Chemical product market analysis method and system based on big data

Abstract

The invention relates to the technical field of big data analysis, in particular to a chemical product market analysis method and system based on big data, and the method comprises the following steps: obtaining online service data of a chemical product based on a chemical product online service platform, the online service data comprising online browsing information and transaction data of the chemical product; establishing a purchase driving force analysis model according to the browsing information and the transaction data, and analyzing the demand condition of the chemical product through the purchase driving force analysis model; constructing a product sales prospect prediction model according to the browsing information and the transaction data, and analyzing the sales condition of the chemical product by using the product sales prospect prediction model; the market status of the chemical products of the service platform is evaluated in combination with the demand status and the sales status, and price monitoring and quantity regulation and control of the chemical products are realized. The chemical product market information is analyzed based on the online service platform data, comprehensive and accurate market information is provided for platform chemical products, and chemical production and marketing strategies are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis, and in particular to a chemical product market analysis method and system based on big data. Background Art

[0002] Big data technology has become an important cornerstone of modern society. It can process and analyze massive amounts of data and extract valuable information from them. In the chemical industry, with the popularization of online service platforms and the continuous accumulation of transaction data, various companies have a large amount of data resources, but the data are often scattered, huge and difficult to use directly. How to effectively use existing data resources to mine and analyze chemical market conditions in order to obtain valuable chemical product market information has become a key issue for the sustainable development of chemical companies.

[0003] The chemical product market is known for its high complexity and variability. There are a wide variety of chemical products on the market, prices change frequently, and market demand and supply conditions change rapidly. However, the market currently lacks a mathematical model and system that can conduct in-depth analysis of chemical product purchase demand and expected sales conditions, which makes chemical companies face certain difficulties in seizing market opportunities, formulating production plans and marketing strategies, etc.

[0004] Therefore, the online service platform for chemical products requires an analysis method that can reflect market conditions in real time and accurately. It can not only capture subtle changes in the chemical products market in a timely manner, but also deeply analyze the inherent logic of market demand, thereby helping chemical companies to better grasp the pulse of the market, formulate more accurate and effective production plans and marketing strategies, and achieve sustainable development of the chemical industry. Summary of the invention

[0005] In view of the shortcomings of the existing methods and the needs of practical applications, the present invention constructs and applies a consumption power analysis model and a sales forecast model based on the data resources of the online service platform for chemical products, and conducts a comprehensive and in-depth analysis of the market situation of chemical products, which not only gives full play to the advantages of big data resources, but also improves the accuracy of market analysis to a new height, and provides a decision-making and support basis for the chemical industry, which can more accurately grasp market opportunities and formulate more reasonable and effective production plans and marketing strategies. On the one hand, the present invention provides a chemical product market analysis method based on big data, which includes the following steps: obtaining online service data of chemical products based on the online service platform for chemical products, the online service data including online browsing information and transaction data of chemical products; establishing a purchase driving force analysis model based on the online browsing information and the transaction data, and analyzing the demand status of online consumers for chemical products through the purchase driving force analysis model; constructing a product sales prospect forecasting model based on the online browsing information and the transaction data, and using the product sales prospect forecasting model to analyze the sales status of online chemical products; combining the demand status and the sales status to evaluate the market status of chemical products on the online service platform, thereby realizing price monitoring and quantity control of online chemical products. The present invention provides comprehensive and accurate online market information for chemical companies based on big data analysis methods, obtains accurate market analysis results, helps to improve decision-making efficiency, reduce decision-making risks and enhance overall competitiveness.

[0006] Optionally, the establishment of a purchase driving force analysis model based on the online browsing information and the transaction data includes: obtaining a consumer data set and a chemical product data set of an online service platform based on the online browsing information and the transaction data; and establishing a purchase driving force analysis model based on the consumer data set and the chemical product data set. The present invention can identify the purchase preferences, behavior patterns and demand characteristics of different consumers, thereby conducting more personalized market analysis, optimizing product design, improving product quality, and more accurately positioning the target market for chemical products.

[0007] Optionally, the purchase driving force analysis model satisfies the following relationship: in, Indicates the motivation coefficient of consumers to purchase chemical products, Indicates that consumers Consumption utility of chemical products, express Consumers’ willingness to buy, Indicates that consumers Sensitive parameters of chemical products, express The average consumption value of chemical products. The relevant model of the present invention can reflect the personalized needs of different consumers, and then implement personalized online marketing strategies, provide products and online services that meet consumer needs, and improve the marketing effect of the online service platform.

[0008] Optionally, analyzing the demand status of online consumers for chemical products through the purchase driving force analysis model includes: obtaining the driving force coefficients of online consumers purchasing different chemical products using the purchase driving force analysis model; and analyzing the demand status of online consumers for chemical products according to the driving force coefficients. The present invention and the driving force coefficients of different chemical products can quickly understand the market conditions of various products in the trading market.

[0009] Optionally, constructing a product sales prospect prediction model based on the online browsing information and the transaction data includes: segmenting and analyzing the online browsing information and the transaction data according to a predetermined time interval, and obtaining data analysis results for different time intervals; and constructing a product sales prospect prediction model based on the data analysis results for different time intervals. The present invention reveals the trends and laws of sales of different chemical products based on the data analysis results for different time intervals, and provides a decision-making basis for chemical product production and inventory management.

