User behavior safety early warning analysis system and method based on data feedback

Abstract

The present application discloses a user behavior safety early warning analysis system and method based on data feedback, which belongs to the field of user behavior monitoring. The present application obtains consumption information of a user's historical consumption cycle and the user's current purchase data to perform behavior history matching analysis, performs commodity purchase matching analysis based on attribute data and historical purchase data of purchased commodities and the user's current purchase data, performs user purchase behavior safety analysis based on the behavior history matching analysis results and commodity purchase matching analysis, performs accurate safety analysis of current purchase behavior based on the similarity and fluctuation of historically purchased commodities, and improves the safety and accuracy of consumer behavior.

Description

Technical Field

[0001] The present application belongs to the field of user behavior monitoring, and specifically to a user behavior security early warning analysis system and method based on data feedback. Background Art

[0002] Consumer safety refers to the feeling and experience that consumers can feel safe, comfortable and assured during the consumption process. Specifically, consumer safety mainly includes the following aspects: Product quality safety: the products or services purchased by consumers shall comply with relevant standards and quality requirements, and there shall be no problems such as counterfeiting, shoddy goods and inferior products, to ensure the property safety of consumers; fair prices: when consumers purchase goods or services, the prices shall be fair and reasonable, without fraud, to protect the interests of consumers. However, when consuming, it is often the case that unreasonable goods or goods that do not conform to purchasing habits are purchased without being reminded. Although the existing technology can provide reminders for over-quantity when purchasing in large quantities, it is impossible to perform accurate and safe analysis of the current purchase behavior based on the similarity and fluctuation of historically purchased goods, resulting in the safety and accuracy of consumer behavior not meeting the requirements. Most of the existing technologies have the above problems; in order to solve the problems raised by this background technology, the present application designs a user behavior safety early warning analysis system and method based on data feedback. Summary of the invention

[0003] In order to address the deficiencies in the prior art mentioned in the background technology, the present application proposes a user behavior safety early warning analysis system and method based on data feedback. The present application obtains consumption information of the user's historical consumption cycle and the user's current purchase data to perform a behavior history matching analysis, performs a product purchase matching analysis based on the attribute data and historical purchase data of the purchased goods and the user's current purchase data, performs a user purchase behavior safety analysis based on the behavior history matching analysis results and the product purchase matching analysis, and performs an accurate and safe analysis of the current purchase behavior based on the similarity and fluctuation of historically purchased goods, thereby improving the safety and accuracy of consumer behavior.

[0004] To achieve the above objectives, the present application provides the following technical solutions: In the first aspect, the present application provides a user behavior safety early warning analysis method based on data feedback, which includes the following specific steps:

[0005] Step 1: Obtain the consumption information of the user's historical consumption cycle, the attribute data of the purchased goods and the historical purchase data, and obtain the user's current purchase data;

[0006] Step 2: Obtain the consumption information of the user's historical consumption cycle and the user's current purchase data for behavior history matching analysis;

[0007] Step 3: Perform product purchase matching analysis based on the attribute data of the purchased products, historical purchase data, and the user's current purchase data;

[0008] Step 4: Conduct a security analysis of user purchase behavior based on the behavior history matching analysis results and product purchase matching analysis;

[0009] Step 5: Provide user behavior safety warning based on the user purchase behavior safety analysis results.

[0010] As a preferred technical solution for the user behavior safety early warning analysis method based on data feedback, the specific content of obtaining the consumption information of the user's historical consumption cycle, the attribute data of the purchased goods and the historical purchase data, and obtaining the user's current purchase data is:

[0011] Step 11: Obtain the types, quantities and attribute data of historical commodities purchased by the user on the platform, where the types refer to the classification data of commodities; the attribute data refers to the function data described in the product manual;

[0012] Step 12: Obtain the user's current purchase data, wherein the current purchase data includes the type, quantity and attribute data of the purchased goods.

