Method for automatically collecting applet information, intelligent terminal and storage medium
Based on the events of the events of the future target date based on historical data prediction mini -programs, automatically collect and push relevant product information, and determine whether the data collection mechanism is triggered according to the user's operating information. Low problems have achieved efficient and accurate information collection and push.
Patent Information
- Application Number
- CN202411450476.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-17
- Publication Date
- 2025-01-24
AI Technical Summary
Existing mini programs push a large number of products irregularly when users open it, resulting in users needing to independently capture key information, which is cumbersome. At the same time, real-time data collection leads to huge and messy information, which makes it impossible to extract effective information in time, and the information collection efficiency is low.
By predicting the event data of the mini program under the future target date based on historical data, it automatically collects and pushes relevant product information, and determines whether to trigger the data collection mechanism based on the user's operation information to achieve automatic collection of target data, and store it in a remote server after processing.
It narrows the scope of product information pushed by users, improves the accuracy of product push, reduces the complexity of information, reduces the amount of data collection, improves the efficiency and accuracy of information collection, and reduces the space occupied by data.
Smart Images
Figure CN119359338A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of information collection and processing, and specifically relates to a method for automatically collecting mini-program information, an intelligent terminal and a storage medium. Background Art
[0002] With the development of the Internet, many industries are using information technology to improve efficiency and service quality. For example, education, transportation, medical care, news, and government affairs are all undergoing digital transformation, which has led to the emergence of various new application mini-programs. These application mini-programs provide users with a wealth of services and functions, and have become an important part of mobile Internet applications.
[0003] These application mini-programs generate a large amount of data during use. In order to extract useful information from this data, we can optimize the user experience and perform performance monitoring based on this data, timely discover potential problems in the application and meet user preferences.
[0004] Existing mini programs, especially commodity shopping mini program platforms, will push a large number of commodities irregularly when users open them, and users are required to independently capture key information for searching, which is very cumbersome. In addition, for the large amount of data generated during use, real-time data collection results in a huge and messy information database, which makes it impossible to extract effective information in time, and the information collection efficiency is low. It is impossible to proactively push commodity information in a timely manner according to user needs, and the accuracy is not high. Summary of the invention
[0005] The purpose of the present invention is to provide a method, intelligent terminal and storage medium for automatically collecting mini-program information with a simple structure and reasonable design in order to solve the above-mentioned problems.
[0006] The present invention achieves the above-mentioned purpose through the following technical solutions:
[0007] A method for automatically collecting mini-program information, comprising:
[0008] Predict the event data of the mini program at a future target date based on historical data;
[0009] Push relevant product information to users based on predicted event data information, and obtain user operation information when the mini program is running;
[0010] Determine and analyze whether the user's operation information triggers the data collection mechanism, and automatically collect the target data generated when the mini program is running based on the analysis results;
[0011] Process the collected target data to obtain effective information;
[0012] The obtained valid information is sent to the remote server for storage.
[0013] As a further optimization scheme of the present invention, the future target date is a future event date set by the user in the mini-program calendar, including birthdays, holidays, and anniversaries.
[0014] As a further optimization scheme of the present invention, the prediction model of the event data of the applet on the future target date based on the historical data prediction is a multiple linear regression model, and the form of the multiple linear regression model is:
[0015] Y=β0+β1X1+β2X2+…+β k X k +ε
[0016] Among them, Y represents the dependent variable, X1, X2, …, X k represents the independent variable, β0, β2, …, β k represents the regression coefficient, and ε represents the error term.
[0017] As a further optimization scheme of the present invention, the independent variables of the multivariate linear regression model include the following data: seasonal factors of future event dates, historical purchase frequency of goods, price range of goods, age, gender and spending power of the recipients of the goods.
[0018] As a further optimization scheme of the present invention, the dependent variable of the multivariate linear regression model is the purchase probability of the product, and the relevant product information pushed to the user is pushed in order from large to small purchase probability.
[0019] As a further optimization solution of the present invention, the user's operation information includes the number of keyword searches, the number of clicks to view, and the sliding frequency.
[0020] As a further optimization solution of the present invention, the data collection mechanism is a threshold trigger algorithm model, and the step of judging and analyzing whether the user's operation information triggers the data collection mechanism includes:
[0021] When the number of keyword searches x(a) of the user is greater than A, the following analysis result is obtained: the keyword is the user's target preference keyword, and the data collection mechanism is triggered, where A is the preset number of keyword searches, and A≥2;
[0022] Or, when the number of times x(b) the user clicks on the product link>B, the following analysis result is determined: the viewed product is the user's target favorite product, and the data collection mechanism is triggered, where B is the preset number of times the product is clicked, and B≥2;
[0023] Or, when the page sliding frequency X(c) of the user when viewing a product is greater than C, the following analysis result is obtained: the viewed product is the user's target favorite product, and the data collection mechanism is triggered, where C is the preset product page sliding frequency, and C≥3.
