CN112837097A - Shop revenue data determination method and system and electronic equipment - Google Patents
Shop revenue data determination method and system and electronic equipment Download PDFInfo
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Abstract
The embodiment of the application provides a shop revenue data determining method and system and electronic equipment. Wherein the method comprises the following steps: determining a plurality of second stores; wherein the second store is a similar store to the first store; obtaining multi-dimensional data for the plurality of second stores; determining the multidimensional data of the first store according to the multidimensional data of the plurality of second stores; analyzing the multidimensional data of the first store to obtain revenue data of the first store; and displaying the first store earning data on an interactive interface. The shop revenue data obtained by the technical scheme provided by the embodiment of the application has higher precision and the scheme is simple and efficient.
Description
Technical Field
The application relates to the technical field of computers, in particular to a method and a system for determining shop revenue data and electronic equipment.
Background
The earnings refer to various business earnings obtained by providing various services or selling products in the production and operation activities of the stores, are related to the survival and development of the stores, and have important significance for the store operation.
Conventionally, when a new store is analyzed, similar stores are determined mainly by adopting a man-made subjective judgment mode, so that the new store revenue is analyzed according to the revenue of the similar stores. However, the above method has strong subjectivity, requires a lot of manpower and time, and has the problems of low accuracy, high cost and the like.
Disclosure of Invention
In view of the above, the present application provides a store revenue data determination method, system and electronic device that solve the above problems, or at least partially solve the above problems.
In one embodiment of the present application, a store revenue data determination method is provided. The method comprises the following steps:
determining a plurality of second stores; wherein the second store is a similar store to the first store;
obtaining multi-dimensional data for the plurality of second stores;
determining the multidimensional data of the first store according to the multidimensional data of the plurality of second stores;
analyzing the multidimensional data of the first store to obtain revenue data of the first store;
and displaying the first store earning data on an interactive interface.
In an embodiment of the present application, a store revenue data determination system is provided. The system comprises:
the server is used for determining a plurality of second stores; wherein the second store is a similar store to the first store; obtaining multi-dimensional data for the plurality of second stores; determining the multidimensional data of the first store according to the multidimensional data of the plurality of second stores; analyzing the multidimensional data of the first store to obtain revenue data of the first store;
and the client is used for providing an interactive interface and displaying the earning data of the first store on the interactive interface.
In one embodiment of the present application, an electronic device is provided. The electronic device includes: a memory and a processor, wherein,
the memory is used for storing programs;
the processor, coupled with the memory, to execute the program stored in the memory to:
determining a plurality of second stores; wherein the second store is a similar store to the first store;
obtaining multi-dimensional data for the plurality of second stores;
determining the multidimensional data of the first store according to the multidimensional data of the plurality of second stores;
analyzing the multidimensional data of the first store to obtain revenue data of the first store;
and displaying the first store earning data on an interactive interface.
According to the technical scheme, after the multi-dimensional data of the first store are determined based on the multi-dimensional data of a plurality of second stores similar to the first store, the multi-dimensional data of the first store are analyzed to obtain revenue data of the first store, and the revenue data of the first store are visually displayed on an interactive interface. The shop revenue data in the scheme is obtained by a scientific and objective mode, the precision is high, and the scheme is simple and efficient.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required to be utilized in the description of the embodiments or the prior art are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained according to the drawings without creative efforts for those skilled in the art.
Fig. 1 is a schematic flow chart of a method for determining store revenue data according to an embodiment of the present disclosure;
fig. 2a is a block diagram of a system for determining revenue data of a store according to an embodiment of the present disclosure;
fig. 2b is a schematic diagram of a specific form of a shop revenue data determining system according to an embodiment of the present application;
fig. 3 is a block diagram illustrating a structure of an apparatus for determining store revenue data according to an embodiment of the present disclosure;
fig. 4 is a block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.
