CN113570262A - Method and device for exhibition of industry based on artificial intelligence, computer equipment and storage medium - Google Patents
Method and device for exhibition of industry based on artificial intelligence, computer equipment and storage medium Download PDFInfo
- Publication number
- CN113570262A CN113570262A CN202110873980.6A CN202110873980A CN113570262A CN 113570262 A CN113570262 A CN 113570262A CN 202110873980 A CN202110873980 A CN 202110873980A CN 113570262 A CN113570262 A CN 113570262A
- Authority
- CN
- China
- Prior art keywords
- exhibition
- area
- target
- business
- industry
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06398—Performance of employee with respect to a job function
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06311—Scheduling, planning or task assignment for a person or group
Abstract
The application belongs to the technical field of artificial intelligence, and provides an exhibition industry method, an exhibition industry device, computer equipment and a storage medium based on artificial intelligence, wherein the method comprises the following steps: when a exhibition industry scheduling request is received, acquiring track data corresponding to each salesman, and generating exhibition industry tracks corresponding to each salesman according to the track data; determining a plurality of exhibition areas to be selected according to the exhibition track, and acquiring evaluation factors corresponding to the exhibition areas to be selected; evaluating each to-be-selected exhibition industry area according to the evaluation factor to obtain an evaluation result corresponding to each to-be-selected exhibition industry area; determining a target exhibition industry area from a plurality of exhibition industry areas to be selected according to the evaluation result; and inquiring the target to-be-dispatched salesman matched with the target exhibition area to dispatch to the target exhibition area. The method and the system can not only provide convenience for the manager to master the exhibition trends of the subordinate salesmen, but also realize intelligent salesmen scheduling, and can meet the scheduling decision requirements of the manager.
Description
Technical Field
The present application relates to the field of artificial intelligence technologies, and in particular, to an artificial intelligence-based method and apparatus for exhibition of business, a computer device, and a storage medium.
Background
How to master the actual situation of the salesman in the exhibition industry is a problem which is very concerned by managers in various sales industries at present. The solutions in the market at present include modes such as 'timing card punching', 'telephone drawing inspection post', 'working daily report' and the like. The method is very dependent on the initiative of the salesman, and is easy to disturb the normal operation exhibition rhythm of the salesman; the 'telephone drawing inspection post' is the condition that a manager makes a call randomly to inquire exhibition on the day of the exhibition of a subordinate salesman, the working difficulty of the manager is increased by the mode, and the exhibition of the salesman is also influenced very much; the 'working daily report' is a written report formed by the business member on the daily exhibition track and the achievement after the daily exhibition is finished, the mode can reduce the influence of the manager on the exhibition process to the maximum extent, but the business member is very dependent on whether the business member reports really or not, and because the business member pays attention to the day, the timely intervention on the daily exhibition situation is insufficient. Therefore, on the premise that the actual conditions of the operator in the exhibition industry cannot be known in time, the manager cannot effectively make scheduling decisions of the operator.
Disclosure of Invention
The application mainly aims to provide an artificial intelligence-based exhibition industry method, an artificial intelligence-based exhibition industry device, a computer device and a readable storage medium, and aims to solve the technical problem that effective decision-making cannot be carried out on the scheduling of an operator due to the fact that the actual situation of the operator in the exhibition industry is difficult to grasp in time.
In a first aspect, the present application provides an artificial intelligence-based exhibition industry method, including:
when a exhibition industry scheduling request is received, acquiring track data corresponding to each salesman, and generating exhibition industry tracks corresponding to each salesman according to the track data;
determining a plurality of exhibition areas to be selected according to the exhibition track, and acquiring evaluation factors corresponding to the exhibition areas to be selected;
evaluating each exhibition area to be selected according to the evaluation factor to obtain an evaluation result corresponding to each exhibition area to be selected;
determining a target exhibition industry area from the plurality of exhibition industry areas to be selected according to the evaluation result;
and inquiring the target to-be-dispatched salesman matched with the target exhibition area to dispatch to the target exhibition area.
In a second aspect, the present application further provides an artificial intelligence-based exhibition industry device, the device comprising:
the generation module is used for acquiring the track data corresponding to each salesman when the exhibition service scheduling request is received, and generating the exhibition service track corresponding to each salesman according to the track data;
the acquisition module is used for determining a plurality of exhibition areas to be selected according to the exhibition track and acquiring evaluation factors corresponding to the exhibition areas to be selected;
the evaluation module is used for evaluating each exhibition area to be selected according to the evaluation factors to obtain an evaluation result corresponding to each exhibition area to be selected;
the determining module is used for determining a target exhibition industry area from the plurality of exhibition industry areas to be selected according to the evaluation result;
and the scheduling module is used for inquiring the target business to be scheduled matched with the target exhibition area and scheduling the target business to be scheduled to the target exhibition area.
In a third aspect, the present application further provides a computer device comprising a processor, a memory, and a computer program stored on the memory and executable by the processor, wherein the computer program, when executed by the processor, implements the steps of the artificial intelligence based exhibition method as described above.
In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, where the computer program, when executed by a processor, implements the artificial intelligence based exhibition method as described above.
