CN112651699A - OA (office automation) business management data processing method and device - Google Patents

Abstract

The invention discloses an OA business management method and a device, wherein the method comprises the following steps: establishing a logistic regression model; a step of receiving and recording customer data; a step of customer analysis; a task distribution step; and (5) task acceptance. Analyzing through a logistic regression model to obtain an optimal follow-up scheme, an optimal communication frequency and a client weight, and quickly analyzing an optimal scheme; and all client data are recorded to the server, so that the client data loss caused by the user leaving the job is avoided. Because the server can not send the client data which does not belong to the user, each user can only receive the client data sent by the user, and the secret leakage event caused by the fact that the user obtains all the client data is avoided; meanwhile, the client data is compared with the database, the included client data is eliminated, and the client data is configured for the users, so that each client corresponds to a unique user, and the occurrence of a ticket collision event is avoided.

Description

OA (office automation) business management data processing method and device

Technical Field

The present invention relates to the field of data processing technologies, and in particular, to a method and an apparatus for processing OA service management data.

Background

OA office automation is mainly applied to the aspects of information crawling, file processing, file distribution, online meeting, attendance management, enterprise management and the like, and the office efficiency is greatly improved.

In the current sales business, the collection intention customer data and the management intention customer data are recorded only by each sales person, and the mode can cause the problems that a plurality of sales business persons of the same company simultaneously follow the same intention customer, conflict is generated among the business persons due to the fact that the customers are contended, the intention customer data are lost, and the like. If the sales service would like customer data to be shared, a compromise event would be triggered. In addition, the scheme that business personnel follow up to the customers is judged completely by own subjective consciousness, and cannot comprehensively and systematically analyze customer data to find out the optimal follow-up scheme under the leading of personal habits and current moods.

Disclosure of Invention

The invention aims to provide an OA business management data processing method and device which can collect and record the intention customer data, distribute the customer follow-up task and find out the optimum scheme of the intention customer follow-up.

The invention provides an OA business management data processing method, which comprises the following steps:

establishing a logistic regression model: setting a plurality of preset follow-up schemes, and performing model iteration according to historical data to generate a function, wherein the logistic regression model comprises a client intention degree model, a preset follow-up scheme model and a communication frequency model, and the function comprises a client intention degree function, a preset follow-up scheme function and a communication frequency function;

client data recording step: the server receives client data sent by a user, compares the client data with a database to eliminate the included client data, and configures the client data to the user;

step of customer analysis

Calculating an optimal follow-up scheme and an optimal communication frequency according to the client data, a preset follow-up scheme function and a communication frequency function;

and a task distribution step: determining customer weight through customer data and a customer intention function, and distributing tasks for users according to the customer weight and preset workload parameters;

task acceptance step: the task that has been completed is checked.

Further, the client data recording step is as follows: the user sends the client data to the server; the server compares the client data with client data of a database one by one, if the client data are not stored in the database, the client data are matched with the user, a first feedback result is sent to the user, and if the client data are stored in the database, a second feedback result is sent to the user.

Further, the customer analysis step is:

substituting the client data into the preset follow-up scheme function to select an optimal follow-up scheme;

and substituting the client data into the communication frequency function to obtain the optimal communication frequency.

Further, the task distributing step includes:

presetting task parameters, wherein the task parameters comprise workload parameters and total workload values corresponding to a preset follow-up scheme;

substituting the client data into an intention model function to obtain an intention weight;

and the server generates the day task according to the intention weight, the workload parameter, the total workload value, the optimal follow-up scheme and the optimal communication frequency and sends the day task to the user.

Further, the task acceptance step is as follows:

s1, generating a random check object and sending an instruction to a user;

s2, uploading screenshot information after receiving the instruction, wherein the screenshot information comprises WeChat chat records and/or mobile phone call records;

the S3 server identifies the communication time data in the screenshot information and calculates the current communication frequency according to the communication time data;

and the S4 server compares the current communication frequency with the optimal communication frequency and sends the comparison result to the user.