[0013] As a preferred technical solution of the user behavior safety early warning analysis method based on data feedback, the acquisition of the consumption information of the user's historical consumption cycle and the user's current purchase data for behavior history matching analysis includes the following specific steps:

[0014] Step 21: Obtain the type and quantity data of the goods purchased this time, and at the same time obtain the quantity data of the same type of historically purchased goods in the consumption information of the user's historical consumption cycle;

[0015] Step 22: Obtain the quantity data of the same type of historically purchased goods in the consumption information of the historical consumption cycle and the quantity data of the currently purchased goods, and import them into the purchase quantity abnormal value calculation formula to calculate the purchase quantity abnormal value, wherein the purchase quantity abnormal value calculation formula is: , where xz is the quantity data of the purchased goods this time, n is the number of historical consumption cycles, xi is the number of historical purchased goods in the i-th historical consumption cycle, and re is the historical consumption standard deviation. The calculation formula of the historical consumption standard deviation is: ;

[0016] Step 23: Obtain the calculated purchase quantity abnormal value of all types of goods purchased by the user this time, add up all of them and divide by the number of types to obtain the overall quantity abnormal value.

[0017] As a preferred technical solution of the user behavior safety early warning analysis method based on data feedback, the commodity purchase matching analysis based on the attribute data and historical purchase data of the purchased commodity and the user's current purchase data includes the following specific steps:

[0018] Step 31: Obtain the attribute data and unit price data of the purchased goods, obtain the attribute data and unit price data of the goods purchased by the user in history, import the attribute data of the purchased goods and the attribute data of the goods purchased by the user in history into the attribute similarity calculation formula to calculate the attribute similarity, wherein the attribute similarity calculation formula of the attribute of the cth purchased goods and the attribute of the mth purchased goods in the user's history is: , where Rc is the number of attributes of the cth purchased product, ur is the rth attribute data of the mth purchased product, and pr is the rth attribute data of the cth purchased product;

[0019] Step 32: Obtain historically purchased products whose attribute similarity with the purchased product is greater than or equal to the set attribute similarity threshold, set them as similar purchased products, obtain the unit price data of the corresponding similar purchased products, and simultaneously obtain the unit price data of the currently purchased product, and import them into the purchase unit price abnormal value calculation formula to calculate the purchase unit price abnormal value, wherein the purchase unit price abnormal value calculation formula of the jth purchased product is: , where Bj is the number of similar purchased products to the jth purchased product, Kjb is the attribute similarity between the attributes of the jth purchased product and the bth similar purchased product, yj is the purchase unit price of the jth purchased product, and yjb is the purchase unit price of the bth similar purchased product to the jth purchased product;

[0020] Step 33: Get the purchase price abnormality of all commodities purchased this time, add them up and divide them by the number of categories to get the overall price abnormality.

[0021] As a preferred technical solution of the user behavior safety early warning analysis method based on data feedback, the user purchase behavior safety analysis based on the behavior history matching analysis results and the commodity purchase matching analysis includes the following specific contents: obtaining the calculated overall quantity abnormal value and overall unit price abnormal value and importing them into the behavior abnormal value calculation formula to calculate the behavior abnormal value, wherein the behavior abnormal value calculation formula is: , where Xw is the behavior outlier, a is the unit price weight, Dj is the overall unit price outlier, and Sl is the overall quantity outlier;

[0022] The calculated behavior anomaly value is obtained and compared with the set behavior anomaly threshold. If the obtained behavior anomaly value is greater than or equal to the set behavior anomaly threshold, the user's purchase behavior is evaluated as unsafe. If the obtained behavior anomaly value is less than the set behavior anomaly threshold, the user's purchase behavior is evaluated as safe.

[0023] As a preferred technical solution for the user behavior safety warning analysis method based on data feedback, the user behavior safety warning based on the user purchasing behavior security analysis results includes the following specific contents: obtaining unsafe information about user purchasing behavior, issuing safety warning prompts to the platform and user's mobile phone, and prompting users that their purchasing behavior is unsafe.

[0024] In the second aspect, the present application provides a user behavior safety warning analysis system based on data feedback, which is implemented based on the above-mentioned user behavior safety warning analysis method based on data feedback, and specifically includes a data acquisition module, a product purchase matching analysis module, a behavior history matching analysis module, a behavior safety analysis module and a behavior safety warning module; wherein, the data acquisition module is used to obtain consumption information of the user's historical consumption cycle, attribute data of purchased products and historical purchase data, and simultaneously obtain the user's current purchase data; the product purchase matching analysis module performs product purchase matching analysis based on the attribute data and historical purchase data of purchased products and the user's current purchase data; the behavior history matching analysis module is used to obtain consumption information of the user's historical consumption cycle and the user's current purchase data for behavior history matching analysis; the behavior safety analysis module performs user purchase behavior safety analysis based on the behavior history matching analysis results and product purchase matching analysis; the behavior safety warning module performs user behavior safety warning based on the user purchase behavior safety analysis results.

