CN116797324A - Online intelligent analysis recommendation method and system for store renting line of mall based on Internet of things - Google Patents
Online intelligent analysis recommendation method and system for store renting line of mall based on Internet of things Download PDFInfo
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Abstract
The invention relates to the technical field of store renting in a mall, and particularly discloses an online intelligent analysis recommendation method and system for store renting in a mall based on the Internet of things, wherein the method comprises the following steps: the method not only analyzes the mall, but also has higher geographical position of the residence of the client and geographical position attention of the mall, further analyzes the intention leasing shop of the client, overcomes the defects of the prior art, improves the satisfaction of the client to a certain extent, ensures the income of leasing platforms on the mall line, judges the value of the shop by combining the value of the mall and the position of the shop in the mall through the intention leasing shop analysis of the client, and further ensures the accuracy of the position suitability analysis of the shop.
Description
Technical Field
The invention relates to the technical field of store renting in a mall, in particular to an online intelligent analysis recommendation method and system for store renting in a mall based on the Internet of things.
Background
With the development of economy and society, the quality requirements of people on living standards are gradually improved, so that a plurality of consumer markets are generated, daily shopping of people is met, a plurality of stores exist in the consumer markets, the stores are managed in a leasing way, a certain fee is charged, and nowadays, with the development of science and technology, a plurality of store on-line leasing platforms are layered, on the store on-line leasing platform, clients can select leasing stores intended by themselves according to own needs, and how to improve the viscosity and leasing yield of the clients becomes the store on-line leasing platform is important, if the use feeling of the clients on the store on-line leasing platform is poor, not only the income of the store on-line leasing platform is affected, but also the flow of the store on-line leasing platform is affected, so that the intended leasing of the clients needs to be analyzed.
The existing analysis of the intention leasing store of the client has certain defects, and the concrete steps are as follows: (1) In the prior art, the intent lease stores of the clients are mostly analyzed by means of a business circle to recommend stores to the clients, the influence of the geographical position of the residence places of the clients and the positions of the businesses on the selection stores of the clients is ignored, and further the problem that the recommended lease stores are too far away from the residence places of the clients is caused, the traffic trip of the clients is inconvenient, the satisfaction degree of the clients is difficult to ensure, the success rate of the recommended lease stores and the clients is reduced, and the income of lease platforms on the businesses is influenced to a certain extent.
(2) The existing analysis of the intention leasing shops of clients is mostly to judge the value of the shops according to the value of a business district, the position analysis of the shops in the business district is not deep enough, such as the distance between the shops and the exit of the business district and the layer value of the shops, so that the analysis of the proper position values of the shops is not accurate enough, the reliability guarantee cannot be provided for pushing the intention leasing shops of the subsequent clients, the grasping degree of the intention leasing shops of the clients is reduced to a certain extent, and the use feeling of the clients is influenced.
Disclosure of Invention
In order to overcome the defects in the background technology, the embodiment of the invention provides an online intelligent analysis recommendation method and system for transferring shops of a mall to rents based on the Internet of things, which can effectively solve the problems related to the background technology.
The aim of the invention can be achieved by the following technical scheme: the invention provides an online intelligent analysis recommendation method for transferring a store to a renting line of a mall based on the Internet of things, which comprises the following steps: s1, acquiring a mall position: the position of each development area is obtained from a store management center of the mall, a coordinate system is established by taking the central point of each development area as an origin, and then the position coordinates of each mall in each development area are obtained.
S2, analyzing geographic position priority coefficients of the mall: and analyzing geographic position priority coefficients corresponding to the malls based on the position coordinates of the malls in the development areas.
S3, store position priority coefficient analysis: and establishing a three-dimensional coordinate system by taking the center point of each mall as an origin, further obtaining the three-dimensional coordinates of the center point of each store and the center point of each outlet in each mall, and analyzing the position priority coefficients corresponding to each store in each mall by combining the geographic position priority coefficients corresponding to each mall.
S4, mall recommendation index analysis: and extracting the living address corresponding to the demand customer from the store management center of the mall, acquiring the three-dimensional coordinates of the central point corresponding to the living address of the demand customer in each mall, marking the three-dimensional coordinates as the target three-dimensional coordinates of each mall, and further analyzing the recommendation index of each mall.
S5, target shop analysis: and obtaining information to be leased of each store in each mall from a mall management center, wherein the information to be leased comprises a leasing area, a leasing ending time point, leasing and leasing payment types, analyzing corresponding leasing time points of each store in each mall to accord with coefficients, further analyzing each store in each mall, and marking each store with the leasing time point meeting the requirements of the coefficients as each target store.
S6, store recommendation index analysis: and acquiring information to be leased of each target store in each mall, acquiring demand leasing information of a demand client, and further analyzing recommendation indexes corresponding to each target store in each mall.
S7, analyzing a proper shop: and analyzing each proper store corresponding to the demand client based on the recommended index corresponding to each target store in each mall.
S8, processing in a proper shop: and sequencing all proper stores corresponding to the demand clients according to the order of the recommendation indexes from high to low, and displaying the proper stores on the display pages of the demand clients from high to low.
Further, the geographic position priority coefficient corresponding to each mall is specifically analyzed by the following steps: s21: the distance between each mall and each development area is analyzed, and the calculation formula is as follows:wherein JL id Expressed as the distance of the ith mall from the d development area, (x) id ,y id ,z id ) Expressed as the position coordinates of the ith mall in the d development area, i expressed as the number of each mall, i=1, 2.
