Data value analysis method and system based on machine learning

Abstract

The invention relates to the technical field of machine learning, and discloses a data value analysis method and system based on machine learning, and the method comprises the steps: collecting customer data of an agricultural product e-commerce platform; generating a keyword group of the customer according to the customer data; generating a purchase intention combination of the customer based on a decision tree model and the keyword group; performing importance analysis on the keyword group according to the purchase intention combination to obtain an importance score of the keyword in the keyword group; target words in the keywords are screened out according to a screening mechanism of the importance scores; and generating the predicted sales volume of the agricultural products according to the target word and the customer data. According to the invention, market strategies can be optimized based on machine learning, and maximization of data values is realized.

Description

Technical Field

[0001] The present invention relates to the field of machine learning technology, and in particular to a data value analysis method and system based on machine learning. Background Art

[0002] In agricultural products e-commerce platforms and supply chain management, sales forecasting is an important basis for determining inventory management, production scheduling and marketing strategies. Existing technologies usually adopt static sales forecasting models based on historical data, mainly including time series analysis, regression analysis and machine learning-based forecasting methods. These methods rely on fixed historical data sets to establish static models to predict future sales.

[0003] In the process of data-driven value mining, the dynamic evolution of market demand plays a decisive role in the accuracy of prediction. However, the traditional static prediction model has significant bottlenecks in fully releasing the value of data, which are mainly reflected in the following two aspects:

[0004] First, external interference factors such as promotional activities have a strong influence on market demand, but static forecasting methods lack a real-time response mechanism to such dynamic changes, and are unable to obtain and update the value labels of agricultural products in a timely manner, resulting in a lag effect between the forecast results and the actual market demand, making it difficult to match the immediate status of the market; second, existing methods usually use a fixed-length time window for forecasting, and the sampling of historical data in a fixed time window has limitations. When market demand fluctuates greatly, short-term market changes are difficult to be captured in a timely manner by a fixed window, resulting in a slow response of the forecasting model to dynamic changes in the market. For example, if the window length is too long, the impact of the latest market trends is diluted. If the window is too short, the model is easily affected by short-term fluctuations, resulting in unstable forecasts. Summary of the invention

[0005] The present invention provides a data value analysis method and system based on machine learning, the main purpose of which is to solve the problem in the prior art that the value labels of agricultural products cannot be obtained and updated in time due to the lack of a real-time response mechanism to dynamic changes, resulting in a lag effect between the prediction results and the actual market demand.

[0006] To achieve the above object, the present invention provides a data value analysis method based on machine learning, comprising:

[0007] Collect customer data of agricultural product e-commerce platforms;

[0008] generating a keyword group for a customer based on the customer data;

[0009] Generating the customer's purchase intention combination based on the decision tree model and the keyword group;

[0010] Performing importance analysis on the keyword group according to the purchase intention combination to obtain importance scores of the keywords in the keyword group;

[0011] Filtering out target words from the keywords according to the importance score screening mechanism;

[0012] generating predicted sales of agricultural products based on the target words and the customer data;

[0013] The real-time sales volume of the agricultural product is monitored, the importance score of the target word is updated based on the predicted sales volume and the real-time sales volume, and the target word is updated according to the updated importance score to obtain a value tag of the agricultural product.

[0014] Optionally, generating the customer's purchase intention combination based on the decision tree model and the keyword group includes:

[0015] T1. Generate a data set of the customer according to the keywords and the customer data, wherein the keywords include: agricultural product A, agricultural product B and agricultural product C, and the customer data includes: purchase history, browsing time and browsing times of agricultural products;

[0016] T2, inputting the data set into a decision tree model;

[0017] T3. When the keyword has a purchase history, the keyword is marked as Y. When the keyword does not have a purchase history, the browsing time judgment algorithm is entered, wherein the browsing time judgment algorithm is:

[0018] When the browsing time of the keyword is ≥10, the keyword is marked as Y;

[0019] When the browsing time of the keyword is within 2-10s, the browsing times judgment algorithm is entered, wherein the browsing times judgment algorithm is: when the browsing times of the keyword is ≥3 times, the keyword is marked as Y, and when the browsing times of the keyword is less than times, the keyword is marked as N;

[0020] When the browsing time of the keyword is ≤2s, the keyword is marked as N;

[0021] T4. Add the keyword marked as Y to the customer's purchase intention combination.