The invention also provides an OA business management data processing device, which comprises a logistic regression model establishing device, a client data recording device, a client analyzing device, a task distributing device and a task acceptance device;

the logistic regression model establishing device is used for setting a plurality of preset follow-up schemes and carrying out model iteration according to historical data to generate functions, the logistic regression model comprises a client intention degree model, a preset follow-up scheme model and a communication frequency model, and the functions comprise a client intention degree function, a preset follow-up scheme function and a communication frequency function;

the client data recording device is used for the server to receive client data sent by a user, compare the client data with a database, eliminate the recorded client data and configure the client data to the user;

the client analysis device is used for calculating an optimal follow-up scheme and an optimal communication frequency through client data, a preset follow-up scheme function and a communication frequency function;

the task distribution device is used for determining customer weight through customer data and a customer intention function and distributing tasks to users according to the customer weight and preset workload parameters;

the task acceptance device is used for checking the completed task.

Further, the customer data listing apparatus includes:

the user sends the client data to the server; the server compares the client data with client data of a database one by one, if the client data are not stored in the database, the client data are matched with the user, a first feedback result is sent to the user, and if the client data are stored in the database, a second feedback result is sent to the user.

Further, the customer analysis device includes:

substituting the client data into the preset follow-up scheme function to select an optimal follow-up scheme;

and substituting the client data into the communication frequency function to obtain the optimal communication frequency.

Further, the task distributing apparatus includes:

presetting task parameters, wherein the task parameters comprise workload parameters and total workload values corresponding to a preset follow-up scheme;

substituting the client data into an intention model function to obtain an intention weight;

and the server generates the day task according to the intention weight, the workload parameter, the total workload value, the optimal follow-up scheme and the optimal communication frequency and sends the day task to the user.

Further, the task acceptance device comprises:

generating a random spot check object and sending an instruction to a user; uploading screenshot information after a user receives an instruction, wherein the screenshot information comprises WeChat chat records and/or mobile phone call records; the server identifies communication time data in the screenshot information and calculates the current communication frequency according to the communication time data; and the server compares the current communication frequency with the optimal communication frequency and sends a comparison result to the user.

The beneficial effects obtained by the invention are as follows: analyzing through a logistic regression model to obtain an optimal follow-up scheme, an optimal communication frequency and a client weight, and quickly analyzing an optimal scheme; and all client data are recorded to the server, so that the client data loss caused by the user leaving the job is avoided. Because the server can not send the client data which does not belong to the user, each user can only receive the client data sent by the user, and the secret leakage event caused by the fact that the user obtains all the client data is avoided; meanwhile, the client data is compared with the database, the included client data is eliminated, and the client data is configured for the users, so that each client corresponds to a unique user, and the occurrence of a ticket collision event is avoided.

Drawings

FIG. 1 is a flow chart of the present invention;

FIG. 2 is a flow chart of the logistic regression model building of the present invention;

FIG. 3 is a flow chart of client data entry in the present invention;

FIG. 4 is a flow diagram of customer analysis and task distribution in the present invention;

FIG. 5 is a flow chart of task acceptance in the present invention.

Detailed Description

The present invention will be further described with reference to the following embodiments.

The first embodiment is as follows:

as shown in fig. 1: an OA business management data processing method, the method comprising:

as shown in fig. 2: establishing a logistic regression model: setting a plurality of preset follow-up schemes, and performing model iteration according to historical data to generate a function, wherein the logistic regression model comprises a client intention degree model, a preset follow-up scheme model and a communication frequency model, and the function comprises a client intention degree function, a preset follow-up scheme function and a communication frequency function.

The client intention evaluation indexes of different industries are different. The embodiment is intellectual property agency industry, and the customer intention evaluation index comprises the following customer data: communication frequency, registered capital, number of patents, number of trademarks, follow-up plans, whether the scope of operation includes development and or design, etc.

