities can be determined. This helps companies and merchants understand the current market demand and adjust inventory and marketing strategies accordingly.

[0025] Using web crawler technology, we can capture hot topics on multiple social media platforms such as Weibo, WeChat official accounts, Xiaohongshu, Douyin, Kuaishou, etc. in real time. This ensures the comprehensiveness and real-time nature of the data, allowing the analysis to keep up with market trends and consumer interests. By screening the first target topic containing fresh keywords, we can accurately locate discussions related to fresh products. This helps reduce noise data and improve the accuracy of analysis. The number of likes, reposts and comments of the first target topic is weighted and summed to obtain the heat value, and the hot fresh topics with heat values ​​exceeding the threshold are screened out. This method objectively and quantitatively evaluates the popularity of the topic and avoids the interference of subjective judgment. The sentiment tendency analysis of the hot fresh topics can identify the emotions in the user discussion, including positive, negative or neutral. This helps to understand consumers' attitudes and sentiment tendencies towards fresh products and provides an important reference for formulating marketing strategies. Identify the second target topics with positive emotions and rising discussion heat, and determine the types and quantities of fresh products based on these topics. This method can accurately capture the hot spots of consumer demand and provide a scientific basis for the procurement and inventory management of fresh products. Through the above process, enterprises can timely understand consumer needs and preferences, so as to make more accurate and effective decisions, which helps to enhance the market competitiveness of enterprises and increase sales and profits.

[0026] Optionally, determining the weather influencing factors according to the forecast weather data within a second preset time period after the current moment, and determining the holiday influencing factors and the seasonal influencing factors according to the current date includes: Obtain weather data within a second preset time period after the current time provided by the meteorological department, analyze the weather data and determine weather factors affecting the sales and preservation of fresh products, the weather data including temperature, humidity, precipitation, wind speed, wind direction, air pressure, etc.; Obtain holiday schedule information, combine holiday types, historical sales data and consumer behavior analysis to determine holiday influencing factors; The current season is determined according to the current date and weather data, the seasonal characteristics corresponding to the current season are determined, and the seasonal influencing factors are determined according to the seasonal characteristics and historical sales data.

[0027] Obtain detailed weather data for the second preset time period (e.g., the next week, two weeks, or one month) after the current moment from the meteorological department or professional weather forecast service providers. These weather data should include key meteorological indicators such as temperature, humidity, precipitation, wind speed, wind direction, and air pressure. Conduct in-depth analysis of the obtained weather data to identify weather factors that may have a significant impact on the sales and preservation of fresh products. For example, high temperature and humidity may cause some fresh products (such as fruits and vegetables) to rot quickly, while rainfall may affect transportation and storage conditions. Based on the above analysis, determine the weather factors that will have the most direct and significant impact on the sales and preservation of fresh products. These factors will serve as an important basis for the subsequent formulation of sales strategies and inventory management plans. Obtain holiday schedule information for the current and future period from government announcements, calendar applications, or professional holiday information providers. Analyze the impact of different holiday types (such as Spring Festival, National Day, Mid-Autumn Festival, etc.) on fresh product sales. Use historical sales data to identify fresh products whose sales will increase or decrease significantly during holidays. Understand consumers' purchasing behavior and preferences during holidays through social media, questionnaires, or customer feedback. Analyze the specific reasons why these behaviors affect the demand and sales of fresh products. Based on the above analysis, determine the specific factors that affect the sales of fresh products during holidays, such as holiday customs, family gatherings, travel, etc. Determine the current season (such as spring, summer, autumn, winter) based on the current date and weather data (such as temperature, precipitation, etc.). Analyze the unique climate conditions and consumer behavior patterns of each season, such as hot and humid summers and cold and dry winters. These characteristics will directly affect the shelf life, transportation costs, and consumer demand of fresh products. Using historical sales data, analyze the fresh products that will significantly increase or decrease sales in each season. Identify the direct impact of seasonal changes on the demand for fresh products. Based on the above analysis, determine the specific factors that affect the sales of fresh products in the current season, such as seasonal product demand, changes in consumer preferences, etc.