The application discloses an exhibition method, a device, computer equipment and a storage medium based on artificial intelligence, wherein when an exhibition scheduling request is received, trajectory data corresponding to each salesman is obtained, an exhibition trajectory corresponding to each salesman is generated according to the trajectory data corresponding to each salesman, a plurality of exhibition areas to be selected are determined according to the exhibition trajectory corresponding to each salesman, evaluation factors corresponding to the exhibition areas to be selected are obtained, the exhibition areas to be selected are evaluated according to the evaluation factors, evaluation results corresponding to the exhibition areas to be selected are obtained, target exhibition areas are determined from the exhibition areas to be selected according to the evaluation results, and finally the target employee to be scheduled matched with the target exhibition areas is inquired and scheduled to the target exhibition areas. Through the mode, on one hand, the exhibition track of the business personnel going out of the exhibition is visually displayed and checked, convenience is provided for a manager to master the exhibition trends of the subordinate business personnel, on the other hand, the target exhibition area is accurately obtained based on the exhibition track, and then the matched target business personnel to be dispatched are dispatched to the target exhibition area in a targeted mode, so that the exhibition efficiency of the business personnel can be effectively improved, the intelligent dispatching of the business personnel is realized, and the dispatching decision requirement of the manager can be met.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic flow chart diagram illustrating an embodiment of an artificial intelligence-based exhibition method according to the present application;
fig. 2a is an exemplary diagram of an exhibition APP uploading trajectory data of a salesman to a server according to an embodiment of the exhibition method based on artificial intelligence of the present application;
FIG. 2b is an exemplary diagram illustrating an exhibition business trajectory and an exhibition business scheduling suggestion of a serviceman obtained by a manager through an exhibition business APP according to an embodiment of the artificial intelligence-based exhibition business method;
FIG. 3 is a schematic flow chart illustrating another embodiment of an artificial intelligence-based exhibition method according to the present application;
FIG. 4 is a schematic block diagram of an artificial intelligence based exhibition apparatus according to an embodiment of the present application;
fig. 5 is a block diagram schematically illustrating a structure of a computer device according to an embodiment of the present application.
The implementation, functional features and advantages of the objectives of the present application will be further explained with reference to the accompanying drawings.
Detailed Description
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, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. 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 flow diagrams depicted in the figures are merely illustrative and do not necessarily include all of the elements and operations/steps, nor do they necessarily have to be performed in the order depicted. For example, some operations/steps may be decomposed, combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
It is to be understood that the terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification of the present application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
The embodiment of the application provides an artificial intelligence-based exhibition industry method and device, computer equipment and a storage medium. The exhibition method based on artificial intelligence is mainly applied to exhibition equipment based on artificial intelligence, the exhibition equipment based on artificial intelligence can be terminal equipment with a data processing function, such as a terminal (such as a mobile terminal, a PC and the like) and a server, and the exhibition equipment based on artificial intelligence bears an exhibition system.
The exhibition system can become an exhibition application program or a part of the exhibition application program and is installed in the terminal, so that the terminal has the exhibition function; the exhibition system can also be applied to a background server of the exhibition application program, so that the server provides the exhibition function for the exhibition application program in the terminal.
Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the features of the embodiments can be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a schematic flow chart of an artificial intelligence-based exhibition method according to an embodiment of the present disclosure.
As shown in fig. 1, the artificial intelligence-based exhibition industry method includes steps S101 to S105.
Step S101, when receiving a exhibition industry scheduling request, obtaining track data corresponding to each salesman, and generating exhibition industry tracks corresponding to each salesman according to the track data.
The application of the artificial intelligence-based exhibition method to a server is taken as an example for explanation.
As shown in fig. 2a, when any salesman in the salesman team goes out of the exhibition industry, the salesman needs to log in an exhibition application (exhibition industry APP) on a terminal (such as a smart phone) of the salesman, and after the login is successful, the exhibition application displays prompt information requesting to grant positioning permission on a page where the salesman has operation permission in a popup mode, so that the salesman can select whether to grant the positioning permission of the exhibition application; when the exhibition application program receives a confirmation instruction that the operator grants the positioning permission, a service process for collecting the geographic position and reporting the geographic position to the server is started at the background, namely the geographic position of the operator is collected once every preset time, and the collected time and the geographic position are uploaded to the server, wherein the preset time can be flexibly taken according to actual needs, for example, 10 minutes, and the geographic position comprises longitude and latitude, a detailed address and the like.
After receiving the acquisition time and the geographic position acquired by the exhibition application program each time, the server associates the acquisition time and the geographic position acquired each time with the identifier of the operator as trajectory data and stores the trajectory data in a preset database, such as a Redis database. The identification of the operator can be an account number of the operator logging in the exhibition application program. Therefore, the preset database stores the track data of the staff member team.
It can be understood that as long as the exhibition application program obtains positioning authorization once, the exhibition application program defaults to permanently have positioning authority (unless the positioning authorization is released), and then does not pop up to request the salesperson to grant the positioning authority, so that the trajectory data of the salesperson going out of the exhibition can be automatically uploaded to the server for storage without disturbing the exhibition rhythm of the salesperson, and for the salesperson, the salesperson only needs to concentrate on the exhibition and does not need to pay attention to when to get a card and report.
In some embodiments, in order to improve transmission security, the exhibition application program encrypts the acquisition time and the geographic position of each acquisition by using an encryption algorithm agreed with the server and uploads the encrypted acquisition time and geographic position to the server. Illustratively, the preset encryption algorithm may be an AES encryption algorithm. And after receiving the encrypted acquisition time and the encrypted geographic position, the server decrypts the acquisition time and the geographic position by adopting a decryption algorithm corresponding to the encryption algorithm, uses the decrypted acquisition time and the decrypted geographic position as track data, associates the track data with the identifier of the corresponding salesman and stores the track data in a preset database.