[0024] As a further optimization scheme of the present invention, the step of processing the collected target data includes:
[0025] Cache to obtain the collected target data;
[0026] Check whether the corresponding target data exists in the historical data. If not, re-cache the missing part of the target data and output it as valid information.
[0027] The present invention also provides an intelligent terminal for automatically collecting mini-program information, comprising:
[0028] A memory, wherein the memory is used to store a data acquisition program;
[0029] A processor, the processor is used to execute the data collection program, wherein when the data collection program is executed by the processor, each step of the above-mentioned method for automatically collecting mini-program information is implemented.
[0030] The present invention also provides a storage medium, on which a data acquisition program is stored, wherein when the data acquisition program is executed by a processor, each step of the above-mentioned method for automatically collecting mini-program information is implemented.
[0031] The present invention has at least the following beneficial effects: the present invention provides a method, a smart terminal and a storage medium for automatically collecting information of a mini program. In the method for automatically collecting information of a mini program, event data of the mini program on a future target date is predicted based on historical data, thereby narrowing the scope of product information pushed to users, improving the accuracy of product push, avoiding confusing information, and effectively reducing the subsequent amount of data collection. In addition, data collection is performed when a data collection mechanism is triggered based on user operation information, and valid information is obtained based on data processing, thereby ensuring high efficiency and accuracy of information collection, reducing the space occupied by the collected data, and realizing effective data collection and storage. BRIEF DESCRIPTION OF THE DRAWINGS
[0032] Figure 1 It is a flowchart of a method for automatically collecting mini-program information provided by the present invention. DETAILED DESCRIPTION
[0033] The present application is further described in detail below in conjunction with the accompanying drawings. It is necessary to point out here that the following specific implementation methods are only used to further illustrate the present application and cannot be understood as limiting the scope of protection of the present application. Technical personnel in this field can make some non-essential improvements and adjustments to the present application based on the above application content.
[0034] like Figure 1 As shown, the present invention provides a method for automatically collecting mini-program information, comprising:
[0035] Predict the event data of the mini program at a future target date based on historical data;
[0036] Push relevant product information to users based on predicted event data information, and obtain user operation information when the mini program is running;
[0037] Determine and analyze whether the user's operation information triggers the data collection mechanism, and automatically collect the target data generated when the mini program is running based on the analysis results;
[0038] Process the collected target data to obtain effective information;
[0039] The obtained valid information is sent to the remote server for storage.
[0040] In the above embodiment, by predicting the event data of the mini program on a future target date based on historical data, the scope of product information pushed to users is narrowed, information confusion is avoided, and the amount of subsequent data collection is effectively reduced. In addition, based on the user's operation information, data collection is performed when the data collection mechanism is triggered, and valid information is obtained based on the processing of the data, thereby ensuring the efficiency and accuracy of information collection, reducing the space occupied by the collected data, and realizing effective collection and storage of data.
[0041] Exemplarily, the future target date is the future event date set by the user in the mini-program calendar, including birthdays, holidays, and anniversaries. In the mini-program calendar, users can record memos according to the future occurrence time of the corresponding event. Birthdays include birthdays of friends, family members, and customers. Holidays include Valentine's Day, Mid-Autumn Festival, Christmas, Teacher's Day, National Day, etc. Anniversaries include love anniversaries, wedding anniversaries, acquaintance anniversaries, and other days with special significance that require preparing gifts for each other. The content recorded in the memo at the corresponding time includes additional information such as the age, gender, preferences, and spending power of the gift recipient. The additional information recorded in these memos can be used as a basis for subsequent prediction of event data. Of course, the redundant description of these additional information can be supplemented according to actual needs, especially for customers who need to give gifts, who pay great attention to their preferences and social status. Therefore, as much additional information as possible is supplemented.
[0042] It should be noted that, taking into account the time cycle of express delivery and the fact that gifts generally need to be delivered before the target time, users need to reserve sufficient time for express delivery. They can set a certain day before the future event date as the target time and make a memo within the target time to remind users to browse the mini program for purchase within that time to ensure that the gifts are delivered before the holiday.
[0043] For example, the prediction model for the event data of the applet at the future target date based on historical data is a multiple linear regression model, and the form of the multiple linear regression model is:
[0044] Y=β0+β1X1+β2X2+…+β k X k +ε
[0045] Among them, Y represents the dependent variable, X1, X2, …, X k represents the independent variable, β0, β2, …, β k Represents the regression coefficient, where β0 is the constant term, represents the intercept, which is also the weight of each independent variable, and ε represents the error term.