[0010] Optionally, the product sales prospect prediction model satisfies the following relationship: in, represents the predicted sales volume of chemical products in a specific time interval, express The corresponding error coefficient is, Indicates the sales volume of chemical products in the first time period in history, express The corresponding error coefficient is, Indicates the sales volume of chemical products in the second historical time interval, express The corresponding error coefficient is, Indicates the sales volume of chemical products in the third historical time interval, express The corresponding error coefficient is, In history Sales volume of chemical products in the time interval, Indicates the number of intervals of the historical selected time series. The present invention uses multiple product sales indicators as model parameters, which helps to capture the long-term trend, periodic changes and possible emergencies of product sales data, thereby improving the accuracy of the prediction results.

[0011] Optionally, the analyzing the sales of online chemical products using the product sales prospect prediction model includes: obtaining predicted sales volumes of different online chemical products within a specific time interval through the product sales prospect prediction model; and analyzing the sales of online chemical products based on the predicted sales volumes. The predicted sales results of the present invention can more accurately analyze product inventory requirements, thereby avoiding inventory backlogs or shortages, and further ensuring that product supply matches market demand, reducing inventory costs and improving operational efficiency.

[0012] Optionally, the evaluating the market status of chemical products on the online service platform in combination with the demand status and the sales status includes: obtaining the initial sales price and product sales quantity of different chemical products in the online service platform for chemical products in combination with the demand status and the sales status; and evaluating the market status of chemical products on the online service platform based on the initial sales price and the product sales quantity. The present invention evaluates the market status of chemical products on the online service platform in combination with the demand status and the sales status, which helps to grasp the market dynamics more comprehensively and accurately, optimize market strategies, and reduce the probability of industry risks.

[0013] Optionally, the price monitoring and quantity control of online chemical products include: obtaining the preparation costs of different chemical products in combination with the online service platform for chemical products; monitoring and controlling the price and quantity of online chemical products according to the preparation costs, the initial sales prices, the product sales quantities and the market status. The present invention monitors the prices and controls the quantities of chemical products, which helps to better grasp the market dynamics, optimize cost control, maintain a fair competition environment, achieve a balance between supply and demand, and protect the interests of consumers, thereby promoting the healthy development of the chemical market.

[0014] In the second aspect, in order to efficiently execute the chemical product market analysis method based on big data provided by the present invention, the present invention also provides a chemical product market analysis system based on big data, including a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the chemical product market analysis method based on big data as described in the first aspect of the present invention. The chemical product market analysis system based on big data of the present invention has a compact structure and stable performance, and can stably execute the chemical product market analysis method based on big data provided by the present invention, thereby improving the overall applicability and practical application ability of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of the chemical product market analysis method based on big data of the present invention; Figure 2 This is a structural diagram of the chemical product market analysis system based on big data of the present invention. DETAILED DESCRIPTION

[0016] The specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are only for illustration and are not intended to limit the present invention. In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is obvious to those of ordinary skill in the art that these specific details do not need to be adopted to implement the present invention. In other examples, in order to avoid confusing the present invention, known circuits, software or methods are not specifically described.

[0017] Throughout the specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment of the present invention. Therefore, the phrases "in one embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily all refer to the same embodiment or example. In addition, particular features, structures, or characteristics may be combined in one or more embodiments or examples in any suitable combination and / or subcombination. In addition, it should be understood by those of ordinary skill in the art that the figures provided herein are for illustrative purposes and that the figures are not necessarily drawn to scale.

[0018] See also Figure 1 In order to provide real-time market data of online chemical products so that relevant enterprises can respond quickly to market changes, market factors such as price fluctuations and demand changes of chemical products are analyzed based on online service data, so as to timely adjust the production plan and marketing strategy of chemical products, thereby optimizing market resource allocation, improving production efficiency and avoiding potential market risks. The present invention provides a chemical product market analysis method based on big data, which comprises the following steps: S1. Obtain online service data of chemical products based on the online service platform for chemical products. The above online service data mainly includes online browsing information and transaction data of chemical products. The specific implementation steps and contents are as follows: The online service platform for chemical products mentioned in this example refers to the Jiaoyou Mall program. The above program is committed to providing professional services for online transactions of chemical products, with a special focus on online transactions and services for rubber products. By building a user-friendly online trading platform, Jiaoyou Mall provides buyers and sellers with efficient and convenient trading channels, supplemented by comprehensive and accurate market information.

[0019] Based on the online service data of chemical products obtained from the above-mentioned online service platform for chemical products, we can gain in-depth understanding of the market dynamics, customer needs and market competition situation of chemical products, which is helpful to optimize the operation strategy of chemical products. The online service data mainly includes the online browsing information and transaction data of chemical products.

[0020] Online browsing information plays a key role in gaining insights into online users' interest in and demand for chemical products. By collecting and analyzing data such as users' browsing time, browsing frequency, click paths, and search keywords on the platform, we can gain an in-depth understanding of different users' focus, interests, and changing trends in market demand for different chemical products and categories. This not only helps to discover potential market opportunities for the chemical product business and provide strong support for the development of new chemical products that meet market demand, but also helps to accurately optimize the design and function of existing products to meet users' actual needs and expectations.