In some of the flows described in the specification, claims, and above-described figures of the present application, a number of operations are included that occur in a particular order, which operations may be performed out of order or in parallel as they occur herein. The sequence numbers of the operations, e.g., 101, 102, etc., are used merely to distinguish between the various operations, and do not represent any order of execution per se. Additionally, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first", "second", etc. in this document are used for distinguishing different messages, devices, modules, etc., and do not represent a sequential order, nor limit the types of "first" and "second" to be different. In the present application, the term "or/and" is only one kind of association relationship describing the associated object, and means that three relationships may exist, for example: a or/and B, which means that A can exist independently, A and B can exist simultaneously, and B can exist independently; the "/" character in this application generally indicates that the objects associated with each other are in an "or" relationship. In addition, the embodiments described below are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The technical solutions of the embodiments of the present application will be described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart illustrating a store revenue data determination method according to an embodiment of the present disclosure. The method can be executed by the shop revenue data determination device, the device can be realized by software and/or hardware, the device can be configured in electronic equipment such as a computer, a server and the like, and the method provided by the embodiment can be applied to any scene for determining the shop revenue data. As shown in fig. 1, the method comprises the steps of:
101. determining a plurality of second stores; wherein the second store is a similar store to the first store;
102. obtaining multi-dimensional data for the plurality of second stores;
103. determining the multidimensional data of the first store according to the multidimensional data of the plurality of second stores;
104. analyzing the multidimensional data of the first store to obtain revenue data of the first store;
105. and displaying the first store earning data on an interactive interface.
In the above 101, the first store is a store newly opened or to be newly opened in a certain area, such as a business or a merchant, and is collectively referred to as a new store. In practical applications, in order to better grasp the economic benefits and the development trend of the first store, an enterprise or a merchant often analyzes revenue data that may be generated by the first store, so as to adjust the operation strategy of the first store based on the analysis result, thereby improving the competitiveness. However, in the process of analyzing revenue data of a first store, a conventional scheme mainly determines a second store similar to the first store by using an artificial subjective method, and then analyzes the revenue data of the first store based on the revenue data of the second store, but the artificial subjective method for determining the similar stores has strong subjectivity, and the obtained revenue data of the first store has low accuracy. In order to solve the problem, in the process of determining the second store similar to the first store, the similarity between the existing store and the first store is calculated by using, for example, a KNN nearest neighbor algorithm based on the attribute characteristics of the existing store and the attribute characteristics of the first store, so as to determine the second store similar to the first store according to the similarity calculation result.
That is, in a specific implementation technical solution, the step 101 "determining a plurality of second stores" may specifically include:
1011. acquiring attribute characteristics of a plurality of existing stores and attribute characteristics of the first store;
1012. vectorizing the attribute characteristics of the existing stores and the attribute characteristics of the first store, and calculating the similarity between the existing stores and the first store according to a vectorization result;
1013. and taking the existing stores with the similarity larger than a threshold value as the second store.
In 1011 above, the attribute characteristics of the existing store or the first store may include, but are not limited to: the region where the store is located, the types of the services which can be provided in the region where the store is located, the density of the houses in the region where the store is located, the number of the population in the region where the store is located, the number of the competitive store stores in the region where the store is located and the like; the service categories refer to types or categories of services, and the auction stores may be defined by the layout of existing stores, such as the distance between stores and the number of service categories provided by stores. In specific implementation, the attribute characteristics of the existing store or the first store may be obtained by using the AI big data, but may also be obtained by using other methods, such as a manual research method, and is not limited herein. However, in consideration of the accuracy of the similarity calculation result, it is necessary to ensure that the obtained attribute features have comprehensiveness and accuracy, and therefore, in this embodiment, the AI big data method is preferably used to obtain the attribute features of the existing store or the first store. It should be noted that the existing store and the first store may be located in the same area or different areas, where the area may be a direct district city, a downtown, a county, a street, and the like, and this embodiment is not limited.