As can be appreciated, the exhibition application enables multi-level rights management from manager to team of business personnel by setting user rights. That is, the administrator has the operation right of the management page in the exhibition application program and has the right of managing the staff member team.
When the manager needs to know the exhibition situation of the staff member in due time and make a scheduling decision, as shown in fig. 2b, the manager may trigger an exhibition scheduling request on a management page of the exhibition application installed at the terminal thereof to request exhibition trajectories and exhibition suggestions from the server. The exhibition industry scheduling request carries a target exhibition industry time period, the target exhibition industry time period can be any time period for the business staff to carry out exhibition industry work, and the manager can select the target exhibition industry time period according to actual requirements, for example, the exhibition industry time period from the past two hours to the current time. When the server receives the exhibition scheduling request, acquiring the track data of each salesman in the salesman team in the target exhibition time period from a preset database, and generating the exhibition track corresponding to each salesman according to the track data of each salesman in the target exhibition time period.
Illustratively, track data corresponding to each salesman in a target exhibition period is searched in a preset database according to the identification of each salesman, and then the track data with the stay time of each salesman exceeding a preset time threshold is selected from the searched track data corresponding to each salesman. And then, according to the sequence relation of the acquisition time of the trajectory data corresponding to each selected salesman, directionally connecting the geographic positions on the map in pairs to generate an exhibition business trajectory corresponding to each salesman, displaying the exhibition business trajectory on an activity trajectory page, and generating display parameters of the exhibition business trajectory corresponding to each salesman, wherein the display parameters comprise salesman identifications and indicators for indicating the real-time positions of the salesman. Therefore, the manager can visually check the exhibition industry track of each salesman in the salesman team on the map by clicking the activity track page entering the management page, so that the exhibition industry dynamics of the salesman team when going out of the exhibition industry can be mastered.
Step S102, determining a plurality of exhibition areas to be selected according to the exhibition track, and obtaining evaluation factors corresponding to the exhibition areas to be selected.
And then screening out a plurality of exhibition business areas to be selected according to the exhibition business tracks corresponding to the business personnel.
In some embodiments, as shown in fig. 3, the determining a plurality of exhibition areas to be selected according to the exhibition trajectory includes substeps S1021 to S1024.
And a substep S1021, selecting track points with the total amount of customers larger than a preset threshold value from the exhibition business track.
And traversing the exhibition trajectories corresponding to the salesmen, and comparing the exhibition trajectories with the pre-stored customer distribution data so as to find out the trajectory points matched with the target areas in the pre-stored customer distribution data from the exhibition trajectories. The pre-stored customer distribution data comprises a target area and a total amount of customers in the target area, wherein the target area can be an area where an intended or transaction customer group is located, and the total amount of customers can be the total amount of consulted customers, intended customers or transaction customers. And comparing the total amount of the clients in the target area matched with the found track points in the pre-stored client distribution data with a preset threshold value, so as to screen out the track points of which the total amount of the clients is greater than the preset threshold value.
The substep S1022 is to cluster the screened track points by adopting a preset first clustering algorithm so as to aggregate the screened track points into a plurality of track point clusters;
and S1023, calculating the central point of each track point cluster by adopting a preset second clustering algorithm, clustering the screened track points by taking each central point as a clustering center, and aggregating the screened track points into a plurality of new track point clusters.
Further, clustering is carried out on the screened track points, and a mode of combining a preset first clustering algorithm and a preset second clustering algorithm is adopted during clustering. For example, the preset first clustering algorithm may be a DBSCAN algorithm, and the preset second clustering algorithm may be a K-means algorithm. For ease of understanding, the DBSCAN algorithm and the K-means algorithm are first introduced, and the DBSCAN (Density-Based Clustering of Applications with Noise) algorithm is a relatively representative Density-Based Clustering algorithm, which defines clusters as the largest set of Density-connected points, can cluster regions with sufficiently high Density into clusters, and can find clusters of arbitrary shapes in a noisy Spatial database, and the emphasis of the DBSCAN algorithm is to select an appropriate aggregation radius parameter and a threshold PtMins (minimum number of a points) number that needs to be specified for aggregation. The K-means algorithm is a typical distance-based clustering algorithm, and adopts distance as an evaluation index of similarity, that is, the closer the two objects are, the greater the similarity of the two objects is, the closer the two objects are, the cluster is considered to be composed of the objects, so that the compact and independent cluster is taken as a final target, and the K-means algorithm focuses on the selection of a K value at the clustering center.
Considering that track points screened from exhibition tracks are too dispersed and a proper K value is difficult to select, the DBSCAN algorithm is adopted to cluster the screened track points, and then the clustering result based on the DBSCAN algorithm is adopted to cluster the track points by adopting the K-means algorithm.
When the DBSCAN algorithm is adopted to cluster the screened track points, the spherical distance is adopted to measure the distance of the screened track points to be used as a radius parameter of the aggregation, for example, 2 kilometers is used as the radius parameter of the aggregation, and the MinPts number can be set according to actual requirements. Then, according to the set radius parameter and the MinPts number, the screened track points are clustered by adopting a DBSCAN algorithm, so that the screened track points are aggregated into a plurality of track point clusters.