[0021] Transaction data provides the actual transaction status of the online market for chemical products. By sorting out the transaction records in the online platform, we can understand the key information such as the transaction volume, transaction price, and transaction time of different online chemical products. This not only helps to evaluate the transaction scale and product potential of the chemical market, but also reveals the sales differences and competitive situation between different products. Based on the analysis results of transaction data, we can formulate targeted marketing strategies, adjust the pricing and promotion strategies of chemical products, and effectively increase the market share and profitability of chemical products.

[0022] In addition, online user portraits and market segments can be analyzed based on online service data. According to the users' browsing behavior and transaction habits, the characteristics and needs of different user groups can be identified, and personalized products and service plans can be formulated, which will help online service platforms improve customer satisfaction and scientificity, and enhance the influence and market competitiveness of platform services.

[0023] In order to ensure the accuracy and availability of online service data, a data preprocessing link is also set in this embodiment. In this process, data will be strictly screened, including identifying and eliminating abnormal data caused by system errors, human operation errors, etc., to ensure the uniqueness, authenticity and accuracy of online service data. At the same time, online service data of different dimensions will be standardized to eliminate the impact of dimensional differences on data analysis, making online service data more comparable, thereby ensuring an accurate, consistent and standardized online service data set, laying a solid foundation for subsequent data comparison and in-depth analysis.

[0024] In this embodiment, the method of obtaining online service data based on the online service platform for chemical products is only an optional condition of the present invention. In one or some other embodiments, the data acquisition method can be changed according to the actual situation of the data platform and the market analysis requirements, ensuring that the present invention can adapt to various complex and changeable actual application scenarios. The data acquisition method is adjusted according to the actual situation of the platform, which can more effectively extract and utilize data resources and improve the accuracy and reliability of data analysis.

[0025] S2. Establish a purchase driving force analysis model based on the online browsing information and transaction data, and then analyze the demand of online consumers for chemical products through the purchase driving force analysis model. The specific implementation steps and contents are as follows; First, based on online browsing information and transaction data, we obtain the consumer data set and chemical product data set of the online service platform. Then, we establish a purchase driving force analysis model based on the consumer data set and chemical product data set. The specific implementation content is as follows: Based on the online browsing information and transaction data in the chemical product online service platform, two core data sets can be obtained, which mainly include consumer data sets and chemical product data sets in the embodiment. The construction of the consumer data set mainly depends on the browsing behavior and transaction records of users on the online service platform, which not only includes the basic information of the current users, including but not limited to user registration information, browsing history, login logs and other information, but also can deeply analyze the purchase preferences, behavior patterns and consumption habits of online users. In an optional embodiment, the chemical product pages browsed by users, the length of stay, search keywords and other information are obtained through the chemical product online service platform. Based on this, the attention and interest trends of different users to a certain category or several categories of chemical products are analyzed, and at the same time, the transaction data of different users, including historical purchase frequency, purchase volume and repurchase probability, etc., are combined to further provide strong support for subsequent precision marketing and personalized services.

[0026] The chemical product data set mainly includes detailed transaction information of all chemical products on the online service platform, including but not limited to multi-dimensional data such as the name, specifications, price, sales volume, and evaluation of online chemical products. By organizing and analyzing the chemical product data, we can fully understand the performance of various chemical products in the market, mainly including sales trends, market share, competitive situation, etc. It not only helps to identify online best-selling products and slow-selling products, but also provides an important basis for the adjustment strategy of chemical products and market trend analysis.

[0027] Further application of data analysis techniques such as association analysis and cluster analysis can deeply explore the potential relationship between consumers and chemical products, which will help to discover and analyze the market demand and product opportunities for chemical products, thereby providing strong data support for the market strategy, product development and marketing promotion of chemical products.

[0028] In order to deeply analyze the purchasing motivations and preferences of different consumers and online users for chemical plant products, a purchasing driving force analysis model is further constructed based on the consumer data set and the chemical product data set. The above model is not only conducive to understanding the internal logic of consumer purchasing decisions, analyzing the demand characteristics and purchasing trends of online users for different chemical plant products, but also can more flexibly respond to the diversity of the chemical product market, price changes, and the ever-changing market demand and supply.

[0029] When building a purchase driving force analysis model, it is necessary to extract feature information related to consumer purchase behavior. The feature information includes but is not limited to consumers' personal information, browsing time, number of clicks, purchase frequency, purchase volume, etc., which can more accurately reflect consumers' purchase driving force. Next, a machine learning algorithm will be used to build a purchase driving force analysis model. The model needs to predict the possibility of consumers purchasing chemical products based on the extracted feature information. In the process of model construction, a suitable algorithm is selected, and model parameters are adjusted and optimized to improve the prediction performance of the purchase driving force analysis model.

[0030] On the other hand, in order to ensure that the purchase driving force analysis model can accurately capture the inherent laws of consumer purchasing behavior, the historical data accumulated by the online service platform is used to train the model. Based on this, it can not only learn the general characteristics of consumer purchasing behavior but also deeply explore the deep-seated laws and patterns behind it, thereby obtaining more accurate and reliable prediction results, providing strong data support for the market analysis of chemical products.

[0031] The above purchase driving force analysis model satisfies the following relationship: in, Indicates the motivation coefficient of consumers to purchase chemical products, Indicates that consumers Consumption utility of chemical products, express Consumers’ willingness to buy, Indicates that consumers Sensitive parameters of chemical products, express Average consumption value of chemical products.