In 1012, in order to facilitate similarity calculation based on the attribute features of the existing stores and the attribute characteristics of the first store, vectorization processing needs to be performed on the attribute features of the existing stores and the attribute features of the first store before calculating the similarity between the existing stores and the first store, in specific implementation, vectorization may be performed on the attribute features of the existing stores and the attribute features of the first store by using an existing vectorization method to obtain the attribute feature vectors corresponding to the existing stores and the attribute feature vectors corresponding to the first store, and a specific vectorization process may refer to the prior art. Further, when calculating the similarity between each existing store and the first store according to the vectorization result, a KNN nearest neighbor algorithm may be adopted, and specifically, the similarity between each existing store and the first store may be calculated by using a cosine similarity algorithm between vectors, using the KNN nearest neighbor algorithm, where the cosine similarity algorithm corresponds to the following calculation formula:
in the above formula1Is an attribute feature vector of an existing store,/2Is the attribute feature vector of the first store, d represents the number of attribute features in the attribute feature vector, l1iThe ith attribute feature, l, in the attribute feature vector representing the existing store2iRepresenting the ith attribute feature in the attribute feature vector for the first store. sim (l)1,l2) Attribute feature vector l for existing stores1And attribute feature vector l of first store2Cosine similarity between them.
Besides, the similarity between the existing stores and the first store is calculated by using a cosine similarity algorithm between vectors, other algorithms may be used to calculate the similarity between the existing stores and the first store, such as a shortest distance (single link), a longest distance (complete link), a Jaccard similarity (Jaccard), a K-S (Kolmogorov-Smirnov) test, a K-L (Kullback-Leibler) divergence, and the like, which is not limited in this embodiment. The greater the similarity value between the existing store and the first store, the greater the similarity between the existing store and the first store.
In 1013 above, after calculating the similarity between each existing store and the first store, the existing store with the similarity greater than a threshold value may be used as the second store, where the threshold value may be determined according to actual needs, for example, the threshold value may be 0.7, 0.85, and the like, and is not limited herein. In addition, the existing stores may be ranked from large to small according to the similarity between the existing stores and the first store, and the top N existing stores ranked in the front may be taken as the second store.
In the step 102, the multidimensional data corresponding to each of the plurality of second stores may be automatically and directly acquired from the internet by using an AI big data method. The multidimensional data has relevance to the shop providing service, and specifically, the multidimensional data may include, but is not limited to, at least part of the following data: the number of users placing orders in the area where the store is located, the number of orders completed in the area where the store is located, the number of competitive stores in the area where the store is located, the number of people who can provide services corresponding to the store in the area where the store is located, the types of services provided in the area where the store is located, the number of people who can provide various services in the area where the store is located, the number of people in the area where the store is located, the density of rooms in the area where the store is located, the business area of the store and the like. The number of outstanding orders may be the number of lost orders, i.e. the number of orders cancelled by the user.
Generally, when revenue data of one store is analyzed, multidimensional data of the store is often synthesized for analysis, but considering that the first store is a new store and lacks corresponding operation data, it is difficult to obtain multidimensional data of the first store comprehensively, and since a store similar to the first store is a second store, multidimensional data of the second store and multidimensional data of the second store have a large correlation, multidimensional data of the first store can be determined based on multidimensional data of the second store. That is, in a specific implementation solution, the step 103 "determining the multidimensional data of the first store according to the multidimensional data of the plurality of second stores" may specifically include the following steps:
1031. determining a weight coefficient corresponding to each second store according to the similarity between each second store and the first store;
1032. and determining the multidimensional data of the first store based on the multidimensional data of the second stores and the corresponding weight coefficients.
In a specific implementation, the multidimensional data of the first store may be obtained by performing weighted average calculation on the multidimensional data of each second store based on the multidimensional data of each second store and the corresponding weight coefficient. For example, if the plurality of second stores specifically include a second store a and a second store B, where the multidimensional data of the second store a includes a1, a2, and a3, the multidimensional data of the second store B includes B1, B2, and B3, and the weight coefficient corresponding to the second store a is determined to be 0.7 according to the similarity between the second store a and the first store C, and the weight coefficient corresponding to the second store B is determined to be 0.3 according to the similarity between the second store B and the first store C, then correspondingly, according to the multidimensional degrees of the second store a and the second store B and their respective corresponding weight coefficients, the multidimensional data of the first store can be determined as follows:
c1＝(0.7*a1+0.3*b1)/2；c2＝(0.7*a2+0.3*b2)/2；c3＝(0.7*a3+0.3*b3)/2
wherein, a1 and b1, a2 and b2, and a3 and b3 are the same type dimension data respectively.
