CN111881184A

CN111881184A - Data analysis method and device, computer equipment and storage medium

Info

Publication number: CN111881184A
Application number: CN202010739323.8A
Authority: CN
Inventors: 韩冰
Original assignee: Ping An International Financial Leasing Co Ltd
Current assignee: Ping An International Financial Leasing Co Ltd
Priority date: 2020-07-28
Filing date: 2020-07-28
Publication date: 2020-11-03

Abstract

The application discloses a data analysis method and device, computer equipment and a storage medium, and belongs to the technical field of big data. According to the data analysis method, the task type associated with the task request is inquired according to the task request sent by the user equipment, so that the object to be analyzed corresponding to the task request is determined, and therefore, different analysis objects are analyzed in a targeted mode, so that the conditions of transaction information such as viscosity, liveness and the like between the user equipment and different types of user parties, the conditions of service information between the user equipment and different types of user parties, behavior information, service information and the like of the user parties associated with the user equipment are known conveniently, the corresponding analysis information is fed back to the user equipment, and unified and effective management of the user equipment on different user types is achieved. The invention also relates to a block chain technology, and the first type user information, the second type user information, the behavior information and/or the service information can be stored in the block chain node.

Description

Data analysis method and device, computer equipment and storage medium

Technical Field

The present application relates to the field of big data, and in particular, to a data analysis method, apparatus, computer device, and storage medium.

Background

With the development of internet technology, more and more transaction behaviors can be realized online, such as online business handling, for example, the handling of rental business and rented business.

However, for an outworker such as a salesperson or an employee who often has an outing visit to a customer, the current management system cannot uniformly manage personal behaviors (such as attendance) of the employee and different types of transaction services, cannot intuitively know the quality of a transaction party which the employee is responsible for, the activity between the employee and the transaction party, the attendance condition of the employee, and the like, and cannot uniformly and effectively manage and analyze personal information of different transaction parties and employees.

Disclosure of Invention

Aiming at the problem that the existing management system can not uniformly and effectively manage various other information associated with the user, the data analysis method, the device, the computer equipment and the storage medium which aim at uniformly managing and analyzing various other user information associated with the user are provided.

In order to achieve the above object, the present application provides a data analysis method, including:

the method comprises the steps of obtaining a task request sent by user equipment, and inquiring a task type associated with the task request, wherein the task type comprises a first analysis task corresponding to a first class of users, a second analysis task corresponding to a second class of users and a management analysis task corresponding to a third class of users;

analyzing first type user information of the first type users associated with the user equipment according to the first analysis task, generating first statistical information of the first type users, and sending the first statistical information to the user equipment;

analyzing second type user information of the second type users associated with the user equipment according to the second analysis task, generating second statistical information of the second type users, and sending the second statistical information to the user equipment;

and analyzing the behavior information and/or the service information of the third type of users associated with the user equipment according to the management analysis task, generating management evaluation information of the third type of users, and sending the management evaluation information to the user equipment.

In order to achieve the above object, the present application also provides a data analysis apparatus, including:

the task management system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring a task request sent by user equipment and inquiring a task type associated with the task request, and the task type comprises a first analysis task corresponding to a first class of users, a second analysis task corresponding to a second class of users and a management analysis task corresponding to a third class of users;

the renting analysis unit is used for analyzing the first type user information of the first type user associated with the user equipment according to the first analysis task, generating first statistical information of the first type user, and sending the first statistical information to the user equipment;

the lease analysis unit is used for analyzing second type user information of the second type users related to the user equipment according to the second analysis task, generating second statistical information of the second type users and sending the second statistical information to the user equipment;

and the management analysis unit is used for analyzing the behavior information and/or the service information of the third type of users related to the user equipment according to the management analysis task, generating management evaluation information of the third type of users, and sending the management evaluation information to the user equipment.

To achieve the above object, the present application also provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the above method when executing the computer program.

To achieve the above object, the present application also provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the above method.

According to the data analysis method, the data analysis device, the computer equipment and the storage medium, the task type associated with the task request is inquired according to the task request sent by the user equipment to determine the object to be analyzed corresponding to the task request, so that the targeted analysis of different analysis objects is realized, the transaction information conditions such as the viscosity, the liveness and the like between the user equipment and different types of user parties, the service information conditions between the user equipment and different types of user parties, the behavior information, the service information and the like of the user parties associated with the user equipment are conveniently known, the corresponding analysis information is fed back to the user equipment, and the unified and effective management of the user equipment on different user types is realized.

Drawings

FIG. 1 is a flow chart of one embodiment of a data analysis method described herein;

FIG. 2 is a flowchart of one embodiment of the present application for analyzing first type user information of first type users associated with a user;

FIG. 3 is a flowchart of one embodiment of analyzing second type user information of a second type user associated with a user according to the present application;

fig. 4 is a flowchart of an embodiment of analyzing behavior information and service information of a user according to the present application;

FIG. 5 is a flow diagram illustrating one embodiment of rating an account to generate management rating information according to the present application;

FIG. 6 is a block diagram of one embodiment of a data analysis device according to the present application;

fig. 7 is a hardware architecture diagram of an embodiment of a computer device according to the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit 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.