[0028] By obtaining detailed weather data (such as temperature, humidity, precipitation, wind speed, wind direction, air pressure, etc.) provided by the meteorological department, the specific impact of weather on the sales and preservation of fresh goods can be fully analyzed. This accurate prediction helps companies adjust inventory in advance, optimize logistics distribution and preservation measures, and reduce commodity losses and sales fluctuations caused by weather changes. Combined with holiday types, historical sales data and consumer behavior analysis, the impact of holidays on fresh product sales can be accurately identified. This helps companies develop targeted marketing strategies, such as launching holiday specials, adding holiday specialty products, etc., to attract consumers and increase sales. Determine the current season based on the current date and weather data, and combine seasonal characteristics and historical sales data to deeply analyze the impact of seasonal changes on fresh product sales. This effective grasp helps companies adjust product structure and optimize supply chain management according to seasonal changes to meet the seasonal needs of consumers. By comprehensively considering multiple influencing factors such as weather, holidays and seasons, companies can develop more comprehensive and scientific sales strategies and inventory management plans. This helps improve the company's operational efficiency and market competitiveness and achieve sustainable development. The above process makes full use of big data and data analysis technology to achieve automation and intelligence in the entire process from data collection, analysis to decision-making. This data-driven decision optimization helps companies grasp market dynamics and consumer demands more accurately, and improve the scientific nature and accuracy of decision-making.

[0029] S120, obtaining first sales data of a target store and second sales data of a competitor within a preset range of the target store, and determining a sales influencing factor according to the second sales data and the first sales data; The first sales data of the target store refers to the sales data of the target store in a specific time period, including key indicators such as sales, sales volume, average customer price, product types, and inventory. Data is directly exported through the internal sales system or ERP system of the target store. The second sales data of competitors refers to the sales data of competitors within the preset range of the target store (such as the same business district, the same city, or the same region) in a specific time period. The sales data of competitors can be obtained indirectly through market research, questionnaires, or consumer interviews, or by using the data services of third-party data providers or industry research institutions. When obtaining the second sales data of competitors, it is necessary to comply with relevant laws, regulations, and ethical standards. Clean, organize, and standardize the sales data obtained to ensure the accuracy and consistency of the data. Convert the data into a format suitable for analysis, such as tables, charts, or databases. Sales and sales volume comparison: compare the sales and sales volume of the target store and competitors, and analyze the performance of the target store in the market. Average customer price analysis: calculate the average average customer price of the target store and competitors, and analyze consumers' purchasing power and consumption habits. Product category and inventory analysis: Compare the product category and inventory of the target store and competitors, and analyze the product structure and inventory management strategy of the target store. Price competition analysis: Analyze the product prices of the target store and competitors, and evaluate the effectiveness of the price competition strategy. Promotion effect analysis: Analyze the promotion effect of the target store and competitors, and evaluate the impact of the promotion strategy on sales. Based on the above analysis, determine the main factors that affect the sales of the target store, such as product quality, pricing strategy, promotion activities, inventory management, consumer demand, etc.

[0030] Optionally, the acquiring first sales data of a target store and second sales data of a competitor within a preset range of the target store, and determining a sales influencing factor according to the second sales data and the first sales data includes: Using a web crawler to crawl the competitor's price list, comparing the selling prices of the same fresh products in the competitor and the target store, and calculating the price difference rate; Assessing the risk level of a potential price war based on the price difference rate, wherein the risk levels include low risk, medium risk and high risk; The sales influencing factors are determined according to the risk level, the sales growth rate of the competitor, and the sales growth rate of the target store. The sales influencing factors include price, market demand, and brand image.

[0031] Use web crawler technology to crawl competitor price lists from their official websites, e-commerce platforms or other public channels. These price lists usually contain information such as the names, specifications, and prices of fresh products sold by competitors. Compare the crawled competitor price lists with the prices of the same fresh products in the target store. This requires that the fresh products sold by the target store and competitors are comparable in terms of name and specifications. Through comparison, calculate the price difference rate, that is, the percentage difference between the competitor's price and the target store's price. The price difference rate can reflect the relative position of the target store and competitors in price competition. According to the calculated price difference rate, analyze the price competition situation between the target store and competitors. If the price difference rate is small, it means that the prices of both parties are similar, and the potential price war risk may be high; if the price difference rate is large, it may mean that the two parties have differences in market positioning, target customer groups, etc., and the price war risk is relatively low. According to the size of the price difference rate, the risk level of potential price war is divided into low risk, medium risk and high risk. Low risk means that the price competition between the target store and competitors is relatively mild, medium risk means that the price competition between the two parties is relatively fierce, and high risk means that the two parties may be involved in a fierce price war. Combined with the results of the price war risk level assessment, analyze the impact of price factors on sales. If the risk level is high, it means that price competition has a greater impact on sales, and the target store may need to adjust its pricing strategy to cope with competition. In addition to price factors, market demand is also an important factor affecting sales. By analyzing the sales growth rates of competitors and target stores, you can understand the changing trend of market demand. If the sales growth rate is high, it means that the market demand is strong, and the target store can seize the opportunity to expand sales; if the sales growth rate is low or negative, it may be necessary to adjust the product structure or marketing strategy to cope with changes in market demand. Brand image is one of the important factors that influence consumer purchasing decisions. In a market environment with fierce price competition, a good brand image can help target stores improve consumer loyalty and reduce the negative impact of price wars on sales. Therefore, when analyzing the factors affecting sales, the role of brand image also needs to be considered.