S22: comparing the distance between each mall and each development area with the preset matching distance between the mall and the development area, if the distance between a certain mall and a certain development area is smaller than or equal to the matching distance between the mall and the development area, marking the development area as the association area of the mall, further counting to obtain each association area of each mall, and counting the number of the association areas of each mall.
S23: the position coordinates of each mall in each associated area are obtained, and the average distance between the mall and the associated area is analyzed according to the position coordinates, wherein the calculation formula is as follows:where PJ is expressed as the average distance of the malls from the associated areas, p is expressed as the number of each associated area, p=1, 2,..q, n is expressed as the number of malls, q is expressed as the number of associated areas, (x ip ′,y ip ′,z ip ') is expressed as the position coordinates of the ith mall in the p-th associated area.
S23: the geographic position priority coefficient corresponding to each mall is analyzed, and the calculation formula is as follows:wherein YX i Expressed as geographic location priority coefficient corresponding to the ith mall, SL i Expressed as the number, lambda, of associated areas of the ith mall 1 、λ 2 Respectively representing the preset quantity of the associated areas and the weight factors of the average distance of the mall from the associated areas.
Further, the method for analyzing the position priority coefficient corresponding to each store in each mall comprises the following specific steps: s31: according to the three-dimensional coordinates of the center points of all shops and the center points of all exits in all shops, the distances between all shops and all exits in all shops are analyzed, and the calculation formula is as follows:wherein CK is imp Expressed as the distance between the mth store and the jth exit in the ith mall, (xc) im ,yc im ,zc im ) Three-dimensional coordinates expressed as the m-th store center point in the i-th store, (xk) ij ,yk ij ,zk ij ) Three-dimensional coordinates expressed as the center point of the j-th exit in the i-th mall, m expressed as the number of each store, m=1, 2.
S32: the outlet convenience coefficients corresponding to all shops in all shops are analyzed, and the calculation formula is as follows:wherein CY im Expressed as the exit convenience coefficient corresponding to the m store in the ith mall, k is expressed as the number of exits, and e is expressed as a natural constant.
S34: acquiring layer values corresponding to stores in each mall, matching the layer values with convenience factors corresponding to the layer values stored in a cloud database, matching the convenience factors corresponding to the stores in each mall, and marking the convenience factors as beta im 。
S33: analyzing position priority coefficients corresponding to stores in each mall, wherein the calculation formula is as follows: WY im ＝ln(1+YX i *γ 1 +CY im *γ 2 +β im *γ 3 ) Wherein WY im Denoted as the firstPosition priority coefficient corresponding to m store in i malls, gamma 1 、γ 2 、γ 3 The method is respectively expressed as the influence factors of the preset geographic position of the mall, the convenience in export and the convenience in layer value.
Further, the recommendation index of each mall comprises the following specific analysis method: s41: according to the three-dimensional coordinates of each mall target, analyzing the distance suitability index corresponding to each mall, wherein the calculation formula is as follows: Wherein JS i Expressed as the distance fitness index corresponding to the ith mall, (xm) i ,ym i ,zm i ) The three-dimensional coordinate of the target in the ith mall is expressed, and epsilon is expressed as a blocking factor corresponding to a preset unit distance.
S42: the business turnover and the people flow of each business corresponding to each month are extracted from the business store management center of each business store, and then the appropriate index of the campaigns corresponding to each business store is analyzed according to the business turnover and the people flow, and the calculation formula is as follows:wherein YD i Expressed as the index of the appropriate revenue corresponding to the ith mall, YY ir 、RL ir The business and the flow of people are respectively expressed as the business and the flow of people of the ith mall corresponding to the r month, r is the number of each month, r=1, 2, & gt, w and w are the number of months, and θ 1 、θ 2 Respectively expressed as a proper duty factor of the preset turnover and the camping of the people flow.
S43: the recommendation indexes of all malls are comprehensively analyzed, and the specific calculation formula is as follows: recommendation index, τ, expressed as the ith mall 1 、τ 2 Respectively expressed as correction factors to which preset distance is suitable and nutrient is suitable.
Further, the corresponding lease time points of each store in each mall accord with coefficients, and the specific analysis method comprises the following steps: s51: and extracting a lease ending time point from information to be leased of each store in each mall, and extracting an interval duration threshold value from a cloud database.
S52: and obtaining the predicted lease time point of the demand client from the store re-lease platform of the mall and obtaining the current time point.
S53: substituting the lease ending time point of each store in each mall, the predicted lease time point of a demand client, the interval time length threshold value, the current time point and the preset proper adjustment time length into the lease time point coincidence coefficient corresponding to each store in each mall, wherein the calculation formula is as follows:wherein SJ im Expressed as a lease time point coincidence coefficient corresponding to an mth store in an ith mall, JH im The method is characterized in that the method is expressed as a lease ending time point of an mth store in an ith mall, XY is expressed as a predicted lease time point of a demand client, DQ is expressed as a current time point, and JG and TZ are respectively expressed as interval duration thresholds and proper adjustment durations.
Further, the analysis method specifically analyzes each store meeting the requirement of the coefficient at the lease time point in each mall comprises the following steps: comparing the corresponding lease time point coincidence coefficient of each store in each mall with a preset lease time point coincidence threshold, and if the lease time point coincidence coefficient of a store in a certain mall is larger than or equal to the lease time point coincidence threshold, judging that the lease time point coincidence coefficient of the store in the mall meets the requirement, thereby obtaining each store with the lease time point coincidence coefficient meeting the requirement.