It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.

The data analysis method, the data analysis device, the computer equipment and the storage medium are suitable for the fields of leasing business, selling business, commodity supply business and the like, and the task type associated with the task request is inquired according to the task request sent by the user equipment so as to determine the object to be analyzed corresponding to the task request, so that the targeted analysis of different analysis objects is realized; when the object to be analyzed is a first-class user, first-class user information of the first-class user associated with the user equipment can be analyzed to generate first statistical information of the first-class user, and the viscosity and the activity between the user equipment and the first-class user are known through the first statistical information; when the object to be analyzed is a second class user, second class user information of the second class user associated with the user equipment can be analyzed to generate second statistical information of the second class user, and cooperation conditions (such as service type, payment amount and the like) of the second class user currently responsible by the user equipment are known through the second statistical information; when the object to be analyzed is a third-class user, the behavior information and/or the service information of the third-class user can be analyzed to generate management evaluation information of the third-class user, so that the working condition (such as attendance) of the third-class user associated with the user equipment can be known.

Example one

Referring to fig. 1, a data analysis method includes:

s1, acquiring a task request sent by user equipment, and inquiring a task type associated with the task request.

The task types comprise a second analysis task corresponding to a second class of users, a first analysis task corresponding to a first class of users and a management analysis task corresponding to a third class of users; the user device corresponds to the user that triggered the task request, i.e. the user device through which the user is bound or logged in triggers the task request.

In this step, the task request is a request triggered by an account user of the user equipment, and an object to be analyzed corresponding to the task request is determined according to the type of the task request. The task request comprises a task identifier, and the task identifier corresponds to the task type.

Specifically, in an embodiment, a task identifier in a task request may be extracted by obtaining the task request sent by the user equipment, and a task type corresponding to the task identifier may be obtained by querying a type form according to the task identifier.

It should be noted that the type table stores a plurality of task types, for example: the system comprises a first analysis task corresponding to a first class of users, a second analysis task corresponding to a second class of users and a management analysis task corresponding to a third class of users.

By way of example and not limitation, for a rental business, the account user of the user device may be an intermediate service person such as a customer manager, the first class of users may be tenants, the second class of users may be tenants, and the third class of users may be employees of the user device or the account user himself/herself.

S2, analyzing the first type user information of the first type user associated with the user equipment according to the first analysis task, generating first statistical information of the first type user, and sending the first statistical information to the user equipment.

In this embodiment, when the object to be analyzed corresponding to the task type is a first type user, the first type user information of the first type user associated with the account of the user equipment may be analyzed to generate first statistical information of the first type user, and the stickiness and the liveness between the user and the first type user are known through the first statistical information. The liveness refers to whether the first type user has a transaction behavior (such as a rental transaction) with other users within a preset time period (such as 2 months, 3 months or 6 months), and the stickiness refers to whether the transaction amount of the first type user with other users within the preset time period reaches a desired transaction amount (such as a rent amount). After the account user receives the first statistical information corresponding to the first type of user through the user equipment, the account user can perform targeted business operation on the first type of user based on the first statistical information.

In this embodiment, taking a lease service as an example, the first type of user may be a tenant, and correspondingly, the first type of user information is tenant information; in this case, the liveness refers to whether the second type users have rented transactions for several months (e.g. 2 months, 3 months or 6 months), and the stickiness refers to whether the second type users reach the expected rent amount for several months. The account user receives the renting information corresponding to the renter through the user equipment, can know the transaction condition of the renter in a few months, and further can contact the corresponding renter to carry out renting maintenance; for example, for a lessee who has a lease transaction in a few months and has a large transaction amount, the account user can take the lessee as a high-quality lessee to perform high-quality maintenance, so that better service is improved, and the lessee can be actively contacted to continuously realize the implementation of the lease service when the lease period is close.

Further, in an embodiment, referring to fig. 2, taking a rental service as an example, the first type of user is a tenant, the first type of user information is tenant information, and step S2 may include the following steps:

s21, according to the first analysis task, obtaining the tenant information of the tenant party associated with the user equipment, wherein the tenant information comprises: the type of the lease, the data of the account of the lease, the data of the credit, the amount of the rent and the data of the address of the lease.

Specifically, an account of one user device may be responsible for multiple tenants, so the account of the user device may be associated with multiple tenants, each tenant having respective tenant information; the type of the lessons is related to the type of lessons equipment (such as laser printers, numerical control machines, injection molding machines and the like), and the lessons can comprise: printers, machines, injection molding machines, and the like; the tenant account data may be a company name, a company tax number, a company account name, or the like of the tenant; the credit granting data refers to the auditing values (such as 7 points, 8 points, 9 points, 10 points and the like) of the lessees, each lessee needs to audit the assets, credit information and the like of the lessee before the lessee, and the auditing values are generated after the auditing; the rent amount is the rent amount of the equipment for rent; the tenant address data is the address (e.g., city address) of the tenant device.

