Evaluation and analysis system based on network security

Abstract

The invention discloses an evaluation and analysis system based on network security, which relates to the technical field of shopping systems, and comprises a data acquisition module, a data preprocessing module, a feature extraction module, a model training module and an evaluation and analysis module, according to the method, features of commodities in a picture are extracted through commodity positioning, effective information including commodity brands, commodity prices, commodity dates, commodity shelf lives and commodity purposes is rapidly extracted from all obtained information of the commodities, and brands, marks and characters carried by the commodities in the picture are all disassembled through characters and a commodity cutting algorithm, so that the commodity information is obtained. According to the method, feature extraction is performed on the commodity body, and price search of the commodity identified by the identification frame in other channels is displayed to the user interface through channel matching for the user to acquire, so that the user purchases a commodity worthy of purchase in a cold attitude, impulsive consumption of the user is avoided, and the user can be helped to make a more intelligent choice.

Description

Technical Field

[0001] The present invention relates to the technical field of shopping systems, and in particular to an evaluation and analysis system based on network security. Background Art

[0002] Live streaming sales refers to the sales of goods during live streaming entertainment. The reason for the emergence of live streaming sales is the popularity of mobile Internet and the fact that young people are generally watching live streaming on their phones every day. Some early e-commerce companies cooperated with anchors, who helped merchants to sell goods and received commissions. This is the origin of live streaming sales. However, the popularity of the live streaming industry did not raise the threshold of this industry, which led to the sale of low-quality goods through live streaming sales, resulting in consumers being cheated and the problem of online shopping safety becoming more serious. When selling goods through live streaming, the host will set a fixed time after a product is put on the shelves. Only those who purchase the product within the fixed time period of the host can enjoy the discount. During this fixed time period, consumers do not have enough time to compare the prices of the same products in other shopping channels, and they cannot even read the instructions carefully to understand the product. As a result, consumers buy the product without enough understanding, resulting in impulse purchases. Summary of the invention

[0003] The purpose of the present invention is to provide an evaluation and analysis system based on network security to solve the problems raised in the above background technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: an evaluation and analysis system based on network security, comprising a data acquisition module, a data preprocessing module, a feature extraction module, a model training module and an evaluation and analysis module, wherein the feature extraction module comprises a plurality of recognition frames, wherein at least one recognition frame is used to recognize an image, and the recognition process of the recognition frame comprises a commodity positioning unit, a text and commodity cutting algorithm and a channel matching; Extract features of the product in the image through product positioning, and quickly extract valid information from all the acquired product information, wherein the valid information includes product brand, product price, product date, product shelf life, and product use; The brand, logo and text of the product in the picture are separated by the text and product segmentation algorithm, and the product features are extracted. Through channel matching, the price search of the product identified by the identification box in other channels is displayed on the user interface for users to obtain.

[0005] According to the above technical solution, the data collection module includes a preferential information extraction unit, a plurality of commodity sales channel price information extraction units, a live commodity information extraction unit, and a commodity sales video extraction unit; The preferential information extraction unit obtains the preferential calculation method of the live broadcast product, directly helping the user to obtain the method of how to purchase at the lowest price; Acquire the price of the same commodity in other channels within the same time period through the multiple commodity selling channel price information extraction units; The live broadcast product information extraction unit is used to obtain all information about the product being broadcast live, including all text extraction of the product introduction page; The commodity selling video extraction unit is used to obtain the whole process video of the commodity selling being broadcast live.

[0006] According to the above technical solution, the commodity sales video extraction unit has an automatic deletion function. The commodity sales video extraction unit chooses to adopt a separated video recording method to divide the entire live sales video into several short videos of equal length.

[0007] According to the above technical solution, the data preprocessing module includes a time node reservation unit; The time node reservation unit is used to frame the live broadcast time period for users to purchase goods, and the entire live broadcast time period is divided into a time period for users to confirm purchase of goods and a time period without confirmation. By dividing the time periods, the data preprocessing module is given priority for processing data.

[0008] According to the above technical solution, the data preprocessing module also includes a commodity information data clearing unit and a commodity information data modularization unit; The information obtained by the data collection module is cleaned by the commodity information data cleaning unit to ensure that the information obtained by the data collection module is usable information and to avoid the appearance of garbled data; The commodity information data modular unit analyzes the information collected by the data collection module, and obtains commodities and commodity prices of the same type in other channels.