[0022] Optionally, performing importance analysis on the keyword group according to the purchase intention combination to obtain importance scores of the keywords in the keyword group includes:

[0023] The purchase history impact score, browsing time impact score and browsing times impact score of the keywords in the keyword group are generated according to the purchase intention combination, wherein:

[0024] The calculation formula for the purchase history impact score is as follows:

[0025]

[0026] in, Indicates the first Keywords, () indicates that purchase history affects the score. Indicates the customer, Indicates the purchase history of the keyword. Indicates that the customer has a purchase history of the keyword. Indicates that the customer has no purchase history for the keyword;

[0027] The calculation formula for browsing time impact score is as follows:

[0028]

[0029] in, Indicates the first Keywords, Indicates that browsing time affects the rating. Indicates the browsing time of the keyword;

[0030] The calculation formula for the impact of the number of views on the score is as follows:

[0031]

[0032] in, Indicates the first Keywords, Indicates that the number of views affects the rating. Indicates the number of views of the keyword;

[0033] The purchase history impact score, the browsing time impact score, and the browsing times impact score are weighted and summed to obtain the importance score of the keyword, wherein the calculation formula for the weighted sum is as follows:

[0034]

[0035] in, Indicates the first Keywords, represents the importance score of the keyword, , , They respectively represent the weight coefficients of the purchase history affecting the score, the browsing time affecting the score, and the browsing times affecting the score.

[0036] Optionally, the screening mechanism is as follows:

[0037]

[0038] in, Indicates the first Keywords, Indicates the preset importance threshold, Represents the target word after filtering, represents the importance score.

[0039] Optionally, generating predicted sales volume of agricultural products according to the target word and the customer data includes:

[0040] Substitute the target word and the customer data into a sales forecasting algorithm to obtain the predicted sales of agricultural products, wherein the sales forecasting algorithm is as follows:

[0041]

[0042] in, Indicates the first Keywords, Represents the word vector corresponding to the target word, represents the data vector corresponding to the customer data, Indicates The predicted sales volume corresponding to the keywords, Represents a mapping function.

[0043] Optionally, monitoring the real-time sales volume of the agricultural product, and updating the importance score of the target word based on the predicted sales volume and the real-time sales volume, comprises:

[0044] Based on the sliding window algorithm, data collection is performed on the sales data of the agricultural product for the current month to obtain the real-time sales data of the agricultural product for the current month;

[0045] The importance score of the target word is updated based on the predicted sales volume and the real-time sales volume, wherein the update formula of the importance score is as follows:

[0046]

[0047] in, Indicates the first Keywords, represents the importance score of the target word, Indicates real-time sales volume. Indicates the predicted sales volume, Indicates the total number of days in the month. Indicates the total number of days for collecting the sales data. represents the promotion indicator variable, Indicates the correction coefficient of real-time sales error, The correction factor representing the impact of promotional activities, Represents the updated importance score.

[0048] Optionally, the calculation formula of the sliding window algorithm is as follows:

[0049]

[0050] in, Indicates the total number of days in the month. Indicates the total number of days for collecting the sales data. represents the adjustment factor, represents rounding down, Indicates the minimum number of days for data collection. Indicates the maximum number of days for data collection.

[0051] Optionally, the calculation formula of the adjustment coefficient is as follows:

[0052]

[0053] in, represents the adjustment factor, Represents a random number.