Further, a Bayesian network can be adopted to evaluate the assets and credit information of the lessee and generate the auditing score. And inputting the asset parameters and the credit parameters of the lessee into a Bayesian network, formally representing the Bayesian network as a directed acyclic graph, and representing each node of the directed acyclic graph as a random variable. The link between two nodes indicates the relationship between variables and the direction representing causality. Unconnected nodes represent variables that are conditionally independent of each other. If a node has a known value, it is called an evidence node. Each node may be associated with a conditional probability distribution that represents the node's parameter dependency with the node's parent. The probability distribution can be continuous or discrete, and the corresponding audit score of the lessee is determined based on the probability distribution. A bayesian network can handle cases where variables are incomplete or missing.

And S22, respectively carrying out account rating corresponding to the lessee account data associated with the credit data according to the level of the credit data, and generating a lessee rating score according to all the account ratings.

Specifically, credit granting data distribution of each lessee associated with the account of the user equipment is matched with a preset rating interval, and the prediction rating interval is a plurality of mutually independent intervals divided according to the audit score; and acquiring the prediction rating interval with the maximum number of lessees, calculating the average value of the auditing scores in the prediction rating interval, and taking the average value as the rating score of the prediction rating interval.

By way of example and not limitation, the credited data is a review score: the method comprises the steps of 1 point, 2 points, 3 points, 4 points, 5 points, 6 points, 7 points, 8 points, 9 points, 10 points and 10 points of full points, wherein the lower the score is, the lower the credit is, the higher the score is, the higher the credit is, a prediction rating interval is divided into 3 grades, 1 point-5 points, 6 points-8 points and 9 points-10 points, the lower the credit is, the higher the score is, all lessees related to an account of user equipment are classified according to the rating interval, 3 rating sets are generated, the average value of the audit scores of the rating interval with the largest number of the lessees is obtained, and the average value is used as the score of the lessees.

S23, acquiring the rent amount corresponding to each renting area, and counting the total rent amount of each renting area.

Specifically, the method matches the renting address data in the renting information with the renting areas (such as province and city), wherein the renting areas are areas divided according to province, city, county, district and town; and associating the lessees associated with the lessee address data according to the matching result, counting the total rent amount of the corresponding lessees in each province, city, county, district and town, and arranging from large to small according to the total rent amount so that the user can know the rent distribution condition of the lessees of the current lessees.

It should be noted that, in step S23, the rent amount corresponding to the rental area may be counted according to a preset time period (e.g., 03.23-04.22; about 30 days, etc.), so as to know the situation of the recent rental party and filter the influence of the history data.

And S24, acquiring the corresponding rental account data of each rental area, and counting the number of the tenants in the rental area according to the rental account data.

Specifically, matching the tenant address data in the tenant information with the tenant area data, associating the tenants associated with the tenant address data according to the matching result, counting the number of corresponding tenants in each province, city, county, district and town, and arranging from large to small according to the number of the tenants so that the user can know the tenant distribution condition of the current tenants.

It should be noted that, in step S24, the number of the corresponding tenants in the tenant area may be counted according to a preset time period (e.g., 03.23-04.22; about 30 days, etc.), so as to know the distribution of the tenants in the near term and filter the influence of the historical data.

And S25, generating the first statistical information according to the account rating value, the total rent amount and the number of the tenants, and sending the first statistical information to the user equipment.

In a preferred embodiment, after the step S24 is executed in the step S2, the following steps may be further included:

and S26, acquiring the corresponding rental account data, credit granting data, rent amount and rental address data of each rental type, and counting the type rent amount, the number of type renters and the type address data associated with the rental type.

In step S26, based on the tenant type, the type rent amount, the number of type tenants, and the type address data of the tenant associated with the user are counted to obtain the rent amount, the number of the tenants, and the area distribution of the tenant corresponding to each tenant type.

And S27, generating a type scoring score of the rent type according to the type rent amount and the number of the type tenants associated with the rent type.

In step S27, the type rent amount associated with each rental type is divided by the number of the type tenants to obtain a type rent average value, and a rating level preset by the type rent average value is matched to obtain a rating level matched therewith, where the rating score corresponding to the rating level is the type rating score of the rental type.

And S28, generating first statistical information according to the account rating score, the total rent amount, the number of the type tenants and the type rating score, and sending the first statistical information to the user equipment.

And S3, analyzing second type user information of the second type user associated with the user equipment according to the second analysis task, generating second statistical information of the second type user, and sending the second statistical information to the user equipment.

The second statistical information may include the number of users of the second type and the type pre-trading total amount.

In this embodiment, when the object to be analyzed is a second type user, the second type user information of the second type user associated with the account of the user equipment may be analyzed to generate second statistical information of the second type user, and the cooperation condition (such as the service type, the payment amount, and the like) of the second type user currently responsible for the user is known through the second statistical information.