Furthermore, each cluster of track points is used as a new input, the central point of each cluster of track points is calculated by utilizing iterative aggregation of a K-means algorithm, then each central point is used as a clustering center, the distance between each screened track point and each clustering center is calculated, each screened track point is allocated to the clustering center closest to the screened track point, and when the distribution of the screened track points is completed, the screened track points can be aggregated into a plurality of new track point clusters.
And S1024, determining a plurality of to-be-selected exhibition areas according to the new track point clusters.
And taking the area covered by each new track point cluster as a to-be-selected exhibition area, thereby obtaining a plurality of to-be-selected exhibition areas.
After a plurality of exhibition areas to be selected are determined, the plurality of exhibition areas to be selected are not taken as a final exhibition area, and in order to improve the accuracy of the exhibition area, evaluation factors of the exhibition areas to be selected are also obtained so as to evaluate the exhibition areas to be selected.
In some embodiments, the evaluation factors include a transaction rate and a customer density, and the obtaining of the evaluation factor corresponding to each to-be-selected exhibition business area specifically includes: acquiring customer visit volume and customer volume corresponding to each to-be-selected exhibition area, and calculating the volume rate corresponding to each to-be-selected exhibition area according to the customer visit volume and the customer volume; and acquiring the total amount of customers corresponding to each exhibition area to be selected, and calculating the customer density corresponding to each exhibition area to be selected according to the total amount of the customers.
The client visit volume and the client volume of each to-be-selected exhibition area are obtained, the volume of the to-be-selected exhibition area can be calculated according to the client visit volume and the client volume of each to-be-selected exhibition area and by combining the following calculation formula,
R＝n/m*100％
wherein, R represents the transaction rate of the to-be-selected exhibition business area, m represents the customer visit volume of the to-be-selected exhibition business area, and n represents the customer transaction volume of the to-be-selected exhibition business area.
The total amount of the customers in each to-be-selected exhibition area is obtained, the customer density of each to-be-selected exhibition area can be calculated according to the total amount of the customers in each to-be-selected exhibition area and by combining the calculation formula shown in the specification,
ρ＝c/πr2
wherein rho represents the customer density of the exhibition business area to be selected, c represents the total quantity of customers in the exhibition business area to be selected, and r represents the radiation radius of the exhibition business area to be selected.
And S103, evaluating each to-be-selected exhibition industry area according to the evaluation factors to obtain an evaluation result corresponding to each to-be-selected exhibition industry area.
After the evaluation factor corresponding to each exhibition area to be selected is obtained, each exhibition area to be selected is evaluated according to the evaluation factor corresponding to each exhibition area to be selected, and the evaluation result of each exhibition area to be selected is obtained.
In some embodiments, the evaluating each exhibition industry area to be selected according to the evaluation factor to obtain an evaluation result corresponding to each exhibition industry area to be selected specifically includes: respectively configuring a first weight value corresponding to the transaction rate and a second weight value corresponding to the customer density; according to the transaction rate, the first weight value, the customer density and the second weight value, combining a preset scoring calculation formula s-mu1R+μ2Rho, calculating to obtain the score value corresponding to each exhibition area to be selected as an evaluation result, wherein s represents the score value corresponding to the exhibition area to be selected, R represents the transaction rate of the exhibition area to be selected, and mu1Represents the first weight value, p represents the customer density of the exhibition area to be selected, mu2Representing the second weight value.
And evaluating each to-be-selected exhibition industry area, wherein the evaluation can be realized by adopting a mode of distributing weights and adding. That is, the transaction rate is set to correspond to the first weight value (in μ)1Expressed) and a second weight value (in μ) corresponding to the customer density2Express), mu1And mu2Is taken to satisfy mu1+μ21 is enough. Therefore, the scoring value of each to-be-selected exhibition area can be calculated according to the following calculation formula,
s＝μ1R+μ2ρ
wherein s represents the score value corresponding to the to-be-selected exhibition business area.
Therefore, the calculated score value of each exhibition area to be selected is used as the corresponding evaluation result of each exhibition area to be selected.
And S104, determining a target exhibition industry area from the plurality of exhibition industry areas to be selected according to the evaluation result.
After the evaluation result corresponding to each to-be-selected exhibition area is obtained, the target exhibition area can be further screened out from the to-be-selected exhibition areas according to the evaluation result, and the purpose of improving the accuracy of the exhibition range is achieved.
For example, the score values of each to-be-selected exhibition area may be sorted, and then a preset number of to-be-selected exhibition areas are selected as target exhibition areas in a descending order of the score values, where the preset number may be flexibly set according to actual needs, for example, the score values of the to-be-selected exhibition areas arranged in the top five places. And comparing the score value of each exhibition area to be selected with a preset score threshold value, and taking the exhibition area to be selected with the score value exceeding the preset score threshold value as a target exhibition area.
And S105, inquiring a target to-be-dispatched salesman matched with the target exhibition area and dispatching the target to-be-dispatched salesman to the target exhibition area.
And after the target exhibition business area is determined, inquiring the target to-be-dispatched salesman matched with the target exhibition business area to dispatch to the target exhibition business area.
In some embodiments, querying a target to-be-scheduled salesman matched with the target exhibition area to schedule to the target exhibition area specifically includes: determining the preference service personnel type of the target exhibition area; and selecting the service personnel belonging to the preference service personnel type, and scheduling the service personnel to be scheduled to the target exhibition area as the service personnel to be scheduled matched with the target exhibition area.