After obtaining the multidimensional data of the first store, the multidimensional data of the first store may be analyzed to obtain revenue data of the first store, and in specific implementation, parameters corresponding to the respective dimensional data of the first store may be set, so that the revenue data of the first store is obtained based on the respective dimensional data of the first store and the parameters corresponding to the respective dimensional data. Accordingly, in an implementation solution, the step 104 "analyzing the multidimensional data of the first store to obtain the revenue data of the first store", may specifically include:
1041. setting parameters corresponding to the dimensional data of the first store;
1042. and obtaining the earning data of the first store according to the dimensional data of the first store and the parameters corresponding to the dimensional data.
In 1041, one or more combined manners may be adopted to set the parameter corresponding to each piece of dimensional data of the first store, for example, the parameter corresponding to each piece of dimensional data of the first store may be defined and set according to the influence degree of each piece of dimensional data on the store revenue data singly, or the parameter corresponding to each piece of dimensional data of the first store may be set in a comprehensive and customized manner by combining other manners, for example, combining the type to which each piece of dimensional data belongs, which is not limited herein. The following description will be given taking as an example the parameters corresponding to the respective dimensional data for which the first store is defined and set according to the type to which the respective dimensional data for the first store belongs. Specifically, for example, in the step 103, the multidimensional data of the first store C obtained based on the multidimensional data of the second store a and the second store B includes C1, C2, and C3, where C1 is set to represent the business area of the first store, C2 represents the number of people in the area where the first store can provide services corresponding to the stores, and C3 represents the number of completed orders in the area where the first store is located, when the parameters corresponding to C1, C2, and C3 are set by self-definition, the following method may be specifically used:
when the parameter corresponding to c1 is set, the parameters corresponding to c1 can be self-defined by referring to the lawn effect indicators of similar stores; wherein the plateau effect index can reflect the productivity of store area, for example, setting the business area of a store as 100m2When the daily income is 9000 yuan, the daily plateau of the store is 9000 yuan/100 m290 yuan/m2. When setting the parameter corresponding to c2, the parameter corresponding to c2 may be defined by referring to human effectiveness indicators of similar stores, where the human effectiveness indicators may reflect the rationality of marketing ability and typesetting people of people who can provide the service corresponding to the store, for example, setting the business income of a store to 9000 yuan per day, and the number of people who can provide the service corresponding to the store to 9 people, so that the human effectiveness of the store is 9000 yuan/9 people/1000 yuan per day. Similarly, the parameters corresponding to the setting c3 may be defined by referring to the order effectiveness index of the similar store, where the order effectiveness index may reflect the consumption ability of the consumer, the pricing corresponding to the goods or the services provided, and the like, for example, if the daily income of a store is 9000 yuan, and the number of completed orders is 20, the daily order effectiveness of the store is 9000 yuan/20 yuan or 450 yuan.
It should be noted here that the multidimensional data of the first store shown in the above example is only an example, and in practice, the multidimensional data of the first store is not limited thereto. In addition, with reference to the above description, the parameters corresponding to the other dimension data of the first store can be customized.
1042, the first shop revenue data may be obtained by performing weighted summation on the dimensional data of the first shop and the parameters corresponding to the dimensional data. Specifically, the step 1042 "obtaining the revenue data of the first store according to the dimensional data of the first store and the parameters corresponding to the dimensional data" may be implemented by specifically adopting the following steps:
a11, summing the product values of the dimensional data and the corresponding parameters to obtain a sum value;
a12, determining a numerical value interval to which the sum belongs;
a13, based on the numerical value interval to which the sum belongs, normalizing the sum to obtain the earning data of the first store.
For example, continuing with the above example, if the multidimensional data of the first store C includes C1, C2, and C3, and the parameter corresponding to C1 is w1, the parameter corresponding to C2 is w2, and the parameter corresponding to C3 is w3, the sum Num obtained by summing the products of the multidimensional data and the corresponding parameters is:
Num＝w1*c1+w2*c2+w3*c3
further, normalization processing is carried out according to the numerical range of the Num, and therefore the earning data of the first store are obtained according to the normalization result. Specifically, if Num belongs to the value range [ a, b ] and the normal range [ min, max ], Num can be normalized to the range [ min, max ] according to the following formula:
the Num' represents a result obtained by normalizing Num, that is, revenue data of the first store C.