[0032] The motivation coefficient of consumers to purchase chemical products refers to a comprehensive quantitative indicator of various factors that affect consumers' purchasing decisions. It directly reflects the strength of consumers' internal driving force when purchasing a certain chemical product. It can be comprehensively analyzed and calculated based on multi-dimensional information such as consumers' personal information, purchase history, and online behavior, so as to better grasp the market dynamics of chemical products.

[0033] In this embodiment, the online service platform users are distinguished, and the users who purchase chemical products on the platform are consumers. In this embodiment, the consumer information corresponding to the Jiaoyou Mall program satisfies the following relationship: in, Represents the consumer data set of the online service platform, Indicates the first consumer information, Indicates the second consumer information, Indicates third-party consumer information, Indicates Consumer information.

[0034] The consumer data set of the online service platform refers to the consumer data set about the Jiaoyou Mall program, including but not limited to the consumer's purchase history, browsing history, search behavior, comments and ratings, personal information and transaction information. It is also necessary to intercept the browsing information of the Jiaoyou Mall program in a specific time period, organize and sort the consumer information according to the browsing time, set the consumer order according to the browsing or clicking time, and organize the corresponding information.

[0035] Similarly, in the embodiment, there are a total of Online chemical products, where the chemical product set satisfies the following relationship: in, Represents the chemical product data set of the online service platform, Indicates the first chemical product information, Indicates the second chemical product information, Indicates the third chemical product information, Indicates According to the specific time of intercepting the consumer information, the chemical products on the online transaction applet are sorted, wherein the first chemical product is set according to the online time or the latest update time of the chemical product, and the corresponding information is matched and sorted.

[0036] The chemical product data set of the online service platform refers to the data set about chemical products on the online service platform, including but not limited to basic information, price and transaction information, inventory and logistics information, and market demand and trend analysis of chemical products.

[0037] The consumer utility of a chemical product refers to the degree of satisfaction or benefit that the consumer obtains in the process of purchasing a chemical product. The above utility index is the consumer's subjective evaluation of the product. Information can be collated and analyzed through after-sales tracking of online service platforms and questionnaires, which directly reflects the degree of consumer recognition of chemical products. In addition, the consumer's purchasing intention refers to the consumer's subjective tendency and willingness to purchase a chemical product, reflecting the consumer's interest, demand and purchase possibility for different chemical products. Understanding and grasping the consumer's purchasing intention can be achieved through market research, user interviews, etc., so as to more accurately predict the purchase of different chemical products by different consumers.

[0038] Consumers' sensitive parameters for online chemical products include but are not limited to product price, quality, performance, safety and after-sales service. Different sensitive parameters play different roles in consumers' purchasing decision-making process.

[0039] The average consumption value of chemical products refers to the average purchase quantity or payment amount of a certain chemical product by online consumers during a certain period of time. It is used to understand consumers' consumption levels of different chemical products, thereby formulating more precise market strategies and improving the cost-effectiveness of chemical products.

[0040] Through the purchase driver analysis model, we can observe the online market dynamics of chemical products in real time, quickly grasp the purchasing intention and preferences of online consumers, and provide strong support for the formulation of flexible and changeable market strategies. This will help chemical product companies to calmly respond to various changes and challenges in the market, improve the company's market response speed, and enhance market competitiveness.

[0041] Then, the purchase driving force analysis model is used to obtain the driving force coefficients of online consumers purchasing different chemical products. The demand for chemical products by online consumers is analyzed based on the driving force coefficients. The specific implementation contents are as follows: By inputting the relevant online service data into the purchase driving force analysis model, the power coefficients of different online consumers to purchase different chemical products can be quickly obtained. Since different chemical products differ in performance, use, price and other factors, the corresponding consumer purchase power coefficients will also be different. With the changes in the online transaction market environment and consumer demand, the purchase power coefficients of different chemical products will also change dynamically. In the embodiment, the online transaction data is regularly monitored and updated based on the mathematical model, so as to maintain a keen observation of the dynamics of the chemical product market, so as to adjust the market strategy of chemical products in a timely manner.

[0042] Analyze the demand of online consumers for chemical products based on the power coefficient. As a quantitative indicator to measure consumer purchasing power, the power coefficient can directly reflect the intensity of consumer demand for chemical products. By comparing the power coefficients of different chemical products, it can be found that online consumers show higher demand for certain types of products. The corresponding chemical products may have unique performance, a wide range of applications, or environmental protection requirements that meet current market trends. Secondly, the changing trend of the power coefficient can reveal the changing dynamics of consumer demand. When the power coefficient of a chemical product continues to rise, it means that consumer demand for the product is increasing, which may be driven by factors such as technological progress, price reduction, or marketing activities. On the contrary, if the power coefficient of a chemical product shows a downward trend, it means that the market is saturated or consumer demand is shifting to other substitutes. On the other hand, analyzing the power coefficient of consumers purchasing chemical products can also further understand the differences in demand for chemical products among different consumer groups.

[0043] In this embodiment, the purchasing driving force analysis model is used as a powerful forecasting tool for in-depth analysis of the demand status of online consumers for chemical products. It is only an optional condition of the present invention. In one or some other embodiments, the market analysis method can be adjusted according to the market status of chemical products and online data to make it more flexible and adaptable, which is conducive to finding the most suitable analysis method for the current situation, thereby ensuring the accuracy and effectiveness of the analysis results.