[0054] Optionally, the real-time sales volume is calculated as follows:

[0055]

[0056] in, Indicates the real-time sales volume on the first day of the month. Indicates the real-time sales volume on the second day of the month. Indicates the real-time sales volume on the nth day of the month. Indicates real-time sales.

[0057] In order to solve the above problems, the present invention also provides a data value analysis system based on machine learning, the system comprising:

[0058] The collection module is used to collect customer data of agricultural product e-commerce platforms;

[0059] A keyword group generating module, used for generating a keyword group for a customer according to the customer data;

[0060] A purchase intention combination generating module, used for generating the customer's purchase intention combination based on a decision tree model and the keyword group;

[0061] An importance score generating module, used for performing importance analysis on the keyword group according to the purchase intention combination to obtain importance scores of the keywords in the keyword group;

[0062] A screening module, used to screen out target words from the keywords according to the screening mechanism of the importance score;

[0063] A sales forecasting module, used for generating a forecasted sales volume of agricultural products according to the target words and the customer data;

[0064] The value label generation module is used to monitor the real-time sales volume of the agricultural product, update the importance score of the target word based on the predicted sales volume and the real-time sales volume, and update the target word according to the updated importance score to obtain the value label of the agricultural product.

[0065] Compared with the prior art, the present invention has the following beneficial effects:

[0066] 1. When the relationship between real-time sales and predicted sales changes, the importance score of the target word is increased or decreased accordingly, and the keywords are screened and sorted again, and the keywords corresponding to the top three importance scores are selected as value tags. This real-time monitoring and dynamic update mechanism can adjust the value tags in time according to the actual sales situation of the market, so that the value tags can reflect the actual needs of the market in real time, avoiding the lag effect between the prediction results and the actual needs of the market;

[0067] 2. Use the sliding window algorithm to collect the sales data of agricultural products in the current month to obtain real-time sales. By setting the minimum and maximum data collection days to limit the size of the sliding window, it ensures that the collected data has a sufficient time span to capture long-term trends such as seasonal changes and cyclical fluctuations, avoiding unstable or misleading predictions due to insufficient data collection, and avoiding the problem of historical data diluting the latest market dynamics due to too long a window, so that the model has a better response capability to the latest market trends;

[0068] 3. The total number of days for sales data collection can be randomly selected between the minimum and maximum data collection days, effectively avoiding the situation where the updated importance score fluctuates dramatically due to abnormal data in a fixed window. In this way, the update formula of the importance score can be more robust, successfully avoiding the interference of extreme data values ​​on the update formula of the importance score, and ensuring the stability and reliability of the update process of the importance score. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 A flowchart of a data value analysis method based on machine learning provided by one embodiment of the present invention;

[0070] Figure 2 A functional module diagram of a data value analysis system based on machine learning provided by one embodiment of the present invention;

[0071] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0072] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0073] The embodiment of the present application provides a data value analysis method based on machine learning. The execution subject of the data value analysis method based on machine learning includes but is not limited to at least one of the electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided in the embodiment of the present application. In other words, the data value analysis method based on machine learning can be executed by software or hardware installed on a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms.

[0074] Reference Figure 1 FIG. 1 is a flow chart of a data value analysis method based on machine learning provided by an embodiment of the present invention. In this embodiment, the data value analysis method based on machine learning includes:

[0075] S1. Collect customer data of agricultural products e-commerce platforms.

[0076] In the embodiment of the present invention, the collecting of customer data of the agricultural products e-commerce platform includes:

[0077] The backend log and front-end Cookie technology of the agricultural products e-commerce platform are used to collect customer data on the agricultural products e-commerce platform, including the purchase history, browsing time and number of browsing times of agricultural products.