[0009] According to the above technical solution, the model training module includes a learning library, a confirmation unit and an evaluation unit; The learning library records the user's habit of purchasing goods, and the same type of goods are grouped as a whole. The user's purchasing habits are extracted from the same type of goods. The purchasing habits include price selection and product selection. The price selection habit is determined by the model training module by analyzing whether the user will take price as the first priority when purchasing the same type of goods. The product selection habit is determined by the model training module by analyzing whether the user will take product quality as the first priority when purchasing the same type of goods. Updating the user's purchasing habits through the confirmation unit; Divide the user purchase habit record data stored in the learning library into time periods: L1, confirm the duration of the time period; L2, the learning library records the user's purchasing habits in the previous three time periods and analyzes and confirms the user's purchasing habits; L3: Analyze the user purchase habits recorded in the fourth time period, confirm the user's purchase habits, and compare them with the previously confirmed user purchase habits. If the two users' purchase habits are consistent, execute according to the latest confirmed user purchase habits. If the two users' purchase habits are different, execute according to the old purchase habits. Take the fourth time period as the starting time period, record the user purchase habits in the three time periods through the learning library again, and re-analyze and confirm the user's purchase habits. L4, by confirming the user’s purchase history, helps the user confirm whether the selected product is in line with their purchasing habits.

[0010] According to the above technical solution, the evaluation unit includes a test scoring unit and a tracing unit; The test scoring unit is used to regularly test the user's purchasing habits. The purchasing habit test is to manually design a questionnaire page for the user to test. The tracing unit is used to determine the time point of each purchase habit test. After the user's purchase habits within three time periods are recorded again through the learning library each time, a purchase habit test is conducted on the user. The model training module is used to double-confirm the results of the user's purchase habit analysis and the results of the user evaluation to accurately locate the user's purchase habits. In case of result deviation, the user evaluation result shall prevail.

[0011] According to the above technical solution, the evaluation and analysis module includes evaluation results and analysis reports; The evaluation results provide users with a quick understanding of whether they need to buy the product. Some products sold through live streaming are time-sensitive. The analysis report allows users to understand the advantages and disadvantages of the products being sold.

[0012] Compared with the prior art, the beneficial effect achieved by the present invention is as follows: the present invention, by setting up, when the user watches the live broadcast of selling goods, saves the product information by taking a screenshot, quickly scans and identifies the picture through the identification box, obtains all the information of the product, and displays the products without additional items, the prices of products from different channels, etc. for the user to view. The user quickly browses the information and chooses whether to buy such products in conjunction with the results given by the evaluation and analysis module. The rational results given by the evaluation and analysis module reduce the impact of the live broadcast environment on the user. By using big data analysis and machine learning technology, the value of the product can be quickly and accurately evaluated, so that the user can buy a product worth buying with a calm attitude, avoid impulsive consumption, and help the user make a wiser choice. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 It is a schematic diagram of the basic flow of commodity evaluation of the present invention; Figure 2 is a schematic diagram of a commodity evaluation and analysis system of the present invention; Figure 3 It is a detailed flow chart of commodity evaluation of the present invention. DETAILED DESCRIPTION

[0014] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0015] See also Figure 1-3 , the present invention provides a technical solution: an evaluation and analysis system based on network security, including a data acquisition module, a data preprocessing module, a feature extraction module, a model training module and an evaluation and analysis module, the feature extraction module includes a plurality of recognition frames, at least one of which is used to recognize a picture, and the recognition process of the recognition frame includes a commodity positioning unit, a text and commodity cutting algorithm and a channel matching; Through product positioning, feature extraction is performed on the products in the image, and effective information is quickly extracted from all the product information obtained. The effective information includes product brand, product price, product date, product shelf life, and product use; The brand, logo and text of the product in the picture are separated by the text and product segmentation algorithm, and the product features are extracted. Through channel matching, the price search of the product identified by the identification box in other channels is displayed on the user interface for users to obtain; For example, for a shirt product, the brand, logo and text on the shirt product are removed to obtain a shirt without any additional attachments. The identification box identifies the shirt without any additional attachments and obtains the price of the shirt without additional attachments for users to obtain. The price of the shirt without additional attachments is compared with that of the shirt product, giving users a variety of choices. The disadvantages of live streaming sales are low after-sales guarantee, many low-quality products, and the possibility of purchasing solid-color clothing without any additional marks at a low price. The additional marks are used to set arbitrary prices, causing consumers to buy low-quality products at high prices. The identification box compares the additional products with the non-additional products for users to choose.