The method comprises the steps of determining the preference salesman type of a target exhibition area, then selecting the salesman to be dispatched from the salesman to be dispatched, wherein the salesman to be dispatched belongs to the preference salesman type, and dispatching the selected salesman to be dispatched to the target exhibition area as the target salesman to be dispatched, wherein the target salesman to be dispatched is matched with the target exhibition area.
In some embodiments, the determining the preferred type of the servicer in the target exhibition area specifically includes: acquiring business data corresponding to each business member acquiring the deal in the target exhibition area; determining the preference degree of the target exhibition business area to each trader who acquires the deal according to the business data corresponding to each trader who acquires the deal; determining the preference business personnel of the target exhibition area from all business personnel who obtain deals according to the preference degree; and acquiring the characteristic data corresponding to the preference service member, and determining the preference service member type of the target exhibition service area according to the characteristic data corresponding to the preference service member.
Namely, business data corresponding to each businessman who succeeds in exhibition in the target exhibition area, namely, who obtains the deal is obtained, and then the preference degree of the target exhibition area for each businessman who obtains the deal is measured according to the business data corresponding to each businessman who obtains the deal.
In some embodiments, the business data includes a volume of bargain and a volume of customer bargain, and the determining, according to the business data corresponding to each salesman who obtains the bargain, a preference degree of the target exhibition business area for each salesman who obtains the bargain specifically includes: according to the corresponding transaction amount of each waiter obtaining the transaction and the client transaction amount, combining with a preset preference degree calculation formula
Calculating to obtain the preference degree of the target exhibition area to each waiter who obtains deals, wherein diThe preference degree of the target exhibition area to the business member I for acquiring the deal is represented, I represents the business member set for acquiring the deal in the target exhibition area, I belongs to I, giRepresenting the amount of customer trades in the target exhibition area by the trader i who has taken the trades, aiThe trading volume of the trader i who gets the trading in the target exhibition area is shown, and the lambda represents the weight corresponding to the trading volume.
The business data comprises the client volume and volume of bargain in the target exhibition area, the preference degree of the target exhibition area to each businessman who acquires the bargain is calculated according to the business data corresponding to each businessman who acquires the bargain and by combining the following preference degree calculation formula,
wherein d isiIndicating the preference of the target exhibition area for the trader i who gets the deal, diThe value of (a) is between 0 and 1;
i represents a salesman set for getting deals in a target exhibition area, and I belongs to I;
gishowing the client volume of the trader i in the target exhibition area；
aiShowing the transaction amount of the transaction acquisition salesman i in the target exhibition area;
the lambda is expressed as the weight corresponding to the sum of the quotients, and can be flexibly set according to practical experience, such as 0.5.
After the preference degree of the target exhibition area to each trader acquiring the deal is obtained through calculation in the mode, the preference trader of the target exhibition area is determined from each trader acquiring the deal according to the preference degree of each trader acquiring the deal. Illustratively, the preference degrees are sorted, and then a preset number of the salesmen are selected from the salesmen who have made the deal in the order of the preference degrees from large to small, and are used as the preference salesmen of the target exhibition area.
Further, the characteristic data of the preference service personnel is obtained, and the characteristic data can be regarded as the reflection and description of the preference service personnel from different angles, including age, gender, education degree, working seniority and the like. The characteristic data of the preference facilitator is tagged to determine the type of preference facilitator. For example, a preset tag template may be obtained, the feature data of the preference service provider is filled into the preset tag template, so as to obtain a feature tag corresponding to the preference service provider, and then the preference service provider is classified according to the feature tag, for example, the tag content of the class a preference service provider is (gender is female, 30 years to 35 years old, the subject calendar, the working is more than 2 years and less than 5 years old), and the tag content of the class B preference service provider is (gender is male, 35 years to 40 years old, the subject calendar, the working is more than 5 years old).
Therefore, the characteristic data corresponding to the waiter to be dispatched is obtained, then the characteristic data corresponding to the waiter to be dispatched is labeled to obtain the characteristic label corresponding to the waiter to be dispatched, and then the characteristic label corresponding to the waiter to be dispatched is compared with the characteristic labels of various preference waiters, so that the waiter to be dispatched belonging to the preference waiter type is selected from the waiter to be dispatched and is used as the target waiter to be dispatched, which is matched with the target exhibition area, to be dispatched to the target exhibition area.
And loading the exhibition industry suggestion for dispatching the target to-be-dispatched salesman to the target exhibition industry area to a management page of an exhibition industry application program for reference of a manager. Therefore, intelligent staff scheduling is realized.
According to the exhibition method based on artificial intelligence provided by the embodiment, when an exhibition scheduling request is received, trajectory data corresponding to each salesman is obtained, an exhibition trajectory corresponding to each salesman is generated according to the trajectory data corresponding to each salesman, a plurality of exhibition areas to be selected are determined according to the exhibition trajectory corresponding to each salesman, an evaluation factor corresponding to each exhibition area to be selected is obtained, each exhibition area to be selected is evaluated according to the evaluation factor, an evaluation result corresponding to each exhibition area to be selected is obtained, a target exhibition area is determined from the plurality of exhibition areas to be selected according to the evaluation result, and finally the target employee to be scheduled matched with the target exhibition area is inquired and scheduled to the target exhibition area. Through the mode, on one hand, the exhibition track of the business personnel going out of the exhibition is visually displayed and checked, convenience is provided for a manager to master the exhibition trends of the subordinate business personnel, on the other hand, the target exhibition area is accurately obtained based on the exhibition track, and then the matched target business personnel to be dispatched are dispatched to the target exhibition area in a targeted mode, so that the exhibition efficiency of the business personnel can be effectively improved, the intelligent dispatching of the business personnel is realized, and the dispatching decision requirement of the manager can be met.