Customer data sheet

Whether the scope of operation includes development and or design: 1 denotes that the operating range includes development and or design, 0 denotes that the operating range does not include development and design: whether proxy cooperation is achieved: 1 indicates that proxy cooperation is achieved, and 0 indicates that proxy cooperation is not achieved.

Solve using MATLAB's fitglm command. The code is as follows:

Clear；

Filename＝’train_model.xls’；

[num，txt]＝xlsread(filename)；

X＝num(：，1：end-1)；

Y＝num(：，end)；

N＝1：size(X，2)；

N0＝[]；

flag＝1；

mdl＝fitglm(X，Y，′linear′，′distr′，′binomial′，

′Link′，′logit′)；

while flag＝＝1

pValue＝mdl.Coefficients.pValue；

pValue_gt05＝pValue>0.05；

if sum(pValue_gt05)＝＝0；

flag＝0；

break；

end

[t，index]＝max(pValue，[]，1)；

N0＝[N0 index+sum(index>＝N0)]；

if index-1～＝0

removeVariance＝mdl.CoefficientNames

{1，index}；

else

removeVariance＝′1′；

end

mdl＝removeTerms(mdl，removeVariance)；

end

mdl2＝stepwiseglm(X，Y，′constant′，′Distribution′，′

binomial′，′Link′，′logit′)；

mdl3＝stepwiseglm(X，Y，′linear′，′Distribution′，′

binomial′，′Link′，′logit′)；

Index1＝setdiff(N，N0-1)；

ynew＝feval(mdl，X(：，Index1))；

ynew1＝[1-ynew′；ynew′]；

Y1＝full(ind2vec(Y′+1))；

plotconfusion(Y1，ynew1)

figure(2)

plotroc(Y1，ynew1)

and obtaining a preset follow-up scheme function and a communication frequency function.

And deleting two columns of the follow-up scheme and the communication frequency in the data table, and running the program again to obtain a client intention function.

As shown in fig. 3: client data recording step: the user sends the client data to the server; the server compares the client data with client data of a database one by one, if the client data are not stored in the database, the client data are matched with the user, a first feedback result is sent to the user, and if the client data are stored in the database, a second feedback result is sent to the user.

As shown in fig. 4: customer analysis and task distribution steps: substituting the client data into the preset follow-up scheme function to select an optimal follow-up scheme; and substituting the client data into the communication frequency function to obtain the optimal communication frequency. Presetting task parameters, wherein the task parameters comprise workload parameters and total workload values corresponding to a preset follow-up scheme; substituting the client data into an intention model function to obtain an intention weight; and the server generates the day task according to the intention weight, the workload parameter, the total workload value, the optimal follow-up scheme and the optimal communication frequency and sends the day task to the user.

As illustrated in fig. 5: task acceptance step: s1, generating a random check object and sending an instruction to a user; s2, uploading screenshot information after receiving the instruction, wherein the screenshot information comprises WeChat chat records and/or mobile phone call records; the S3 server identifies the communication time data in the screenshot information and calculates the current communication frequency according to the communication time data; and the S4 server compares the current communication frequency with the optimal communication frequency and sends the comparison result to the user.

Example two:

as shown in fig. 1: the invention also provides an OA business management data processing device which comprises a logistic regression model establishing device, a client data recording device, a client analyzing device, a task distributing device and a task acceptance device.

As shown in fig. 2: the logistic regression model establishing device is used for setting a plurality of preset follow-up schemes and performing model iteration according to historical data to generate functions, the logistic regression model comprises a client intention degree model, a preset follow-up scheme model and a communication frequency model, and the functions comprise a client intention degree function, a preset follow-up scheme function and a communication frequency function.

The client intention evaluation indexes of different industries are different. The embodiment is intellectual property agency industry, and the customer intention evaluation index comprises the following customer data: communication frequency, registered capital, number of patents, number of trademarks, follow-up plans, whether the scope of operation includes development and or design, etc.