For example, assuming that Num is 17500 nt and belongs to a numerical range of [10000 nt, 20000 nt ] and a normal range of less than 10000 nt, i.e., [0 nt, 1000 nt ], the normalization result corresponding to Num is 7500 nt, i.e., the revenue data of the first store is 7500 nt, according to the above normalization formula
It should be noted that the obtained first store revenue data may be revenue data that may be generated by taking time, day, month, season, year, or the like as a statistical time period, and specifically, the obtained first store revenue data is determined by a time period corresponding to the multidimensional data of the first store, that is, by a time period corresponding to the acquired multidimensional data of the second store similar to the first store. For example, when the day is taken as the statistical time period, the multidimensional data of each second store in a certain day is obtained, the multidimensional data of the first store in the corresponding day can be obtained based on the multidimensional data of each second store, and thus the revenue data of the first store obtained based on the multidimensional data of the first store is also the business income which can be generated by the first store in the corresponding day.
In the above 105, the first store revenue data is displayed on the interactive interface, and the obtained first store revenue data is visually presented to the user.
According to the technical scheme provided by the embodiment, after the multi-dimensional data of the first store is determined based on the multi-dimensional data of a plurality of second stores similar to the first store, the multi-dimensional data of the first store is analyzed to obtain the earning data of the first store, and the earning data of the first store is displayed on an interactive interface. The method has the advantages that the earning data of the stores in the scheme are obtained in a scientific and objective mode, the accuracy is high, the scheme is simple and efficient, and therefore multi-dimensional effect references such as expected profits, public praise and overall strategic layout of the stores can be provided for enterprises or merchants through the earning data of the first store.
In another implementation solution, the revenue data of the first store may be determined according to the revenue data of a plurality of second stores, specifically, the weight coefficient corresponding to each second store may be determined according to the similarity between each second store and the first store, and the revenue data of each second store and the corresponding weight coefficient may be combined to obtain the revenue data of the first store.
That is, further, the method provided in this embodiment further includes the following steps:
106. acquiring revenue data of a plurality of second stores;
107. determining a weight coefficient corresponding to each second store according to the similarity between each second store and the first store;
108. and obtaining the revenue data of the first store according to the revenue number of each second store and the corresponding weight coefficient.
In specific implementation, on the basis of determining a plurality of second stores similar to the first store, revenue and earning data corresponding to different statistical time periods of the plurality of second stores can be acquired by using an AI big data technology, wherein the time periods can be set according to actual needs, and specifically, time, day, month, season, year or the like can be taken as a time period; and then, further determining a weight coefficient corresponding to each second store according to the similarity between each second store and the first store, so as to obtain a weighted average value between revenue data of different time periods of each second store and the corresponding weight coefficient to obtain revenue data corresponding to the corresponding time period of the first store.
For example, assuming that the second store a and the second store B are stores similar to the first store C, and the AI big data means is used to respectively obtain the monthly revenue of the second store a as Rev _ a and the monthly revenue of the second store B as Rev _ B, and respectively determine that the weight coefficient corresponding to the second store a is 0.7 according to the similarity between the second store a and the first store C, and determine that the weight coefficient corresponding to the second store B is 0.3 according to the similarity between the second store B and the first store C, the monthly revenue Rev _ C of the first store C can be obtained according to the following formula:
Rev_C＝(0.7*Rev_A+0.3*Rev_B)/2
based on the above related content, the second store is determined in a scientific objective manner and has a higher similarity with the first store, and thus the revenue data of the second store has a stronger correlation with the revenue data of the first store, so that the first store revenue data obtained according to the revenue data of the second store and the corresponding weight coefficient has higher accuracy, and the scheme is simple and efficient. In addition, the earning data of the first store can provide a multidimensional effect reference such as expected profit, public praise, overall strategic layout of stores and the like for enterprises or merchants.
