Digital barrier gate advertisement putting position planning system and method based on big data

Abstract

The invention relates to the technical field of advertisement putting planning, in particular to a digital barrier gate advertisement putting position planning system and method based on big data. Target-grade advertisement putting areas are accurately screened out according to customer requirements, corresponding positions are obtained, effective advertisement putting area searching processing is carried out based on customer storefront operation position data and target-grade advertisement putting area position data, and effective advertisement putting area position data are generated; and performing crowd feature data matching processing on the collected client advertisement putting target crowd feature data and the advertisement effective putting area position data to generate target advertisement putting area position data, pushing the target advertisement putting area position data to a digital barrier gate advertisement putting position planning platform, and meanwhile, ending the digital barrier gate advertisement putting position planning operation. Accurate matching of advertisement putting areas and scientific planning of digital barrier gate advertisement putting positions are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of advertising placement planning, and specifically to a digital gate advertising placement location planning system and method based on big data. Background Art

[0002] There are no advertising media of any form around digital gate advertisements. While ensuring the advertising placement effect, it also achieves the effect of reducing advertising placement costs. In the existing digital gate advertising placement location planning technology, factors such as the flow of people, regional location, and the matching degree between the advertising target audience and the characteristics of the regional population are often not comprehensively considered, resulting in poor advertising placement effects and insufficient intelligence in the advertising placement location planning process.

[0003] The Chinese patent application with the publication number CN118246987A introduces an advertising placement location management system based on placement effect analysis. The target population that has not watched the placed advertisement is screened out by the audience population screening module according to the monitoring information. Then, the population behavior collection module obtains the trajectory information of the target population around the office building according to the monitoring information. Finally, by obtaining the new hobbies of the target population that has not watched the placed advertisement, verification advertisements of interest are placed for the target population and the advertising placement effect is analyzed, and the advertising placement location is adjusted according to the advertising placement effect, further improving the advertising placement effect. However, the influence of the flow of people and the advertising placement area location is not considered during the advertising placement process, which easily leads to a large deviation between the advertising placement effect and the actual effect and cannot meet the expectations of customers. Summary of the Invention

[0004] (I) Technical Problems to be Solved

[0005] To solve the deficiencies in the background art, the present invention provides a digital gate advertising placement location planning system and method based on big data, realizing the precise matching of the advertising placement area and the scientific planning of the digital gate advertising placement location.

[0006] (II) Technical Solutions

[0007] A digital gate advertising placement location planning method based on big data includes the following steps:

[0008] S1. Collect the digital gate unit cycle pedestrian flow data;

[0009] S2. Classify each advertising placement area according to the digital gate unit cycle pedestrian flow data to obtain the advertising placement area classification data;

[0010] S3. Screen out the target level advertising placement areas from the advertising placement area classification data according to customer requirements, and collect the target level advertising placement area location data;

[0011] S4. Collect the data of the business location of the customer's storefront, and perform search processing on the effective advertising delivery area based on the target-level advertising delivery area location data and the business location data of the customer's storefront to generate the effective advertising delivery area location data.

[0012] S5. Collect the characteristic data of the target population for the customer to place advertisements.

[0013] S6. Perform matching processing on the characteristic data of the target population for the customer to place advertisements and the effective advertising delivery area location data to generate the target advertising delivery area location data.

[0014] S7. Push the target advertising delivery area location data to the digital gate advertising delivery location planning platform, and end this digital gate advertising delivery location planning operation.

[0015] According to the collected digital gate unit-period pedestrian flow data, the present invention divides each advertising delivery area into levels, accurately screens out the target-level advertising delivery areas according to customer requirements and obtains the corresponding locations, performs search processing on the effective advertising delivery area based on the business location data of the customer's storefront and the target-level advertising delivery area location data to generate the effective advertising delivery area location data, performs matching processing on the collected characteristic data of the target population for the customer to place advertisements and the effective advertising delivery area location data to generate the target advertising delivery area location data, and pushes it to the digital gate advertising delivery location planning platform, and at the same time ends this digital gate advertising delivery location planning operation, realizing the accurate matching of the advertising delivery area and the scientific planning of the digital gate advertising delivery location.

[0016] Preferably, the specific steps for collecting the digital gate unit-period pedestrian flow data are as follows:

[0017] S11. Set the digital gate pedestrian flow measurement period, install infrared sensors near the digital gates in each advertising delivery area, and collect the pedestrian flow data of the digital gates in each advertising delivery area during the previous digital gate pedestrian flow measurement period through the infrared sensors to obtain the digital gate unit-period pedestrian flow data set A = {a 1 , a 2 , …, a i , …, a k}, where a i represents the pedestrian flow data of the digital gate in the i-th advertising delivery area, and k represents the total number of advertising delivery areas; the unit of the pedestrian flow data is person-times, and when collecting the pedestrian flow data of the digital gate, it is set that passing through the digital gate is 1 person-time and passing by the digital gate is 0.5 person-time.