Further, in an embodiment, taking a rental service as an example, the second type of user is a renter, and the second type of user information is rental information, where step S3 referring to fig. 3 may include the following steps:

s31, according to the second analysis task, obtaining the leasing information of a leasing party associated with the user equipment, wherein the leasing information can include: rental type, rental account data, and prepayment data.

Specifically, an account of one user device may be responsible for multiple renters, so the user may be associated with multiple renters, each renter having respective rental information; the rental type is related to the type of rental equipment (such as a laser printer, a numerical control machine, an injection molding machine, etc.), and can comprise: printers, machines, injection molding machines, and the like; the rental account data may be a company name, a company tax number, a company account name, or the like of the tenant; the pre-payment data is a pre-payment that is delivered prior to the transaction being generated.

And S32, acquiring rental account data and prepayment data corresponding to each rental type, and counting the number of type renters and the total amount of type prepayment related to the rental type.

Specifically, according to different leasing types, the leasing parties associated with the account of the user equipment can be divided, and the total amount of prepaid of all the leasing parties corresponding to each leasing type is obtained, so that the account of the user equipment can know the prepaid condition of the leasing party responsible for the leasing type at present.

And S33, generating the second statistical information according to the number of the type leasing parties and the type pre-transaction total amount, and sending the second statistical information to the user equipment.

It should be noted that: the second statistical information may also include a lease rating score;

further, in an embodiment, after the step S32 is executed in the step S3, the following steps may be further included:

s34, according to the second analysis task, obtaining a leasing list of each leasing party related to the user equipment.

The renting list comprises account data to be rented, wherein the account data to be rented can be the company name, the company tax number or the company account name of a party to be rented and the like;

in this step, the renter pushes a potential account to be rented to the account of the user equipment by means of the renting list, and the account user of the user equipment before the renter can contact the corresponding account to be rented according to the account data to be rented in the renting list provided by the renter, so as to facilitate the renting transaction service.

S35, matching the account data to be rented in the rental list with the rented account data in the rented information associated with the user equipment to obtain an account matching value.

In this step, account data to be rented in a rental list provided by the renter is extracted, the extracted account data to be rented is respectively matched with the rental account data currently associated with the account of the user equipment to obtain an account matching value (namely, matching degree), and the success rate of the rental transaction service in the rental list provided by the renter can be reflected through the account matching value.

And S36, grading the leaseholder corresponding to the lease list according to the account matching value, and generating the lease grade score.

In the step, the account matching value of the rental list is used as the rental rating score of the renter, the higher the rental rating score is, the higher the quality of the renter is, and preferential service functions of prepayment sign-free, front-stage receipt-free, ticket-increase first-order-opening and the like can be provided for the renter with high quality.

S37, generating second statistical information according to the number of the type leasing parties, the type pre-transaction total amount and the leasing rating score, and sending the second statistical information to the user equipment.

And S4, analyzing the behavior information and/or the service information of the third type of users associated with the user equipment according to the management analysis task, generating management evaluation information of the third type of users, and sending the management evaluation information to the user equipment.

In this step, when the object to be analyzed is the third type of user associated with the account of the user device, the behavior information and/or the service information of the third type of user may be analyzed to generate management evaluation information of the third type of user, so as to know the working condition of the user (e.g., attendance).

Further, in an embodiment, step S4 with reference to fig. 4 may include the following steps:

and S41, acquiring the management authority of the user equipment according to the management analysis task.

Specifically, the management authority is a management authority corresponding to a management level of an account of the user equipment, for example: when the account of the user equipment is the area manager, the account of the user equipment can manage the behavior information and/or the business information of each store owner below the area manager; when the account of the user equipment is the store owner, the account of the user equipment can manage the behavior information and/or the business information of each store employee in the store; when the account of the user device is a clerk, the account of the user device can only manage the behavior information and/or the business information of the user.

And S42, inquiring the authority task type corresponding to the management authority, wherein the authority task type comprises an attendance task, a visit task and a rent task.

The attendance task is the attendance task of all manageable employees corresponding to the current management level of the account of the user equipment; the visit task is the visit task of all manageable employees corresponding to the current management level of the account of the user equipment; the rent task is the rent task of all the manageable employees corresponding to the current management level of the account of the user equipment.

S43, according to the authority task type, counting attendance information of the third class users related to the management authority, and generating an attendance form.

Specifically, the third type of user associated with the management authority is a manageable employee account corresponding to the current user management level; the attendance form can be generated according to a preset period (such as one week and one month) or a set time range (03.20-03.29).

And S44, counting the first class user visit records and the second class user visit records of the third class users associated with the management authority according to the authority task type to generate a visit form.

The first class user visit records are times of visiting the first class users, and the second class user visit records are times of visiting the second class users;

and S45, counting the total rent amount of the third class of users associated with the management authority according to the authority task type to generate a rent form.

The total rent amount is the total rent amount of each manageable employee account corresponding to the account management level of the current user equipment within a preset time range (such as 1 year, one month, one week and the like).