Further, the demand leasing information of the demand client specifically includes a demand leasing area, a demand lease and a demand lease payment type.
Further, the recommendation indexes corresponding to the target stores in the malls are analyzed by the following steps: s61: and extracting lease areas, lease rents and lease payment types from lease information of each target store in each mall.
S62: and extracting the demand leasing area, the demand leasing and the demand leasing payment type from the demand leasing information of the demand clients.
S63: and matching the rent payment type of each target store in each mall with the demand rent payment type of the demand client, if the rent payment type of a certain target store in a certain mall is successfully matched with the demand rent payment type of the demand client, marking the rent payment type matching index of the target store in the mall as rho, otherwise marking the rent payment type matching index as rho'.
S64: acquiring lease payment type matching indexes of target stores in each mall and marking the lease payment type matching indexes as eta ia Wherein eta ia The number of each target store, a=1, 2,...
S65: the method comprises the steps of obtaining position priority coefficients corresponding to target stores in each mall, comparing the leasing area and leasing rent of the target stores in each mall with the demand leasing area and demand rent of a demand client respectively, and further analyzing recommendation indexes corresponding to the target stores in each mall, wherein the calculation formula is as follows: Wherein ZJ ia Expressed as recommendation index, WY, corresponding to the a-th target store in the i-th mall ia ' expressed as a position priority coefficient, eta, corresponding to the a-th target store in the i-th mall ia Lease payment type matching index, MJ, expressed as the ith target store in the ith mall ia 、MY ia Respectively expressed as the leasing area and leasing rent of an a target store in an i-th mall, and MJ and MY respectively expressed as the demand leasing area, the demand rent and omega of a demand customer 1 、ω 2 、ω 3 、ω 4 、ω 5 Respectively representing the preset target store position priority coefficient, the store recommendation index, the rent payment type matching index, the area coincidence and the rent coincidence as the proportion coefficient.
Further, the recommendation index corresponding to each target store in each mall analyzes each suitable store corresponding to the client of the demand, and the specific analysis method comprises the following steps: comparing the recommendation index corresponding to each target store in each mall with a preset recommendation index threshold, and if the recommendation index corresponding to a certain target store in a certain mall is greater than or equal to the recommendation index threshold, marking the target store in the mall as a proper store of a demand customer, thereby obtaining each proper store corresponding to the demand customer.
The second aspect of the invention provides an online intelligent analysis recommendation system for transferring a store to a renting line of a mall based on the Internet of things, which comprises the following steps: the mall position acquisition module is used for acquiring the position of each development area from a mall store management center, and establishing a coordinate system by taking the central point of each development area as an origin, so as to acquire the position coordinates of each mall in each development area.
The mall geographic position priority coefficient analysis module is used for analyzing geographic position priority coefficients corresponding to the malls based on the position coordinates of the malls in the development areas.
The store position priority coefficient analysis module is used for establishing a three-dimensional coordinate system by taking the center point of each store as an origin, further obtaining the three-dimensional coordinates of the center point of each store and the center point of each outlet in each store, and analyzing the position priority coefficient corresponding to each store in each store by combining the geographic position priority coefficient corresponding to each store.
The mall recommendation index analysis module is used for extracting residence addresses corresponding to the demand clients from the mall store management center, acquiring three-dimensional coordinates of center points corresponding to the residence addresses of the demand clients in each mall, marking the three-dimensional coordinates as target three-dimensional coordinates of each mall, and further analyzing recommendation indexes of each mall.
The target store analysis module is used for acquiring information to be leased of each store in each store from the store management center, wherein the information to be leased comprises a leasing area, a leasing ending time point, a leasing rent and a rent payment type, analyzing the corresponding lease time point coincidence coefficient of each store in each store, further analyzing each store with the lease time point coincidence coefficient meeting the requirement in each store, and marking the stores as each target store.
The store recommendation index analysis module is used for acquiring information to be leased of each target store in each mall, acquiring demand leasing information of a demand client and further analyzing recommendation indexes corresponding to each target store in each mall.
The proper store analysis module is used for analyzing each proper store corresponding to the demand client based on the recommended index corresponding to each target store in each mall.
The proper shop processing module is used for sequencing all proper shops corresponding to the demand clients according to the order of the recommendation indexes from high to low, and displaying the proper shops on the display pages of the demand clients from high to low.
The cloud database is used for storing convenience factors corresponding to the layers of values and storing interval duration thresholds.
Compared with the prior art, the embodiment of the invention has at least the following advantages or beneficial effects: (1) According to the method, analysis is carried out on the intent leasing shops of the clients, the business circles are analyzed, the attention degree on the geographical positions of the living places of the clients and the geographical positions of the malls is higher, and then the intent leasing shops of the clients are analyzed.
(2) According to the invention, the value of the store is judged by combining the value of the business district and the position of the store in the mall through analysis of the intention lease store of the client, so that the problem that the position analysis of the store in the mall is not deep enough in the prior art is solved, the accuracy of the position proper value analysis of the store is further ensured, the reliability guarantee is provided for pushing the intention lease store of the subsequent client, the grasping degree of the intention lease store of the client is improved to a certain extent, and the influence on the use feeling of the client is reduced.
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The invention will be further described with reference to the accompanying drawings, in which embodiments do not constitute any limitation of the invention, and other drawings can be obtained by one of ordinary skill in the art without inventive effort from the following drawings.