[0018] Preferably, the specific steps for classifying each advertising area according to the digital barrier unit-period pedestrian flow data to obtain the advertising area classification data are as follows:

[0019] S21. Establish a digital barrier pedestrian flow scale level data set Among them, represents that the digital barrier pedestrian flow scale level data corresponding to the i-th pedestrian flow data interval [c i1 , c i2 is b i , c i1 and c i2 respectively represent the minimum and maximum pedestrian flows in the i-th pedestrian flow data interval [c i1 , c i2 , and l represents the total number of digital barrier pedestrian flow scale levels;

[0020] S22. Through a two-way search algorithm, numerically match each digital barrier unit-period pedestrian flow data in the digital barrier unit-period pedestrian flow data set with the pedestrian flow data intervals corresponding to the digital barrier pedestrian flow scale level data in the digital barrier pedestrian flow scale level data set, search for the digital barrier pedestrian flow scale level data that matches each digital barrier unit-period pedestrian flow data, and classify each advertising area according to the digital barrier pedestrian flow scale level data that matches each digital barrier unit-period pedestrian flow data, generating an advertising area classification data matrix D as follows:

[0021]

[0022] Among them, represents the j-th advertising area with the digital barrier pedestrian flow scale level data of b i , and d i represents the total number of advertising areas with the digital barrier pedestrian flow scale level data of b i ;

[0023] By regularly collecting the pedestrian flow data of the digital barriers in each advertising area, scientifically classifying each advertising area based on the collected pedestrian flow data, enabling customers to intuitively feel the pedestrian flow distribution of the digital barriers in each advertising area, and helping customers select the digital barrier pedestrian flow scale level with the highest cost performance according to their own needs.

[0024] Preferably, the specific steps for screening out the target-level advertising areas from the advertising area classification data according to customer needs and collecting the target-level advertising area location data are as follows:

[0025] S31. Online collect the digital gate traffic scale level data selected by the customer through the digital gate advertising placement planning platform, screen out all advertising placement areas corresponding to the digital gate traffic scale level data selected by the customer from the advertising placement area classification data matrix D, and collect the location data corresponding to the screened advertising placement areas to obtain the target level advertising placement area location data set Among them, represents the location data of the i-th advertising placement area in the digital gate traffic scale level data selected by the customer, and p represents the total number of advertising placement areas in the digital gate traffic scale level data selected by the customer.

[0026] Preferably, collect the customer store operation location data, and perform advertising effective placement area search processing according to the target level advertising placement area location data and the customer store operation location data. The specific steps for generating the advertising effective placement area data are as follows:

[0027] S41. Online collect the operation location data of the customer store through the digital gate advertising placement planning platform to obtain the customer store operation location data

[0028] S42. Import the customer store operation location data and each target level advertising placement area location data in the target level advertising placement area location data set into the map software, and calculate the straight-line distance between the customer store operation location data and each target level advertising placement area location data in the target level advertising placement area location data set to obtain the target level advertising placement area straight-line distance data set Among them, represents the straight-line distance between the location data of the i-th advertising placement area in the digital gate traffic scale level data selected by the customer and the customer store operation location data. The map software includes but is not limited to any one of Amap, Baidu Map, and Tencent Map;

[0029] S43. Set the advertising effective placement radius as R, perform numerical comparison processing on the advertising effective placement radius R and each target level advertising placement area straight-line distance data in the target level advertising placement area straight-line distance data set, and generate the advertising effective placement area location data set D'={d' 1 , d' 2 , …, d' i , …, d' o} according to the numerical comparison result. Among them, d' i represents the i-th advertising effective placement area location data, and o represents the total number of advertising effective placement area location data;

[0030] S431. Select the data in the straight-line distance dataset of the target-level advertising placement area and perform a numerical comparison with the effective advertising placement radius R;

[0031] If d i > R, it means that the position of the i-th advertising placement area in the digital barrier pedestrian flow scale level data selected by the customer does not meet the effective radiation requirement for advertising placement, and the position data of this advertising placement area is excluded;

[0032] If then it means that the position of the i-th advertising placement area in the digital barrier pedestrian flow scale level data selected by the customer meets the effective radiation requirement for advertising placement, and the position data of this advertising placement area is retained;

[0033] S432. Repeat the steps in S431 until all the straight-line distance data of the target-level advertising placement areas in the straight-line distance dataset of the target-level advertising placement area are traversed, and then combine all the retained advertising placement area position data to generate an effective advertising placement area position dataset.

[0034] Import the advertising placement area position data corresponding to the digital barrier pedestrian flow scale selected by the customer and the customer store operation position data into the map software, accurately calculate the straight-line distance between each advertising placement area and the customer store, and perform a numerical comparison with the preset effective advertising placement radius. According to the numerical comparison result, scientifically exclude the advertising placement areas with poor advertising radiation effect to ensure the effectiveness and reliability of the digital barrier advertising placement position planning result.

[0035] Preferably, the specific steps for collecting the target population characteristic data of the customer's advertising are as follows:

[0036] S51. Online collect the target population characteristic data for the customer's advertising through the digital barrier advertising placement position planning platform to generate the target population characteristic data E of the customer's advertising. The target population characteristic data includes, but is not limited to, age, gender, occupation, and consumption ability.