Referring to fig. 4, fig. 4 is a schematic block diagram of an artificial intelligence-based exhibition device according to an embodiment of the present disclosure.
As shown in fig. 4, the apparatus 400 includes: a generation module 401, an acquisition module 402, an evaluation module 403, a determination module 404 and a scheduling module 405.
The generation module 401 is configured to, when receiving a exhibition industry scheduling request, obtain trajectory data corresponding to each salesman, and generate an exhibition industry trajectory corresponding to each salesman according to the trajectory data;
an obtaining module 402, configured to determine multiple exhibition areas to be selected according to the exhibition trajectory, and obtain an evaluation factor corresponding to each exhibition area to be selected;
the evaluation module 403 is configured to evaluate each exhibition area to be selected according to the evaluation factor to obtain an evaluation result corresponding to each exhibition area to be selected;
a determining module 404, configured to determine a target exhibition industry area from the multiple exhibition industry areas to be selected according to the evaluation result;
and the scheduling module 405 is configured to query that the target to-be-scheduled salesman matched with the target exhibition business area is scheduled to the target exhibition business area.
It should be noted that, as will be clear to those skilled in the art, for convenience and brevity of description, the specific working processes of the apparatus and the modules and units described above may refer to the corresponding processes in the aforementioned embodiment of the exhibition method based on artificial intelligence, and are not described herein again.
The apparatus provided by the above embodiments may be implemented in the form of a computer program that can be run on a computer device as shown in fig. 5.
Referring to fig. 5, fig. 5 is a schematic block diagram of a computer device according to an embodiment of the present disclosure. The computer device may be a Personal Computer (PC), a server, or the like having a data processing function.
As shown in fig. 5, the computer device includes a processor, a memory, and a network interface connected by a system bus, wherein the memory may include a nonvolatile storage medium and an internal memory.
The non-volatile storage medium may store an operating system and a computer program. The computer program includes program instructions that, when executed, cause a processor to perform any one of the artificial intelligence based approaches to exhibition of business.
The processor is used for providing calculation and control capability and supporting the operation of the whole computer equipment.
The internal memory provides an environment for the execution of a computer program on a non-volatile storage medium, which when executed by the processor, causes the processor to perform any one of the artificial intelligence based methods of exhibition of business.
The network interface is used for network communication, such as sending assigned tasks and the like. Those skilled in the art will appreciate that the architecture shown in fig. 5 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
It should be understood that the Processor may be a Central Processing Unit (CPU), and the Processor may be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, etc. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Wherein, in one embodiment, the processor is configured to execute a computer program stored in the memory to implement the steps of:
when a exhibition industry scheduling request is received, acquiring track data corresponding to each salesman, and generating exhibition industry tracks corresponding to each salesman according to the track data; determining a plurality of exhibition areas to be selected according to the exhibition track, and acquiring evaluation factors corresponding to the exhibition areas to be selected; evaluating each exhibition area to be selected according to the evaluation factor to obtain an evaluation result corresponding to each exhibition area to be selected; determining a target exhibition industry area from the plurality of exhibition industry areas to be selected according to the evaluation result; and inquiring the target to-be-dispatched salesman matched with the target exhibition area to dispatch to the target exhibition area.
In some embodiments, the processor, when determining a plurality of exhibition areas to be selected according to the exhibition business trajectory, is configured to:
screening track points with the total amount of customers larger than a preset threshold value from the exhibition track;
clustering the screened track points by adopting a preset first clustering algorithm so as to aggregate the screened track points into a plurality of track point clusters;
calculating the central point of each track point cluster by adopting a preset second clustering algorithm, and clustering the screened track points by taking each central point as a clustering center so as to aggregate the screened track points into a plurality of new track point clusters;
and determining a plurality of to-be-selected exhibition areas according to the new track point clusters.
In some embodiments, when the target to-be-scheduled salesman who inquires the target exhibition area and matches the query is scheduled to the target exhibition area, the processor is configured to:
determining the preference service personnel type of the target exhibition area;
and selecting the waited businessman belonging to the preference businessman type, and serving as the target waited businessman matched with the target exhibition business area to be dispatched to the target exhibition business area.
In some embodiments, said processor, when implementing said determining a preferred employee type for said target exhibition area, is configured to implement:
acquiring business data corresponding to each business member acquiring the deal in the target exhibition area;
determining the preference degree of the target exhibition business area to each trader who acquires the deal according to the business data corresponding to each trader who acquires the deal;
determining the preference business personnel of the target exhibition area from all business personnel who obtain deals according to the preference degree;
and acquiring the characteristic data corresponding to the preference service member, and determining the preference service member type of the target exhibition service area according to the characteristic data corresponding to the preference service member.