[0025] In a third aspect, the present application provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0026] The processor executes the above-mentioned user behavior safety early warning analysis method based on data feedback by calling the computer program stored in the memory.

[0027] In a fourth aspect, the present application provides a computer-readable storage medium storing instructions, which, when executed on a computer, enables the computer to execute the user behavior safety warning analysis method based on data feedback as described above.

[0028] Compared with the prior art, the beneficial effects of this application are:

[0029] This application obtains the consumption information of the user's historical consumption cycle and the user's current purchase data to perform behavior history matching analysis, performs product purchase matching analysis based on the attribute data and historical purchase data of the purchased goods and the user's current purchase data, performs user purchase behavior security analysis based on the behavior history matching analysis results and product purchase matching analysis, and performs accurate and safe analysis of current purchase behavior based on the similarity and fluctuation of historically purchased goods, thereby improving the security and accuracy of consumer behavior. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings;

[0031] Figure 1 This is a schematic diagram of the overall process of the user behavior security early warning analysis method based on data feedback in this application;

[0032] Figure 2 This is a flowchart of step 2 of the user behavior safety early warning analysis method based on data feedback in this application;

[0033] Figure 3 This is a flowchart of step 3 of the user behavior safety early warning analysis method based on data feedback in this application;

[0034] Figure 4 This is a schematic diagram of the overall framework of the user behavior safety early warning analysis system based on data feedback in this application. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present application and its application or use.

[0036] Example 1

[0037] In order to solve the technical problems raised in the background technology, the present application provides a preferred embodiment: Figure 1-Figure 3 As shown, the user behavior security early warning analysis method based on data feedback includes the following specific steps:

[0038] Step 1: Obtain the consumption information of the user's historical consumption cycle, the attribute data of the purchased goods and the historical purchase data, and obtain the user's current purchase data;

[0039] In this embodiment, the consumption information of the user's historical consumption cycle, the attribute data of the purchased goods and the historical purchase data are obtained, and the specific content of the user's current purchase data is obtained as follows:

[0040] Step 11: Obtain the types, quantities and attribute data of historical commodities purchased by the user on the platform, where the types refer to the classification data of commodities; the attribute data refers to the function data described in the product manual;

[0041] Step 12: Obtain the user's current purchase data, wherein the current purchase data includes the type, quantity and attribute data of the goods purchased this time;

[0042] Step 2: Obtain the consumption information of the user's historical consumption cycle and the user's current purchase data for behavior history matching analysis;

[0043] In this embodiment, obtaining the consumption information of the user's historical consumption cycle and the user's current purchase data for behavior history matching analysis includes the following specific steps:

[0044] Step 21: Obtain the type and quantity data of the goods purchased this time, and at the same time obtain the quantity data of the same type of historically purchased goods in the consumption information of the user's historical consumption cycle;

[0045] Step 22: Obtain the quantity data of the same type of historically purchased goods in the consumption information of the historical consumption cycle and the quantity data of the currently purchased goods, and import them into the purchase quantity abnormal value calculation formula to calculate the purchase quantity abnormal value, wherein the purchase quantity abnormal value calculation formula is: , where xz is the quantity data of the purchased goods this time, n is the number of historical consumption cycles, xi is the number of historical purchased goods in the i-th historical consumption cycle, and re is the historical consumption standard deviation. The calculation formula of the historical consumption standard deviation is: In this formula, the number of historical purchases of the same product and the fluctuation of the purchased product are used to evaluate whether the purchase quantity of the product is normal. For example, if a customer always buys eight pieces, but this time he buys eighty pieces at one time, this is definitely abnormal. On the other hand, if a customer's purchases fluctuate greatly, then buying eighty pieces at one time may be normal.