Here, it should be noted that: the method provided by the embodiment can be applied to any application scenario in which store operation data needs to be determined, where the type of the store may be, but is not limited to, a home administration type, a catering type, an express delivery type, a hairdressing and beauty type, a sports and fitness type, a hotel type, an education type, a retail goods type, a real estate type, an information consulting service type, a travel industry type, and the like, and this embodiment is not particularly limited thereto.
The technical scheme provided by the embodiment of the method can be realized based on the following hardware system. Specifically, fig. 2a and 2b show schematic structural diagrams of a store revenue data determination system provided by an embodiment of the present application. As shown in fig. 2a, the system specifically includes:
the server 201 is used for determining a plurality of second stores; wherein the second store is a similar store to the first store; obtaining multi-dimensional data for the plurality of second stores; determining the multidimensional data of the first store according to the multidimensional data of the plurality of second stores; analyzing the multidimensional data of the first store to obtain revenue data of the first store;
and the client 202 is used for providing an interactive interface, and displaying the first store revenue data on the interactive interface.
In specific implementation, referring to fig. 2b, the server 201 includes a device capable of performing data processing and having a communication function. In some embodiments, the server device 201 may be implemented as a conventional server, a cloud host, a virtual center, and other devices, which is not limited in this embodiment. The Cloud server is a computer set based on Cloud Computing, that is, the Cloud server is composed of a large number of hosts or network servers based on Cloud Computing (Cloud Computing), wherein the Cloud Computing is one of distributed Computing and is a super virtual computer composed of a group of loosely coupled computers.
The client 202 may be a device capable of interacting with a user and having a communication function. The implementation of the client 202 may vary in different application scenarios. For example, in some scenarios, client 201 may be: a mobile phone, a tablet computer, a Personal Digital Assistant (PDA), a desktop computer, a notebook computer, an intelligent wearable device (such as an intelligent glasses, an intelligent watch, etc.), etc., which is not limited in this embodiment.
In the system for determining the shop revenue data according to this embodiment, the data interaction process between the server 201 and the client 202 can be implemented based on the communication connection relationship established between the server 201 and the client 202. The specific communication connection mode may depend on the actual application scenario.
In some exemplary embodiments, the server 201 and the client 202 may communicate with each other in a wired communication manner and a wireless communication manner. The WIreless communication mode includes short-distance communication modes such as bluetooth, ZigBee, infrared, WiFi (WIreless-Fidelity), long-distance WIreless communication modes such as LORA, and WIreless communication mode based on a mobile network. When the mobile network is connected through communication, the network format of the mobile network may be any one of 3d (gsm), 3G (WCDMA, TD-SCDMA, CDMA2000, UTMS), 4G (LTE), 4G + (LTE +), 5G, WiMax, and the like.
Further, the server 201 is further configured to obtain revenue of a plurality of second stores; determining a weight coefficient corresponding to each second store according to the similarity between each second store and the first store; and obtaining the revenue data of the first store according to the revenue amount of each second store and the corresponding weight coefficient.
Here, it should be noted that: for the content of each step in the store revenue data determining system provided in this embodiment, which is not described in detail in the above embodiments, reference may be made to the corresponding content in the above embodiments, and details are not described here again. In addition, the system for determining store revenue data provided in this embodiment may further include, in addition to the above steps, some or all of the other steps in the above embodiments, for which reference may be specifically made to corresponding contents in the above embodiments, and details are not described here.
Fig. 3 is a block diagram showing a structure of a store revenue data determination apparatus according to an embodiment of the present application. As shown in fig. 3, the apparatus specifically includes: ,
a first determining module 301, configured to determine a plurality of second stores; wherein the second store is a similar store to the first store;
a first obtaining module 302, configured to obtain multidimensional data of the plurality of second stores;
a second determination model 303, configured to determine multidimensional data of the first store according to multidimensional data of the plurality of second stores;
the analysis module 304 is configured to analyze the multidimensional data of the first store to obtain revenue data of the first store;
and a display module 305, configured to display the revenue data of the first store on an interactive interface.