[0032] Using web crawler technology to capture competitors' price lists, real-time monitoring of competitors' price information is achieved. This not only improves the efficiency of obtaining price data, but also ensures the timeliness and accuracy of the data. Compare the selling prices of the same fresh products in competitors and target stores, calculate the price difference rate, and provide basic data for subsequent risk assessment and influencing factor analysis. Evaluate the risk level of potential price wars based on the price difference rate, including low risk, medium risk and high risk. This quantitative evaluation method helps companies understand the price competition situation more intuitively and provides a scientific basis for formulating response strategies. Combined with the risk level, the sales growth rate of competitors and the sales growth rate of target stores, a comprehensive analysis of sales influencing factors is conducted. This method not only takes into account price factors, but also takes into account other important factors such as market demand and brand image, making the analysis results more comprehensive and accurate. Through the above analysis process, companies can keep abreast of competitors' dynamics, evaluate the risks of price wars, and formulate corresponding sales strategies and procurement volumes based on sales influencing factors. This helps companies maintain their competitive advantage and increase market share and profitability.

[0033] S130, determining the predicted demand for various fresh products in a second preset time period after the current moment according to the sales influencing factors, the demand hot spots, the weather influencing factors, the holiday influencing factors and the seasonal influencing factors through a preset prediction model; The preset forecasting model is a tool that predicts the future demand for fresh products based on a variety of influencing factors. The model comprehensively considers sales influencing factors (such as price, market competition, brand image, etc.), demand hotspots (such as hot topics on social media), weather influencing factors (such as temperature, humidity, precipitation, etc.), holiday influencing factors (such as holiday types, consumer behavior, etc.) and seasonal influencing factors (such as seasonal characteristics, historical sales data, etc.). By integrating these multi-dimensional data, the model can more accurately predict the demand for various fresh products in the future. The preset forecasting model adds an attention mechanism to the original framework, automatically screens key features, and strengthens the allocation of decision weights, thereby effectively responding to the impact of emergencies on the stability of the supply chain.

[0034] The process of building a preset forecasting model: collect historical sales data, social media data, meteorological data, holiday schedule information, etc., and clean, organize and normalize them. Fill in missing data and correct abnormal data to ensure the accuracy and completeness of the data. Extract features related to the demand for fresh goods from the collected data, such as price difference rate, sales growth rate, weather conditions, holiday types, seasonal characteristics, etc. Quantify the features, such as converting weather conditions into specific values ​​such as temperature and humidity, and converting holiday types into time periods before and after holidays. According to the characteristics of the data and the forecasting needs, select a suitable forecasting model, such as a time series analysis model, a machine learning model (such as random forest, support vector machine, neural network, etc.) or a deep learning model, use historical data to train the model, adjust the model parameters, and improve the model's forecasting accuracy.

[0035] The sales influencing factors, demand hot spots, weather influencing factors, holiday influencing factors and seasonal influencing factors in the second preset time period after the current moment are used as input data. The input data is preprocessed to ensure that it is consistent with the data format used in model training. The preprocessed input data is input into the trained prediction model. The model calculates the predicted demand for various fresh products in the future based on the input data. Output the prediction results, including the predicted demand for various fresh products in the future.

[0036] S140. Determine the purchase quantity according to the predicted demand and the inventory of the target store, and determine the purchase plan according to the purchase quantity and supplier information, wherein the supplier information includes the type of supplied goods, price and supplier location.