[0046] Step 23: Obtain the calculated purchase quantity abnormal value of all types of goods purchased by the user this time, add them all up and divide by the number of types to obtain the overall quantity abnormal value;

[0047] Step 3: Perform product purchase matching analysis based on the attribute data of the purchased products, historical purchase data, and the user's current purchase data;

[0048] In this embodiment, the commodity purchase matching analysis based on the attribute data of the purchased commodity, the historical purchase data, and the current purchase data of the user includes the following specific steps:

[0049] Step 31: Obtain the attribute data and unit price data of the purchased goods, obtain the attribute data and unit price data of the goods purchased by the user in history, import the attribute data of the purchased goods and the attribute data of the goods purchased by the user in history into the attribute similarity calculation formula to calculate the attribute similarity, wherein the attribute similarity calculation formula of the attribute of the cth purchased goods and the attribute of the mth purchased goods in the user's history is: , where Rc is the number of attributes of the cth purchased item, ur is the rth attribute data of the mth purchased item, and pr is the rth attribute data of the cth purchased item; in this formula, the similarity of purchased items is evaluated by analyzing the attributes of the purchased items;

[0050] Step 32: Obtain historically purchased products whose attribute similarity with the purchased product is greater than or equal to the set attribute similarity threshold, set them as similar purchased products, obtain the unit price data of the corresponding similar purchased products, and simultaneously obtain the unit price data of the currently purchased product, and import them into the purchase unit price abnormal value calculation formula to calculate the purchase unit price abnormal value, wherein the purchase unit price abnormal value calculation formula of the jth purchased product is: , where Bj is the number of similar purchased products to the jth purchased product, Kjb is the attribute similarity between the attributes of the jth purchased product and the bth similar purchased product, yj is the purchase unit price of the jth purchased product, and yjb is the purchase unit price of the bth similar purchased product to the jth purchased product;

[0051] Step 33: Obtain the purchase price abnormality values ​​of all commodities purchased this time, add them up and divide them by the number of categories to obtain the overall price abnormality value;

[0052] Step 4: Conduct a security analysis of user purchase behavior based on the behavior history matching analysis results and product purchase matching analysis;

[0053] In this embodiment, the user purchase behavior security analysis based on the behavior history matching analysis results and the commodity purchase matching analysis includes the following specific contents: Obtain the calculated overall quantity outlier value and the overall unit price outlier value and import them into the behavior outlier value calculation formula to calculate the behavior outlier value, wherein the behavior outlier value calculation formula is: , where Xw is the behavior outlier, a is the unit price weight, Dj is the overall unit price outlier, and Sl is the overall quantity outlier;

[0054] Obtain the calculated behavior anomaly value and compare it with the set behavior anomaly threshold. If the obtained behavior anomaly value is greater than or equal to the set behavior anomaly threshold, the user's purchase behavior is evaluated to be unsafe. If the obtained behavior anomaly value is less than the set behavior anomaly threshold, the user's purchase behavior is evaluated to be safe.

[0055] Step 5: Provide user behavior safety warning based on the user purchase behavior safety analysis results;

[0056] In this embodiment, the user behavior safety warning based on the user purchase behavior security analysis results includes the following specific contents: obtaining unsafe information about the user's purchase behavior, issuing a safety warning prompt to the platform and the user's mobile phone, and prompting the user that the purchase behavior is unsafe.

[0057] The preset parameters in this embodiment are obtained through experiments, which is a conventional technical means in the field. The preferred method of obtaining the preset parameters (such as abnormal behavior thresholds, unit price ratio weights, etc.) is: obtaining consumption information of several groups of users' historical consumption cycles, attribute data of purchased goods, and historical purchase data, and obtaining the user's current purchase data at the same time, calculating the abnormal behavior values ​​through these parameters, and obtaining the judgment results of whether the purchase behavior of these users is safe, importing the abnormal behavior values ​​and the judgment results of whether the purchase behavior of these users is safe into the fitting software, and outputting the values ​​of the preset parameters (such as abnormal behavior thresholds, unit price ratio weights, etc.) that meet the maximum judgment accuracy.

[0058] The advantages of this embodiment over the prior art are as follows: obtaining consumption information of the user's historical consumption cycle and the user's current purchase data for behavior history matching analysis, performing product purchase matching analysis based on the attribute data and historical purchase data of the purchased products and the user's current purchase data, performing user purchase behavior security analysis based on the behavior history matching analysis results and product purchase matching analysis, and performing accurate and safe analysis of current purchase behavior based on the similarity and fluctuation of historically purchased products, thereby improving the security and accuracy of consumer behavior.