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a block diagram of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention provides an online intelligent analysis recommendation method for transferring a store to a renting line of a mall based on the internet of things, which comprises the following steps: s1, acquiring a mall position: the position of each development area is obtained from a store management center of the mall, a coordinate system is established by taking the central point of each development area as an origin, and then the position coordinates of each mall in each development area are obtained.
S2, analyzing geographic position priority coefficients of the mall: and analyzing geographic position priority coefficients corresponding to the malls based on the position coordinates of the malls in the development areas.
In a specific embodiment of the present invention, the geographic location priority coefficient corresponding to each mall is specifically analyzed by: s21: the distance between each mall and each development area is analyzed, and the calculation formula is as follows:wherein JL id Expressed as the distance of the ith mall from the d development area, (x) id ,y id ,z id ) Expressed as the position coordinates of the ith mall in the d development area, i expressed as the number of each mall, i=1, 2.
S22: comparing the distance between each mall and each development area with the preset matching distance between the mall and the development area, if the distance between a certain mall and a certain development area is smaller than or equal to the matching distance between the mall and the development area, marking the development area as the association area of the mall, further counting to obtain each association area of each mall, and counting the number of the association areas of each mall.
S23: the position coordinates of each mall in each associated area are obtained, and the average distance between the mall and the associated area is analyzed according to the position coordinates, wherein the calculation formula is as follows:where PJ is expressed as the average distance of the malls from the associated areas, p is expressed as the number of each associated area, p=1, 2,..q, n is expressed as the number of malls, q is expressed as the number of associated areas, (x ip ′,y ip ′,z ip ') is expressed as the position coordinates of the ith mall in the p-th associated area.
S23: the geographic position priority coefficient corresponding to each mall is analyzed, and the calculation formula is as follows:wherein YX i Expressed as geographic location priority coefficient corresponding to the ith mall, SL i Expressed as the number, lambda, of associated areas of the ith mall 1 、λ 2 Respectively representing the preset quantity of the associated areas and the weight factors of the average distance of the mall from the associated areas.
S3, store position priority coefficient analysis: and establishing a three-dimensional coordinate system by taking the center point of each mall as an origin, further obtaining the three-dimensional coordinates of the center point of each store and the center point of each outlet in each mall, and analyzing the position priority coefficients corresponding to each store in each mall by combining the geographic position priority coefficients corresponding to each mall.
In a specific embodiment of the present invention, the method for analyzing the position priority coefficient corresponding to each store in each mall includes: s31: according to the three-dimensional coordinates of the center points of all shops and the center points of all exits in all shops, the distances between all shops and all exits in all shops are analyzed, and the calculation formula is as follows: Wherein CK is imp Expressed as the distance between the mth store and the jth exit in the ith mall, (xc) im ,yc im ,zc im ) Three-dimensional coordinates expressed as the m-th store center point in the i-th store, (xk) ij ,yk ij ,zk ij ) Three-dimensional coordinates expressed as the center point of the j-th exit in the i-th mall, m expressed as the number of each store, m=1, 2.
S32: the outlet convenience coefficients corresponding to all shops in all shops are analyzed, and the calculation formula is as follows:wherein CY im Expressed as the exit convenience coefficient corresponding to the m store in the ith mall, k is expressed as the number of exits, and e is expressed as a natural constant.
S34: acquiring layer values corresponding to stores in each mall, matching the layer values with convenience factors corresponding to the layer values stored in a cloud database, matching the convenience factors corresponding to the stores in each mall, and marking the convenience factors as beta im 。
S33: analyzing position priority coefficients corresponding to stores in each mall, wherein the calculation formula is as follows: WY im ＝ln(1+YX i *γ 1 +CY im *γ 2 +β im *γ 3 ) Wherein WY im Expressed as a position priority coefficient corresponding to the mth store in the ith mall, gamma 1 、γ 2 、γ 3 The method is respectively expressed as the influence factors of the preset geographic position of the mall, the convenience in export and the convenience in layer value.
According to the invention, the value of the store is judged by combining the value of the business district and the position of the store in the mall through analysis of the intention lease store of the client, so that the problem that the position analysis of the store in the mall is not deep enough in the prior art is solved, the accuracy of the position proper value analysis of the store is further ensured, the reliability guarantee is provided for pushing the intention lease store of the subsequent client, the grasping degree of the intention lease store of the client is improved to a certain extent, and the influence on the use feeling of the client is reduced.
S4, mall recommendation index analysis: and extracting the living address corresponding to the demand customer from the store management center of the mall, acquiring the three-dimensional coordinates of the central point corresponding to the living address of the demand customer in each mall, marking the three-dimensional coordinates as the target three-dimensional coordinates of each mall, and further analyzing the recommendation index of each mall.
In a specific embodiment of the present invention, the recommendation index of each mall is specifically analyzed by: s41: according to the three-dimensional coordinates of each mall target, analyzing the distance suitability index corresponding to each mall, wherein the calculation formula is as follows:wherein JS i Expressed as the distance fitness index corresponding to the ith mall, (xm) i ,ym i ,zm i ) The three-dimensional coordinate of the target in the ith mall is expressed, and epsilon is expressed as a blocking factor corresponding to a preset unit distance.