S46, according to attendance information of a third type of users, first type of user visit records related to the third type of users, second type of user visit records related to the third type of users and the total rent amount, grading the third type of users to generate management evaluation information, and sending the second statistical information to the user equipment.

Specifically, in one embodiment, step S46 with reference to fig. 5 may include the following steps:

s461, matching the attendance information of the third class of users with an attendance threshold value to obtain attendance scores.

S462, combining the first class user visit records associated with the third class users with the second class user visit records associated with the third class users, and comparing the combined records with a preset visit threshold value to obtain visit scores.

S463, calculating the rent proportion score according to the total rent amount of the third type of users and the number of the first type of users corresponding to the account.

S464, generating the management evaluation information of the third type of users according to the attendance value, the visit value and the rent proportion value, and sending the second statistical information to the user equipment.

In step S464, the attendance score, the visit score, and the rent proportion score are added to obtain management evaluation information of each account.

In the embodiment, the data analysis method queries the task type associated with the task request according to the task request sent by the user equipment to determine the object to be analyzed corresponding to the task request, so that different analysis objects are analyzed in a targeted manner; when the object to be analyzed is a first-class user, first-class user information of the first-class user associated with the user equipment can be analyzed to generate first statistical information of the first-class user, so that the viscosity and the activity between the user equipment and the first-class user can be known through the first statistical information; when the object to be analyzed is a second class user, second class user information of the second class user associated with the user equipment can be analyzed to generate second statistical information of the second class user, so that cooperation conditions (such as service type, payment amount and the like) of the user equipment and the second class user currently responsible for the user equipment can be known through the second statistical information; when the object to be analyzed is a third-class user, the behavior information and/or the service information of the third-class user can be analyzed to generate management evaluation information of the third-class user, so that the working condition (such as attendance) of the third-class user associated with the user equipment can be known, the corresponding analysis information is fed back to the user equipment, and the user equipment can uniformly and effectively manage different user types. The embodiment better analyzes the first class users, the second class users and the employees by combining big data analysis so as to give targeted opinions to the users, improve the client stickiness, mine potential first class users and second class users, and help the users find assistance and methods for improving the performance.

Further, in an embodiment, in order to further ensure the privacy and security of the first type of user information, the second type of user information, the behavior information, and the service information, the first type of user information, the second type of user information, the behavior information, and the service information may also be stored in a node of a block chain. The block chain referred by the application 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.

Example two

Referring to fig. 6, a data analysis apparatus 1 includes: an acquisition unit 11, a rental analysis unit 12, a rental analysis unit 13, and a management analysis unit 14;

the acquiring unit 11 is configured to acquire a task request sent by user equipment, and query a task type associated with the task request.

The task types comprise a first analysis task corresponding to a first class of users, a second analysis task corresponding to a second class of users and a management analysis task; the user device corresponds to the user that triggered the task request, i.e. the user device through which the user is bound or logged in triggers the task request. In this embodiment, the task request is a request triggered by an account user of the user equipment, and an object to be analyzed corresponding to the task request is determined according to the type of the task request. The task request comprises a task identifier, and the task identifier corresponds to the task type.

Specifically, the obtaining unit 11 is used to obtain a task request sent by the user equipment, extract a task identifier in the task request, and obtain a task type corresponding to the task identifier by querying a type form according to the task identifier.

It should be noted that: the type table stores a plurality of task types, for example: the system comprises a first analysis task corresponding to a first class of users, a second analysis task corresponding to a second class of users and a management analysis task corresponding to a third class of users.

The tenant analysis unit 12 analyzes the first class user information of the first class user associated with the user equipment according to the first analysis task, generates first statistical information of the first class user, and sends the first statistical information to the user equipment.

In this embodiment, when the object to be analyzed corresponding to the task type is a first type user, the first type user information of the first type user associated with the account of the user equipment may be analyzed to generate first statistical information of the first type user, and the stickiness and the liveness between the user and the first type user are known through the first statistical information. The liveness refers to whether the first class user has transaction behaviors with other users within a preset time period, and the stickiness refers to whether the transaction amount of the first class user with other users within the preset time period reaches an expected transaction amount.

In this embodiment, taking a lease service as an example, the first type of user may be a tenant, and correspondingly, the first type of user information is tenant information; in this case, the liveness means whether the second class users have a lease transaction for a few months, and the stickiness means whether the second class users have reached the expected lease amount for a few months.

Further, in an embodiment, taking a lease service as an example, the first type of user is a tenant, and the first type of user information is tenant information.

The tenant analysis unit 12 may obtain tenant information of the tenant associated with the user equipment according to the first analysis task, where the tenant information includes: the method comprises the following steps of (1) renting type, account data, credit data, rent amount and address data;

The lessee analysis unit 12 can also respectively perform account rating corresponding to lessee account data associated with the credit data according to the level of the credit data, and generate a lessee rating score according to all the account ratings;

The lease analysis unit 12 is configured to obtain a lease amount corresponding to each lease area, and count a total lease amount of each lease area;

It should be noted that, in this embodiment, the amount of the rent corresponding to the renting area may be counted according to a preset time period (e.g., 03.23-04.22; about 30 days, etc.), so as to know the situation of the recent renter and filter the influence of the historical data.