[0059] Example 2

[0060] like Figure 4 As shown, the user behavior safety early warning analysis system based on data feedback is implemented based on the above-mentioned user behavior safety early warning analysis method based on data feedback, and specifically includes a data acquisition module, a product purchase matching analysis module, a behavior history matching analysis module, a behavior safety analysis module and a behavior safety early warning module; wherein the data acquisition module is used to obtain the consumption information of the user's historical consumption cycle, the attribute data and historical purchase data of the purchased products, and the user's current purchase data at the same time; the product purchase matching analysis module performs product purchase matching analysis based on the attribute data and historical purchase data of the purchased products and the user's current purchase data; the behavior history matching analysis module is used to obtain the consumption information of the user's historical consumption cycle and the user's current purchase data for behavior history matching analysis; the behavior safety analysis module performs user purchase behavior safety analysis based on the behavior history matching analysis results and product purchase matching analysis; the behavior safety early warning module performs user behavior safety early warning based on the user purchase behavior safety analysis results; it should be noted that Figure 4 The arrow in the figure indicates the data transmission direction of the system. The specific steps of the system module in this embodiment have been fully mentioned in the above method embodiment, so they will not be repeated here in detail.

[0061] Example 3

[0062] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0063] The processor executes the above-mentioned user behavior safety early warning analysis method based on data feedback by calling the computer program stored in the memory.

[0064] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors and one or more memories, wherein the memory stores at least one computer program, and the computer program is loaded and executed by the processor to implement the user behavior safety early warning analysis method based on data feedback provided by the above method embodiment. The electronic device may also include other components for realizing the functions of the device, for example, the electronic device may also have components such as a wired or wireless network interface and an input and output interface to input and output data. This embodiment will not be described in detail here.

[0065] Example 4

[0066] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0067] When the computer program runs on a computer device, the computer device executes the above-mentioned user behavior safety early warning analysis method based on data feedback.

[0068] For example, the computer readable storage medium can be a read-only memory, a random access memory, a read-only CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0069] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0070] The terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or also includes elements inherent to such process, method, article, or apparatus.

[0071] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of application involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned application concept. For example, the above features are replaced with (but not limited to) technical features with similar functions applied in the present application.