According to the technical scheme provided by the embodiment, after the multi-dimensional data of the first store is determined based on the multi-dimensional data of a plurality of second stores similar to the first store, the multi-dimensional data of the first store is analyzed to obtain the earning data of the first store, and the earning data of the first store is displayed on an interactive interface. The shop revenue data in the scheme is obtained by utilizing a scientific and objective mode, the precision is high, and the scheme is simple and efficient.
Further, when the first determining module 301 is configured to determine a plurality of second stores, specifically:
acquiring attribute characteristics of a plurality of existing stores and attribute characteristics of the first store;
vectorizing the attribute characteristics of the existing stores and the attribute characteristics of the first store to calculate the similarity between the existing stores and the first store according to a vectorization result;
and taking the existing stores with the similarity larger than a threshold value as the second store.
Wherein the attribute features include: the method comprises the following steps of providing service categories in the area where the store is located, providing room density in the area where the store is located, the number of population in the area where the store is located and the number of competitive stores in the area where the store is located.
Further, when the second determining module 303 is configured to determine the multidimensional data of the first store according to the multidimensional data of the plurality of second stores, the second determining module is specifically configured to: determining a weight coefficient corresponding to each second store according to the similarity between each second store and the first store; and determining the multidimensional data of the first store based on the multidimensional data of the second stores and the corresponding weight coefficients.
Further, the multidimensional data includes at least part of the following data: the number of users placing orders in the area of the store, the number of orders completed in the area of the store, the number of competitive stores in the area of the store, the number of personnel providing services corresponding to the store in the area of the store, the types of services provided in the area of the store, the number of personnel providing various services in the area of the store, the number of population in the area of the store, the density of rooms in the area of the store, and the business area of the store.
Further, the analysis module 304, when configured to analyze the multidimensional data of the first store to obtain the revenue data of the first store, is specifically configured to: setting parameters corresponding to the dimensional data of the first store; and obtaining the earning data of the first store according to the dimensional data of the first store and the parameters corresponding to the dimensional data.
Further, the analysis module 304 is specifically configured to, when being configured to obtain the revenue data of the first store according to the dimensional data of the first store and the parameters corresponding to the dimensional data: summing the product values of the dimensional data and the corresponding parameters to obtain a sum value; determining a numerical value interval to which the sum belongs; and based on the numerical value interval to which the sum value belongs, carrying out normalization processing on the sum value to obtain the revenue data of the first store.
Further, the apparatus provided in this embodiment further includes:
the second acquisition module is used for acquiring the earnings of a plurality of second stores;
the third determining module is used for determining the weight coefficient corresponding to each second store according to the similarity between each second store and the first store;
and the obtaining module is used for obtaining the revenue data of the first store according to the revenue amount of each second store and the corresponding weight coefficient.
Here, it should be noted that: the device for determining store revenue data according to this embodiment may execute the method for determining store revenue data according to the embodiment shown in fig. 1, and the implementation principle and technical effect are not repeated. The specific implementation manner of the operation performed by each module or unit in the store revenue data determination device in the above embodiment has been described in detail in the embodiment related to the method, and will not be described in detail here.
Fig. 4 shows a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 4, the electronic apparatus includes: a memory 401 and a processor 402. The memory 401 may be configured to store other various data to support operations on the sensors. Examples of such data include instructions for any application or method operating on the sensor. The memory 401 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The processor 402, coupled to the memory 401, is configured to execute the program stored in the memory 401 to:
determining a plurality of second stores; wherein the second store is a similar store to the first store;
obtaining multi-dimensional data for the plurality of second stores;
determining the multidimensional data of the first store according to the multidimensional data of the plurality of second stores;
analyzing the multidimensional data of the first store to obtain revenue data of the first store;
and displaying the first store earning data on an interactive interface.
When the processor 402 executes the program in the memory 401, in addition to the above functions, other functions may be implemented, which may be specifically referred to the description of the foregoing embodiments.
Further, as shown in fig. 4, the electronic device further includes: communication components 403, display 404, power components 405, and audio components 406, among other components. Only some of the components are schematically shown in fig. 4, and the electronic device is not meant to include only the components shown in fig. 4.