[0044] S3. Construct a product sales prospect prediction model based on online browsing information and transaction data, and use the above product sales prospect prediction model to analyze the sales of online chemical products. The specific implementation steps and contents are as follows: The online browsing information and transaction data are segmented and analyzed according to the predetermined time intervals, and the data analysis results of different time intervals are obtained. Then, a product sales prospect prediction model is constructed based on the data analysis results of different time intervals in the embodiment. The specific implementation content is as follows: In order to understand the sales dynamics of chemical products, the online browsing information and transaction data are segmented and analyzed according to predetermined time intervals. Segmented analysis helps to capture changes in consumer behavior and market trends in different time periods. Reasonable time intervals are set according to the basic operating mode of the chemical product online service platform and business transaction conditions. The above time intervals can be flexibly adjusted according to the business needs of chemical products, including but not limited to division in the form of days, weeks, months or quarters. The online browsing information and transaction data in each time interval are extracted separately for subsequent detailed analysis.

[0045] In this embodiment, data is sorted based on days as the time unit. For a sample data example table of online service data, please see Table 1 for details.

[0046] Table 1 Sample data sales example table Next, analyze the data of each time interval, including key indicators such as statistical page views, click-through rates, and conversion rates of different chemical products, to reveal consumers’ browsing and purchasing trends in different time periods. At the same time, it is necessary to further analyze the weight of the impact of information such as market promotions and online activities on product sales. Then extract feature information closely related to the sales volume of chemical products from online service data in different time intervals. The above features include but are not limited to product prices, categories, historical sales, and relevant information on market promotion activities and online marketing activities. The above feature information is crucial for analyzing the market trends of chemical products.

[0047] Furthermore, comparing the data analysis results of different time intervals is conducive to analyzing the sales patterns and quantity trends of different chemical products in different time periods. In an optional embodiment, if the sales volume of a chemical product increases significantly in a certain period of time, a more targeted marketing strategy is needed to adjust the price, quantity and sales pattern of the chemical product.

[0048] In this embodiment, the segmented analysis method can not only understand the specific situation of the historical chemical market, but also help predict future market trends. It is only an optional condition of the present invention. In one or some other embodiments, the sales forecasting method can be adjusted according to the actual inspection situation and the status of chemical products, which can not only ensure the accuracy and effectiveness of the sales quantity forecast results, but also utilize data resources and adapt to the dynamic changes of the chemical product market to achieve continuous innovation and market optimization of chemical products.

[0049] In this embodiment, the data characteristics of chemical products are fully considered when selecting the prediction algorithm, and modeling can be based on a series of algorithms, in which linear regression is used to explore the linear relationship between sales volume and price, logistic regression is used to deal with binary classification problems, decision trees and random forests are used to capture nonlinear and complex interactions in the data, and deep learning algorithms are used to process large-scale high-dimensional data and mine potential patterns.

[0050] In order to ensure that the product sales prospect prediction model has good generalization ability, the embodiment adopts cross-validation and other methods to evaluate the model, and through multiple iterations and optimizations, it is ensured that the selected model not only performs well on the training data, but also can maintain stable prediction performance on unknown data, so that it can better utilize and process the relevant data of the chemical product online service platform, so as to formulate more scientific and effective market strategies.

[0051] Furthermore, the above product sales prospect prediction model satisfies the following relationship: in, represents the predicted sales volume of chemical products in a specific time interval, express The corresponding error coefficient is, Indicates the sales volume of chemical products in the first time period in history, express The corresponding error coefficient is, Indicates the sales volume of chemical products in the second historical time interval, express The corresponding error coefficient is, Indicates the sales volume of chemical products in the third historical time interval, express The corresponding error coefficient is, In history Sales volume of chemical products in the time interval, Indicates the number of intervals of the historically selected time series.

[0052] The forecast sales volume of chemical products in a specific time period refers to the sales volume of different chemical products in a certain time period predicted by the product sales prospect forecast model. There are different external influencing factors in different forecast time periods, including but not limited to the historical sales trend of the product, changes in market demand, price changes, promotional activities, seasonal factors, competitor activities and macroeconomic environment. By comprehensively considering relevant factors to forecast sales volume in different time periods and combining market dynamics, it is conducive to timely regulation of the market layout of chemical products.

[0053] Among them, the sales volume of chemical products in the first historical time period refers to the actual sales volume of chemical products in a specific time period in the past. The above time period can be other different time units such as days, weeks, months, quarters or years. The setting of the time interval mainly depends on the demand for chemical products and actual purposes. Understanding the sales of different chemical products in the past period, including but not limited to the fluctuations in sales volume, growth trends and the relationship with other market factors such as price, promotional activities, seasonal changes, etc., can provide strong support for future market planning and decision-making.

[0054] The error coefficient corresponding to sales volume in different time intervals refers to a quantitative indicator that analyzes the degree of deviation or error between actual sales volume and predicted sales volume for each specific time interval such as day, week, month, quarter, etc. when analyzing the sales volume of chemical products. Since the error coefficients in different time intervals vary due to various external factors, including but not limited to the speed of change of the chemical product market, changes in platform sales models, and differences in time intervals, the error coefficients in different time intervals are of great significance for optimizing the forecasting model and improving accuracy, so as to ensure the forecasting accuracy and reliability in different time intervals.