[0046] It should be noted that the independent variables of the multivariate linear regression model include the following data: seasonal factors of the future event date, historical purchase frequency of goods, price range of goods, age, gender, and spending power of the recipients of the goods. Specifically, the seasonal factors of the future event date, because the demand for some goods may change when the seasons change, the seasonal factors can be months, quarters, holidays, etc. The higher the frequency of historical purchase of goods, the higher the satisfaction of the objects involved in the event corresponding to the target date, and the stronger the demand for the goods. The price range of goods will highlight the range of goods that the users involved can accept, as well as the consideration of multiple factors such as avoiding financial pressure on the recipients when returning gifts. The age and gender of the recipients of the goods can indicate the applicable population of the goods, thereby avoiding pushing goods that are not suitable for customers.
[0047] The historical data is the data information of the above-mentioned independent variables under the event corresponding to the target date in the historical time. Based on this historical data, the regression coefficient of each independent variable is determined, and the relevant data of similar products in the historical data are substituted into the above-mentioned multivariate linear regression model to calculate the Y value, that is, the probability of users purchasing similar products in the event under the future target date. Based on this, the relevant information of similar products is pushed to the user.
[0048] By referring to historical data and making predictions for new similar products, we use a multivariate linear regression model to digitally analyze the range of products that users want to buy on future target dates, thereby greatly reducing the push of irrelevant products, improving the matching degree of the types of products that users want to buy, and improving data extraction efficiency.
[0049] Among them, the relevant product information pushed to users is pushed in order from the highest to the lowest purchase probability. This is convenient for users to consider the most relevant product information pushed by the mini program at the first time. This is to indirectly remind users of the types of products and price ranges recommended for purchase under the target future event. This is used as a reference to assist users in making choices.
[0050] It should be noted that in actual application, for the products pushed by the mini program, users can swipe to view them, and for the products they are interested in, they can click to enter the detailed page of the product to view them. When viewing, they can swipe the page to view the product information in detail, including product performance, function introduction, and function, etc. For information that requires detailed understanding or special attention, they will stop and browse for a while, and even after understanding it, when returning to the product push browsing interface, they will focus on searching for keywords for the product through the search box, thereby further increasing the browsing attention of the product.
[0051] In summary, the user's operation information includes the number of keyword searches, the number of clicks to view, and the sliding frequency. According to the user's operation information, the user's attention to and purchase tendency of the corresponding product can be reflected indirectly. Therefore, it is judged and analyzed whether the user's operation information triggers the data collection mechanism, and the target data generated when the mini program is running is automatically collected according to the analysis results, so as to realize targeted automatic data collection, which can be used as historical data for predicting event data under the next target date, improve data collection efficiency, and avoid data collection at the beginning of the mini program, avoid too much cumbersome data, and increase the amount of data processing tasks.
[0052] Specifically, the data collection mechanism is a threshold trigger algorithm model, and the step of judging and analyzing whether the user's operation information triggers the data collection mechanism includes:
[0053] When the number of keyword searches x(a) of the user is greater than A, the following analysis result is obtained: the keyword is the user's target preference keyword, and the data collection mechanism is triggered, where A is the preset number of keyword searches, and A≥2;
[0054] Or, when the number of times x(b) the user clicks on the product link>B, the following analysis result is determined: the viewed product is the user's target favorite product, and the data collection mechanism is triggered, where B is the preset number of times the product is clicked, and B≥2;
[0055] Or, when the page sliding frequency X(c) of the user when viewing a product is greater than C, the following analysis result is obtained: the viewed product is the user's target favorite product, and the data collection mechanism is triggered, where C is the preset product page sliding frequency, and C≥3.
[0056] It should be noted that as long as at least one of the above conditions such as the number of keyword searches or the number of product clicks or product page scrolling frequency is met, the data collection mechanism can be triggered. Considering the user's usage habits, for example, some users are used to searching for keywords, some users are used to browsing product page information, and some users are used to clicking on products to view them repeatedly, the number of keyword searches or product clicks or product page scrolling frequency can be automatically adjusted as needed to ensure that the user is interested in the product and has a tendency to buy it. When the user's operation information triggers the data collection mechanism, by setting the above data collection mechanism, data is collected only when the mechanism conditions are met, which can ensure that the collected data is more relevant and of actual value. The target data generated when the mini program is currently running is automatically collected to avoid unnecessary data collection, improve the accuracy of data collection, and save subsequent data storage space and processing capabilities.