Claims

1. A user behavior safety early warning analysis method based on data feedback, characterized in that: It includes the following specific steps: Step 1: Obtain the consumption information of the user's historical consumption cycle, the attribute data of the purchased goods and the historical purchase data, and obtain the user's current purchase data; Step 2: Obtain the consumption information of the user's historical consumption cycle and the user's current purchase data for behavior history matching analysis; Step 3: Perform product purchase matching analysis based on the attribute data of the purchased products, historical purchase data, and the user's current purchase data; Step 4: Conduct a security analysis of user purchase behavior based on the behavior history matching analysis results and product purchase matching analysis; Step 5: Perform a user behavior security warning based on the user purchase behavior security analysis result; the acquisition of the user's consumption information of the historical consumption cycle and the user's current purchase data for behavior history matching analysis includes the following specific steps: Obtain the type and quantity data of the goods purchased this time, and at the same time obtain the quantity data of the same type of historically purchased goods in the consumption information of the user's historical consumption cycle; The quantity data of historically purchased commodities of the same type in the consumption information of the historical consumption cycle and the quantity data of the commodities purchased this time are imported into the purchase quantity abnormal value calculation formula to calculate the purchase quantity abnormal value, wherein the purchase quantity abnormal value calculation formula is: , where xz is the quantity data of the purchased goods this time, n is the number of historical consumption cycles, xi is the number of historical purchased goods in the i-th historical consumption cycle, and re is the historical consumption standard deviation. The calculation formula of the historical consumption standard deviation is: ; Obtain the calculated purchase quantity abnormal values ​​of all types of goods purchased by the user this time, add them all up and divide them by the number of types to obtain the overall quantity abnormal value; the commodity purchase matching analysis based on the attribute data and historical purchase data of the purchased goods and the user's current purchase data includes the following specific steps: Obtain the attribute data and unit price data of the purchased goods, obtain the attribute data and unit price data of the user's historical purchased goods, import the attribute data of the purchased goods and the attribute data of the user's historical purchased goods into the attribute similarity calculation formula to calculate the attribute similarity, where the attribute similarity calculation formula between the attribute of the cth purchased goods and the attribute of the mth purchased goods in the user's history is: , where Rc is the number of attributes of the cth purchased product, ur is the rth attribute data of the mth purchased product, and pr is the rth attribute data of the cth purchased product; Obtain the historically purchased products whose attribute similarity with the purchased products is greater than or equal to the set attribute similarity threshold, set them as similar purchased products, obtain the corresponding unit price data of similar purchased products, and at the same time obtain the unit price data of the current purchased products, import them into the purchase unit price abnormal value calculation formula to calculate the purchase unit price abnormal value, where the purchase unit price abnormal value calculation formula of the jth purchased product is: , where Bj is the number of similar purchased products to the jth purchased product, Kjb is the attribute similarity between the attributes of the jth purchased product and the bth similar purchased product, yj is the purchase unit price of the jth purchased product, and yjb is the purchase unit price of the bth similar purchased product to the jth purchased product; Obtain the purchase price anomaly values ​​of all commodities purchased this time, add them up and divide them by the number of categories to obtain the overall price anomaly value; the user purchase behavior security analysis based on the behavior history matching analysis results and the commodity purchase matching analysis includes the following specific contents: Obtain the calculated overall quantity anomaly value and overall price anomaly value and import them into the behavior anomaly value calculation formula to calculate the behavior anomaly value, wherein the behavior anomaly value calculation formula is: , where Xw is the behavior outlier, a is the unit price weight, Dj is the overall unit price outlier, and Sl is the overall quantity outlier; The calculated behavior anomaly value is obtained and compared with the set behavior anomaly threshold. If the obtained behavior anomaly value is greater than or equal to the set behavior anomaly threshold, the user's purchase behavior is evaluated as unsafe. If the obtained behavior anomaly value is less than the set behavior anomaly threshold, the user's purchase behavior is evaluated as safe.

2. The user behavior safety early warning analysis method based on data feedback according to claim 1, characterized in that: The user behavior safety warning based on the user purchase behavior safety analysis result includes the following specific contents: obtaining the unsafe information of the user's purchase behavior, issuing a safety warning prompt to the platform and the user's mobile phone, and prompting the user that the purchase behavior is unsafe.

3. The user behavior safety early warning analysis method based on data feedback according to claim 2, characterized in that: The specific contents of obtaining the consumption information of the user's historical consumption cycle, the attribute data of the purchased goods and the historical purchase data, and obtaining the user's current purchase data are as follows: Obtain the types, quantities, and attribute data of historical goods purchased by users on the platform. The types here refer to the classification data of goods; The user's current purchase data is obtained, wherein the current purchase data includes the type, quantity and attribute data of the purchased goods.

4. A user behavior safety early warning analysis system based on data feedback, which is implemented based on the user behavior safety early warning analysis method based on data feedback as claimed in any one of claims 1 to 3, characterized in that: It specifically includes a data acquisition module, a product purchase matching analysis module, a behavior history matching analysis module, a behavior safety analysis module and a behavior safety warning module; wherein the data acquisition module is used to obtain consumption information of the user's historical consumption cycle, attribute data of purchased products and historical purchase data, and simultaneously obtain the user's current purchase data; the product purchase matching analysis module performs product purchase matching analysis based on the attribute data and historical purchase data of purchased products and the user's current purchase data; the behavior history matching analysis module is used to obtain consumption information of the user's historical consumption cycle and the user's current purchase data to perform behavior history matching analysis; the behavior safety analysis module performs user purchase behavior safety analysis based on the behavior history matching analysis results and product purchase matching analysis; the behavior safety warning module performs user behavior safety warning based on the user purchase behavior safety analysis results.

5. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; It is characterized in that the processor executes the user behavior safety early warning analysis method based on data feedback as described in any one of claims 1-3 by calling the computer program stored in the memory.

6. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer executes the user behavior safety early warning analysis method based on data feedback as described in any one of claims 1-3.

Images