Customer data sheet

Whether the scope of operation includes development and or design: 1 denotes that the operating range includes development and or design, 0 denotes that the operating range does not include development and design: whether proxy cooperation is achieved: 1 indicates that proxy cooperation is achieved, and 0 indicates that proxy cooperation is not achieved.

Solve using MATLAB's fitglm command. The code is as follows:

Clear；

Filename＝’train_model.xls’；

[num，txt]＝xlsread(filename)；

X＝num(：，1：end-1)；

Y＝num(：，end)；

N＝1：size(X，2)；

N0＝[]；

flag＝1；

mdl＝fitglm(X，Y，′linear′，′distr′，′binomial′，

′Link′，′logit′)；

while flag＝＝1

pValue＝mdl.Coefficients.pValue；

pValue_gt05＝pValue>0.05；

if sum(pValue_gt05)＝＝0；

flag＝0；

break；

end

[t，index]＝max(pValue，[]，1)；

N0＝[NO index+sum(index>＝N0)]；

if index-1～＝0

removeVariance＝mdl.CoefficientNames

{1，index}；

else

removeVariance＝′1′；

end

mdl＝removeTerms(mdl，removeVariance)；

end

mdl2＝stepwiseglm(X，Y，′constant′，′Distribution′，′

binomial′，′Link′，′logit′)；

mdl3＝stepwiseglm(X，Y，′linear′，′Distribution′，′

binomial′，′Link′，′logit′)；

Index1＝setdiff(N，N0-1)；

ynew＝feval(mdl，X(：，Index1))；

ynew1＝[1-ynew′；ynew′]；

Y1＝full(ind2vec(Y′+1))；

plotconfusion(Y1，ynew1)

figure(2)

plotroc(Y1，ynew1)

and obtaining a preset follow-up scheme function and a communication frequency function.

And deleting two columns of the follow-up scheme and the communication frequency in the data table, and running the program again to obtain a client intention function.

The client data listing apparatus shown in fig. 3 includes a user transmitting client data to a server; the server compares the client data with client data of a database one by one, if the client data are not stored in the database, the client data are matched with the user, a first feedback result is sent to the user, and if the client data are stored in the database, a second feedback result is sent to the user.

As shown in fig. 4: the customer analysis device includes: substituting the client data into the preset follow-up scheme function to select an optimal follow-up scheme; and substituting the client data into the communication frequency function to obtain the optimal communication frequency.

As shown in fig. 4: the task distributing device comprises: presetting task parameters, wherein the task parameters comprise workload parameters and total workload values corresponding to a preset follow-up scheme; substituting the client data into an intention model function to obtain an intention weight; and the server generates the day task according to the intention weight, the workload parameter, the total workload value, the optimal follow-up scheme and the optimal communication frequency and sends the day task to the user.

As shown in fig. 5: the task acceptance device comprises: generating a random spot check object and sending an instruction to a user; uploading screenshot information after a user receives an instruction, wherein the screenshot information comprises WeChat chat records and/or mobile phone call records; the server identifies communication time data in the screenshot information and calculates the current communication frequency according to the communication time data; and the server compares the current communication frequency with the optimal communication frequency and sends a comparison result to the user.

The present invention is not limited to the above preferred embodiments, and any modifications, equivalent replacements, improvements, etc. within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. An OA business management data processing method is characterized in that the method comprises the following steps:

a logistic regression model establishing step: setting a plurality of preset follow-up schemes, and performing model iteration according to historical data to generate a function, wherein the logistic regression model comprises a client intention degree model, a preset follow-up scheme model and a communication frequency model, and the function comprises a client intention degree function, a preset follow-up scheme function and a communication frequency function;

client data recording step: the server receives client data sent by a user, compares the client data with a database, eliminates the included client data and configures the client data to the user;

a customer analysis step: calculating an optimal follow-up scheme and an optimal communication frequency according to the client data, a preset follow-up scheme function and a communication frequency function;

and a task distribution step: determining customer weight through customer data and a customer intention function, and distributing tasks for users according to the customer weight and preset workload parameters;

task acceptance step: the task that has been completed is checked.