[0037] Calculate the net demand for each fresh commodity based on the forecast demand (i.e., the expected sales volume of various fresh commodities in the future) and the current inventory of the target store. Net demand = forecast demand - current inventory. When calculating the net demand, the safety stock factor needs to be taken into account. Safety stock is the inventory reserved to cope with risks such as uncertainty in market demand and supply chain disruptions. Therefore, when calculating the current inventory, the safety stock should be subtracted from the actual inventory to obtain the effective inventory that can be used to meet the forecast demand. Add the net demand for each fresh commodity to obtain the total purchase volume. It should be noted that the purchase volume should take into account factors such as procurement cycle and transportation time to ensure that the goods can arrive on time and be put on the shelves for sale before the forecast demand period arrives. According to the supplier information (including the type of goods supplied, price and supplier location), select suppliers who can provide the required fresh commodities. In the screening process, it is necessary to comprehensively consider factors such as the supplier's price, quality, reputation, and delivery ability. Send an inquiry request to the selected suppliers to collect quotation information. Based on the quotation information, combined with other cost factors such as transportation costs and tariffs, calculate the total cost of each procurement plan. Consider factors such as supplier delivery time, transportation method, and transportation distance to evaluate the delivery time of different procurement plans. At the same time, it is also necessary to evaluate potential risks such as supplier stability and the risk of supply chain disruption. Comprehensively consider factors such as cost, delivery time, and risk to develop the best procurement plan. The procurement plan should include specific details such as procurement volume, supplier selection, price negotiation, delivery time, and transportation method.

[0038] Optionally, determining the purchase quantity according to the predicted demand quantity and the inventory quantity of the target store includes: Calculate the difference between the predicted demand and the inventory, set the safety stock coefficient of the target fresh product according to the shelf life of the target fresh product, determine the safety stock quantity according to the safety stock coefficient, and add the difference to the safety stock quantity to obtain the purchase quantity.

[0039] Obtain the forecasted demand for various fresh products in the future (such as a week, a month, etc.) from the forecasting model. At the same time, obtain the current inventory of various fresh products from the inventory system of the target store. For each fresh product, calculate the difference between its forecasted demand and current inventory. This difference represents the amount of additional purchases required to meet future demand without considering safety stock. The shelf life of different fresh products may be different. For example, vegetables and fruits may be shorter, while meat, seafood, etc. may be slightly longer. The shorter the shelf life of the product, the lower its safety stock coefficient may need to be set to avoid inventory backlog and loss. Set a reasonable safety stock coefficient based on the shelf life of the target fresh product, historical sales data, supply chain stability and other factors. The safety stock coefficient is usually a value between 0 and 1, which is used to adjust the size of the safety stock. The higher the coefficient, the more safety stock needs to be reserved. For each fresh product, multiply its forecasted demand (or average demand in a certain period of time) by the safety stock coefficient to obtain the safety stock. This safety stock is reserved to deal with risks such as uncertainty in market demand and supply chain disruptions. The difference between the forecast demand and inventory calculated above is added to the safety stock to get the final purchase quantity. This purchase quantity takes into account both the basic quantity to meet future demand and the safety stock to deal with potential risks.

[0040] Safety stock coefficients and purchase volumes are not static. Companies should make dynamic adjustments based on market changes, supply chain stability and other factors. For example, during holidays or promotional events, it may be necessary to increase safety stock coefficients and purchase volumes to cope with increased demand. When setting safety stock coefficients and calculating purchase volumes, cost control also needs to be considered. Too high safety stock may lead to inventory backlogs and capital occupation, while too low purchase volumes may not meet market demand and lead to sales losses. Companies should establish close cooperative relationships with suppliers to ensure the stability and reliability of the supply chain. This helps reduce the risk of supply chain disruptions, thereby reducing reliance on safety stock.

[0041] By calculating the difference between the predicted demand and the inventory, we can accurately understand the demand gap for fresh products in the target stores in the future. This helps companies replenish stocks in time and avoid out-of-stock situations, thereby meeting consumers' purchasing needs. Considering that fresh products have a short shelf life and are prone to deterioration and loss, it is very important to set a safety stock coefficient and determine the safety stock accordingly. The setting of safety stock can reserve a certain buffer space for enterprises to cope with risks such as uncertainty in market demand and supply chain disruptions. In this way, even if an emergency occurs, the company can maintain a certain inventory level to ensure that sales are not affected. By adding the difference to the safety stock to get the purchase volume, the company can manage inventory more scientifically. This helps companies avoid inventory backlogs and out-of-stock situations, and improve inventory turnover and capital utilization. At the same time, a reasonable purchase volume can also reduce losses caused by expired inventory. With a clear purchase volume, companies can organize procurement activities more efficiently. This includes selecting suitable suppliers, negotiating prices, and determining delivery times. Efficient procurement activities can shorten the procurement cycle and improve procurement efficiency, thereby ensuring that fresh products can arrive and be put on the shelves for sale in a timely manner. By accurately calculating the purchase volume, companies can avoid over-purchasing or under-purchasing. This helps companies reduce inventory costs, transportation costs and other expenses, and improve overall profitability.