[0037] Preferably, the specific steps for performing population characteristic data matching processing on the target population characteristic data of the customer's advertising and the effective advertising placement area position data to generate the target advertising placement area position data are as follows:

[0038] S61. Collect the characteristic data of the people living around each effective advertising placement area position data in the effective advertising placement area position dataset to generate an effective advertising placement area living population characteristic dataset Among them, It represents the characteristic data of the living population around the i-th advertising placement area in the digital barrier traffic scale level data selected by the customer. The characteristic data includes, but is not limited to, age, gender, occupation, and consumption ability;

[0039] S62. Perform population characteristic data matching processing on the characteristic data of the target population for advertising placement E of the customer and the characteristic data of the living population in the effective advertising placement area in the set of characteristic data of the living population in the effective advertising placement area, and generate the target advertising placement area location data F mubiao ;

[0040] S621. Construct a search water flow population for the effective advertising placement area, set the population size as N, the current iteration number as t, the maximum iteration number as t max , and the search space dimension of the characteristic data of the living population in the effective advertising placement area is P;

[0041] Take the set of characteristic data of the living population in the effective advertising placement area as the search space of the characteristic data of the living population in the effective advertising placement area. Randomly generate N pieces of characteristic data of the living population in the effective advertising placement area in the search space of the characteristic data of the living population in the effective advertising placement area. Each piece of characteristic data of the living population in the effective advertising placement area corresponds to an individual of the search water flow in the effective advertising placement area in the search water flow population for the effective advertising placement area;

[0042] S622. Calculate the fitness value of each individual of the search water flow in the effective advertising placement area in the search water flow population for the effective advertising placement area. Arrange each individual of the search water flow in the effective advertising placement area in the search water flow population for the effective advertising placement area in descending order according to the fitness value, and select the individual of the search water flow in the effective advertising placement area with the highest fitness value as the current optimal individual. The fitness value calculation formula is as follows:

[0043]

[0044] Among them, f i represents the fitness value of the i-th individual of the search water flow in the effective advertising placement area in the search water flow population for the effective advertising placement area. n represents the number of characteristic data in the characteristic data of the target population for advertising placement of the customer. λ ij and η ij respectively represent the population proportion of the characteristic data of the living population in the effective advertising placement area corresponding to the i-th individual of the search water flow in the effective advertising placement area that matches the j-th type of population characteristic data in the characteristic data of the target population for advertising placement of the customer and the weight coefficient corresponding to this type of population characteristic data;

[0045] S623. Update the water consumption coefficient α of each search water flow individual for the effective advertising placement area in the search water flow population during the current iteration process. The update formula is as follows:

[0046]

[0047] Among them, α represents the water consumption coefficient of each search water flow individual for the effective advertising placement area in the search water flow population during the current iteration process;

[0048] S624. Calculate the water flow penetration coefficient β of each search water flow individual for the effective advertising placement area in the search water flow population in the search space of the characteristic data of the people living in the effective advertising placement area i ; The calculation formula of the water flow penetration coefficient is as follows:

[0049]

[0050] Among them, β i represents the water flow penetration coefficient of the i-th search water flow individual for the effective advertising placement area in the search water flow population, f max and f min respectively represent the maximum fitness value and the minimum fitness value of each search water flow individual for the effective advertising placement area in the search water flow population;

[0051] Each search water flow individual for the effective advertising placement area in the search water flow population will select a corresponding update method for position update according to the magnitude of the water flow penetration coefficient in the search space of the characteristic data of the people living in the effective advertising placement area; The position update formula is as follows:

[0052]

[0053] Among them, represents the position after the position update of the i-th search water flow individual for the effective advertising placement area in the search water flow population, and respectively represent the current positions of the i-th and j-th search water flow individuals for the effective advertising placement area in the search water flow population, rand 1 、rand 2 and rand all represent random numbers uniformly distributed between (0, 1), ub and lb respectively represent the upper limit and the lower limit of the search space, X best represents the position of the current optimal individual;

[0054] S625. Each search water flow individual in the search water population of the effective advertising placement area will update its position by probabilistically changing the water volume in the basin in the search space of the characteristic data of the people living in the effective advertising placement area. The position update formula is as follows:

[0055]

[0056] where rand 3 and rand 4 both represent random numbers uniformly distributed between (0, 1);

[0057] S626. Calculate the fitness value of each search water flow individual in the search water population of the effective advertising placement area after position update. If the fitness value of a search water flow individual after position update is greater than the original fitness value, replace the original position with the new position of this search water flow individual; otherwise, retain the original position.

[0058] Re - arrange each search water flow individual in the search water population of the effective advertising placement area from largest to smallest according to the fitness value, and select the search water flow individual with the highest fitness value as the new current optimal individual.

[0059] S627. Determine whether the current iteration number t is less than the maximum iteration number t max . If the current iteration number t is less than the maximum iteration number t max , then increment the current iteration number t by 1 and return to S623; otherwise, take the current optimal individual as the global optimal solution, output the position data of the effective advertising placement area corresponding to the characteristic data of the people living in the effective advertising placement area corresponding to the global optimal solution and perform data identification to generate the target advertising placement area position data F mubiao .

[0060] Through the flood optimization algorithm, the characteristic data of the target population for the customer to place advertisements and the position data of the effective advertising placement area are subjected to population characteristic data matching processing, scientifically searching for the position data of the effective advertising placement area that conforms to the population targeted by the customer's advertisement, realizing the precise matching of the effective advertising placement area, and at the same time improving the speed and efficiency of the matching process, quickly and accurately giving the final digital gate advertising placement position result that meets the customer's needs.

[0061] Preferably, the specific steps of pushing the target advertising placement area position data to the digital gate advertising placement position planning platform and ending this digital gate advertising placement position planning operation are as follows:

[0062] S71. Push the target advertising area location data F to the digital gate advertising placement location planning platform through the wireless communication network, and end the current digital gate advertising placement location planning operation. mubiao Push it to the digital gate advertising placement location planning platform, and end the current digital gate advertising placement location planning operation.