[0016] The data collection module includes a discount information extraction unit, a price information extraction unit for multiple commodity sales channels, a live commodity information extraction unit, and a commodity sales video extraction unit; The discount information extraction unit obtains the discount calculation method of the live broadcast product, directly helping users to find out how to buy it at the lowest price; Obtain the price of the same product in other channels during the same time period through multiple product sales channel price information extraction units; The live broadcast product information extraction unit is used to obtain all the information of the product being broadcast live, including all the text extraction of the product introduction page; The product sales video extraction unit is used to obtain the video of the entire product sales process being live broadcast.

[0017] The commodity sales video extraction unit has an automatic deletion function. The commodity sales video extraction unit chooses to adopt a separated video recording method. Through the separated video recording method, the entire live sales video is divided into several short videos of equal length, which is convenient for users to find and delete videos later.

[0018] The data preprocessing module includes a time node reservation unit; The live broadcast time period when users purchase goods is framed by time node reservation units, and the entire live broadcast time period is divided into a user purchase confirmation time period and a non-confirmation time period. By dividing the time periods, the data preprocessing module is given priority for processing data. The user purchase confirmation time period is the video time period of the live broadcast selling the goods purchased by the user. The retention of this video time period can be used as evidence for after-sales rights protection. The non-confirmation time period is when the user watches the live broadcast selling video but does not purchase. This video time period can be used as the object for data preprocessing module to clean up data. The data preprocessing module cleans data regularly to ensure that the database space meets normal use and will not harm user interests.

[0019] The data preprocessing module also includes a commodity information data clearing unit and a commodity information data modularization unit; The information obtained by the data collection module is cleaned through the commodity information data cleaning unit to ensure that the information obtained by the data collection module is usable information and to avoid garbled data; The information collected by the data collection module is analyzed through the commodity information data modular unit, and the same type of commodities and commodity prices in other channels are obtained.

[0020] The model training module includes a learning library, a confirmation unit, and an evaluation unit; The learning library records the user's purchasing habits, and the same type of goods are grouped as a whole. The user's purchasing habits are extracted from the same type of goods. Purchasing habits include price selection and product selection. The price selection habit is determined by the model training module by analyzing whether the user purchases the same type of goods based on price as the first priority criterion, that is, if the user purchases goods multiple times, the probability of choosing low-priced goods in the same type of goods exceeds 85%, and the user is determined to be a price-oriented user. The product selection habit is determined by the model training module by analyzing whether the user purchases the same type of goods based on product quality as the first priority criterion, that is, if the user purchases goods multiple times, the probability of choosing high-priced goods in the same type of goods exceeds 60%, and the user is determined to be a product-oriented user. Update users’ purchasing habits through confirmation units; Divide the user purchasing habits records stored in the learning library into time periods: L1, confirm the time period duration; L2, the learning library records the user's purchasing habits in the previous three time periods and analyzes and confirms the user's purchasing habits; L3: Analyze the user purchase habits recorded in the fourth time period, confirm the user's purchase habits, and compare them with the previously confirmed user purchase habits. If the two users' purchase habits are consistent, execute according to the latest confirmed user purchase habits. If the two users' purchase habits are different, execute according to the old purchase habits. Take the fourth time period as the starting time period, record the user purchase habits in the three time periods through the learning library again, and re-analyze and confirm the user's purchase habits. L4, by confirming the user's purchase history, helps the user confirm whether the selected product is in line with their purchase habits. Products that are in line with the user's purchase habits have a low probability of impulse purchase and are more in line with the user's purchase habits.

[0021] The evaluation unit includes a test scoring unit and a tracing unit; The test scoring unit is used to regularly test the purchasing habits of users. The purchasing habit test is to manually design a questionnaire page for users to test. The time point of each purchase habit test is determined by the tracing unit. After the user's purchase habits within three time periods are recorded through the learning library each time, a purchase habit test is conducted on the user. The model training module uses the results of the user purchase habit analysis and the results of the user evaluation to double confirm the user's purchase habits, and the user's purchase habits are accurately located. In case of result deviation, the user evaluation result shall prevail.

[0022] The evaluation and analysis module includes evaluation results and analysis reports; The evaluation results are used to help users quickly understand whether they need to buy the product. Some products sold through live streaming are time-sensitive. By displaying the evaluation results, users can quickly know the answer and avoid impulse purchases caused by users considering the product in a short period of time. Through analysis reports, users can understand the advantages and disadvantages of the products being sold. For products that are not time-sensitive, users can first understand the advantages and disadvantages of the products before making a choice. Through the final evaluation and analysis module, the probability of users' impulse purchases can be reduced, and the problem of selling inferior products to users through live streaming can be solved.