[0055] Then, the product sales prospect prediction model is used to obtain the predicted sales volume of different chemical products within a specific time period. The sales situation of online chemical products is analyzed based on the above predicted sales volume. The specific implementation content is as follows: The sales volume of different chemical products in a specific time interval is predicted by the product sales prospect prediction model. In the embodiment, based on a large amount of online platform historical data, online browsing information, transaction data, market trends and other multi-dimensional information, the prediction result of the sales volume in a specific time interval is finally obtained.

[0056] The predicted sales volume for the above-mentioned specific time periods is integrated, and the sales of online chemical products are analyzed in depth. On the one hand, from the perspective of the overall sales trend, the predicted sales volume is conducive to understanding the overall development trend of the chemical product market. If the predicted sales volume shows an upward trend, it means that the online chemical product market is expanding and consumers' acceptance of online purchases of chemical products is increasing. On the contrary, if the predicted sales volume declines, it means that market competition is intensifying or consumer demand is changing. Chemical companies need to pay close attention to market trends and adjust their chemical product sales strategies in a timely manner.

[0057] On the other hand, based on the predicted sales volume of different chemical products, analyzing the sales situation of different chemical products and comparing the predicted sales volume of different products will help adjust the product structure of the chemical products online service platform. At the same time, for products with poor sales, formulate improvement strategies or consider adjusting the market positioning, so as to more accurately grasp the market dynamics and ensure the normal operation of the online service platform.

[0058] Finally, forecasting sales helps predict future market trends and potential risks of chemical products. By comparing forecast sales in different time periods, we can find cyclical changes or seasonal patterns in the market, and plan product production and sales plans in advance. On the contrary, if the forecast sales volume fluctuates greatly or changes abnormally, we need to be alert to possible market risks and formulate countermeasures in advance.

[0059] Furthermore, in the present embodiment, the method for predicting the sales volume of chemical products is only an optional condition of the present invention. In one or some other embodiments, the sales volume prediction method can be optimized according to market trends and product conditions. In actual applications, market changes are complex and changeable. Different chemical products and different market environments require different prediction strategies and methods to more accurately predict the sales trends of chemical products.

[0060] S4. Evaluate the market status of chemical products on the online service platform based on demand and sales conditions, so as to achieve the purpose of price monitoring and quantity regulation of online chemical products. The specific implementation steps and contents are as follows: First, the initial sales price and sales volume of different chemical products in the chemical product online service platform are obtained based on the demand and sales conditions. The specific implementation contents are as follows: In order to obtain the initial sales price of different chemical products, it is necessary to perform product cost accounting. According to factors such as the production cost, transportation cost, and taxes of different chemical products, the cost price corresponding to each chemical product in the online service platform is calculated; based on this, the profit targets of different chemical products are pre-set. In this embodiment, reasonable profits and markup ratios of different chemical products are set according to the profit targets of the chemical product online service platform, market competition conditions, and historical product data. Finally, the initial sales price of each chemical product in the online service platform is determined.

[0061] In another optional embodiment, the future sales volume of different chemical products in the online service platform is predicted. Based on the demand and sales of different chemical products in the online service platform, combined with market trends and forecasting models, the sales volume of different chemical products in the future is predicted.

[0062] Through the above steps, we can obtain the initial sales price and product sales quantity of different chemical products in the online service platform. The above data will provide strong support for the formulation of sales strategies, inventory management and market promotion of online chemical products. In addition, it is also necessary to make reasonable adjustments as the market changes and competitive situation change.

[0063] Secondly, based on the above initial sales price and product sales volume, the market status of chemical products on the online service platform is evaluated. The specific implementation contents are as follows: Compare the initial sales prices of similar products on different platforms or markets, and evaluate the price level of chemical products on online service platforms. If the initial sales price is higher than the market average, it is necessary to formulate a scientific price adjustment strategy, or add use value or brand influence to high-priced products; conversely, if the initial sales price is lower than the market average, reasonable adjustments can be made to factors such as product cost control and market competition strategy.

[0064] Furthermore, we compare the changes in the initial sales prices of the same type or the same chemical products in different time periods, and analyze the patterns and reasons for the fluctuations in the initial sales prices. The increase in the initial sales price will be affected by factors such as raw material costs, market demand and product effects, while the price decrease is related to factors such as market competition intensity, product manufacturing technology and product use value.

[0065] Based on the above product sales forecast, the sales volume of chemical products is analyzed. According to the forecast sales volume of different chemical products, the market share and sales scale of chemical products in the online service platform are analyzed. By comparing the sales data of different products and different periods, the competitive position of different chemical products in the market and future market trends are understood. In addition, it is necessary to calculate the sales growth rate of different chemical products and analyze the growth rate of demand in the chemical market. A high growth rate means that the market demand is strong and the product has great market potential; a low growth rate reflects the current market saturation or intensified competition.

[0066] Combine the initial sales price and sales volume to comprehensively evaluate the current status of the chemical product market in the online service platform. In this embodiment, the sales volume and growth rate of different chemical products in the online service platform are used to determine the market demand for chemical products and analyze the stability, growth or volatility of product demand; based on the sales price and average price level of different chemical products in the online service platform, the competitiveness of the chemical product market in the online service platform is evaluated, and the pricing strategies, market share and other information of competitors are compared and analyzed; in addition, based on the initial sales price and predicted sales volume of different chemical products in the online service platform, the market positioning of the chemical products of the online service platform in the market is further clarified.