[0063] The present invention further includes a digital gate advertising placement location planning system based on big data, including a digital gate unit-period pedestrian flow collection module, an advertising area grading module, a target-level advertising area location locking module, an effective advertising area location search module, a customer's target population characteristic data collection module for advertising placement, a target advertising area location matching module, and a target advertising area location data pushing module;

[0064] The digital gate unit-period pedestrian flow collection module collects the pedestrian flow data of the digital gates in each advertising area within the previous digital gate pedestrian flow measurement period through an infrared sensor, and obtains the digital gate unit-period pedestrian flow data;

[0065] The advertising area grading module performs numerical matching processing on each digital gate unit-period pedestrian flow data in turn with the pedestrian flow data intervals corresponding to the digital gate pedestrian flow scale level data through a two-way search algorithm, searches for the digital gate pedestrian flow scale level data that matches each digital gate unit-period pedestrian flow data, and classifies each advertising area to generate advertising area grading data;

[0066] The target-level advertising area location locking module online collects the digital gate pedestrian flow scale level data selected by the customer through the digital gate advertising placement location planning platform, filters out all advertising areas corresponding to the digital gate pedestrian flow scale level data selected by the customer from the advertising area grading data, and collects the location data corresponding to the filtered advertising areas to obtain the target-level advertising area location data;

[0067] The effective advertising area location search module online collects the business location data of the customer's store through the digital gate advertising placement location planning platform to obtain the customer's store business location data, imports the customer's store business location data and the target-level advertising area location data into a map software and calculates the straight-line distance to obtain the target-level advertising area straight-line distance data, and at the same time makes a numerical comparison with the set effective advertising radius, and generates the effective advertising area location data according to the numerical comparison result;

[0068] The customer's target population characteristic data collection module for advertising placement online collects the target population characteristic data for which the customer places advertisements through the digital gate advertising placement location planning platform to generate the customer's target population characteristic data for advertising placement;

[0069] The target advertising area location matching module performs population characteristic data matching processing on the customer's target population characteristic data for advertising placement and the population characteristic data of the living population in the effective advertising placement area through a flood optimization algorithm, and generates target advertising area location data;

[0070] The target advertising area location data pushing module pushes the target advertising area location data to the digital gate advertising placement location planning platform through a wireless communication network, and ends the current digital gate advertising placement location planning operation.

[0071] (III) Beneficial effects

[0072] 1. According to the collected digital gate unit cycle pedestrian flow data, the present invention divides each advertising placement area into levels, accurately screens out the target level advertising placement areas according to customer requirements and obtains the corresponding locations, performs advertising effective placement area search processing based on the customer store operation location data and the target level advertising placement area location data, generates advertising effective placement area location data, performs population characteristic data matching processing on the collected customer's target population characteristic data for advertising placement and the advertising effective placement area location data, generates target advertising area location data, and pushes it to the digital gate advertising placement location planning platform, and at the same time ends the current digital gate advertising placement location planning operation, realizing the accurate matching of the advertising placement area and the scientific planning of the digital gate advertising placement location;

[0073] 2. By regularly collecting the pedestrian flow data of the digital gates in each advertising placement area, scientifically dividing each advertising placement area based on the collected pedestrian flow data, enabling customers to intuitively feel the pedestrian flow distribution of the digital gates in each advertising placement area, and helping customers select the digital gate pedestrian flow scale level with the highest cost performance according to their own needs;

[0074] 3. Importing the advertising placement area location data corresponding to the digital gate pedestrian flow scale level selected by the customer and the customer store operation location data into the map software, accurately calculating the straight-line distance between each advertising placement area and the customer store, comparing the numerical value with the preset advertising effective placement radius, and scientifically eliminating the advertising placement areas with poor advertising radiation effects according to the numerical comparison result, ensuring the effectiveness and reliability of the digital gate advertising placement location planning result;

[0075] 4. The flood optimization algorithm is used to perform population characteristic data matching processing on the customer's target population characteristic data for advertising placement and the advertising effective placement area location data, scientifically search for the advertising effective placement area location data that meets the population targeted by the customer's advertisement, achieve precise matching of the advertising effective placement area, and at the same time improve the speed and efficiency of the matching process, quickly and accurately give the final digital gate advertising placement location result that meets the customer's needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] To more clearly illustrate the technical solutions of the embodiments of the invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the invention, and for those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0077] Figure 1 It is a flowchart of a method for planning digital gate advertising placement positions based on big data provided by the present invention;

[0078] Figure 2 It is a schematic diagram of the modules of a system for planning digital gate advertising placement positions based on big data provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0079] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, rather than all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0080] In the description of the present invention, it should be understood that the terms "opening", "upper", "lower", "top", "middle", "inner", etc. indicating orientation or positional relationships are only for the convenience of describing the invention and simplifying the description, rather than indicating or implying that the components or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention.