The renting analysis unit 12 can be further configured to obtain renting account data corresponding to each renting area, and count the number of renters in the renting area according to the renting account data;

It should be noted that, in this embodiment, the number of the tenants corresponding to the tenant area may be counted according to a preset time period (e.g., 03.23-04.22; about 30 days, etc.), so as to know the distribution of the tenants in the near term and filter the influence of the historical data.

The lease analysis unit 12 may further generate the first statistical information according to the account rating score, the total lease amount, and the number of the tenants, and send the first statistical information to the user equipment.

In a preferred embodiment, the first statistical information may further include a type score, specifically:

the lease analysis unit 12 is configured to obtain lease account data, credit granting data, lease amount, and lease address data corresponding to each lease type, and count type lease amount, type tenant party number, and type address data associated with the lease type;

in this embodiment, based on the tenant type, the type rent amount, the number of the type tenants, and the type address data of the tenant associated with the user are counted based on the tenant type, so as to obtain the rent amount condition, the number of the tenants, and the area distribution condition of the tenant corresponding to each tenant type.

The rental analysis unit 12 may generate the type score of the rental type according to a type rental amount and a number of type tenants associated with the rental type.

In this embodiment, the amount of the type rent associated with each rental type is divided by the number of the type tenants to obtain a type rent average value, and a rating level preset by the type rent average value is matched to obtain a rating level matched with the type rent average value, where the rating score corresponding to the rating level is the type rating score of the rental type.

The lease analysis unit 12 may further generate the first statistical information according to the account rating score, the total rent amount, the number of the type tenants, and the type rating score, and send the first statistical information to the user equipment.

A lease analysis unit 13, configured to analyze, according to the second analysis task, second type user information of the second type user associated with the user equipment, generate second statistical information of the second type user, and send the second statistical information to the user equipment;

and the second statistical information comprises the number of the type second type users and the type pre-transaction total amount.

Further, taking a rental service as an example, the second type of user is a renter, the second type of user information is rental information, and at this time, the rental analysis unit 13 may obtain the rental information of the renter associated with the user equipment according to the second analysis task, where the rental information: rental type, rental account data, and prepayment data;

the lease analysis unit 13 is further configured to obtain lease account data and prepaid data corresponding to each lease type, and count the number of type leasers and the total amount of type pre-transaction associated with the lease type.

The leasing analysis unit 13 further generates the second statistical information according to the number of the type leasing parties and the type pre-transaction total amount, and sends the second statistical information to the user equipment.

further: in one embodiment, the lease analysis unit 13 may obtain a lease list of each of the leasers associated with the user equipment according to the second analysis task;

the lease analysis unit 13 may match the account data to be leased in the lease list with the lease account data in the lease information associated with the user equipment to obtain an account matching value;

the lease analysis unit 13 may rank the leasers corresponding to the lease list according to the account matching value, and generate the lease rating score.

In the embodiment, the account matching value of the rental list is used as the rental rating score of the renter, and higher rental rating score indicates higher quality of the renter, so that preferential service functions of prepayment, non-previous receipt, ticket increase and first order purchase and the like can be provided for the renter with high quality.

The lease analysis unit 13 may further generate the second statistical information according to the number of the type leasers, the type pre-transaction total amount, and the lease rating score, and send the second statistical information to the user equipment.

And the management analysis unit 14 is configured to analyze the behavior information and/or the service information of the third type of user associated with the user equipment according to the management analysis task, generate management evaluation information of the third type of user, and send the management evaluation information to the user equipment.

In this embodiment, when the object to be analyzed is the third type of user associated with the account of the user device, the behavior information and/or the service information of the third type of user may be analyzed to generate management evaluation information of the third type of user, so as to know the working condition of the user (e.g., attendance).

Further, the management analysis unit 14 may obtain the management authority of the user equipment according to the management analysis task;

specifically, the management authority is an management authority corresponding to a management level of an account of the user equipment, for example: when the account of the user equipment is the area manager, the account of the user equipment can manage the behavior information and/or the business information of each store owner below the area manager; when the account of the user equipment is the store owner, the account of the user equipment can manage the behavior information and/or the business information of each store employee in the store; when the account of the user device is a clerk, the account of the user device can only manage the behavior information and/or the business information of the user.

The management analysis unit 14 may query the authority task types corresponding to the management authority, where the authority task types include an attendance task, a visit task, and a rent task;

The management analysis unit 14 may count attendance information of the third class of users associated with the management authority according to the authority task type to generate an attendance form;

The management analysis unit 14 can also count the first class user visit records and the second class user visit records of the third class users associated with the management authority according to the authority task type to generate a visit form;

the management analysis unit 14 may count the total rent amount of the third type of users associated with the management authority according to the authority task type to generate a rent form;

The management analysis unit 14 may rank the third type of user according to the attendance information of the third type of user, the first type of user visit record associated with the third type of user, the second type of user visit record associated with the third type of user, and the total rent amount to generate management evaluation information, and send the second statistical information to the user equipment.