[0078] In detail, the customer data of the agricultural products e-commerce platform is collected through the background logs and front-end Cookie technology of the agricultural products e-commerce platform, including the purchase history, browsing time and number of views of agricultural products. Among them, the purchase history of agricultural products records the customer's order information in detail, including the type, quantity, transaction time and payment amount of purchased products; the browsing time of agricultural products records the time when the customer visits the agricultural product details page; the number of views of agricultural products records the cumulative number of times the customer browses a certain agricultural product on the agricultural products e-commerce platform. Customer data can not only help the agricultural products e-commerce platform better understand consumer needs and behavior patterns, but also be converted into actual business value through data mining and machine learning technologies, and promote the agricultural products e-commerce platform to achieve precision marketing, scientific inventory management and dynamic decision-making optimization.

[0079] For example, customer data is collected through front-end Cookie technology, and data records show that the customer has a purchase history of a certain agricultural product, which indicates that the customer has successfully purchased the product and has certain consumption preferences; at the same time, the data also records that the customer browsed the page for the agricultural product for 20 seconds, which shows that the customer showed a high degree of attention during the browsing process; in addition, the customer browsed the product a total of 5 times, further reflecting his potential purchase intention and continued interest.

[0080] Based on the above information, customer data not only reflects historical purchasing behavior, but also reveals the degree of attention to products through browsing time and number of views. This information is of great significance for building accurate customer portraits. Based on this, agricultural products e-commerce platforms can use customer data for targeted recommendations and precision marketing, while providing strong decision-making support for sales forecasting and inventory management, further improving the operational efficiency and customer conversion rate of agricultural products e-commerce platforms.

[0081] S2. Generate a keyword group for the customer based on the customer data.

[0082] In the embodiment of the present invention, generating a keyword group for a customer based on the customer data includes:

[0083] Remove noise data, duplicate records and irrelevant data from the customer data to ensure the accuracy of the customer data, and perform unified coding processing on the customer data (e.g., convert to lowercase and remove special symbols) to provide standardized input for word segmentation and subsequent analysis;

[0084] Extracting features related to customer behavior and preferences from the customer data, such as extracting the types, brands, specifications, and purchase frequency of purchased agricultural products from the purchase history of agricultural products, and extracting the degree of customer attention to different agricultural product pages from the browsing time and number of browsing of agricultural products;

[0085] When the customer data contains text information such as reviews and messages, the text information is segmented, POS tagged, and stop words are removed to facilitate subsequent keyword extraction;

[0086] Use a keyword extraction algorithm (such as SF-IDF algorithm) to extract important words from the processed customer data to form a keyword group. The expression of the keyword group is as follows:

[0087]

[0088] in, Indicates the first keyword, Indicates the nth keyword, n indicates the number of keywords, Indicates a keyword group.

[0089] S3. Generate a purchase intention combination of the customer based on a decision tree model and the keyword group.

[0090] In the embodiment of the present invention, generating the customer's purchase intention combination based on the decision tree model and the keyword group includes:

[0091] T1. Generate a data set of the customer according to the keywords and the customer data, wherein the keywords include: agricultural product A, agricultural product B and agricultural product C, and the customer data includes: purchase history, browsing time and browsing times of agricultural products;

[0092] T2, inputting the data set into a decision tree model;

[0093] T3. When the keyword has a purchase history, the keyword is marked as Y. When the keyword does not have a purchase history, the browsing time judgment algorithm is entered, wherein the browsing time judgment algorithm is:

[0094] When the browsing time of the keyword is ≥10, the keyword is marked as Y;

[0095] When the browsing time of the keyword is within 2-10s, the browsing times judgment algorithm is entered, wherein the browsing times judgment algorithm is: when the browsing times of the keyword is ≥3 times, the keyword is marked as Y, and when the browsing times of the keyword is less than times, the keyword is marked as N;

[0096] When the browsing time of the keyword is ≤2s, the keyword is marked as N;

[0097] T4. Add the keyword marked as Y to the customer's purchase intention combination.