[0081] Embodiment 1 is as follows:

[0082] Please refer to Figure 1 , a method for planning digital gate advertising placement positions based on big data, including the following steps:

[0083] S1. Collect the digital gate unit cycle pedestrian flow data;

[0084] S11. Set the digital barrier traffic measurement period. Install infrared sensors near the digital barriers in each advertising area. Collect the traffic flow data of the digital barriers in each advertising area during the previous digital barrier traffic measurement period through the infrared sensors, and obtain the digital barrier unit period traffic flow data set A = {a 1 , a 2 , …, a i , …, a k}, where a i represents the traffic flow data of the digital barrier in the i-th advertising area, and k represents the total number of advertising areas; the unit of the traffic flow data is person-times. When collecting the traffic flow data of the digital barrier, it is set that passing through the digital barrier is 1 person-time and passing by the digital barrier is 0.5 person-time.

[0085] S2. Classify each advertising area according to the digital barrier unit period traffic flow data to obtain the advertising area classification data;

[0086] S21. Establish a digital barrier traffic scale level data set where represents that the digital barrier traffic scale level data corresponding to the i-th traffic flow data interval [c i1 , c i2 is b i , and c i1 and c i2 respectively represent the minimum traffic flow and the maximum traffic flow in the i-th traffic flow data interval [c i1 , c i2 , and l represents the total number of digital barrier traffic scale levels;

[0087] S22. Use the two-way search algorithm to perform numerical matching processing on each digital barrier unit period traffic flow data in the digital barrier unit period traffic flow data set with the traffic flow data intervals corresponding to the digital barrier traffic scale level data in the digital barrier traffic scale level data set, search for the digital barrier traffic scale level data that matches each digital barrier unit period traffic flow data, and classify each advertising area according to the digital barrier traffic scale level data that matches each digital barrier unit period traffic flow data, and generate the advertising area classification data matrix D as follows:

[0088]

[0089] where represents the j-th advertising area with the digital barrier traffic scale level data of b i , and d i represents the digital barrier traffic scale level data of bi The total number of advertising placement areas.

[0090] S3. Screen out the target-level advertising placement areas from the advertising placement area grading data according to customer requirements, and collect the location data of the target-level advertising placement areas;

[0091] S31. Online collect the digital gate traffic scale level data selected by the customer through the digital gate advertising placement location planning platform, screen out all advertising placement areas corresponding to the digital gate traffic scale level data selected by the customer from the advertising placement area grading data matrix D, and collect the location data corresponding to the screened advertising placement areas to obtain the target-level advertising placement area location data set Among them, represents the location data of the i-th advertising placement area in the digital gate traffic scale level data selected by the customer, and p represents the total number of advertising placement areas in the digital gate traffic scale level data selected by the customer.

[0092] S4. Collect the customer store operation location data, and perform advertising effective placement area search processing according to the target-level advertising placement area location data and the customer store operation location data to generate advertising effective placement area location data;

[0093] S41. Online collect the operation location data of the customer store through the digital gate advertising placement location planning platform to obtain the customer store operation location data

[0094] S42. Import the customer store operation location data and each target-level advertising placement area location data in the target-level advertising placement area location data set into the map software, and calculate the straight-line distance between the customer store operation location data and each target-level advertising placement area location data in the target-level advertising placement area location data set to obtain the target-level advertising placement area straight-line distance data set Among them, represents the straight-line distance between the location data of the i-th advertising placement area in the digital gate traffic scale level data selected by the customer and the customer store operation location data, and the map software includes but is not limited to any one of Amap, Baidu Map, and Tencent Map;

[0095] S43. Set the advertising effective placement radius as R, perform numerical comparison processing on the advertising effective placement radius R and each target-level advertising placement area straight-line distance data in the target-level advertising placement area straight-line distance data set, and generate the advertising effective placement area location data set D'={d' according to the numerical comparison result1 , d' 2 , …, d' i , …, d' o} where d' i represents the position data of the i-th effective advertising placement area, and o represents the total number of position data of the effective advertising placement area;

[0096] S431. Select the in the set of linear distance data of the target-level advertising placement areas and compare it numerically with the effective advertising placement radius R.

[0097] If it means that the position of the i-th advertising placement area in the digital gate traffic volume scale level data selected by the customer does not meet the effective radiation requirement for advertising placement, and the position data of this advertising placement area is excluded;

[0098] If it means that the position of the i-th advertising placement area in the digital gate traffic volume scale level data selected by the customer meets the effective radiation requirement for advertising placement, and the position data of this advertising placement area is retained;

[0099] S432. Repeat the steps in S431 until all the linear distance data of the target-level advertising placement areas in the set of linear distance data of the target-level advertising placement areas are traversed, and then combine all the retained position data of the advertising placement areas to generate a set of position data of the effective advertising placement area.

[0100] S5. Collect the characteristic data of the target population for the customer's advertising placement;

[0101] S51. Online collect the characteristic data of the target population for the customer's advertising placement through the digital gate advertising placement location planning platform to generate the characteristic data E of the target population for the customer's advertising placement. The target population characteristic data includes, but is not limited to, age, gender, occupation, and consumption ability.