[0057] Exemplarily, the step of processing the collected target data includes:
[0058] Cache to obtain the collected target data;
[0059] Check whether the corresponding target data exists in the historical data. If not, re-cache the missing part of the target data and output it as valid information.
[0060] In the above embodiment, the data processing of the above steps is performed on the collected target data, and the collected target data is obtained through the cache to avoid data loss during the data processing process. The target data is compared with the historical data, and the cached target data corresponding to the data already stored in the historical data is deleted. For the data not in the historical data, the cached target data corresponding to the data not in the historical data is re-buffered and output as valid information, thereby further reducing the repeated collection of data and improving the efficiency and accuracy of collecting valid data when the commodity shopping applet is running.
[0061] The acquired valid information is sent to the remote server for storage, thereby reducing the storage space occupied by irrelevant data and improving the remote server's data storage performance.
[0062] In one embodiment, the present invention further provides a smart terminal for automatically collecting mini-program information, including:
[0063] A memory, wherein the memory is used to store a data acquisition program;
[0064] A processor, the processor is used to execute the data collection program, wherein when the data collection program is executed by the processor, each step of the above-mentioned method for automatically collecting mini-program information is implemented.
[0065] In one embodiment, the present invention further provides a storage medium having a data acquisition program stored thereon, wherein when the data acquisition program is executed by a processor, each step of the above-mentioned method for automatically collecting mini-program information is implemented.
[0066] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.
Claims
1. A method for automatically collecting mini-program information, characterized in that: include: Predict the event data of the mini program at a future target date based on historical data; Push relevant product information to users based on predicted event data information, and obtain user operation information when the mini program is running; Determine and analyze whether the user's operation information triggers the data collection mechanism, and automatically collect the target data generated when the mini program is running based on the analysis results; Process the collected target data to obtain effective information; The obtained valid information is sent to the remote server for storage.
2. A method for automatically collecting mini-program information according to claim 1, characterized in that: The future target date is the future event date set by the user in the mini-program calendar, including birthdays, holidays, and anniversaries.
3. The method for automatically collecting mini-program information according to claim 2, characterized in that: The prediction model based on historical data to predict the event data of the applet on the future target date is a multiple linear regression model, and the form of the multiple linear regression model is: Y=β0+β1X1+β2X2+…+β k X k +e Among them, Y represents the dependent variable, X1, X2, …, X k represents the independent variable, β0, β2, …, β k represents the regression coefficient, and ε represents the error term.
4. A method for automatically collecting mini-program information according to claim 3, characterized in that: The independent variables of the multivariate linear regression model include the following data: seasonal factors of the future event date, historical purchase frequency of the product, price range of the product, age, gender, and spending power of the recipient of the product.
5. A method for automatically collecting mini-program information according to claim 4, characterized in that: The dependent variable of the multivariate linear regression model is the purchase probability of the product, and the relevant product information pushed to the user is pushed in order from the largest to the smallest purchase probability.
6. A method for automatically collecting mini-program information according to claim 5, characterized in that: The user's operation information includes the number of keyword searches, the number of clicks to view, and the sliding frequency.
7. A method for automatically collecting mini-program information according to claim 6, characterized in that: The data collection mechanism is a threshold trigger algorithm model. The steps of judging and analyzing whether the user's operation information triggers the data collection mechanism include: When the number of keyword searches x(a) of the user is greater than A, the following analysis result is obtained: the keyword is the user's target preference keyword, and the data collection mechanism is triggered, where A is the preset number of keyword searches, and A≥2; Or, when the number of times x(b) the user clicks on the product link>B, the following analysis result is determined: the viewed product is the user's target favorite product, and the data collection mechanism is triggered, where B is the preset number of times the product is clicked, and B≥2; Or, when the page sliding frequency X(c) of the user when viewing a product is greater than C, the following analysis result is obtained: the viewed product is the user's target favorite product, and the data collection mechanism is triggered, where C is the preset product page sliding frequency, and C≥3.
8. The method for automatically collecting mini-program information according to claim 7, characterized in that: The steps of processing the collected target data include: Cache to obtain the collected target data; Check whether the corresponding target data exists in the historical data. If not, re-cache the missing part of the target data and output it as valid information.
9. An intelligent terminal for automatically collecting mini-program information, characterized in that: include: A memory, wherein the memory is used to store a data acquisition program; A processor, the processor is used to execute the data collection program, wherein when the data collection program is executed by the processor, the various steps of the method for automatically collecting mini-program information as described in any one of claims 1 to 8 are implemented.
10. A storage medium, characterized in that: The storage medium stores a data collection program, wherein when the data collection program is executed by the processor, each step of the method for automatically collecting mini-program information as described in any one of claims 1 to 8 is implemented.