[0098] In detail, a data set of the customer is generated based on the keywords and the customer data, wherein, taking the customer as a unit, the corresponding keyword is associated with the customer's agricultural product purchase history (whether the relevant agricultural products have been purchased), browsing time (browsing time for the agricultural products corresponding to the keyword), and number of views (number of views of the agricultural products corresponding to each keyword) to form a set containing the keywords and the corresponding customer data, which is the customer's data set.

[0099] For example, taking customer 1 as an example, the data set of customer 1 may include the keyword "agricultural product A" and customer 1's purchase history of agricultural product A (assuming it has been purchased), browsing time (such as 30 minutes), and number of browsing times (such as 5 times).

[0100] S4. Performing importance analysis on the keyword group according to the purchase intention combination to obtain importance scores of the keywords in the keyword group.

[0101] In the embodiment of the present invention, the importance analysis of the keyword group according to the purchase intention combination to obtain the importance score of the keywords in the keyword group includes:

[0102] The purchase history impact score, browsing time impact score and browsing times impact score of the keywords in the keyword group are generated according to the purchase intention combination, wherein:

[0103] The calculation formula for the purchase history impact score is as follows:

[0104]

[0105] in, Indicates the first Keywords, () indicates that purchase history affects the score. Indicates the customer, Indicates the purchase history of the keyword. Indicates that the customer has a purchase history of the keyword. Indicates that the customer has no purchase history for the keyword;

[0106] The calculation formula for browsing time impact score is as follows:

[0107]

[0108] in, Indicates the first Keywords, Indicates that browsing time affects the rating. Indicates the browsing time of the keyword;

[0109] The calculation formula for the impact of the number of views on the score is as follows:

[0110]

[0111] in, Indicates the first Keywords, Indicates that the number of views affects the rating. Indicates the number of views of the keyword;

[0112] The purchase history impact score, the browsing time impact score, and the browsing times impact score are weighted and summed to obtain the importance score of the keyword, wherein the calculation formula for the weighted sum is as follows:

[0113]

[0114] in, Indicates the first Keywords, represents the importance score of the keyword, , , They respectively represent the weight coefficients of the purchase history affecting the score, the browsing time affecting the score, and the browsing times affecting the score.

[0115] In detail, ,and 1, , , The value of can be adjusted dynamically according to the application's goals. If a certain scoring factor (such as purchase history) is more influential than other factors (such as number of views or time), the weighting coefficient can be adjusted to make the model more sensitive to this scoring factor.

[0116] For example, when purchase history is critical in predicting purchase intent, The value of is set larger, and when the browsing time has less impact on the purchase intention, The number of views will be adjusted based on the actual analysis results.

[0117] S5. Filter out target words from the keywords according to the importance scoring screening mechanism.

[0118] In an embodiment of the present invention, the screening mechanism is as follows:

[0119]

[0120] in, Indicates the first Keywords, Indicates the preset importance threshold, Represents the target word after filtering, represents the importance score.

[0121] S6. Generate predicted sales volume of agricultural products according to the target word and the customer data.

[0122] In the embodiment of the present invention, generating predicted sales volume of agricultural products according to the target word and the customer data includes:

[0123] Substitute the target word and the customer data into a sales forecasting algorithm to obtain the predicted sales of agricultural products, wherein the sales forecasting algorithm is as follows:

[0124]

[0125] in, Indicates the first Keywords, Represents the word vector corresponding to the target word, represents the data vector corresponding to the customer data, Indicates The predicted sales volume corresponding to the keywords, Represents a mapping function.

[0126] In detail, represents the mapping function of the neural network, is a model, The specific structure and form depends on the architecture of the neural network. It learns the complex mapping relationship from input to output, processes the input data (target word vector and customer data vector) through multiple layers (convolutional layer, fully connected layer, etc.), and finally outputs the predicted sales volume.

[0127] S7. Monitor the real-time sales volume of the agricultural product, update the importance score of the target word based on the predicted sales volume and the real-time sales volume, and update the target word according to the updated importance score to obtain a value tag for the agricultural product.