[0102] S6. Perform population characteristic data matching processing on the characteristic data of the target population for the customer's advertising placement and the position data of the effective advertising placement area to generate target advertising placement area position data;

[0103] S61. Collect the characteristic data of various aspects of the living population around each position data of the effective advertising placement area in the set of position data of the effective advertising placement area to generate a set of characteristic data of the living population in the effective advertising placement area where represents the characteristic data of various aspects of the living population around the i-th advertising placement area in the digital gate traffic volume scale level data selected by the customer. The various characteristic data includes, but is not limited to, age, gender, occupation, and consumption ability;

[0104] S62. Perform population characteristic data matching processing on the advertising effective placement area living population characteristic data in the customer's advertising target population characteristic data E and the advertising effective placement area living population characteristic data set through a flood optimization algorithm to generate target advertising placement area location data F mubiao ;

[0105] S621. Construct a search water flow population for the advertising effective placement area, set the population size as N, the current iteration number as t, and the maximum iteration number as t max . The search space dimension of the advertising effective placement area living population characteristic data is P;

[0106] Use the advertising effective placement area living population characteristic data set as the search space for the advertising effective placement area living population characteristic data. Randomly generate N advertising effective placement area living population characteristic data in the search space for the advertising effective placement area living population characteristic data. Each advertising effective placement area living population characteristic data corresponds to an advertising effective placement area search water flow individual in the advertising effective placement area search water flow population;

[0107] S622. Calculate the fitness value of each advertising effective placement area search water flow individual in the advertising effective placement area search water flow population. Arrange each advertising effective placement area search water flow individual in the advertising effective placement area search water flow population in descending order according to the fitness value, and select the advertising effective placement area search water flow individual with the highest fitness value as the current optimal individual; the fitness value calculation formula is as follows:

[0108]

[0109] Among them, f i represents the fitness value of the i-th advertising effective placement area search water flow individual in the advertising effective placement area search water flow population, n represents the number of characteristic data in the customer's advertising target population characteristic data, λ ij and η ij respectively represent the population proportion of the advertising effective placement area living population characteristic data corresponding to the i-th advertising effective placement area search water flow individual in the advertising effective placement area search water flow population that matches the j-th type of population characteristic data in the customer's advertising target population characteristic data and the weight coefficient corresponding to this type of population characteristic data;

[0110] S623. Update the water consumption coefficient α of each advertising effective placement area search water flow individual in the advertising effective placement area search water flow population during the current iteration; the update formula is as follows:

[0111]

[0112] Among them, α represents the water consumption coefficient of each individual of the search water flow for the effective advertising placement area in the search water flow population of the effective advertising placement area in the current iteration process;

[0113] S624. Calculate the water flow penetration coefficient β of each individual of the search water flow for the effective advertising placement area in the search water flow population of the effective advertising placement area in the search space of the characteristic data of the people living in the effective advertising placement area i ; The calculation formula of the water flow penetration coefficient is as follows:

[0114]

[0115] Among them, β i represents the water flow penetration coefficient of the i-th individual of the search water flow for the effective advertising placement area in the search water flow population of the effective advertising placement area, and f max and f min respectively represent the maximum fitness value and the minimum fitness value of each individual of the search water flow for the effective advertising placement area in the search water flow population of the effective advertising placement area;

[0116] Each individual of the search water flow for the effective advertising placement area in the search water flow population of the effective advertising placement area will select a corresponding update method for position update according to the magnitude of the water flow penetration coefficient in the search space of the characteristic data of the people living in the effective advertising placement area; The position update formula is as follows:

[0117]

[0118] Among them, represents the position after the position update of the i-th individual of the search water flow for the effective advertising placement area in the search water flow population of the effective advertising placement area, and respectively represent the current positions of the i-th and j-th individuals of the search water flow for the effective advertising placement area in the search water flow population of the effective advertising placement area, and rand 1 、rand 2 and rand all represent random numbers uniformly distributed between (0, 1), ub and lb respectively represent the upper limit and the lower limit of the search space, and X best represents the position of the current optimal individual;

[0119] S625. Each individual of the search water flow for the effective advertising placement area in the search water flow population of the effective advertising placement area will update the position by changing the water volume of the basin with probability in the search space of the characteristic data of the people living in the effective advertising placement area; The position update formula is as follows:

[0120]

[0121] where rand 3 and rand 4 both represent random numbers that follow a uniform distribution between (0, 1);

[0122] S626. Calculate the fitness value of each search water flow individual in the search water flow population for the effective advertising placement area after position update. If the fitness value of a search water flow individual in the search water flow population for the effective advertising placement area after position update is greater than the original fitness value, then replace the original position with the new position of this search water flow individual for the effective advertising placement area; otherwise, retain the original position;

[0123] Re - arrange each search water flow individual in the search water flow population for the effective advertising placement area in descending order of fitness value, and select the search water flow individual with the highest fitness value as the new current optimal individual;

[0124] S627. Determine whether the current iteration number t is less than the maximum iteration number t max . If the current iteration number t is less than the maximum iteration number t max , then increment the current iteration number t by 1 and return to S623; otherwise, take the current optimal individual as the global optimal solution, output the location data of the effective advertising placement area corresponding to the characteristic data of the target population for advertising placement in the effective advertising placement area, and perform data identification to generate the target advertising placement area location data F mubiao .

[0125] S7. Push the target advertising placement area location data to the digital gate advertising placement location planning platform, and end this digital gate advertising placement location planning operation;

[0126] S71. Push the target advertising placement area location data F mubiao to the digital gate advertising placement location planning platform through a wireless communication network, and end this digital gate advertising placement location planning operation.