[0042] Optionally, determining a procurement plan according to the procurement volume and supplier information includes: Determine a supply distance score according to the distance between the target store and the supplier to be selected, determine a product quality score according to the product quality of the supplier to be selected, and determine a product cost score according to the product price of the supplier to be selected; The supply distance score, product quality score, product cost score of the candidate supplier are weightedly summed with the corresponding weights to obtain the comprehensive score of the candidate supplier, and procurement is carried out from the candidate supplier with the highest comprehensive score.

[0043] The supply distance score is determined based on the geographical distance between the target store and the candidate supplier. The closer the distance, the higher the score, because a shorter supply distance usually means faster delivery speed and lower transportation cost. The supply distance score can be determined by setting a distance threshold and giving a corresponding score based on the comparison between the actual distance and the threshold. The product quality score is determined based on the quality of the goods provided by the candidate supplier. The quality of the goods can be evaluated through historical purchase records, customer feedback, quality inspection reports, etc. The product quality score is usually a relatively subjective score, but it can be ensured by setting a series of quality standards to ensure objectivity and consistency. The product cost score is determined based on the price of the goods provided by the candidate supplier. The lower the price, the higher the score, because a lower price usually means a lower procurement cost. The product cost score can be given a corresponding score by comparing the product price with the market price or the budget price. The weight reflects the importance of each scoring dimension in the comprehensive score. The setting of the weight should be determined according to the company's procurement strategy and goals. For example, if the company attaches great importance to delivery speed, the weight of the supply distance score may be higher; if the company attaches great importance to product quality, the weight of the product quality score may be higher. The supply distance score, product quality score, product cost score and corresponding weights of the candidate supplier are weighted and summed to obtain the comprehensive score of the candidate supplier. Comprehensive score = supply distance score × supply distance weight + product quality score × product quality weight + product cost score × product cost weight. Sort the comprehensive scores of all the candidate suppliers and select the supplier with the highest comprehensive score as the optimal procurement solution. If there are multiple suppliers with similar comprehensive scores, other factors (such as supplier reputation, delivery reliability, etc.) can be further considered to make the final selection.

[0044] By setting the supply distance score, product quality score and product cost score and assigning them different weights, the comprehensive strength of the selected suppliers can be comprehensively and objectively evaluated. This comprehensive evaluation method helps enterprises choose the most suitable supplier to ensure that the purchased goods meet the quality requirements and can be controlled in terms of cost. Selecting suppliers based on comprehensive scores can make procurement decisions more scientific and reasonable. Enterprises no longer rely solely on price factors to select suppliers, but take into account multiple factors such as supply distance, product quality and cost. This helps enterprises reduce procurement risks and improve procurement efficiency. By evaluating the product cost score, enterprises can have a clearer understanding of the quotations of various suppliers, so as to select suppliers with lower costs for procurement. This helps enterprises reduce procurement costs and improve profitability. The supply distance score reflects the transportation distance and time between the supplier and the target store. Selecting suppliers with a shorter supply distance can shorten transportation time and improve supply efficiency. This is especially important for enterprises that need to respond quickly to market demand and maintain inventory turnover. The product quality score is an important indicator for evaluating the quality of suppliers' products. Selecting suppliers with higher product quality scores can ensure that the purchased goods meet the quality requirements of the enterprise and reduce the risks of returns and claims caused by product quality problems. By comprehensively evaluating and selecting suppliers with the highest comprehensive scores for procurement, enterprises can establish long-term and stable cooperative relationships with high-quality suppliers. This helps enterprises enhance the stability of the supply chain and improve the risk resistance of the supply chain.

[0045] Optionally, the method further includes: Obtaining GPS track data of the transport vehicle to determine the current location of the transport vehicle, and determining a potential transport route of the transport vehicle based on the current location and the destination location of the transport vehicle; Based on historical traffic data and real-time traffic information, analyze and calculate the busyness and congestion probability of each potential transportation route; An optimal route is selected from the potential transportation routes according to the busyness and congestion probability.