[0128] In an embodiment of the present invention, monitoring the real-time sales volume of the agricultural product and updating the importance score of the target word based on the predicted sales volume and the real-time sales volume include:

[0129] Based on the sliding window algorithm, data collection is performed on the sales data of the agricultural product for the current month to obtain the real-time sales data of the agricultural product for the current month;

[0130] In detail, the calculation formula of the sliding window algorithm is as follows:

[0131]

[0132] in, Indicates the total number of days in the month. Indicates the total number of days for sales data collection, that is, the size of the window. represents the adjustment factor, represents rounding down, Indicates the minimum number of days for data collection. Indicates the maximum number of days for data collection.

[0133] In detail, the calculation formula of the adjustment coefficient is as follows:

[0134]

[0135] in, Represents a random number, The calculation formula is as follows;

[0136]

[0137] in, Represents the calculation formula of the linear congruential generator, represents the multiplication factor, represents the nth generated pseudo-random number, Indicates the increment, represents the modulus, and , .

[0138] For example, given an initial seed , by continuously iterating the above formula, a pseudo-random number sequence can be generated , ..... , the pseudo-random number generated The value range is 0 to between, A random floating point number between 0 and 1 generated for the nth time.

[0139] In detail, the calculation formula of the real-time sales volume is as follows:

[0140]

[0141] in, Indicates the real-time sales volume on the first day of the month. Indicates the real-time sales volume on the second day of the month. Indicates the month The real-time sales volume of the day, Indicates real-time sales.

[0142] In detail, The value range of is expressed as , by setting , which can ensure that the collected sales data has a sufficient time span, avoid unstable or misleading predictions caused by too little sales data collection, and ensure that the sliding window can collect the sales data from a certain period and capture long-term trends such as seasonal changes and cyclical fluctuations; by setting , which can avoid the impact of the excessively long sliding window on the update of the importance score. For example, if the sales data is collected for too long, the early information in the historical data may dilute the recent dynamic changes in the market, thereby reducing the responsiveness of the update formula of the importance score to the latest trends.

[0143] The importance score of the target word is updated based on the predicted sales volume and the real-time sales volume, wherein the update formula of the importance score is as follows:

[0144]

[0145] in, represents the importance score of the target word, Indicates real-time sales volume. Indicates the predicted sales volume, Indicates the total number of days in the month. Indicates the total number of days for collecting the sales data. represents the promotion indicator variable, Indicates the correction coefficient of real-time sales error, The correction factor representing the impact of promotional activities, represents the updated importance score;

[0146] when , then the importance score of the target word is improved, otherwise it is reduced, the target word is downgraded and re-evaluated as a keyword, the importance score of this keyword and the importance scores of other keywords are bubble sorted, the importance score of the keyword ranked first has the largest value, and the importance score of the keyword ranked last has the smallest value, the keywords corresponding to the top 3 importance scores are selected, and these 3 keywords are recorded as value labels.

[0147] Value tags are the most valuable keywords that best reflect current market trends or demands under current market conditions. By dynamically evaluating the importance of keywords, value tags ensure that agricultural product e-commerce platforms can respond to market changes in a timely manner and optimize agricultural product recommendations, pricing strategies, and marketing activities. This method can effectively avoid lag effects, allowing agricultural product e-commerce platforms to more accurately meet consumer needs and improve customer experience. At the same time, value tags also help agricultural product e-commerce platforms quickly adapt to market fluctuations, improve competitiveness, and ultimately drive sales growth and profitability.

[0148] In detail, Can Random values ​​are taken between , which effectively avoids the situation where the updated importance score has a drastic fluctuation due to abnormal data in a fixed window. In this way, the update formula of the importance score can be more robust, successfully avoiding the interference of extreme data values ​​on the update formula of the importance score, and ensuring the stability and reliability of the update process of the importance score.