In some embodiments, the business data includes a volume of deals and a volume of customer deals, and the processor is configured to, when determining a preference degree of the target exhibition area for each of the business members who make deals according to the business data corresponding to each of the business members who make deals:
according to the corresponding transaction amount of each waiter obtaining the transaction and the client transaction amount, combining with a preset preference degree calculation formula
Calculating to obtain the preference degree of the target exhibition area to each waiter who obtains deals, wherein diThe preference degree of the target exhibition area to the business member I for acquiring the deal is represented, I represents the business member set for acquiring the deal in the target exhibition area, I belongs to I, giRepresenting the amount of customer trades in the target exhibition area by the trader i who has taken the trades, aiThe trading volume of the trader i who gets the trading in the target exhibition area is shown, and the lambda represents the weight corresponding to the trading volume.
In some embodiments, the evaluation factors include a rate of transaction and a customer density, and the processor is configured to, when implementing the obtaining of the evaluation factor corresponding to each to-be-selected exhibition business area, implement:
acquiring customer visit volume and customer transaction volume corresponding to each to-be-selected exhibition business area, and calculating to obtain the transaction rate corresponding to each to-be-selected exhibition business area according to the customer visit volume and the customer transaction volume corresponding to each to-be-selected exhibition business area;
and obtaining the total amount of customers corresponding to each to-be-selected exhibition business area, and calculating the customer density corresponding to each to-be-selected exhibition business area according to the total amount of customers corresponding to each to-be-selected exhibition business area.
In some embodiments, the processor is configured to implement, when the evaluation of each to-be-selected exhibition industry area is performed according to the evaluation factor and an evaluation result corresponding to each to-be-selected exhibition industry area is obtained, the processor is configured to implement:
respectively configuring a first weight value corresponding to the transaction rate and a second weight value corresponding to the customer density;
according to the transaction rate and the first weight value, and the customer densityThe second weight value is combined with a preset scoring calculation formula s ═ mu1R+μ2Rho, calculating to obtain the score value corresponding to each exhibition area to be selected as an evaluation result, wherein s represents the score value corresponding to the exhibition area to be selected, R represents the transaction rate of the exhibition area to be selected, and mu1Represents the first weight value, p represents the customer density of the exhibition area to be selected, mu2Representing the second weight value.
Embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, where the computer program includes program instructions, and a method implemented when the program instructions are executed may refer to various embodiments of the artificial intelligence-based exhibition industry method.
The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiment, for example, a hard disk or a memory of the computer device. The computer readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the computer device.
Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments. While the invention has been described with reference to specific embodiments, the scope of the invention is not limited thereto, and those skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the invention. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. An artificial intelligence-based exhibition industry method is characterized by comprising the following steps:
when a exhibition industry scheduling request is received, acquiring track data corresponding to each salesman, and generating exhibition industry tracks corresponding to each salesman according to the track data;
determining a plurality of exhibition areas to be selected according to the exhibition track, and acquiring evaluation factors corresponding to the exhibition areas to be selected;
evaluating each exhibition area to be selected according to the evaluation factor to obtain an evaluation result corresponding to each exhibition area to be selected;
determining a target exhibition industry area from the plurality of exhibition industry areas to be selected according to the evaluation result;
and inquiring the target to-be-dispatched salesman matched with the target exhibition area to dispatch to the target exhibition area.
2. The artificial intelligence-based exhibition industry method according to claim 1, wherein the determining a plurality of exhibition industry areas to be selected according to the exhibition industry trajectory comprises:
screening track points with the total amount of customers larger than a preset threshold value from the exhibition track;
clustering the screened track points by adopting a preset first clustering algorithm so as to aggregate the screened track points into a plurality of track point clusters;
calculating the central point of each track point cluster by adopting a preset second clustering algorithm, and clustering the screened track points by taking each central point as a clustering center so as to aggregate the screened track points into a plurality of new track point clusters;
and determining a plurality of to-be-selected exhibition areas according to the new track point clusters.
3. The artificial intelligence based exhibition industry method of claim 1, wherein the querying of the target to-be-dispatched salesman matched with the target exhibition industry area to dispatch to the target exhibition industry area comprises:
determining the preference service personnel type of the target exhibition area;
and selecting the waited businessman belonging to the preference businessman type, and serving as the target waited businessman matched with the target exhibition business area to be dispatched to the target exhibition business area.
4. The artificial intelligence based deployment method of claim 3 wherein said determining a preferred facilitator type for said target deployment area comprises:
acquiring business data corresponding to each business member acquiring the deal in the target exhibition area;
determining the preference degree of the target exhibition business area to each trader who acquires the deal according to the business data corresponding to each trader who acquires the deal;
determining the preference business personnel of the target exhibition area from all business personnel who obtain deals according to the preference degree;
and acquiring the characteristic data corresponding to the preference service member, and determining the preference service member type of the target exhibition service area according to the characteristic data corresponding to the preference service member.
5. The artificial intelligence based deployment method of claim 4 wherein the business data includes volume and customer volume;
the determining the preference degree of the target exhibition industry area to each businessman who makes the deal according to the business data corresponding to each businessman who makes the deal comprises the following steps:
according to the corresponding transaction amount of each waiter obtaining the transaction and the client transaction amount, combining with a preset preference degree calculation formula
Calculating to obtain the preference degree of the target exhibition area to each waiter who obtains deals, wherein diThe preference degree of the target exhibition area to the business member I for acquiring the deal is represented, I represents the business member set for acquiring the deal in the target exhibition area, I belongs to I, giRepresenting the amount of customer trades in the target exhibition area by the trader i who has taken the trades, aiThe trading volume of the trader i who gets the trading in the target exhibition area is shown, and the lambda represents the weight corresponding to the trading volume.