[0046] The GPS device installed on the transport vehicle will continuously record the vehicle's geographic location information, including longitude, latitude, speed, direction, etc. Current location determination: By receiving and analyzing GPS trajectory data in real time, the current geographic location of the transport vehicle can be accurately determined. This usually involves converting the received GPS coordinates into a specific location on the map for subsequent operations. Once the current location and destination location of the transport vehicle are determined, a route planning algorithm (such as the Dijkstra algorithm) can be used to calculate multiple possible transport routes. These algorithms take into account factors such as road networks, traffic rules, and turning restrictions to generate a reasonable set of routes. Potential route set: After the algorithm calculates, a series of potential transport routes from the current location to the destination location will be obtained. These routes may include different roads, highways, bridges, and tunnels. Historical traffic data: This data usually includes traffic flow, vehicle speed, congestion, etc. on various roads over a period of time in the past. This data can come from traffic monitoring cameras, induction coils, mobile phone positioning data, etc. Real-time traffic information: This information includes traffic accidents, road construction, weather conditions, etc. on the current road, which will directly affect the road's capacity. Calculation of busyness and congestion probability: By analyzing historical traffic data and real-time traffic information, mathematical models (such as time series analysis, machine learning algorithms, etc.) can be used to calculate the busyness and congestion probability of each potential transportation route. These indicators can reflect the traffic conditions of the route in different time periods. Comprehensive evaluation of multiple factors: When selecting the best route, in addition to considering busyness and congestion probability, other factors such as road quality, driving time, fuel consumption cost, safety, etc. can also be considered. Decision algorithm: Multi-objective optimization algorithms (such as genetic algorithms, particle swarm algorithms, etc.) can be used to comprehensively consider these factors and select one or more optimal transportation routes. These algorithms optimize the objective function according to the set weights and constraints to find the optimal solution. Route recommendation: Finally, the system will recommend the selected best route to the driver or dispatcher of the transportation vehicle. This can be achieved through the on-board display, mobile phone APP or text message.

[0047] To reduce operational risks, this solution adds a supply chain resilience indicator monitoring module to regularly evaluate the status of multiple dimensions such as supply-side reliability, distribution link security, and emergency reserve levels, and timely warn of potential crises to ensure normal operation even in the face of unforeseen events. A special fund is set up to purchase insurance services to cover economic loss compensation in various unexpected situations such as natural disasters and man-made damage; at the same time, a set of online troubleshooting tools is developed to guide front-line staff to quickly locate the source of the problem, shorten maintenance time, and avoid long-term shutdowns that cause customer loss.

[0048] This embodiment also discloses a system for replenishing fresh goods. Figure 2is a module diagram of a system for replenishing fresh goods disclosed in an embodiment of the present application, such as Figure 2 As shown, the system includes a first influencing factor module 201, a second influencing factor module 202, a prediction module 203 and a procurement module 204, wherein: The first influencing factor module 201 is configured to obtain from social media hot topics about fresh food within a first preset time period before the current moment, determine demand hotspots based on the hot topics about fresh food, determine weather influencing factors based on predicted weather data within a second preset time period after the current moment, and determine holiday influencing factors and seasonal influencing factors based on the current date; A second influencing factor module 202 is configured to obtain first sales data of a target store and second sales data of a competitor within a preset range of the target store, and determine a sales influencing factor based on the second sales data and the first sales data; The prediction module 203 is configured to determine the predicted demand for various fresh products within a second preset time period after the current moment according to the sales influencing factors, the demand hot spots, the weather influencing factors, the holiday influencing factors and the seasonal influencing factors through a preset prediction model; The purchasing module 204 is configured to determine the purchasing quantity according to the predicted demand and the inventory of the target store, and determine the purchasing plan according to the purchasing quantity and supplier information, wherein the supplier information includes the type of supplied goods, price and supplier location.

[0049] Optionally, the first influencing factor module 201 is configured to: Use a web crawler to crawl real-time hot topics on Weibo, WeChat public accounts, Xiaohongshu, Douyin, and Kuaishou, and select the first target topic containing the fresh food keyword in the real-time hot topics; Performing a weighted summation on the number of likes, the number of reposts, and the number of comments on the first target topic to obtain a heat value of the first target topic, and screening out hot fresh food topics whose heat values ​​exceed a threshold; Performing sentiment analysis on the fresh food hot topic to identify the emotions in user discussions, where the emotions include positive, negative or neutral; A second target topic with positive sentiment and increasing discussion heat is determined, and the types and quantities of fresh food are determined based on the second target topic.