S42: the business turnover and the people flow of each business corresponding to each month are extracted from the business store management center of each business store, and then the appropriate index of the campaigns corresponding to each business store is analyzed according to the business turnover and the people flow, and the calculation formula is as follows:wherein YD i Expressed as the index of the appropriate revenue corresponding to the ith mall, YY ir 、RL ir The business and the flow of people are respectively expressed as the business and the flow of people of the ith mall corresponding to the r month, r is the number of each month, r=1, 2, & gt, w and w are the number of months, and θ 1 、θ 2 Respectively expressed as a proper duty factor of the preset turnover and the camping of the people flow.
S43: the recommendation indexes of all malls are comprehensively analyzed, and the specific calculation formula is as follows:wherein the method comprises the steps ofRecommendation index, τ, expressed as the ith mall 1 、τ 2 Respectively expressed as correction factors to which preset distance is suitable and nutrient is suitable.
According to the method, analysis is carried out on the intent leasing shops of the clients, the business circles are analyzed, the attention degree on the geographical positions of the living places of the clients and the geographical positions of the malls is higher, and then the intent leasing shops of the clients are analyzed.
S5, target shop analysis: and obtaining information to be leased of each store in each mall from a mall management center, wherein the information to be leased comprises a leasing area, a leasing ending time point, leasing and leasing payment types, analyzing corresponding leasing time points of each store in each mall to accord with coefficients, further analyzing each store in each mall, and marking each store with the leasing time point meeting the requirements of the coefficients as each target store.
The rental payment types include annual payment, ji Fu and monthly payment.
In a specific embodiment of the present invention, the rental time points corresponding to the stores in the malls conform to coefficients, and the specific analysis method thereof is as follows: s51: and extracting a lease ending time point from information to be leased of each store in each mall, and extracting an interval duration threshold value from a cloud database.
S52: and obtaining the predicted lease time point of the demand client from the store re-lease platform of the mall and obtaining the current time point.
S53: substituting the lease ending time point of each store in each mall, the predicted lease time point of a demand client, the interval time length threshold value, the current time point and the preset proper adjustment time length into the lease time point coincidence coefficient corresponding to each store in each mall, wherein the calculation formula is as follows:wherein SJ im Expressed as a lease time point coincidence coefficient corresponding to an mth store in an ith mall, JH im The method is characterized in that the method is expressed as a lease ending time point of an mth store in an ith mall, XY is expressed as a predicted lease time point of a demand client, DQ is expressed as a current time point, and JG and TZ are respectively expressed as interval duration thresholds and proper adjustment durations.
In a specific embodiment of the present invention, the analysis method is that: comparing the corresponding lease time point coincidence coefficient of each store in each mall with a preset lease time point coincidence threshold, and if the lease time point coincidence coefficient of a store in a certain mall is larger than or equal to the lease time point coincidence threshold, judging that the lease time point coincidence coefficient of the store in the mall meets the requirement, thereby obtaining each store with the lease time point coincidence coefficient meeting the requirement.
S6, store recommendation index analysis: and acquiring information to be leased of each target store in each mall, acquiring demand leasing information of a demand client, and further analyzing recommendation indexes corresponding to each target store in each mall.
In a specific embodiment of the present invention, the demand lease information of the demand client includes a demand lease area, a demand lease, and a demand lease payment type.
In a specific embodiment of the present invention, the recommendation index corresponding to each target store in each mall is analyzed by the following method: s61: and extracting lease areas, lease rents and lease payment types from lease information of each target store in each mall.
S62: and extracting the demand leasing area, the demand leasing and the demand leasing payment type from the demand leasing information of the demand clients.
S63: and matching the rent payment type of each target store in each mall with the demand rent payment type of the demand client, if the rent payment type of a certain target store in a certain mall is successfully matched with the demand rent payment type of the demand client, marking the rent payment type matching index of the target store in the mall as rho, otherwise marking the rent payment type matching index as rho'.
S64: acquiring lease payment type matching indexes of target stores in each mall and marking the lease payment type matching indexes as eta ia Wherein eta ia The number of each target store, a=1, 2,...
S65: the method comprises the steps of obtaining position priority coefficients corresponding to target stores in each mall, comparing the leasing area and leasing rent of the target stores in each mall with the demand leasing area and demand rent of a demand client respectively, and further analyzing recommendation indexes corresponding to the target stores in each mall, wherein the calculation formula is as follows:wherein ZJ ia Expressed as recommendation index, WY, corresponding to the a-th target store in the i-th mall ia ' expressed as a position priority coefficient, eta, corresponding to the a-th target store in the i-th mall ia Lease payment type matching index, MJ, expressed as the ith target store in the ith mall ia 、MY ia Respectively expressed as the leasing area and leasing rent of an a target store in an i-th mall, and MJ and MY respectively expressed as the demand leasing area, the demand rent and omega of a demand customer 1 、ω 2 、ω 3 、ω 4 、ω 5 Respectively representing the preset target store position priority coefficient, the store recommendation index, the rent payment type matching index, the area coincidence and the rent coincidence as the proportion coefficient.
S7, analyzing a proper shop: and analyzing each proper store corresponding to the demand client based on the recommended index corresponding to each target store in each mall.
In a specific embodiment of the present invention, the recommendation index corresponding to each target store in each mall analyzes each suitable store corresponding to the demand client, and the specific analysis method includes: comparing the recommendation index corresponding to each target store in each mall with a preset recommendation index threshold, and if the recommendation index corresponding to a certain target store in a certain mall is greater than or equal to the recommendation index threshold, marking the target store in the mall as a proper store of a demand customer, thereby obtaining each proper store corresponding to the demand customer.
S8, processing in a proper shop: and sequencing all proper stores corresponding to the demand clients according to the order of the recommendation indexes from high to low, and displaying the proper stores on the display pages of the demand clients from high to low.