Accordingly, the present application further provides a computer-readable storage medium storing a computer program, where the computer program can implement the steps or functions of the store revenue data determination method provided in the foregoing embodiments when executed by a computer.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (10)
1. A method for determining store revenue data, comprising:
determining a plurality of second stores; wherein the second store is a similar store to the first store;
obtaining multi-dimensional data for the plurality of second stores;
determining the multidimensional data of the first store according to the multidimensional data of the plurality of second stores;
analyzing the multidimensional data of the first store to obtain revenue data of the first store;
and displaying the first store earning data on an interactive interface.
2. The method of claim 1, wherein determining a plurality of second stores comprises:
acquiring attribute characteristics of a plurality of existing stores and attribute characteristics of the first store;
vectorizing the attribute characteristics of the existing stores and the attribute characteristics of the first store, and calculating the similarity between the existing stores and the first store according to a vectorization result;
and taking the existing stores with the similarity larger than a threshold value as the second store.
Wherein the attribute features include: the method comprises the following steps of providing service categories in the area where the store is located, providing room density in the area where the store is located, the number of population in the area where the store is located and the number of competitive stores in the area where the store is located.
3. The method of claim 2, wherein determining the multidimensional data for the first store from the multidimensional data for the plurality of second stores comprises:
determining a weight coefficient corresponding to each second store according to the similarity between each second store and the first store;
and determining the multidimensional data of the first store based on the multidimensional data of the second stores and the corresponding weight coefficients.
4. The method of any one of claims 1 to 4, wherein the multidimensional data comprises at least part of:
the number of users placing orders in the area where the store is located, the number of orders completed in the area where the store is located, the number of competitive stores in the area where the store is located, the number of people who can provide services corresponding to the store in the area where the store is located, the types of services provided in the area where the store is located, the number of people who can provide various services in the area where the store is located, the number of people in the area where the store is located, and the room density in the area where the store is located.
5. The method of claim 4, wherein analyzing the multidimensional data for the first store to derive the first store revenue data comprises:
setting parameters corresponding to the dimensional data of the first store;
and obtaining the earning data of the first store according to the dimensional data of the first store and the parameters corresponding to the dimensional data.
6. The method of claim 4, comprising: obtaining the revenue data of the first store according to the dimensional data of the first store and the parameters corresponding to the dimensional data, including:
summing the product values of the dimensional data and the corresponding parameters to obtain a sum value;
determining a numerical value interval to which the sum belongs;
and based on the numerical value interval to which the sum value belongs, carrying out normalization processing on the sum value to obtain the revenue data of the first store.
7. The method of claim 1, further comprising:
acquiring revenue data of a plurality of second stores;
determining a weight coefficient corresponding to each second store according to the similarity between each second store and the first store;
and obtaining the revenue data of the first store according to the revenue data of the second stores and the corresponding weight coefficient.
8. A store revenue data determination system, comprising:
the server is used for determining a plurality of second stores; wherein the second store is a similar store to the first store; obtaining multi-dimensional data for the plurality of second stores; determining the multidimensional data of the first store according to the multidimensional data of the plurality of second stores; analyzing the multidimensional data of the first store to obtain revenue data of the first store;
and the client is used for providing an interactive interface and displaying the earning data of the first store on the interactive interface.
9. The system of claim 8,
the server is further used for acquiring revenue data of a plurality of second stores; determining a weight coefficient corresponding to each second store according to the similarity between each second store and the first store; and obtaining the revenue data of the first store according to the revenue data of the second stores and the corresponding weight coefficient.
10. An electronic device, comprising: a memory and a processor, wherein,
the memory is used for storing programs;
the processor, coupled with the memory, to execute the program stored in the memory to:
determining a plurality of second stores; wherein the second store is a similar store to the first store;
obtaining multi-dimensional data for the plurality of second stores;
determining the multidimensional data of the first store according to the multidimensional data of the plurality of second stores;
analyzing the multidimensional data of the first store to obtain revenue data of the first store;
and displaying the first store earning data on an interactive interface.
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