[0050] Optionally, the first influencing factor module 201 is configured to: Obtain weather data within a second preset time period after the current time provided by the meteorological department, analyze the weather data and determine weather factors affecting the sales and preservation of fresh products, the weather data including temperature, humidity, precipitation, wind speed, wind direction, air pressure, etc.; Obtain holiday schedule information, combine holiday types, historical sales data and consumer behavior analysis to determine holiday influencing factors; The current season is determined according to the current date and weather data, the seasonal characteristics corresponding to the current season are determined, and the seasonal influencing factors are determined according to the seasonal characteristics and historical sales data.

[0051] Optionally, the second influencing factor module 202 is configured to: Using a web crawler to crawl the competitor's price list, comparing the selling prices of the same fresh products in the competitor and the target store, and calculating the price difference rate; Assessing the risk level of a potential price war based on the price difference rate, wherein the risk levels include low risk, medium risk and high risk; The sales influencing factors are determined according to the risk level, the sales growth rate of the competitor, and the sales growth rate of the target store. The sales influencing factors include price, market demand, and brand image.

[0052] Optionally, the purchasing module 204 is configured to: Calculate the difference between the predicted demand and the inventory, set the safety stock coefficient of the target fresh product according to the shelf life of the target fresh product, determine the safety stock quantity according to the safety stock coefficient, and add the difference to the safety stock quantity to obtain the purchase quantity.

[0053] Optionally, the purchasing module 204 is configured to: Determine a supply distance score according to the distance between the target store and the supplier to be selected, determine a product quality score according to the product quality of the supplier to be selected, and determine a product cost score according to the product price of the supplier to be selected; The supply distance score, product quality score, product cost score of the candidate supplier are weightedly summed with the corresponding weights to obtain the comprehensive score of the candidate supplier, and procurement is carried out from the candidate supplier with the highest comprehensive score.

[0054] Optionally, the system further comprises a line module, wherein the line module is configured to: Obtaining GPS track data of the transport vehicle to determine the current location of the transport vehicle, and determining a potential transport route of the transport vehicle based on the current location and the destination location of the transport vehicle; Based on historical traffic data and real-time traffic information, analyze and calculate the busyness and congestion probability of each potential transportation route; An optimal route is selected from the potential transportation routes according to the busyness and congestion probability.

[0055] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, 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 device 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 repeated here.

[0056] This embodiment also discloses an electronic device, referring to Figure 3 The electronic device may include: at least one processor 301 , at least one communication bus 302 , a user interface 303 , a network interface 304 , and at least one memory 305 .

[0057] The communication bus 302 is used to realize the connection and communication between these components.

[0058] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0059] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0060] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0061] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (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, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an 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 various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may also be optionally at least one storage device located away from the aforementioned processor 301. As Figure 3 As shown, the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program of a method for replenishing fresh goods.

[0062] exist Figure 3 In the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 301 can be used to call the application program storing the method of replenishing fresh goods in the memory 305. When executed by one or more processors 301, the electronic device executes one or more methods in the above-mentioned embodiments.

[0063] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0064] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0065] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0066] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0067] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0068] If 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, 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, which is stored in a memory 305 and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory 305 includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk.

[0069] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for replenishing fresh goods, characterized in that: Applied to a fresh food management platform, the method includes: Acquire hot fresh food topics within a first preset time period before the current moment from social media, determine demand hotspots based on the hot fresh food topics, determine weather influencing factors based on predicted weather data within a second preset time period after the current moment, and determine holiday influencing factors and seasonal influencing factors based on the current date; Obtaining first sales data of a target store and second sales data of a competitor within a preset range of the target store, and determining a sales influencing factor according to the second sales data and the first sales data; Determine the predicted demand for various fresh products in a second preset time period after the current moment according to the sales influencing factors, the demand hot spots, the weather influencing factors, the holiday influencing factors and the seasonal influencing factors through a preset prediction model; The purchase quantity is determined according to the predicted demand and the inventory of the target store, and the purchase plan is determined according to the purchase quantity and supplier information, wherein the supplier information includes the type of goods supplied, the price and the location of the supplier.