[0067] Furthermore, it is necessary to combine the online service platform to obtain the preparation costs of different chemical products. The specific implementation content is as follows: In order to formulate a more scientific pricing strategy and ensure that different chemical products can cover costs and achieve profitability while maintaining market competitiveness, the embodiment will analyze the potential risk factors of chemical products based on the cost data of online services, including but not limited to price fluctuations of raw materials, production costs, and growth equipment costs of chemical products, so as to take countermeasures in advance, formulate chemical online market regulation strategies, and reduce industry operating risks.

[0068] The preparation cost data of different chemical products are collected from the online service platform. The above cost data include but are not limited to various aspects such as raw material cost, production and processing cost, labor cost, transportation cost and comprehensive management fee of chemical products. In an optional embodiment, the cost composition of different chemical products is analyzed, the collected preparation cost data of chemical products is disassembled, and the proportion of each cost in the total cost is analyzed, which is helpful to understand the main components of the cost of chemical products and identify the key links of cost control; the cost change trend of different chemical products is analyzed, the preparation cost data in different time periods are compared, and the trend and main reasons of chemical cost change are analyzed, which is helpful to predict the future trend of chemical product cost change and provide a basis for chemical product market planning and cost control.

[0069] On the other hand, the profit level of the online service platform for chemical products is analyzed, and the gross profit margins of different chemical products are calculated by combining the initial sales prices and product preparation costs of different chemical products. The above gross profit margins directly reflect the profit space that the online service platform can obtain when selling products, and are an important indicator for evaluating product profitability. Based on this, the gross profit margins of different products or different periods are compared, the profit levels of different chemical products are evaluated, and then the market competitive advantages, cost control strategies and market application scope of online chemical products are analyzed.

[0070] Finally, the price and quantity of online and offline chemical products are monitored and regulated based on the above-mentioned preparation cost, initial sales price, product sales volume and market status. The specific implementation contents are as follows: A chemical product market analysis method based on big data also includes establishing a chemical product monitoring mechanism. A special data collection system is established to collect relevant data such as the preparation cost, sales price, sales volume and market status of online chemical products in real time and regularly, and the relevant data is processed and analyzed in combination with data analysis tools to generate visual icons, dynamic curve charts or schematic diagrams, so as to quickly understand the market dynamics and product sales of chemical products.

[0071] By comparing sales prices with preparation costs, analyzing the profit margins of different chemical products, and evaluating price sensitivity in combination with market demand and competition, the initial sales prices of different chemical products are dynamically adjusted according to market changes, product cost changes and price sensitivity, thereby obtaining the final sales price of the chemical products online service platform. When the preparation costs of chemical products rise or market competition intensifies, the sales prices of online chemical products can be appropriately increased; when the preparation costs of chemical products fall or market demand is strong, the prices of chemical products can be appropriately lowered to cope with market trends.

[0072] In this embodiment, the final sales price of online chemical products is adjusted in real time based on the online service platform for chemical products, including but not limited to collating platform data and setting price threshold alarms, so as to respond promptly when prices change significantly, compare the current preparation cost with the final sales price, analyze the profit level of different chemical products, and when costs rise and are not reflected in the sales price, it is necessary to consider adjusting prices to ensure reasonable profits, identify price sensitive intervals in combination with the current market situation, and provide a basis for price adjustment. In an optional embodiment, a price adjustment strategy is formulated based on the costs, profits, market demand and competitive situation of different chemical products. When costs rise, prices can be gradually increased; when market demand is strong, prices can be moderately increased to obtain more profits; and when competition is fierce or demand is weak, appropriate price cuts can be considered for promotion.

[0073] In this embodiment, the sales volume of online chemical products is regularly analyzed to understand the sales trends and fluctuations of different chemical products, which helps to predict product demand and provide a basis for product inventory management and production planning. In another optional embodiment, a reasonable inventory level of chemical products is formulated based on the predicted sales volume and market demand. When the inventory is lower than the safety stock, timely warning and replenishment of inventory are carried out; when the inventory is overstocked, consider adjusting sales strategies or promotional activities to reduce inventory pressure. The production plan can also be adjusted according to the sales volume forecast and inventory situation. Specifically, the production of chemical products can be increased to meet market demand during the peak season of demand; in the off-season of demand, the production of chemical products can be appropriately reduced to reduce inventory costs. In an optional embodiment, the sales volume of online chemical products over a period of time is predicted, and please refer to Table 2 for details.

[0074] Table 2 Forecast sales of online chemical products in any time period In this embodiment, the potential risks of the chemical product online service platform are identified through information such as the market dynamics of chemical products, changes in raw material prices, policy adjustments, etc., and response plans are formulated based on online risks, including but not limited to price adjustments of chemical products, inventory structure optimization, supply chain management, and expansion of new markets.

[0075] Furthermore, in the present embodiment, the chemical product market analysis method based on big data is only an optional condition of the present embodiment. In one or some other embodiments, the chemical product market analysis method can be adjusted according to the online transaction market conditions and market trends of chemical products, and combined with demand forecasts, supply changes, policy impacts and other factors to conduct a more comprehensive and accurate analysis of the chemical product market conditions, which can better capture the market changes of chemical products and provide more accurate and valuable decision-making information.

[0076] The present invention proposes a method for in-depth analysis of the market conditions of chemical products based on big data. By integrating the market data, transaction data and market information on the online service platform for chemical products, the supply and demand changes, price change trends, industry development context and potential risk factors of the chemical products market are accurately captured. The analysis results of the online service platform are compared with historical data, and the key factors closely related to the market conditions are deeply excavated. For the price changes, sales volume and market fluctuations of chemical products, this method not only reveals the inherent characteristics and laws of the market, but also provides a decision-making basis for the market regulation of chemical products, which can more accurately grasp the market dynamics, optimize inventory management and demand forecasting. The chemical product market analysis method proposed in the present invention aims to reduce the unstable factors in the market, maintain the balance of supply and demand, and provide comprehensive and accurate market analysis and decision-making support for enterprises and investors.

[0077] See also Figure 2 In an optional embodiment, in order to efficiently execute the chemical product market analysis method based on big data provided by the present invention, the present invention also provides a chemical product market analysis system based on big data, the system comprising a processor, an input device, an output device and a memory, the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program comprises program instructions, the processor is configured to call the program instructions, and execute the specific steps of the relevant embodiments of the chemical product market analysis method based on big data provided by the present invention. The chemical product market analysis system based on big data of the present invention has a complete structure, is objective and stable, and can efficiently execute the chemical product market analysis method based on big data of the present invention, thereby improving the overall applicability and practical application ability of the present invention.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A chemical product market analysis method based on big data, characterized in that: The steps include: obtaining online service data of chemical products based on the online service platform for chemical products, wherein the online service data includes online browsing information and transaction data of chemical products; Establishing a purchase driving force analysis model based on the online browsing information and the transaction data, and analyzing the demand status of online consumers for chemical products through the purchase driving force analysis model; Building a product sales prospect prediction model based on the online browsing information and the transaction data, and analyzing the sales of online chemical products using the product sales prospect prediction model; Based on the demand situation and the sales situation, the market status of chemical products on the online service platform is evaluated, and price monitoring and quantity regulation of online chemical products are implemented.

2. The chemical product market analysis method based on big data according to claim 1, characterized in that: The establishing of a purchase driving force analysis model based on the online browsing information and the transaction data comprises: Obtaining a consumer data set and a chemical product data set of an online service platform based on the online browsing information and the transaction data; A purchase driving force analysis model is established based on the consumer data set and the chemical product data set.

3. The chemical product market analysis method based on big data according to claim 2 is characterized in that: The purchase driving force analysis model satisfies the following relationship: in, Indicates the motivation coefficient of consumers to purchase chemical products, Indicates that consumers Consumption utility of chemical products, express Consumers’ willingness to buy, Indicates that consumers Sensitive parameters of chemical products, express Average consumption value of chemical products.

4. The chemical product market analysis method based on big data according to claim 3 is characterized in that: Analyzing the demand status of online consumers for chemical products through the purchase driving force analysis model includes: The purchasing driving force analysis model is used to obtain the driving force coefficients of online consumers purchasing different chemical products; The demand status of online consumers for chemical products is analyzed based on the dynamic coefficient.

5. The chemical product market analysis method based on big data according to claim 1, characterized in that: The constructing of a product sales prospect prediction model according to the online browsing information and the transaction data comprises: Analyzing the online browsing information and the transaction data in sections according to predetermined time intervals, and obtaining data analysis results for different time intervals; A product sales prospect prediction model is constructed based on the data analysis results of the different time intervals.

6. The chemical product market analysis method based on big data according to claim 5, characterized in that: The product sales prospect prediction model satisfies the following relationship: in, represents the predicted sales volume of chemical products in a specific time interval, express The corresponding error coefficient is, Indicates the sales volume of chemical products in the first time period in history, express The corresponding error coefficient is, Indicates the sales volume of chemical products in the second historical time interval, express The corresponding error coefficient is, Indicates the sales volume of chemical products in the third historical time interval, express The corresponding error coefficient is, In history Sales volume of chemical products in the time interval, Indicates the number of intervals of the historically selected time series.

7. The chemical product market analysis method based on big data according to claim 6, characterized in that: The use of the product sales prospect prediction model to analyze the sales of online chemical products includes: The predicted sales volume of different online chemical products within a specific time period is obtained through the product sales prospect prediction model; The sales of online chemical products are analyzed based on the predicted sales volume.

8. The chemical product market analysis method based on big data according to claim 1, characterized in that: The evaluation of the market status of chemical products on the online service platform in combination with the demand status and the sales status includes: Combining the demand situation and the sales situation, obtaining the initial sales price and product sales quantity of different chemical products in the chemical product online service platform; The market status of chemical products on the online service platform is evaluated based on the initial sales price and the product sales volume.

9. The chemical product market analysis method based on big data according to claim 8, characterized in that: The realization of online price monitoring and quantity control of chemical products includes: Combining the chemical product online service platform to obtain the preparation costs of different chemical products; The price and quantity of online and offline chemical products are monitored and regulated based on the preparation cost, the initial sales price, the product sales quantity and the market status.

10. A chemical product market analysis system based on big data, characterized in that: The system includes a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the chemical product market analysis method based on big data as described in any one of claims 1 to 9.

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