Specifically, the management analysis unit 14 may match the attendance information of the third class of users with an attendance threshold to obtain an attendance score; comparing the first class user visit record associated with the third class user with the second class user visit record associated with the third class user with a preset visit threshold value to obtain a visit score; calculating a rent proportion score according to the total rent amount of the third type of users and the number of the first type of users corresponding to the account; and generating the management evaluation information of the third type of users according to the attendance score, the visit score and the rent proportion score, and sending the second statistical information to the user equipment.

In this embodiment, the data analysis device 1 queries a task type associated with a task request according to the task request sent by the user equipment to determine an object to be analyzed corresponding to the task request, thereby implementing targeted analysis on different analysis objects; when the object to be analyzed is a first-class user, first-class user information of the first-class user associated with the user equipment can be analyzed to generate first statistical information of the first-class user, so that the viscosity and the activity between the user equipment and the first-class user can be known through the first statistical information; when the object to be analyzed is a second class user, second class user information of the second class user associated with the user equipment can be analyzed to generate second statistical information of the second class user, so that cooperation conditions (such as service type, payment amount and the like) of the user equipment and the second class user currently responsible for the user equipment can be known through the second statistical information; when the object to be analyzed is a third-class user, the behavior information and/or the service information of the third-class user can be analyzed to generate management evaluation information of the third-class user, so that the working condition (such as attendance) of the third-class user associated with the user equipment can be known, the corresponding analysis information is fed back to the user equipment, and the user equipment can uniformly and effectively manage different user types. The embodiment better analyzes the first class users, the second class users and the employees by combining big data analysis so as to give targeted opinions to the users, improve the client stickiness, mine potential first class users and second class users, and help the users find assistance and methods for improving the performance.

EXAMPLE III

In order to achieve the above object, the present application further provides a computer device 2, where the computer device 2 includes a plurality of computer devices 2, components of the data analysis apparatus 1 of the second embodiment may be distributed in different computer devices 2, and the computer device 2 may be a smartphone, a tablet computer, a notebook computer, a desktop computer, a rack-mounted server, a blade server, a tower server, or a rack-mounted server (including an independent server or a server cluster formed by a plurality of servers) that executes a program, and the like. The computer device 2 of the present embodiment includes at least, but is not limited to: a memory 21, a processor 23, a network interface 22, and the data analysis apparatus 1 (refer to fig. 7) that can be communicatively connected to each other through a system bus. It is noted that fig. 7 only shows the computer device 2 with components, but it is to be understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead.

In this embodiment, the memory 21 includes at least one type of computer-readable storage medium, which includes a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the storage 21 may be an internal storage unit of the computer device 2, such as a hard disk or a memory of the computer device 2. In other embodiments, the memory 21 may also be an external storage device of the computer device 2, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like provided on the computer device 2. Of course, the memory 21 may also comprise both an internal storage unit of the computer device 2 and an external storage device thereof. In this embodiment, the memory 21 is generally used for storing an operating system installed in the computer device 2 and various types of application software, such as program codes of the data analysis method in the first embodiment. Further, the memory 21 may also be used to temporarily store various types of data that have been output or are to be output.

The processor 23 may be a Central Processing Unit (CPU), a controller, a microcontroller, a microprocessor, or other data Processing chip in some embodiments. The processor 23 is typically used for controlling the overall operation of the computer device 2, such as performing control and processing related to data interaction or communication with the computer device 2. In this embodiment, the processor 23 is configured to operate the program code stored in the memory 21 or process data, for example, operate the data analysis apparatus 1.

The network interface 22 may comprise a wireless network interface or a wired network interface, and the network interface 22 is typically used to establish a communication connection between the computer device 2 and other computer devices 2. For example, the network interface 22 is used to connect the computer device 2 to an external terminal through a network, establish a data transmission channel and a communication connection between the computer device 2 and the external terminal, and the like. The network may be a wireless or wired network such as an Intranet (Intranet), the Internet (Internet), a Global System of Mobile communication (GSM), Wideband Code Division Multiple Access (WCDMA), a 4G network, a 5G network, Bluetooth (Bluetooth), Wi-Fi, and the like.

It is noted that fig. 7 only shows the computer device 2 with components 21-23, but it is to be understood that not all shown components are required to be implemented, and that more or less components may be implemented instead.

In this embodiment, the data analysis apparatus 1 stored in the memory 21 may be further divided into one or more program modules, and the one or more program modules are stored in the memory 21 and executed by one or more processors (in this embodiment, the processor 23) to complete the present application.

Example four

To achieve the above objects, the present application also provides a computer-readable storage medium including a plurality of storage media such as a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application store, etc., on which a computer program is stored, which when executed by the processor 23, implements corresponding functions. The computer-readable storage medium of the present embodiment is used for storing the data analysis apparatus 1, and when being executed by the processor 23, the computer-readable storage medium implements the data analysis method of the first embodiment.