[0127] Embodiment 2 is as follows:

[0128] Please refer to Figure 2 , a digital gate advertising placement location planning system based on big data, including a digital gate unit - cycle pedestrian flow collection module, an advertising placement area grading module, a target - level advertising placement area location locking module, an effective advertising placement area location search module, a customer - target population characteristic data collection module for advertising placement, a target advertising placement area location matching module, and a target advertising placement area location data pushing module;

[0129] The digital barrier unit-period pedestrian flow collection module collects the pedestrian flow data of the digital barriers in each advertisement placement area within the previous digital barrier pedestrian flow measurement period through an infrared sensor, and obtains the digital barrier unit-period pedestrian flow data;

[0130] The advertisement placement area grading module performs numerical matching processing on the pedestrian flow data of each digital barrier unit period with the pedestrian flow data intervals corresponding to the digital barrier pedestrian flow scale level data in sequence through a two-way search algorithm, searches for the digital barrier pedestrian flow scale level data that matches the pedestrian flow data of each digital barrier unit period, and classifies each advertisement placement area to generate advertisement placement area grading data;

[0131] The target-level advertisement placement area location locking module online collects the digital barrier pedestrian flow scale level data selected by the customer through the digital barrier advertisement placement location planning platform, filters out all advertisement placement areas corresponding to the digital barrier pedestrian flow scale level data selected by the customer from the advertisement placement area grading data, and collects the location data corresponding to the filtered advertisement placement areas to obtain the target-level advertisement placement area location data;

[0132] The advertisement effective placement area location search module online collects the business location data of the customer's store through the digital barrier advertisement placement location planning platform to obtain the customer's store business location data, imports the customer's store business location data and the target-level advertisement placement area location data into a map software and calculates the straight-line distance to obtain the target-level advertisement placement area straight-line distance data, and at the same time makes a numerical comparison with the set advertisement effective placement radius, and generates the advertisement effective placement area location data according to the numerical comparison result;

[0133] The customer advertisement target population characteristic data collection module online collects the target population characteristic data for which the customer places advertisements through the digital barrier advertisement placement location planning platform to generate the customer advertisement target population characteristic data;

[0134] The target advertisement placement area location matching module performs population characteristic data matching processing on the customer advertisement target population characteristic data and the advertisement effective placement area living population characteristic data through a flood optimization algorithm to generate the target advertisement placement area location data;

[0135] The target advertisement placement area location data push module pushes the target advertisement placement area location data to the digital barrier advertisement placement location planning platform through a wireless communication network, and ends this digital barrier advertisement placement location planning operation.

[0136] In the description of this specification, the descriptions referring to the terms "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0137] The preferred embodiments of the invention disclosed above are only used to help explain the invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification in order to better explain the principle and practical application of the invention, so that those skilled in the art can well understand and utilize the invention.

Claims

1. A method for planning the placement of digital gate advertisements based on big data, characterized in that: The steps include: S1. Collect the pedestrian flow data of digital gate per unit period; S2. Classify each advertising placement area according to the unit cycle passenger flow data of the digital gate to obtain advertising placement area classification data; S3. Filtering target-level advertising delivery areas from the advertising delivery area classification data according to customer needs, and collecting target-level advertising delivery area location data; S4, collecting the customer's store business location data, performing an effective advertising delivery area search process based on the target level advertising delivery area location data and the customer's store business location data, and generating effective advertising delivery area location data; S5. Collect characteristic data of the target group for the customer's advertisements; S6, performing a population characteristic data matching process on the target population characteristic data of the customer's advertisement placement and the location data of the effective advertisement placement area to generate target advertisement placement area location data; S7: Push the target advertisement placement area location data to the digital barrier advertisement placement location planning platform, and end the digital barrier advertisement placement location planning operation.

2. According to the method for planning the placement of digital gate advertisements based on big data in claim 1, it is characterized in that: The S1 comprises the following steps: S11, set the digital barrier flow measurement cycle, install infrared sensors near the digital barriers in each advertising area, collect the digital barrier flow data in each advertising area in the previous digital barrier flow measurement cycle through the infrared sensors, and obtain the digital barrier flow data set per unit cycle A = {a1, a2, ..., a i ,…,a k }, where a i represents the pedestrian flow data of the digital barrier in the i-th advertising area, k represents the total number of advertising areas; the unit of the pedestrian flow data is person-times, and when collecting the pedestrian flow data of the digital barrier, it is set that passing through the digital barrier is 1 person-time, and passing through the digital barrier is 0.5 person-times.

3. According to the method for planning the placement of digital gate advertisements based on big data in claim 2, it is characterized in that: The S2 comprises the following steps: S21. Establishing a digital barrier passenger flow scale and level dataset in, represents the flow data interval of the i-th person [c i1 ,c i2 ] The corresponding digital gate passenger flow scale data is b i , c i1 and c i2 Respectively represent the flow data interval of the i-th person [c i1 ,c i2 ], l represents the total number of digital barrier flow scale levels; S22. Through a bidirectional search algorithm, numerical matching processing is performed on the unit cycle pedestrian flow data of each digital barrier in A with the pedestrian flow data intervals corresponding to the digital barrier pedestrian flow scale level data in C in turn, and the digital barrier pedestrian flow scale level data matching the unit cycle pedestrian flow data of each digital barrier is searched out, and each advertising delivery area is graded according to the digital barrier pedestrian flow scale level data matching the unit cycle pedestrian flow data of each digital barrier, so as to generate a graded data matrix D for the advertising delivery area.