2. The method for replenishing fresh goods according to claim 1, characterized in that: The step of obtaining from social media a hot topic about fresh food within a first preset time period before the current moment, and determining a hot demand point according to the hot topic about fresh food includes: Use a web crawler to crawl real-time hot topics on Weibo, WeChat public accounts, Xiaohongshu, Douyin, and Kuaishou, and select the first target topic containing the fresh food keyword in the real-time hot topics; Performing a weighted summation on the number of likes, the number of reposts, and the number of comments on the first target topic to obtain a heat value of the first target topic, and screening out hot fresh food topics whose heat values ​​exceed a threshold; Performing sentiment analysis on the fresh food hot topic to identify the emotions in user discussions, where the emotions include positive, negative or neutral; A second target topic with positive sentiment and increasing discussion heat is determined, and the types and quantities of fresh food are determined based on the second target topic.

3. The method for replenishing fresh goods according to claim 1, characterized in that: The step of determining the weather influencing factors according to the forecast weather data within the second preset time period after the current moment, and determining the holiday influencing factors and the seasonal influencing factors according to the current date includes: Obtain weather data within a second preset time period after the current time provided by the meteorological department, analyze the weather data and determine weather factors affecting the sales and preservation of fresh products, the weather data including temperature, humidity, precipitation, wind speed, wind direction, air pressure, etc.; Obtain holiday schedule information, combine holiday types, historical sales data and consumer behavior analysis to determine holiday influencing factors; The current season is determined according to the current date and weather data, the seasonal characteristics corresponding to the current season are determined, and the seasonal influencing factors are determined according to the seasonal characteristics and historical sales data.

4. The method for replenishing fresh goods according to claim 1, characterized in that: The obtaining of first sales data of a target store and second sales data of a competitor within a preset range of the target store, and determining a sales influencing factor according to the second sales data and the first sales data comprises: Using a web crawler to crawl the competitor's price list, comparing the selling prices of the same fresh products in the competitor and the target store, and calculating the price difference rate; Assessing the risk level of a potential price war based on the price difference rate, wherein the risk levels include low risk, medium risk and high risk; The sales influencing factors are determined according to the risk level, the sales growth rate of the competitor, and the sales growth rate of the target store. The sales influencing factors include price, market demand, and brand image.

5. The method for replenishing fresh goods according to claim 1, characterized in that: Determining the purchase quantity according to the predicted demand quantity and the inventory quantity of the target store includes: Calculate the difference between the predicted demand and the inventory, set the safety stock coefficient of the target fresh product according to the shelf life of the target fresh product, determine the safety stock quantity according to the safety stock coefficient, and add the difference to the safety stock quantity to obtain the purchase quantity.

6. The method for replenishing fresh goods according to claim 1, characterized in that: Determining a procurement plan according to the procurement volume and supplier information includes: Determine a supply distance score according to the distance between the target store and the supplier to be selected, determine a product quality score according to the product quality of the supplier to be selected, and determine a product cost score according to the product price of the supplier to be selected; The supply distance score, product quality score, product cost score of the candidate supplier are weightedly summed with the corresponding weights to obtain the comprehensive score of the candidate supplier, and procurement is carried out from the candidate supplier with the highest comprehensive score.

7. The method for replenishing fresh goods according to claim 1, characterized in that: The method further comprises: Obtaining GPS track data of the transport vehicle to determine the current location of the transport vehicle, and determining a potential transport route of the transport vehicle based on the current location and the destination location of the transport vehicle; Based on historical traffic data and real-time traffic information, analyze and calculate the busyness and congestion probability of each potential transportation route; An optimal route is selected from the potential transportation routes according to the busyness and congestion probability.

8. A system for replenishing fresh goods, characterized in that: It includes a first influencing factor module, a second influencing factor module, a prediction module and a procurement module, wherein: A first influencing factor module is configured to obtain from social media hot topics about fresh food within a first preset time period before the current moment, determine demand hotspots based on the hot topics about fresh food, determine weather influencing factors based on predicted weather data within a second preset time period after the current moment, and determine holiday influencing factors and seasonal influencing factors based on the current date; A second influencing factor module is configured to obtain first sales data of a target store and second sales data of a competitor within a preset range of the target store, and determine a sales influencing factor according to the second sales data and the first sales data; A prediction module configured to determine the predicted demand for various fresh commodities within a second preset time period after the current moment according to the sales influencing factors, the demand hot spots, the weather influencing factors, the holiday influencing factors and the seasonal influencing factors through a preset prediction model; The purchasing module is configured to determine the purchasing quantity according to the predicted demand and the inventory of the target store, and determine the purchasing plan according to the purchasing quantity and supplier information, wherein the supplier information includes the type of supplied goods, price and supplier location.

9. 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 both 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.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is executed.

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