CN115081942A

CN115081942A - Data processing method and related device

Info

Publication number: CN115081942A
Application number: CN202210865565.0A
Authority: CN
Inventors: 郭传亮
Original assignee: Hope Zhizhou Technology Shenzhen Co ltd
Current assignee: Hope Zhizhou Technology Shenzhen Co ltd
Priority date: 2022-07-22
Filing date: 2022-07-22
Publication date: 2022-09-20
Anticipated expiration: 2042-07-22
Also published as: CN115965189A; CN115081942B

Abstract

The application provides a data processing method and a related device, wherein the method comprises the following steps: acquiring production data of target employees; determining at least one evaluation parameter mean value corresponding to the target staff according to the quality evaluation parameters of each batch of output and the actual working condition complexity level during the production of each batch of output, wherein the at least one evaluation parameter mean value comprises: the evaluation parameter mean value corresponding to each preset working condition complexity grade in the at least one preset working condition complexity grade comprises the actual working condition complexity grade during production of each batch of products; determining a target working condition complexity grade corresponding to the target employee from at least one preset working condition complexity grade according to at least one evaluation parameter mean value corresponding to the target employee; and creating a work task event for the target staff according to the target working condition complexity level. The method and the device for distributing the work tasks are beneficial to improving the accuracy and efficiency of distributing the work tasks for the staff.

Description

Data processing method and related device

Technical Field

The present application belongs to the field of data processing technology, and in particular, relates to a data processing method and a related apparatus.

Background

At present, when workers are allocated with work tasks, different work tasks are allocated to different workers by means of subjective feeling, and the work task allocation mode depending on manual work has the problems of low efficiency and high labor cost on one hand, and on the other hand, the same worker is always allocated with the same work task possibly, and the work task is not necessarily matched with the actual production capacity of the worker, so that the production advantages of the workers cannot be brought into full play when the production tasks are allocated to the workers, and the production efficiency is influenced.

Disclosure of Invention

The application provides a data processing method and a related device, aiming at improving the accuracy and efficiency of distributing work tasks for employees.

In a first aspect, the present application provides a data processing method, including:

obtaining production data of a target employee, wherein the production data comprises: the quality evaluation parameters of each batch of output in at least one batch of output produced by the target staff, and the complexity level of the actual working condition during the production of each batch of output;

determining at least one evaluation parameter mean value corresponding to the target staff according to the quality evaluation parameters of each batch of output and the actual working condition complexity level during production of each batch of output, wherein the at least one evaluation parameter mean value comprises: the evaluation parameter mean value corresponding to each preset working condition complexity grade in at least one preset working condition complexity grade, wherein the at least one preset working condition complexity grade comprises the actual working condition complexity grade during production of each batch of products;

determining a target working condition complexity grade corresponding to the target employee from the at least one preset working condition complexity grade according to the at least one evaluation parameter average corresponding to the target employee;

and creating a work task event for the target staff according to the target working condition complexity level.

In a second aspect, the present application provides a data processing apparatus comprising:

the acquisition unit is used for acquiring production data of target employees, and the production data comprises: the quality evaluation parameters of each batch of output in at least one batch of output produced by the target staff, and the complexity level of the actual working condition during the production of each batch of output;

a first determining unit, configured to determine at least one evaluation parameter mean value corresponding to the target employee according to the quality evaluation parameter of each batch of output and the actual working condition complexity level during production of each batch of output, where the at least one evaluation parameter mean value includes: the evaluation parameter mean value corresponding to each preset working condition complexity grade in at least one preset working condition complexity grade, wherein the at least one preset working condition complexity grade comprises the actual working condition complexity grade during production of each batch of products;

the second determining unit is used for determining a target working condition complexity level corresponding to the target staff from the at least one preset working condition complexity level according to the at least one evaluation parameter mean value corresponding to the target staff;

and the creating unit is used for creating a work task event for the target staff according to the target working condition complexity level.

In a third aspect, the present application provides an electronic device, comprising:

one or more processors;

one or more memories for storing programs,

the one or more memories and the program are configured to control the electronic device, by the one or more processors, to execute the instructions of the steps in any of the methods of the first aspect of the embodiments of the present application.

In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, and wherein the computer program causes a computer to perform some or all of the steps as described in any one of the methods of the first aspect of the embodiments of the present application.

In a fifth aspect, the present application provides a computer program, wherein the computer program is operable to cause a computer to perform some or all of the steps as described in any of the methods of the first aspect of the embodiments of the present application. The computer program may be a software installation package.

It can be seen that, in the embodiment of the present application, the electronic device first obtains production data of a target employee, determines at least one evaluation parameter mean value corresponding to the target employee according to a quality evaluation parameter of each batch of output in the production data and an actual working condition complexity level during production of each batch of output, includes an evaluation parameter mean value corresponding to each preset working condition complexity level in at least one preset working condition complexity level, determines a target working condition complexity level corresponding to the target employee from the at least one preset working condition complexity level according to the at least one evaluation parameter mean value corresponding to the target employee, and finally creates a work task event for the target employee according to the determined target working condition complexity level. Therefore, the electronic equipment automatically evaluates the evaluation parameter mean value of the staff under different actual working condition complexity levels according to the quality evaluation parameters of the output produced by the staff and the actual working condition complexity levels during production, further distributes work tasks for the staff according to the evaluation result, reduces the manual determination cost, and improves the accuracy and efficiency of distributing the work tasks for the staff.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure;

fig. 2A is a flowchart of a data processing method provided in an embodiment of the present application;

FIG. 2B is a schematic diagram of a production work order provided by an embodiment of the present application;

fig. 3A is a block diagram of functional units of a data processing apparatus according to an embodiment of the present application

Fig. 3B is a block diagram of functional units of another data processing apparatus according to an embodiment of the present disclosure.