Referring to fig. 2, the invention provides an online intelligent analysis recommendation system for transferring a store to a renting line of a mall based on the internet of things, which comprises the following steps: the system comprises a mall position acquisition module, a mall geographic position priority coefficient analysis module, a store position priority coefficient analysis module, a mall recommendation index analysis module, a target store analysis module, a store recommendation index analysis module, a proper store processing module and a cloud database.
The system comprises a store position acquisition module, a store position priority coefficient analysis module, a store recommendation index analysis module, a target store analysis module, a store recommendation index analysis module and a cloud database, wherein the store position acquisition module is connected with the store position priority coefficient analysis module, the store position priority coefficient analysis module is connected with the store recommendation index analysis module, the store recommendation index analysis module is connected with the target store analysis module, the store recommendation index analysis module is connected with the proper store analysis module, the proper store analysis module is connected with the proper store processing module, and the cloud database is respectively connected with the target store analysis module and the store position priority coefficient analysis module.
The mall position acquisition module is used for acquiring the position of each development area from a mall store management center, and establishing a coordinate system by taking the central point of each development area as an origin, so as to acquire the position coordinates of each mall in each development area.
The mall geographic position priority coefficient analysis module is used for analyzing geographic position priority coefficients corresponding to the malls based on the position coordinates of the malls in the development areas.
The store position priority coefficient analysis module is used for establishing a three-dimensional coordinate system by taking the center point of each store as an origin, further obtaining the three-dimensional coordinates of the center point of each store and the center point of each outlet in each store, and analyzing the position priority coefficient corresponding to each store in each store by combining the geographic position priority coefficient corresponding to each store.
The mall recommendation index analysis module is used for extracting residence addresses corresponding to the demand clients from the mall store management center, obtaining three-dimensional coordinates of center points corresponding to the residence addresses of the demand clients in each mall, marking the three-dimensional coordinates as target three-dimensional coordinates of each mall, and further analyzing recommendation indexes of each mall.
The target store analysis module is used for acquiring information to be leased of each store in each store from the store management center, wherein the information to be leased comprises a leasing area, a leasing ending time point, leasing and leasing payment types, analyzing corresponding leasing time point coincidence coefficients of each store in each store, further analyzing each store with the leasing time point coincidence coefficients meeting requirements in each store, and marking the stores as each target store.
The store recommendation index analysis module is used for acquiring information to be leased of each target store in each mall, acquiring demand leasing information of a demand client and further analyzing recommendation indexes corresponding to each target store in each mall.
The proper store analysis module is used for analyzing all proper stores corresponding to the demand clients based on the recommended indexes corresponding to all target stores in all shops.
And the proper store processing module is used for sequencing all proper stores corresponding to the demand clients according to the order of the recommendation indexes from high to low, and displaying the proper stores on the display pages of the demand clients from high to low.
The cloud database is used for storing convenience factors corresponding to the values of all layers and storing interval duration thresholds.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.
Claims (10)
1. The online intelligent analysis recommendation method for transferring renting of shops of malls based on the Internet of things is characterized by comprising the following steps of:
s1, acquiring a mall position: acquiring the position of each development area from a store management center of the mall, and establishing a coordinate system by taking the central point of each development area as an origin, thereby acquiring the position coordinates of each mall in each development area;
s2, analyzing geographic position priority coefficients of the mall: analyzing geographic position priority coefficients corresponding to the malls based on the position coordinates of the malls in the development areas;
s3, store position priority coefficient analysis: establishing a three-dimensional coordinate system by taking the center point of each mall as an origin, further obtaining three-dimensional coordinates of the center point of each store and the center point of each outlet in each mall, and analyzing the position priority coefficients corresponding to each store in each mall by combining the geographic position priority coefficients corresponding to each mall;
S4, mall recommendation index analysis: the living address corresponding to the demand customer is extracted from the store management center of the mall, the three-dimensional coordinates of the central point corresponding to the living address of the demand customer in each mall are obtained and marked as target three-dimensional coordinates of each mall, and then the recommendation index of each mall is analyzed;
s5, target shop analysis: obtaining information to be leased of each store in each mall from a mall management center, wherein the information to be leased comprises leasing area, leasing ending time points, leasing and leasing payment types, analyzing corresponding leasing time points of each store in each mall to accord with coefficients, further analyzing each store with the leasing time points in each mall meeting the requirements of the coefficients, and marking the stores as target stores;
s6, store recommendation index analysis: acquiring information to be leased of each target store in each mall, acquiring demand leasing information of a demand client, and further analyzing recommendation indexes corresponding to each target store in each mall;
s7, analyzing a proper shop: analyzing each suitable store corresponding to the demand client based on the recommended index corresponding to each target store in each mall;
s8, processing in a proper shop: and sequencing all proper stores corresponding to the demand clients according to the order of the recommendation indexes from high to low, and displaying the proper stores on the display pages of the demand clients from high to low.