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.

Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner.

The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application, or which are directly or indirectly applied to other related technical fields, are included in the scope of the present application.

Claims

1. A method of data analysis, comprising:

2. The data analysis method according to claim 1, wherein the first type of user is a tenant, and the first type of user information is tenant information;

the analyzing the first type of user information of the first type of user associated with the user equipment according to the first analysis task to generate first statistical information of the first type of user, and sending the first statistical information to the user equipment includes:

according to the first analysis task, acquiring the tenant information of the tenant party associated with the user equipment, wherein the tenant information comprises: the method comprises the following steps of (1) renting type, account data, credit data, rent amount and address data;

according to the level of the credit granting data, account rating is respectively carried out corresponding to the lessee account data related to the credit granting data, and a lessee rating score is generated according to all the account ratings;

acquiring the rent amount corresponding to each renting area, and counting the total rent amount of each renting area;

acquiring the corresponding rental account data of each rental area, and counting the number of the tenants in the rental area according to the rental account data;

and generating the first statistical information according to the account rating score, the total rent amount and the number of the lessees, and sending the first statistical information to the user equipment.

3. The data analysis method of claim 2,

the analyzing the first type of user information of the first type of user associated with the user equipment according to the first analysis task to generate first statistical information of the first type of user, and sending the first statistical information to the user equipment, further comprising:

acquiring the corresponding rental account data, credit granting data, rent amount and rental address data of each rental type, and counting the type rent amount, the number of type renters and the type address data associated with the rental type;

generating a type scoring score of the rent type according to the type rent amount and the number of type tenants associated with the rent type;

and generating the first statistical information according to the account rating score, the total rent amount, the number of the type lessees and the type rating score, and sending the first statistical information to the user equipment.

4. The data analysis method of claim 1, wherein the second type of user is a leasing party, and the second type of user information is leasing information;

the analyzing, according to the second analysis task, second type user information of the second type user associated with the user equipment to generate second statistical information of the second type user, and sending the second statistical information to the user equipment, includes:

according to the second analysis task, obtaining the leasing information of a leasing party associated with the user equipment, wherein the leasing information comprises: rental type, rental account data, and prepayment data;

acquiring leasing account data and prepayment data corresponding to each leasing type, and counting the number of type leasing parties and the total amount of type prepayment related to the leasing type;

and generating the second statistical information according to the number of the type leasing parties and the type pre-transaction total amount, and sending the second statistical information to the user equipment.

5. The data analysis method of claim 4,

the analyzing, according to the second analysis task, second type user information of the second type user associated with the user equipment to generate second statistical information of the second type user, and sending the second statistical information to the user equipment, further includes:

according to the second analysis task, obtaining a leasing list of each leasing party associated with the user equipment, wherein the leasing list comprises account data to be leased;

matching the account data to be rented in the rental list with the rented account data in the rented information associated with the user equipment to obtain an account matching value;

grading the leaseholder corresponding to the lease list according to the account matching value to generate a lease grade score;

and generating the second statistical information according to the number of the type leasers, the type pre-transaction total amount and the lease rating score, and sending the second statistical information to the user equipment.

6. The data analysis method according to claim 1, wherein the analyzing, according to the management analysis task, behavior information and/or service information of a third type of user associated with the user equipment to generate management evaluation information of the third type of user, and sending the second statistical information to the user equipment includes:

acquiring the management authority of the user equipment according to the management analysis task;

inquiring an authority task type corresponding to the management authority, wherein the authority task type comprises an attendance task, a visit task and a rent task;

according to the authority task type, counting attendance information of a third class of users associated with the management authority to generate an attendance form;

according to the authority task type, counting first class user visit records and second class user visit records of a third class user associated with the management authority to generate a visit form;

counting the total rent amount of the third type of users associated with the management authority according to the authority task type to generate a rent form;

and grading the third type of users according to the attendance information of the third type of users, the first type of user visit records associated with the third type of users, the second type of user visit records associated with the third type of users and the total rent amount to generate management evaluation information, and sending the second statistical information to the user equipment.

7. The data analysis method of claim 6, wherein the ranking the third type of users according to the attendance information of the third type of users, the first type of user visit records associated with the third type of users, the second type of user visit records associated with the third type of users, and the total rent amount to generate management evaluation information, and sending the second statistical information to the user equipment comprises:

matching the attendance information of the third class of users with an attendance threshold value to obtain an attendance score;

comparing the first class user visit record associated with the third class user with the second class user visit record associated with the third class user with a preset visit threshold value to obtain a visit score;

calculating a rent proportion score according to the total rent amount of the third type of users and the number of the first type of users corresponding to the account;

and generating the management evaluation information of the third type of users according to the attendance score, the visit score and the rent proportion score, and sending the second statistical information to the user equipment.

8. A data analysis apparatus, comprising:

9. A computer arrangement comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method of any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program when executed by a processor implements the steps of the method of any one of claims 1 to 7.