[0149] like Figure 2 , which is a functional module diagram of a data value analysis system based on machine learning provided by one embodiment of the present invention.

[0150] The data value analysis system 100 based on machine learning of the present invention can be installed in an electronic device. According to the functions implemented, the data value analysis system 100 based on machine learning can include a collection module 101, a keyword group generation module 102, a purchase intention combination generation module 103, an importance score generation module 104, a screening module 105, a sales forecast module 106 and a value label generation module 107. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0151] In this embodiment, the functions of each module / unit are as follows:

[0152] The collection module is used to collect customer data of the agricultural products e-commerce platform;

[0153] The keyword group generating module is used to generate a keyword group for a customer based on the customer data;

[0154] The purchase intention combination generating module is used to generate the purchase intention combination of the customer based on the decision tree model and the keyword group;

[0155] The importance score generating module is used to perform importance analysis on the keyword group according to the purchase intention combination to obtain importance scores of the keywords in the keyword group;

[0156] The screening module is used to screen out target words from the keywords according to the screening mechanism of the importance score;

[0157] The sales forecasting module is used to generate the sales forecast of agricultural products according to the target words and the customer data;

[0158] The value label generation module is used to monitor the real-time sales volume of the agricultural product, update the importance score of the target word based on the predicted sales volume and the real-time sales volume, and update the target word according to the updated importance score to obtain the value label of the agricultural product.

[0159] In the several embodiments provided by the present invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0160] The modules described as separate components may or may not be physically separated, and the components shown as modules 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 modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0161] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, 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 hardware plus software functional modules.

[0162] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0163] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0164] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A data value analysis method based on machine learning, characterized in that: The method comprises: Collect customer data of agricultural product e-commerce platforms; generating a keyword group for a customer based on the customer data; Generating the customer's purchase intention combination based on the decision tree model and the keyword group; Performing importance analysis on the keyword group according to the purchase intention combination to obtain importance scores of the keywords in the keyword group; Filtering out target words from the keywords according to the importance score screening mechanism; generating predicted sales of agricultural products based on the target words and the customer data; The real-time sales volume of the agricultural product is monitored, the importance score of the target word is updated based on the predicted sales volume and the real-time sales volume, and the target word is updated according to the updated importance score to obtain a value tag of the agricultural product.

2. A data value analysis method based on machine learning as claimed in claim 1, characterized in that: The generating the customer's purchase intention combination based on the decision tree model and the keyword group includes: T1. Generate a data set of the customer according to the keywords and the customer data, wherein the keywords include: agricultural product A, agricultural product B and agricultural product C, and the customer data includes: purchase history, browsing time and browsing times of agricultural products; T2, inputting the data set into a decision tree model; T3. When the keyword has a purchase history, the keyword is marked as Y. When the keyword does not have a purchase history, the browsing time judgment algorithm is entered, wherein the browsing time judgment algorithm is: When the browsing time of the keyword is ≥10, the keyword is marked as Y; When the browsing time of the keyword is within 2-10 seconds, the browsing count judgment algorithm is entered, wherein the browsing count judgment algorithm is: when the browsing count of the keyword is ≥3 times, the keyword is marked as Y, and when the browsing count of the keyword is <3 times, the keyword is marked as N; When the browsing time of the keyword is ≤2s, the keyword is marked as N; T4. Add the keyword marked as Y to the customer's purchase intention combination.