2. The OA business management data processing method according to claim 2, wherein the customer data including step is: the user sends the client data to the server; the server compares the client data with client data of a database one by one, if the client data are not stored in the database, the client data are matched with the user, a first feedback result is sent to the user, and if the client data are stored in the database, a second feedback result is sent to the user.

3. The OA business management data processing method of claim 3, wherein the customer analyzing step is:

substituting the client data into the preset follow-up scheme function to select an optimal follow-up scheme;

and substituting the client data into the communication frequency function to obtain the optimal communication frequency.

4. The OA traffic management data processing method according to claim 4, wherein the task distributing step comprises:

presetting task parameters, wherein the task parameters comprise workload parameters and total workload values corresponding to a preset follow-up scheme;

substituting the client data into an intention model function to obtain an intention weight;

and the server generates the day task according to the intention weight, the workload parameter, the total workload value, the optimal follow-up scheme and the optimal communication frequency and sends the day task to the user.

5. The OA business management data processing method of claim 5, wherein the task acceptance step is:

s1, generating a random check object and sending an instruction to a user;

s2, uploading screenshot information after receiving the instruction, wherein the screenshot information comprises WeChat chat records and/or mobile phone call records;

the S3 server identifies the communication time data in the screenshot information and calculates the current communication frequency according to the communication time data;

and the S4 server compares the current communication frequency with the optimal communication frequency and sends the comparison result to the user.

6. An OA business management data processing device is characterized by comprising a logistic regression model establishing device, a client data recording device, a client analyzing device, a task distributing device and a task checking device;

the logistic regression model establishing device is used for setting a plurality of preset follow-up schemes and carrying out model iteration according to historical data to generate functions, the logistic regression model comprises a client intention degree model, a preset follow-up scheme model and a communication frequency model, and the functions comprise a client intention degree function, a preset follow-up scheme function and a communication frequency function;

the client data recording device is used for the server to receive client data sent by a user, compare the client data with a database, eliminate the recorded client data and configure the client data to the user;

the client analysis device is used for calculating an optimal follow-up scheme and an optimal communication frequency through client data, a preset follow-up scheme function and a communication frequency function;

the task distribution device is used for determining customer weight through customer data and a customer intention function and distributing tasks to users according to the customer weight and preset workload parameters;

the task acceptance device is used for checking the completed task.

7. The OA traffic management data processing device of claim 6, wherein the client data listing device comprises:

the user sends the client data to the server; the server compares the client data with client data of a database one by one, if the client data are not stored in the database, the client data are matched with the user, a first feedback result is sent to the user, and if the client data are stored in the database, a second feedback result is sent to the user.

8. The OA business management data processing apparatus of claim 7, wherein said customer analysis means comprises:

substituting the client data into the preset follow-up scheme function to select an optimal follow-up scheme;

and substituting the client data into the communication frequency function to obtain the optimal communication frequency.

9. The OA traffic management data processing apparatus of claim 8, wherein the task distributing means comprises:

presetting task parameters, wherein the task parameters comprise workload parameters and total workload values corresponding to a preset follow-up scheme;

substituting the client data into an intention model function to obtain an intention weight;

and the server generates the day task according to the intention weight, the workload parameter, the total workload value, the optimal follow-up scheme and the optimal communication frequency and sends the day task to the user.

10. The OA business management data processing apparatus of claim 9, wherein the task acceptance means comprises:

generating a random spot check object and sending an instruction to a user; uploading screenshot information after a user receives an instruction, wherein the screenshot information comprises WeChat chat records and/or mobile phone call records; the server identifies communication time data in the screenshot information and calculates the current communication frequency according to the communication time data; and the server compares the current communication frequency with the optimal communication frequency and sends a comparison result to the user.

Images