2. The online intelligent analysis recommendation method for transferring shops to rents based on the Internet of things of the invention is characterized by comprising the following steps: the geographic position priority coefficient corresponding to each mall comprises the following specific analysis method:
s21: the distance between each mall and each development area is analyzed, and the calculation formula is as follows:wherein JL id Expressed as the distance of the ith mall from the d development area, (x) id ,y id ,z id ) Expressed as the position coordinates of the ith mall in the d development area, i expressed as the number of each mall, i=1, 2,..n, d expressed as the number of each development area, d=1, 2,..s;
s22: comparing the distance between each mall and each development area with the preset matching distance between the mall and the development area, if the distance between a certain mall and a certain development area is smaller than or equal to the matching distance between the mall and the development area, marking the development area as an associated area of the mall, further counting to obtain each associated area of each mall, and counting the number of the associated areas of each mall;
s23: the position coordinates of each mall in each associated area are obtained, and the average distance between the mall and the associated area is analyzed according to the position coordinates, wherein the calculation formula is as follows:where PJ is expressed as the average distance of the malls from the associated areas, p is expressed as the number of each associated area, p=1, 2,..q, n is expressed as the number of malls, q is expressed as the number of associated areas, (x ip ′,y ip ′,z ip ') is represented as the position coordinates of the ith mall in the p-th associated area;
s23: the geographic position priority coefficient corresponding to each mall is analyzed, and the calculation formula is as follows:wherein YX i Expressed as geographic location priority coefficient corresponding to the ith mall, SL i Expressed as the number, lambda, of associated areas of the ith mall 1 、λ 2 Respectively representing the preset quantity of the associated areas and the weight factors of the average distance of the mall from the associated areas.
3. The online intelligent analysis recommendation method for transferring shops to rents based on the Internet of things of the invention is characterized by comprising the following steps: the method for analyzing the position priority coefficient corresponding to each store in each mall comprises the following specific steps:
s31: according to the three-dimensional coordinates of the center points of all shops and the center points of all exits in all shops, the distances between all shops and all exits in all shops are analyzed, and the calculation formula is as follows:wherein CK is imp Expressed as the distance between the mth store and the jth exit in the ith mall, (xc) im ,yc im ,zc im ) Three-dimensional coordinates expressed as the m-th store center point in the i-th store, (xk) ij ,yk ij ,zk ij ) Expressed as three-dimensional coordinates of the jth exit center point in the ith mall, m is expressed as the number of each store, m=1, 2,..i, j is denoted as the number of each outlet, j=1, 2,..k;
S32: the outlet convenience coefficients corresponding to all shops in all shops are analyzed, and the calculation formula is as follows:wherein CY im The method comprises the steps that the method is expressed as an export convenience coefficient corresponding to an mth store in an ith mall, k is expressed as the number of exports, and e is expressed as a natural constant;
s34: acquiring layer values corresponding to stores in each mall, and matching the layer values with convenience factors corresponding to the layer values stored in the cloud database to match the layer values to the stores in each mallA convenience factor, labeled as beta im ；
S33: analyzing position priority coefficients corresponding to stores in each mall, wherein the calculation formula is as follows: WY im ＝ln(1+YX i *γ 1 +CY im *γ 2 +β im *γ 3 ) Wherein WY im Expressed as a position priority coefficient corresponding to the mth store in the ith mall, gamma 1 、γ 2 、γ 3 The method is respectively expressed as the influence factors of the preset geographic position of the mall, the convenience in export and the convenience in layer value.
4. The online intelligent analysis recommendation method for transferring shops to rents based on the Internet of things of the invention is characterized by comprising the following steps: the recommendation index of each mall comprises the following specific analysis method:
s41: according to the three-dimensional coordinates of each mall target, analyzing the distance suitability index corresponding to each mall, wherein the calculation formula is as follows:wherein JS i Expressed as the distance fitness index corresponding to the ith mall, (xm) i ,ym i ,zm i ) The three-dimensional coordinate of the ith mall target is expressed, and epsilon is expressed as a blocking factor corresponding to a preset unit distance;
s42: the business turnover and the people flow of each business corresponding to each month are extracted from the business store management center of each business store, and then the appropriate index of the campaigns corresponding to each business store is analyzed according to the business turnover and the people flow, and the calculation formula is as follows:wherein YD i Expressed as the index of the appropriate revenue corresponding to the ith mall, YY ir 、RL ir The business and the flow of people are respectively expressed as the business and the flow of people of the ith mall corresponding to the r month, r is the number of each month, r=1, 2, & gt, w and w are the number of months, and θ 1 、θ 2 Respectively representing the proper duty factor of the campaigns of the preset turnover and the people flow;
s43: healdThe recommendation index of each mall is analyzed together, and the specific calculation formula is as follows:wherein->Recommendation index, τ, expressed as the ith mall 1 、τ 2 Respectively expressed as correction factors to which preset distance is suitable and nutrient is suitable.
5. The online intelligent analysis recommendation method for transferring shops to rents based on the Internet of things of the invention is characterized by comprising the following steps: the corresponding lease time points of each store in each mall accord with coefficients, and the specific analysis method comprises the following steps:
s51: extracting lease ending time points from information to be leased of each store in each mall, and extracting interval duration thresholds from a cloud database;
S52: obtaining an estimated lease time point of a demand customer from a store re-lease platform of a mall and obtaining a current time point;
s53: substituting the lease ending time point of each store in each mall, the predicted lease time point of a demand client, the interval time length threshold value, the current time point and the preset proper adjustment time length into the lease time point coincidence coefficient corresponding to each store in each mall, wherein the calculation formula is as follows:wherein SJ im Expressed as a lease time point coincidence coefficient corresponding to an mth store in an ith mall, JH im The method is characterized in that the method is expressed as a lease ending time point of an mth store in an ith mall, XY is expressed as a predicted lease time point of a demand client, DQ is expressed as a current time point, and JG and TZ are respectively expressed as interval duration thresholds and proper adjustment durations.