6. The artificial intelligence based exhibition method of claim 1, wherein the evaluation factors include a rate of transaction and a customer density;
the obtaining of the evaluation factor corresponding to each to-be-selected exhibition industry area includes:
acquiring customer visit volume and customer transaction volume corresponding to each to-be-selected exhibition business area, and calculating to obtain the transaction rate corresponding to each to-be-selected exhibition business area according to the customer visit volume and the customer transaction volume corresponding to each to-be-selected exhibition business area;
and obtaining the total amount of customers corresponding to each to-be-selected exhibition business area, and calculating the customer density corresponding to each to-be-selected exhibition business area according to the total amount of customers corresponding to each to-be-selected exhibition business area.
7. The artificial intelligence-based exhibition industry method according to claim 6, wherein the evaluating each exhibition industry region to be selected according to the evaluation factor to obtain the evaluation result corresponding to each exhibition industry region to be selected comprises:
respectively configuring a first weight value corresponding to the transaction rate and a second weight value corresponding to the customer density;
according to the transaction rate, the first weight value, the customer density and the second weight value, combining a preset scoring calculation formula s-mu1R+μ2Rho, calculating to obtain the score value corresponding to each exhibition area to be selected as an evaluation result, wherein s represents the score value corresponding to the exhibition area to be selected, R represents the transaction rate of the exhibition area to be selected, and mu1Represents the first weight value, p represents the customer density of the exhibition area to be selected, mu2Representing the second weight value.
8. An artificial intelligence based exhibition industry device, characterized in that, said artificial intelligence based exhibition industry device includes:
the generation module is used for acquiring the track data corresponding to each salesman when the exhibition service scheduling request is received, and generating the exhibition service track corresponding to each salesman according to the track data;
the acquisition module is used for determining a plurality of exhibition areas to be selected according to the exhibition track and acquiring evaluation factors corresponding to the exhibition areas to be selected;
the evaluation module is used for evaluating each exhibition area to be selected according to the evaluation factors to obtain an evaluation result corresponding to each exhibition area to be selected;
the determining module is used for determining a target exhibition industry area from the plurality of exhibition industry areas to be selected according to the evaluation result;
and the scheduling module is used for inquiring the target business to be scheduled matched with the target exhibition area and scheduling the target business to be scheduled to the target exhibition area.
9. A computer arrangement comprising a processor, a memory, and a computer program stored on the memory and executable by the processor, wherein the computer program, when executed by the processor, performs the steps of the artificial intelligence based deployment method of any one of claims 1 to 7.
10. A computer-readable storage medium, having a computer program stored thereon, wherein the computer program, when executed by a processor, performs the steps of the artificial intelligence based exhibition method as claimed in any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110873980.6A CN113570262A (en) | 2021-07-30 | 2021-07-30 | Method and device for exhibition of industry based on artificial intelligence, computer equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110873980.6A CN113570262A (en) | 2021-07-30 | 2021-07-30 | Method and device for exhibition of industry based on artificial intelligence, computer equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113570262A true CN113570262A (en) | 2021-10-29 |
Family
ID=78169623
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110873980.6A Pending CN113570262A (en) | 2021-07-30 | 2021-07-30 | Method and device for exhibition of industry based on artificial intelligence, computer equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113570262A (en) |
-
2021
- 2021-07-30 CN CN202110873980.6A patent/CN113570262A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20200402144A1 (en) | Graphical user interface for object discovery and mapping in open systems | |
| US11669928B2 (en) | Fare classes with obscured demand | |
| US20220300281A1 (en) | Software portfolio management system and method | |
| US20130085813A1 (en) | Method, Apparatus and Computer Program Product for Providing a Supply Chain Performance Management Tool | |
| US20160034474A1 (en) | Enterprise Data Mining in a Multi-Tenant Database | |
| US8224866B2 (en) | Idea tracking and management | |
| US20120330707A1 (en) | Web-based communication platform | |
| US11620590B1 (en) | Network value of a flight leg booking | |
| WO2011050482A1 (en) | Enterprise data mining in a hosted multi-tenant database | |
| US20210218748A1 (en) | Method and system for defining roles in an identity and access management system | |
| Rawley et al. | Information technology, productivity, and asset ownership: Evidence from taxicab fleets | |
| Mossalam et al. | Using artificial neural networks (ANN) in projects monitoring dashboards’ formulation | |
| CN112085378B (en) | Resource allocation method, device, computer equipment and storage medium | |
| CN113780998B (en) | Integrated information management method for engineering project execution and implementation | |
| CN111881158A (en) | Management report data processing method and device, computer system and readable storage medium | |
| US20030208394A1 (en) | Sales tracking and forecasting application tool | |
| Khan et al. | Factors affecting organizations adopting human resource information systems: a study in Bangladesh | |
| Cottrell | Contractor process improvement for enhancing construction productivity | |
| CN113570262A (en) | Method and device for exhibition of industry based on artificial intelligence, computer equipment and storage medium | |
| US8620895B1 (en) | Mapping organizational accounting codes to access business information | |
| Guo et al. | A zone design methodology for national freight origin–destination data and transportation modeling | |
| Zavadskas et al. | Simulation of multi-criteria selection of buildings’ maintenance contractor using the game theory | |
| Klimova | Risk-based project management audit | |
| KR102491666B1 (en) | Agent system to improve the matching rate between companies and freelancers | |
| Simonova et al. | Utilization of Six Sigma for data improvement |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |