Intelligent advertising precision push method and system based on big data

Abstract

The present invention proposes an intelligent advertising precision push method and system based on big data, and the method comprises: creating a walking map, wherein the annotation information of the walking map comprises the stay time, the walking route belt, the target building, the building information and the scope of the affected area; obtaining the consumption heat information corresponding to the target building based on the big data, and estimating the environmental consumption level corresponding to the target person in combination with the consumption heat information and the stay time; calculating the adhesion of the target person to the target building, screening out the key buildings with adhesion greater than the preset level, adjusting the environmental consumption level according to the consumption level corresponding to the key buildings, and obtaining the estimated consumption level of the target person; obtaining the network information that the target person is browsing, and in the case that the network information contains product keywords, searching for the corresponding advertising products in combination with the product keywords and the estimated consumption level for push, so as to further improve the degree of adaptation between the pushed products and the target person.

Description

Technical Field

[0001] The present invention relates to the field of big data information processing, and in particular to a method and system for intelligent advertising precision push based on big data. Background Art

[0002] The current method of advertising push is basically to obtain product information that consumers have searched for in shopping software, and push the product information intact to platforms such as WeChat Moments for display. However, the system is unable to push suitable products to consumers based on their preferences and consumption levels, resulting in a relatively single advertising push method. Consumers are unable to obtain information about products that are more suitable for them, and the purpose of accurate advertising push cannot be achieved. Summary of the invention

[0003] The embodiment of the present invention provides an intelligent advertising precision push method and system based on big data to solve the problems existing in related technologies. The technical solution is as follows:

[0004] In a first aspect, an embodiment of the present invention provides an intelligent advertising precision push method based on big data, comprising:

[0005] Obtain the walking data of the target person, and create a walking map based on the walking data. The annotation information of the walking map includes the stay time, walking route, target building, building information and the scope of the affected area;

[0006] Based on big data, the consumption popularity information corresponding to the target building is obtained, and the environmental consumption level corresponding to the target person is estimated by combining the consumption popularity information and the length of stay;

[0007] Calculate the target person's stickiness to the target building, select key buildings with stickiness greater than the preset level, adjust the environmental consumption level according to the consumption level corresponding to the key building, and obtain the target person's estimated consumption level;

[0008] Obtain the network information that the target person is browsing, determine whether there are product keywords in the network information, and if there are product keywords in the network information, find the corresponding advertising products based on the product keywords and the expected consumption level and push them.

[0009] In one implementation, searching for corresponding advertisement products based on product keywords and estimated consumption levels for push notification includes:

[0010] Generate link keywords associated with the advertised product, highlight the link keywords, replace the product keywords in the network information with the link keywords for display, and when the link keyword is clicked, directly jump to the advertising page corresponding to the advertised product according to the specified link.

[0011] In one implementation, obtaining walking data of a target person includes:

[0012] Obtaining GPS data of the mobile terminal, the GPS data includes positioning coordinates and moving speed;

[0013] According to the GPS data, the target coordinates with a moving speed less than a preset value are filtered out to obtain the walking data.

[0014] In one embodiment, creating a walking map based on walking data includes:

[0015] Get the target building within the specified range of the target coordinates and the building coordinates within the range of the target building;

[0016] Integrate the target coordinates to obtain a walking route belt, and map the walking route belt and the building coordinates to a pre-created map layer to obtain a base layer;

[0017] The midpoint coordinates of each target building are calculated according to the building coordinates, and the building range of each target building in the base layer is modified to a specified geometric image with the midpoint coordinates as the origin, and the specified geometric image corresponding to each target building is enlarged by a specified ratio to obtain the influence area range corresponding to each target building;

[0018] The affected area is mapped onto the base layer after transparency is set to obtain a walking map.

[0019] In one embodiment, the adhesion includes a first adhesion, and a calculation method of the first adhesion includes:

[0020] Determine the building type of the target building based on the building information, which includes office buildings, shopping malls, and residential buildings;

[0021] The target person's first degree of attachment to the target building is determined by combining the length of stay and the building type.

[0022] In one embodiment, the adhesion includes a second adhesion, and the calculation method of the second adhesion includes:

[0023] The intersection degree between the walking route belt and the influence range area is calculated based on the walking map to obtain the second adhesion degree.

[0024] In one embodiment, it further includes:

[0025] Obtain the target person's online browsing product data on the specified application;

[0026] Based on big data, offline promotion product information of stores in key buildings is obtained. Key buildings are target buildings with a stickiness greater than a preset level.

[0027] Compare online browsing product data and offline promotion product information to determine whether there are products of interest to the target person in key buildings and the number of products of interest to the target person;

[0028] Calculate the third degree of stickiness of the target person to the key building based on the number of products they pay attention to;

[0029] When the third stickiness is higher than a preset level, the estimated consumption level is optimized according to the sales price of the product of interest.

[0030] In a second aspect, an embodiment of the present invention provides an intelligent advertising precision push system based on big data, which executes the intelligent advertising precision push method based on big data as described above.

[0031] In a third aspect, an embodiment of the present invention provides an electronic device, the device comprising: a memory and a processor. The memory and the processor communicate with each other through an internal connection path, the memory is used to store instructions, the processor is used to execute the instructions stored in the memory, and when the processor executes the instructions stored in the memory, the processor executes the method in any one of the above-mentioned embodiments.

[0032] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a computer, the method in any one of the above-mentioned embodiments is executed.

[0033] The advantages or beneficial effects of the above technical solution include at least:

[0034] The present invention analyzes the environmental consumption level of the external environment that the target person is exposed to through walking data, and judges the adhesion between the target person and each building based on the walking map. A high adhesion level represents a higher possibility that the target person consumes in the building. The environmental consumption level is readjusted according to the consumption level corresponding to the building with a high adhesion level to obtain an estimated consumption level corresponding to the target person. According to the estimated consumption level, commodities suitable for the target person's consumption habits are pushed to the target person, thereby improving the degree of adaptation between the pushed commodities and the target person.

[0035] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the multiple drawings represent the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings only depict some embodiments disclosed in the present invention and should not be regarded as limiting the scope of the present invention.

[0037] Figure 1 It is a flow chart of the intelligent advertising precision push method based on big data of the present invention;

[0038] Figure 2 A schematic diagram of the intersection of the walking route belt and the affected area range of the present invention;

[0039] Figure 3 It is a schematic diagram showing the link keywords of the present invention. DETAILED DESCRIPTION

[0040] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0041] The current method of advertising push is basically to obtain product information that consumers have searched for in shopping software, and push the product information intact to platforms such as WeChat Moments for display. However, the system is unable to push suitable products to consumers based on their preferences and consumption levels, resulting in a relatively single advertising push method. Consumers are unable to obtain information about products that are more suitable for them, and the purpose of accurate advertising push cannot be achieved.

[0042] In order to solve the above problems, this embodiment provides an intelligent advertising precision push method based on big data, which can push products that are more in line with their consumption habits and consumption levels to consumers, improve the accuracy of advertising push, and help consumers find products that are more suitable for them.

[0043] like Figure 1 As shown, the intelligent advertising precision push method based on big data specifically includes the following steps:

[0044] Step S1: Acquire the walking data of the target person, and create a walking map based on the walking data. The annotation information of the walking map includes the stay time, walking route, target building, building information and the scope of the affected area.

[0045] With the permission of the target person, the GPS data of the target person's mobile terminal is obtained, and the target person's mobile terminal can collect his GPS data when he is out. It should be noted that the collected GPS data is only used for advertising push and will not be leaked to the outside to ensure information security.

[0046] The GPS data includes the positioning coordinates, time and moving speed of the target person when the target person moves along the route; if the moving speed is less than a preset value, the target person can be considered to be walking or running, and if the moving speed is greater than the preset value, the target person can be considered to be driving; the preset value can be set according to the normal walking speed of the target person's age group. This embodiment selects the positioning coordinates with a moving speed less than the preset value based on the GPS data and marks them as target coordinates, collects all target coordinates and the time when the target person is at the target coordinates, and obtains walking data, so as to facilitate the subsequent adhesion analysis of the places where the target person walks.

[0047] After obtaining the walking data, create a walking map based on the walking data. The walking map creation method is as follows:

[0048] Step S11: Pre-create a new map layer

[0049] Basic road information, including buildings, roads, etc., is loaded into this map layer, as well as the building information of each building, such as building name, building coordinates, building area, building range, building floors, store conditions in the building, etc.

[0050] Step S12: Mark the target building in the map layer

[0051] Each target coordinate in the walking data is expanded outward by a specified distance to form a specified range corresponding to each target coordinate, and the specified range is overlapped and compared with the building position in the map layer, and the building within the specified range of the target coordinate is marked as the target building. If the specified range of the target coordinate partially overlaps with the building range, the building can also be marked as the target building; this is equivalent to determining the building passed by the target person in the walking map; obtaining the building coordinates of the target building, and highlighting the target building in the walking map according to the building coordinates to distinguish it from other buildings that the target person has not passed.

[0052] Step S13: Mark the walking route belt to obtain the basic layer

[0053] All target coordinates corresponding to continuous time are connected to obtain the walking route corresponding to one trip of the target person. After smoothing the walking route corresponding to each trip, the walking routes corresponding to multiple trips of the target person are collected to generate a walking route belt. The direction of the walking route belt corresponds to the direction of the walking route. The width of the walking route belt is determined according to the degree of repetition of multiple walking routes. The more times a walking route is repeated, the larger the width of the walking route belt is, and the fewer times a walking route is repeated, the smaller the width of the walking route belt is. If a walking route only appears once, the width of the walking route belt is reduced to a line of default width.

[0054] The walking route belt can show the routes that the target person often passes or stays. The walking route belt is mapped to the map layer, and the walking route belt can also be highlighted to obtain the basic layer. The walking route can also be set to a certain degree of transparency to distinguish it from the target building in the map layer. At the same time, the time information corresponding to each target coordinate is marked in the basic layer to indicate the target person's stay time at each location.

[0055] Step S14: Mark the impact area of ​​each target building in the base layer

[0056] The building coordinates, building area and other information corresponding to each target building are loaded in the base layer. The midpoint coordinates of each target building are calculated according to the building coordinates. The building range of each target building in the base layer is modified to a specified geometric image with the midpoint coordinates as the origin. The specified geometric image can be a circle or a regular hexagon. The specified geometric image corresponding to each target building is enlarged. The enlargement method can enlarge all target buildings in equal proportion according to a preset fixed ratio. In this embodiment, the store information corresponding to each target building is collected in advance, that is, the store information of the store address in the target building is obtained. The number of stores in the target building is counted according to the store information corresponding to the target building. The more the number of stores, the larger the scale of the target building, and the greater the corresponding external influence. Therefore, the enlargement ratio of the specified geometric image corresponding to the target building is determined according to the number of stores in the target building. Among them, the enlargement ratio corresponding to different numbers of stores can be set in advance.

[0057] The specified geometric image corresponding to each target building in the walking map is enlarged according to the specified enlargement ratio to obtain the influence area range corresponding to each target building.

[0058] The influence area corresponding to each target building is mapped onto the base layer after a certain transparency setting, so as to obtain a walking map, in which information such as the stay time, walking route, target building, building information and influence area are marked.

[0059] Step S2: Obtain consumption heat information corresponding to the target building based on big data, and estimate the environmental consumption level corresponding to the target person based on the consumption heat information and the length of stay.

[0060] The consumption popularity information includes but is not limited to the flow of people data and the per capita consumption data of the stores. The flow of people data corresponding to each target building is counted based on big data. The target building with a large flow of people has a relatively high consumption popularity. At the same time, the per capita consumption data corresponding to each store in the target building with a flow of people greater than a preset flow value is obtained, and the per capita consumption data corresponding to all stores in the target building are averaged. The calculated per capita consumption average is converted to a level, and the average consumption level corresponding to the stores in the target building can be obtained.

[0061] Count the time the target person stays in the target building, mark the target building where the stay time is greater than the preset time as a stay building, obtain the consumption heat information of the stay building, calculate the average consumption level corresponding to the stores in the stay building based on the consumption heat information, and use the average consumption level as the environmental consumption level corresponding to the target person, which represents the consumption level corresponding to the environment where the target person stays for a long time.

[0062] Step S3: Calculate the target person's adhesion to the target building, select key buildings with adhesion greater than a preset level, adjust the environmental consumption level according to the consumption level corresponding to the key buildings, and obtain the target person's estimated consumption level.

[0063] Since the environmental consumption level corresponding to the target person's environment does not directly represent the target person's own consumption level, it is necessary to further adjust the target person's own consumption level in combination with the target person's adhesion to the target building.

[0064] Stickiness represents the possibility of the target person consuming in a certain building. The higher the stickiness, the higher the possibility of the target person consuming in the building.

[0065] In this embodiment, the first viscosity needs to be calculated. The calculation method of the first viscosity includes:

[0066] Step S31: determining the building type of the target building according to the building information, the building types including office buildings, shopping malls and residential buildings;

[0067] The building information corresponding to each target building is extracted from the walking map, the building type corresponding to each target building is determined according to the building information, and the type property corresponding to the target building is determined according to the building type.

[0068] Step S32: Determine the first adhesion of the target person to the target building in combination with the stay time and the building type.

[0069] Assuming that the building type is an office building, if the target person stays in this type of building, it can be regarded as the target person working in the building for a long time. At this time, it can be considered that the possibility of the target person consuming in the target building is low, and the corresponding first adhesion is low; assuming that the building type is a residence, the first adhesion can be determined to be low; assuming that the building type is a shopping mall, the higher the possibility of the target person consuming in the building, the higher the corresponding first adhesion; assuming that the building type is a mixed type of office buildings and shopping malls, the first adhesion can be determined to be medium.

[0070] In some embodiments, the adhesion includes a second adhesion, and a method for calculating the second adhesion includes:

[0071] Step S33: calculating the degree of intersection between the walking route belt and the influence range area based on the walking map to obtain a second adhesion degree.

[0072] The walking map shows the walking route and the influence area of ​​each building, such as Figure 2 As shown, Figure 2 The pattern indicated by each regular hexagon in the figure represents the influence area of ​​each target building, the curved area represents the walking route belt, and the oblique line in the influence area represents the intersection area between the influence area and the walking route belt. Assuming that there is an intersection between the walking route belt and the influence area of ​​a certain building and the degree of intersection is high, it means that the target person often passes by the target building or often enters the target building, and the possibility of the target person consuming in the building is relatively high; if the degree of intersection between the walking route belt and the influence area of ​​a certain building is low, it means that the target person does not often appear in the influence area of ​​the target building.

[0073] The degree of intersection can be determined by calculating the intersection area between the walking route belt and the influence range area. The larger the intersection area, the higher the degree of intersection, and the second adhesion is a high level; the smaller the intersection area, the lower the degree of intersection, and the second adhesion corresponds to a low level. The conversion relationship between the intersection area and the second adhesion level can be set in advance.

[0074] The calculation of the first viscosity and the second viscosity can be used simultaneously for adjusting the environmental consumption level, and the environmental consumption level can also be adjusted based on the first viscosity or the second viscosity alone.

[0075] Assuming that the first viscosity and the second viscosity are used at the same time, the method for adjusting the environmental consumption level is:

[0076] The key buildings whose first adhesion is greater than the preset level and the key buildings whose second adhesion is greater than the preset level are screened out. In this embodiment, the target buildings with medium and high adhesion are marked as key buildings, and the per capita consumption data of all stores in each key building are obtained, and the average per capita consumption value of all stores in each key building is calculated. Then, the average per capita consumption values ​​corresponding to all key buildings are averaged to obtain the average consumption level. The consumption level is adjusted according to the average consumption level on the basis of the environmental consumption level to obtain the expected consumption level of the target person.

[0077] Assuming that the environmental consumption level is low and the average consumption level is high, the target person's estimated consumption level can be adjusted to a medium level.

[0078] It should be noted that the environmental consumption level and the average consumption level can be divided into multiple levels according to preset rules, and the above-mentioned high, medium and low levels are only used for illustration.

[0079] In some implementations, a third degree of stickiness may be introduced to further optimize the estimated consumption level of the target person. The specific method is as follows:

[0080] Step S34: With the authorization of the target person, obtain the online product browsing data of the target person on a designated application. The designated application may be the online product browsing data of a shopping platform such as Taobao and JD.com.

[0081] Step S35: Based on big data, offline promotion product information of stores in key buildings is obtained. Key buildings are target buildings with a stickiness greater than a preset level; their offline promotion product information includes product names, product sales prices, etc.

[0082] Step S36: Compare the online browsed product data and the offline promoted product information. If the online browsed product is consistent with the offline promoted product, it can be considered that there is a product that the target person is interested in in the key building. The product is marked as a product of interest, the product information of the product of interest is obtained, and the number of products of interest is counted.

[0083] Step S37: Calculate the third adhesion of the target person to the key building based on the number of concerned products; the more concerned products, the higher the possibility that the target person consumes in the key building, and the higher the third adhesion; conversely, the fewer concerned products, the lower the third adhesion. The relationship between the number of concerned products and the level of the third adhesion can be set in advance.

[0084] Step S38: Filter out key buildings whose third adhesion is higher than the preset level, and optimize the expected consumption level according to the sales price of the concerned goods in the key buildings, that is, compare the sales price of the concerned goods with the expected consumption level; if the sales price of the concerned goods is higher than the expected consumption level, then increase the expected consumption level to the consumption level corresponding to the sales price of the concerned goods.

[0085] At this time, the system can search for multiple products of the same type as the product of interest, select products that match the expected consumption level from the multiple products, and push them to the target person's mobile terminal or the designated application platform of the mobile terminal for display. Or when the user actively searches for other products in the shopping software of the mobile terminal, the system can search for products with prices corresponding to the expected consumption level according to the type of the searched product and push them to the target person for display.

[0086] This embodiment can further improve the flexibility of advertisement push, as follows:

[0087] Step S4: With the authorization of the target person, obtain the network information that the target person is browsing, which can be novel text, WeChat chat text, or WeChat Moments text, etc.; perform semantic analysis on the network information to determine whether there are product keywords in the network information. If there are product keywords in the network information, find the corresponding advertising products in combination with the product keywords and the expected consumption level for push. Assuming that the novel text contains the product keyword "TV", and the amount corresponding to the expected consumption level obtained through the above steps S1 to S3 is 10,000 yuan, the system searches for TV products that match the expected consumption level, that is, searches for TV products of about 10,000 yuan, and pushes the corresponding advertising consultation to the target person's mobile terminal or the designated application software of the mobile terminal for display.

[0088] In some embodiments, link keywords associated with the advertised product may also be generated, and the link keywords may be highlighted. Figure 3 As shown, Figure 3 What is shown is the online text that the target person is browsing, and the text contains the product keyword "TV". At this time, the product keyword in the online information is replaced with the link keyword for display. At this time, when the consumer is reading the online information normally, he can choose to click on the link keyword displayed in the online information. When clicking on the link keyword, he will directly jump to the advertising page corresponding to the advertised product according to the designated link bound to the link keyword.

[0089] In some embodiments, a system for intelligent precise advertising push based on big data is also provided, which executes the method for intelligent precise advertising push based on big data as described in Example 1.

[0090] The functions of each module in the system of the embodiment of the present invention can be found in the corresponding description of the above method, which will not be repeated here.

[0091] In some embodiments, an electronic device is also provided, the electronic device comprising: a memory and a processor, wherein the memory stores a computer program that can be run on the processor. When the processor executes the computer program, the intelligent advertising precision push method based on big data in the above embodiment is implemented. The number of the memory and the processor can be one or more.

[0092] The electronic device also includes:

[0093] Communication interface, used to communicate with external devices and perform data exchange transmission.

[0094] If the memory, processor and communication interface are implemented independently, the memory, processor and communication interface can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc.

[0095] Optionally, in a specific implementation, if the memory, processor, and communication interface are integrated on a chip, the memory, processor, and communication interface can communicate with each other through an internal interface.

[0096] An embodiment of the present invention provides a computer-readable storage medium storing a computer program, which implements the method provided in the embodiment of the present invention when executed by a processor.

[0097] An embodiment of the present invention further provides a chip, which includes a processor for calling and executing instructions stored in the memory from the memory, so that a communication device equipped with the chip executes the method provided by the embodiment of the present invention.

[0098] An embodiment of the present invention also provides a chip, including: an input interface, an output interface, a processor and a memory, wherein the input interface, the output interface, the processor and the memory are connected via an internal connection path, and the processor is used to execute the code in the memory. When the code is executed, the processor is used to execute the method provided by the embodiment of the invention.

[0099] It should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. It is worth noting that the processor may be a processor supporting the advanced RISC machines (ARM) architecture.

[0100] Further, optionally, the above-mentioned memory may include a read-only memory and a random access memory, and may also include a non-volatile random access memory. The memory may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may include a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may include a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available. For example, static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM) and direct rambus RAM (DR RAM).

[0101] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it 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. When the computer program instructions are loaded and executed on a computer, the process or function according to the present invention 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. The 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. The actions of obtaining all data (walking-related data, GPS data, consumption heat information, browsed network information, product information, etc.) in this application are all carried out under the premise of complying with the corresponding data protection laws and policies of the country of residence, and with the authorization given by the owner of the corresponding device.

[0102] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0103] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0104] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of various changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An intelligent advertising precision push method based on big data, characterized in that: include: Acquire walking data of a target person, and create a walking map according to the walking data, wherein the annotation information of the walking map includes the stay time, walking route, target building, building information, and the scope of the affected area; wherein acquiring the walking data of the target person includes: acquiring GPS data of a mobile terminal, wherein the GPS data includes positioning coordinates and moving speed; and filtering out target coordinates whose moving speed is less than a preset value according to the GPS data to obtain the walking data; Wherein, creating a walking map according to the walking data includes: obtaining the target building located within the specified range of the target coordinates and the building coordinates within the range of the target building; integrating the target coordinates to obtain the walking route belt, mapping the walking route belt and the building coordinates to a pre-created map layer to obtain a basic layer; calculating the midpoint coordinates of each of the target buildings according to the building coordinates, modifying the building range of each of the target buildings in the basic layer to a specified geometric image with the midpoint coordinates as the origin, and respectively amplifying the specified geometric image corresponding to each of the target buildings by a specified ratio to obtain the influence area range corresponding to each of the target buildings; mapping the influence area range to the basic layer after setting the transparency to obtain the walking map; Acquire the consumption popularity information corresponding to the target building based on big data, and estimate the environmental consumption level corresponding to the target person in combination with the consumption popularity information and the stay time; Calculating the adhesion of the target person to the target building, wherein the adhesion includes a first adhesion, and a calculation method of the first adhesion includes: determining the building type of the target building according to the building information, wherein the building type includes office buildings, shopping malls, and houses; determining the first adhesion of the target person to the target building in combination with the stay time and the building type; screening out key buildings whose adhesion is greater than a preset level, adjusting the environmental consumption level according to the consumption level corresponding to the key buildings, and obtaining the estimated consumption level of the target person; The network information that the target person is browsing is obtained, and it is determined whether there is a product keyword in the network information. If the product keyword exists in the network information, the corresponding advertisement product is searched and pushed based on the product keyword and the expected consumption level.

2. The intelligent advertising precision push method based on big data according to claim 1 is characterized in that: The searching for corresponding advertisement products and pushing them in combination with the product keywords and the estimated consumption level includes: Generate a link keyword associated with the advertised product, highlight the link keyword, replace the product keyword in the network information with the link keyword for display, and when the link keyword is clicked, directly jump to the advertising page corresponding to the advertised product according to the specified link.

3. The intelligent advertising precision push method based on big data according to claim 1 is characterized in that: The adhesion includes a second adhesion, and a calculation method of the second adhesion includes: The second adhesion degree is obtained by calculating the degree of intersection between the walking route belt and the range of the affected area based on the walking map.

4. An intelligent advertising precision push system based on big data, characterized in that: Execute the intelligent advertising precision push method based on big data as described in any one of claims 1 to 3.

5. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores instructions, and the instructions are loaded and executed by the processor to implement the intelligent advertising precision push method based on big data as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the intelligent advertising precision push method based on big data as described in any one of claims 1 to 3 is implemented.

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