4. According to the method of planning the location of digital gate advertisements based on big data in claim 3, it is characterized in that: The S3 comprises the following steps: S31. Collect the digital barrier traffic scale level data selected by the customer online through the digital barrier advertising placement location planning platform, select all advertising placement areas corresponding to the digital barrier traffic scale level data selected by the customer from the D, and collect the location data corresponding to the selected advertising placement areas to obtain the target level advertising placement area location data set in, represents the location data of the i-th advertising placement area in the digital barrier traffic scale level data selected by the customer, and p represents the total number of advertising placement areas in the digital barrier traffic scale level data selected by the customer.

5. According to the method of planning the placement of digital gate advertisements based on big data in claim 4, it is characterized in that: The S4 comprises the following steps: S41. Collect the customer's store business location data online through the digital gate advertising placement planning platform to obtain the customer's store business location data S42, the and stated The location data of each target level advertising area in the map software is imported to calculate the With the The straight-line distance of each target-level advertising delivery area location data in the target-level advertising delivery area is obtained to obtain the straight-line distance dataset of the target-level advertising delivery area in, Indicates the straight-line distance between the location data of the i-th advertising placement area in the digital gate traffic scale level data selected by the customer and the customer's store business location data; S43, setting the effective advertisement delivery radius to R, and comparing the R with the The linear distance data of each target level advertising delivery area in the numerical comparison process is processed, and the effective advertising delivery area location data set D'={d'1,d'2,…,d' i ,…,d' o }, where d' i represents the location data of the i-th effective advertising delivery area, and o represents the total number of location data of the effective advertising delivery areas; S431, select the In Compare the value with the effective advertisement delivery radius R; like This means that the location of the i-th advertising area in the digital gate traffic scale level data selected by the customer does not meet the effective radiation requirements of advertising, and the location data of the advertising area is removed; like This means that the location of the i-th advertising area in the digital gate traffic scale level data selected by the customer meets the effective radiation requirements of advertising, and the location data of the advertising area is retained; S432, repeat the steps in S431 until the After obtaining the straight-line distance data of all target-level advertisement delivery areas, all the retained advertisement delivery area location data are combined to generate the D'.

6. According to the method of planning the placement of digital gate advertisements based on big data in claim 5, it is characterized in that: The S5 comprises the following steps: S51. Collect the target population characteristic data for the customer's advertisement placement online through the digital gate advertisement placement location planning platform to generate the target population characteristic data E for the customer's advertisement placement.

7. According to the big data-based digital gate advertising placement planning method of claim 6, it is characterized by: The S6 comprises the following steps: S61, collecting various characteristic data of the people living around the location data of each effective advertising delivery area in D', and generating a characteristic data set of the people living in the effective advertising delivery area in, Represents various characteristic data of the people living around the i-th advertising area in the digital gate traffic scale level data selected by the customer; S62, using a flood optimization algorithm to reduce the E and The population characteristic data of the effective advertising delivery area in the target advertising delivery area is matched with the population characteristic data to generate the target advertising delivery area location data F mubiao .

8. According to the big data-based digital gate advertising placement planning method of claim 7, it is characterized in that: The S62 comprises the following steps: S621: Construct a water flow population for searching the effective advertising delivery area, set the population size to N, the current number of iterations to t, and the maximum number of iterations to t. max , the search space dimension of the characteristic data of the living population in the effective advertising delivery area is P; The As a search space for characteristic data of people living in an effective advertising delivery area, N characteristic data of people living in an effective advertising delivery area are randomly generated in the search space for characteristic data of people living in an effective advertising delivery area, and each characteristic data of people living in an effective advertising delivery area corresponds to an effective advertising delivery area search water flow individual; S622, calculating the fitness value of each search water flow individual in each effective advertisement delivery area; S623, updating the water consumption coefficient α of each search water flow individual in each effective advertisement delivery area during the current iteration; S624, calculating the water flow permeability coefficient β of each water flow individual in the effective advertising delivery area in the search space of the characteristic data of the living population in the effective advertising delivery area i ; Each search flow individual in the effective advertising delivery area will search for the characteristic data of the living population in the effective advertising delivery area according to the β i The size of the position is updated by selecting the corresponding update method; S625, each water flow individual in the effective advertising delivery area searches for the characteristic data of the population living in the effective advertising delivery area, and the water volume in the watershed is changed according to probability to update the position; S626, calculating the fitness value of each advertising effective delivery area search water flow individual after the position is updated, if the fitness value of the advertising effective delivery area search water flow individual after the position is updated is greater than the original fitness value, then the new position of the advertising effective delivery area search water flow individual will replace the original position; otherwise, the original position will be retained; Arrange the search water flow individuals in each effective advertising delivery area from large to small according to the fitness value, and select the search water flow individual in the effective advertising delivery area with the highest fitness value as the current optimal individual; S627, determine whether t is less than t max , if t is less than t max , then t is increased by 1, and the process returns to S623; otherwise, the current optimal individual is taken as the global optimal solution, and the effective advertising placement area location data corresponding to the characteristic data of the living population in the effective advertising placement area corresponding to the global optimal solution is output and the data is marked to generate the F mubiao .

9. According to the method of digital gate advertisement placement planning based on big data in claim 8, it is characterized in that: The S7 comprises the following steps: S71, transmitting the F mubiao Push to the digital barrier advertising placement planning platform and end the digital barrier advertising placement planning operation.

10. A system for implementing the method for planning digital gate advertising placement locations based on big data as described in any one of claims 1 to 9.

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