3. The data value analysis method based on machine learning according to claim 1, characterized in that: The step of performing importance analysis on the keyword group according to the purchase intention combination to obtain importance scores of the keywords in the keyword group includes: The purchase history impact score, browsing time impact score and browsing times impact score of the keywords in the keyword group are generated according to the purchase intention combination, wherein: The calculation formula for the purchase history impact score is as follows: in, Indicates the first Keywords, () indicates that purchase history affects the score. Indicates the customer, Indicates the purchase history of the keyword. Indicates that the customer has a purchase history of the keyword. Indicates that the customer has no purchase history for the keyword; The calculation formula for browsing time impact score is as follows: in, Indicates the first Keywords, Indicates that browsing time affects the rating. Indicates the browsing time of the keyword; The calculation formula for the impact of the number of views on the score is as follows: in, Indicates the first Keywords, Indicates that the number of views affects the rating. Indicates the number of views of the keyword; The purchase history impact score, the browsing time impact score, and the browsing times impact score are weighted and summed to obtain the importance score of the keyword, wherein the calculation formula for the weighted sum is as follows: in, Indicates the first Keywords, represents the importance score of the keyword, , , They respectively represent the weight coefficients of the purchase history affecting the score, the browsing time affecting the score, and the browsing times affecting the score.

4. The data value analysis method based on machine learning according to claim 1, characterized in that: The screening mechanism is as follows: in, Indicates the first Keywords, Indicates the preset importance threshold, Represents the target word after filtering, represents the importance score.

5. The data value analysis method based on machine learning according to claim 1, characterized in that: The generating the predicted sales volume of agricultural products according to the target word and the customer data includes: Substitute the target word and the customer data into a sales forecasting algorithm to obtain the predicted sales of agricultural products, wherein the sales forecasting algorithm is as follows: in, Indicates the first Keywords, Represents the word vector corresponding to the target word, represents the data vector corresponding to the customer data, Indicates The predicted sales volume corresponding to the keywords, Represents a mapping function.

6. The data value analysis method based on machine learning according to claim 1, characterized in that: Monitoring the real-time sales volume of the agricultural product, and updating the importance score of the target word based on the predicted sales volume and the real-time sales volume, including: Based on the sliding window algorithm, data collection is performed on the sales data of the agricultural product for the current month to obtain the real-time sales data of the agricultural product for the current month; The importance score of the target word is updated based on the predicted sales volume and the real-time sales volume, wherein the update formula of the importance score is as follows: in, Indicates the first Keywords, represents the importance score of the target word, Indicates real-time sales volume. Indicates the predicted sales volume, Indicates the total number of days in the month. Indicates the total number of days for collecting the sales data. represents the promotion indicator variable, Indicates the correction coefficient of real-time sales error, The correction factor representing the impact of promotional activities, Represents the updated importance score.

7. The data value analysis method based on machine learning according to claim 6, characterized in that: The calculation formula of the sliding window algorithm is as follows: in, Indicates the total number of days in the month. Indicates the total number of days for collecting the sales data. represents the adjustment factor, represents rounding down, Indicates the minimum number of days for data collection. Indicates the maximum number of days for data collection.

8. The data value analysis method based on machine learning according to claim 7, characterized in that: The calculation formula of the adjustment coefficient is as follows: in, represents the adjustment factor, Represents a random number.

9. The data value analysis method based on machine learning according to claim 6, characterized in that: The calculation formula of the real-time sales volume is as follows: in, Indicates the real-time sales volume on the first day of the month. Indicates the real-time sales volume on the second day of the month. Indicates the real-time sales volume on the nth day of the month. Indicates real-time sales.

10. A data value analysis system based on machine learning, characterized in that: The system comprises: The collection module is used to collect customer data of agricultural product e-commerce platforms; A keyword group generating module, used for generating a keyword group for a customer according to the customer data; A purchase intention combination generating module, used for generating the customer's purchase intention combination based on a decision tree model and the keyword group; An importance score generating module, used for performing importance analysis on the keyword group according to the purchase intention combination to obtain importance scores of the keywords in the keyword group; A screening module, used to screen out target words from the keywords according to the screening mechanism of the importance score; A sales forecasting module, used for generating a forecasted sales volume of agricultural products according to the target words and the customer data; The value label generation module is used to monitor the real-time sales volume of the agricultural product, update the importance score of the target word based on the predicted sales volume and the real-time sales volume, and update the target word according to the updated importance score to obtain the value label of the agricultural product.

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