[0023] When users watch live broadcasts selling goods, they can save the product information by taking screenshots, quickly scan and identify the image through the identification box, obtain all the information about the product, and display the products without additional items and prices of products from different channels for users to view. Users can quickly browse the information and choose whether to buy such products based on the results given by the evaluation and analysis module. The rational results given by the evaluation and analysis module can reduce the impact of the live broadcast environment on users. By using big data analysis and machine learning technology, the value of the product can be quickly and accurately evaluated, allowing users to buy a worthwhile product with a calm attitude, avoiding impulsive consumption, and helping users make more informed choices.

[0024] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0025] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A network security-based evaluation and analysis system, comprising a data acquisition module, a data preprocessing module, a feature extraction module, a model training module and an evaluation and analysis module, characterized in that: The feature extraction module includes a plurality of recognition frames, at least one of which is used to recognize the image, and the recognition process of the recognition frame includes a product positioning unit, a text and product segmentation algorithm, and a channel matching; Extract features of the product in the image through product positioning, and quickly extract valid information from all the acquired product information, wherein the valid information includes product brand, product price, product date, product shelf life, and product use; The brand, logo and text of the product in the picture are separated by the text and product segmentation algorithm, and the product features are extracted. Through channel matching, the price search of the product identified by the identification box in other channels is displayed on the user interface for users to obtain; The data collection module includes a preferential information extraction unit, a plurality of commodity sales channel price information extraction units, a live commodity information extraction unit, and a commodity sales video extraction unit; The preferential information extraction unit obtains the preferential calculation method of the live broadcast product, directly helping the user to obtain the method of how to purchase at the lowest price; Acquire the price of the same commodity in other channels within the same time period through the multiple commodity selling channel price information extraction units; The live broadcast product information extraction unit is used to obtain all information about the product being broadcast live, including all text extraction of the product introduction page; The commodity selling video extraction unit obtains the live broadcast of the whole process of commodity selling; The commodity selling video extraction unit has an automatic deletion function. The commodity selling video extraction unit selects to adopt a separated video recording method, and divides the entire live selling video into a number of short videos of equal length through the separated video recording method; The data preprocessing module includes a time node reservation unit; The live broadcast time period for users to purchase goods is framed by the time node reservation unit, and the entire live broadcast time period is divided into a time period for users to confirm the purchase of goods and a time period without confirmation. By dividing the time periods, the data preprocessing module is given priority for processing data; The model training module includes a learning library, a confirmation unit and an evaluation unit; The learning library records the user's habit of purchasing goods, and the same type of goods are grouped as a whole. The user's purchasing habits are extracted from the same type of goods. The purchasing habits include price selection and product selection. The price selection habit is determined by the model training module by analyzing whether the user will take price as the first priority when purchasing the same type of goods. The product selection habit is determined by the model training module by analyzing whether the user will take product quality as the first priority when purchasing the same type of goods. Updating the user's purchasing habits through the confirmation unit; Divide the user purchase habit record data stored in the learning library into time periods: L1, confirm the duration of the time period; L2, the learning library records the user's purchasing habits in the previous three time periods and analyzes and confirms the user's purchasing habits; L3: Analyze the user purchase habits recorded in the fourth time period, confirm the user's purchase habits, and compare them with the previously confirmed user purchase habits. If the two users' purchase habits are consistent, execute according to the latest confirmed user purchase habits. If the two users' purchase habits are different, execute according to the old purchase habits. Take the fourth time period as the starting time period, record the user purchase habits in the three time periods through the learning library again, and re-analyze and confirm the user's purchase habits. L4, by confirming the user's purchase history, helps the user confirm whether the selected product is in line with their purchase habits; The evaluation and analysis module includes evaluation results and analysis reports; The evaluation results provide users with a quick understanding of whether they need to buy the product. Some products sold through live streaming are time-sensitive. Provide users with an understanding of the advantages and disadvantages of the products being sold through analytical reports; The evaluation unit includes a test scoring unit and a tracing unit; The test scoring unit is used to regularly test the user's purchasing habits. The purchasing habit test is to manually design a questionnaire page for the user to test. The tracing unit is used to determine the time point of each purchase habit test. After the user's purchase habits within three time periods are recorded again through the learning library each time, a purchase habit test is conducted on the user. The model training module is used to double-confirm the results of the user's purchase habit analysis and the results of the user evaluation to accurately locate the user's purchase habits. In case of result deviation, the user evaluation result shall prevail.

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