6. The online intelligent analysis recommendation method for transferring shops to rents based on the Internet of things of the invention is characterized by comprising the following steps: the analysis method for analyzing each store meeting the requirement of the coefficient at the lease time point in each mall comprises the following steps: comparing the corresponding lease time point coincidence coefficient of each store in each mall with a preset lease time point coincidence threshold, and if the lease time point coincidence coefficient of a store in a certain mall is larger than or equal to the lease time point coincidence threshold, judging that the lease time point coincidence coefficient of the store in the mall meets the requirement, thereby obtaining each store with the lease time point coincidence coefficient meeting the requirement.
7. The online intelligent analysis recommendation method for transferring shops to rents based on the Internet of things of the invention is characterized by comprising the following steps: the demand leasing information of the demand client specifically comprises a demand leasing area, a demand lease and a demand lease payment type.
8. The online intelligent analysis recommendation method for transferring shops to rents based on the Internet of things according to claim 4, wherein the online intelligent analysis recommendation method is characterized by comprising the following steps of: the recommendation indexes corresponding to each target store in each mall are analyzed by the following steps:
s61: extracting lease areas, lease rent and lease payment types from information to be leased of each target store in each mall;
s62: extracting a demand lease area, a demand lease and a demand lease payment type from demand lease information of a demand client;
s63: matching the lease payment type of each target store in each mall with the demand lease payment type of a demand client, if the lease payment type of a certain target store in a certain mall is successfully matched with the demand lease payment type of the demand client, marking the lease payment type matching index of the target store in the mall as rho, otherwise marking the lease payment type matching index as rho';
s64: acquiring lease payment type matching indexes of target stores in each mall and marking the lease payment type matching indexes as eta ia Wherein eta ia =ρ or ρ', a is expressed as the number of each target store, a=1, 2, b;
s65: the method comprises the steps of obtaining position priority coefficients corresponding to target stores in each mall, comparing the leasing area and leasing rent of the target stores in each mall with the demand leasing area and demand rent of a demand client respectively, and further analyzing recommendation indexes corresponding to the target stores in each mall, wherein the calculation formula is as follows:wherein ZJ ia Expressed as recommendation index, WY, corresponding to the a-th target store in the i-th mall ia ' expressed as a position priority coefficient, eta, corresponding to the a-th target store in the i-th mall ia Lease payment type matching index, MJ, expressed as the ith target store in the ith mall ia 、MY ia Respectively expressed as the leasing area and leasing rent of an a target store in an i-th mall, and MJ and MY respectively expressed as the demand leasing area, the demand rent and omega of a demand customer 1 、ω 2 、ω 3 、ω 4 、ω 5 Respectively representing the preset target store position priority coefficient, the store recommendation index, the rent payment type matching index, the area coincidence and the rent coincidence as the proportion coefficient.
9. The online intelligent analysis recommendation method for transferring shops to rents based on the Internet of things of the invention is characterized by comprising the following steps: the recommendation index corresponding to each target store in each mall analyzes each suitable store corresponding to the demand client, and the specific analysis method comprises the following steps: comparing the recommendation index corresponding to each target store in each mall with a preset recommendation index threshold, and if the recommendation index corresponding to a certain target store in a certain mall is greater than or equal to the recommendation index threshold, marking the target store in the mall as a proper store of a demand customer, thereby obtaining each proper store corresponding to the demand customer.
10. Online intelligent analysis recommendation system of mall shop renting based on thing networking, its characterized in that: comprising the following steps:
the mall position acquisition module is used for acquiring the position of each development area from a mall store management center, and establishing a coordinate system by taking the central point of each development area as an origin, so as to acquire the position coordinates of each mall in each development area;
the mall geographic position priority coefficient analysis module is used for analyzing geographic position priority coefficients corresponding to the malls based on the position coordinates of the malls in the development areas;
the store position priority coefficient analysis module is used for establishing a three-dimensional coordinate system by taking the center point of each store as an origin, further obtaining the three-dimensional coordinates of the center point of each store and the center point of each outlet in each store, and analyzing the position priority coefficient corresponding to each store in each store by combining the geographic position priority coefficient corresponding to each store;
the mall recommendation index analysis module is used for extracting residence addresses corresponding to the demand clients from the mall store management center, acquiring three-dimensional coordinates of center points corresponding to the residence addresses of the demand clients in each mall, marking the three-dimensional coordinates as target three-dimensional coordinates of each mall, and further analyzing recommendation indexes of each mall;
The target store analysis module is used for acquiring information to be leased of each store in each store from the store management center, wherein the information to be leased comprises a leasing area, a leasing ending time point, a leasing rent and a rent payment type, analyzing the corresponding lease time point coincidence coefficient of each store in each store, further analyzing each store with the lease time point coincidence coefficient meeting the requirement in each store, and marking the stores as each target store;
the store recommendation index analysis module is used for acquiring information to be leased of each target store in each mall, acquiring demand leasing information of a demand client and further analyzing recommendation indexes corresponding to each target store in each mall;
the proper store analysis module is used for analyzing all proper stores corresponding to the demand clients based on the recommended indexes corresponding to all target stores in all shops;
the proper shop processing module is used for sequencing all proper shops corresponding to the demand clients according to the sequence of the recommendation indexes from high to low, and displaying the proper shops on the display pages of the demand clients from high to low;
the cloud database is used for storing convenience factors corresponding to the layers of values and storing interval duration thresholds.
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