Detailed Description

In order to make the technical solutions of the present application better understood, 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 only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

The terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.

The electronic device in the present application may be configured as shown in fig. 1, and may include a processor 110, a memory 120, a communication interface 130, and one or more programs 121, where the one or more programs 121 are stored in the memory 120 and configured to be executed by the processor 110, and the one or more programs 121 include instructions for executing any step of the foregoing method embodiments. The communication interface 130 is used for supporting communication between the electronic device and other devices. In a specific implementation, the processor 110 is configured to perform any one of the steps performed by the electronic device in the method embodiments described below, and when performing data transmission such as sending, optionally invokes the communication interface 130 to complete the corresponding operation. It should be noted that the structural schematic diagram of the electronic device is merely an example, and more or fewer devices may be specifically included, which is not limited herein.

In a specific implementation, the electronic device described in this application may be specifically a server, and the server may communicate with other user devices (for example, a mobile phone, a computer, a tablet computer, or an intelligent wearable device), so that the other user devices may obtain information, such as a work task event created by the server, from the server.

The embodiment of the present application provides a data processing method, and the following describes the embodiment of the present application in detail.

Referring to fig. 2A, fig. 2A is a flowchart of a data processing method according to an embodiment of the present disclosure. As shown in fig. 2A, the data processing method includes the steps of:

step 201, the electronic device obtains production data of a target employee.

Wherein the production data comprises: the quality evaluation parameters of each batch of output in at least one batch of output produced by the target staff, and the complexity level of the actual working condition when each batch of output is produced.

In specific implementation, the electronic device may specifically obtain the production data of the target employee within a preset time period by obtaining a work order of the target employee within the preset time period, where each production work order may include quality evaluation parameters of one or more batches of products produced by the employee within a certain time period and a work condition complexity level when each batch of products is produced. Specifically, the name or number of the staff corresponding to the work order can be recorded in each work order, and the electronic device can inquire the work order of the target staff through the name or number of the target staff.

Wherein, according to different production stages, the output can be divided into intermediate materials and products, the products can be final products obtained by a plurality of production processes, the intermediate materials are intermediate products generated by each production process before the products are obtained, for example, the production of a certain product needs to pass through continuous P1-Pn production process devices, a batch of output obtained by processing each production device in P1-Pn is sent to the next production device and is used as a raw material of the next production device to continue to enter into the next production deviceLine processing, P1-P _n-1 The output of the production device is intermediate material, and the output obtained by the Pn production device is the product.

Illustratively, fig. 2B shows a schematic diagram of a production work order, each work order includes the number of the staff corresponding to the work order, the type of the output, the work condition complexity level and the quality evaluation parameter of the output, the production work order 1, i.e. the staff 01, produces the output 1 under the work condition with the work condition complexity level of 1, and the quality evaluation parameter of the batch of the output 1 is 80. It should be noted that fig. 2B is only an exemplary illustration, and in practical applications, each information in the production work order may also be identified by using other characters or symbols, for example, the employee number may be an employee name, the type of the output may be a name of a specific output, the quality evaluation parameter may be in a form of percentage, and the like, which is not limited herein.

Step 202, the electronic equipment determines at least one evaluation parameter mean value corresponding to the target staff according to the quality evaluation parameters of each batch of output and the actual working condition complexity level during production of each batch of output.

Wherein the at least one evaluation parameter mean comprises: and the evaluation parameter mean value corresponding to each preset working condition complexity grade in at least one preset working condition complexity grade, wherein the at least one preset working condition complexity grade comprises the actual working condition complexity grade during production of each batch of products.

In specific implementation, multiple working condition complexity levels which may appear in production may be pre-stored in the electronic device, and since a single employee may not necessarily perform production operations under each preset working condition complexity level, that is, the actual working condition complexity level included in the obtained production data of the target employee may only be a part of the preset working condition complexity levels. At this time, the evaluation parameter mean value corresponding to part of the preset working condition complexity levels (i.e., the actual working condition complexity levels) may be determined according to the production data obtained in step 201, and for other working condition complexity levels except the actual working condition complexity levels in the preset working condition complexity levels, the values of the other working condition complexity levels are directly determined as preset values, or the corresponding evaluation parameter mean values may be determined for the other working condition complexity levels according to the actual working condition complexity levels, for example, the evaluation parameter mean value corresponding to the actual working condition complexity level higher than the other working condition complexity levels is determined as the evaluation parameter mean value corresponding to the other working condition complexity levels.

For example, assuming that the preset working condition complexity level is four levels, namely, level a, level B, level C and level D, in sequence from high to low, and assuming that the actual working condition complexity level is level a, level B and level D, wherein the mean values of the evaluation parameters corresponding to level a, level B and level D are a, B and D, respectively, the electronic device may directly determine the mean value of the evaluation parameter corresponding to level C as the preset value C, or may determine the mean value of a and B as the mean value of the evaluation parameter of level C, or may further set the weight of a higher than the weight of B when calculating the mean value of a and B (i.e., the higher the working condition complexity level is, the higher the weight of the mean value of the corresponding evaluation parameter is).

The electronic equipment can count the quality evaluation parameters of each batch of output corresponding to the working condition complexity grade in the production data according to each working condition complexity grade related to the obtained production data of the target staff, and calculate the average value of the quality evaluation parameters of each batch of output to serve as the average value of the evaluation parameters corresponding to the working condition complexity grade.

And 203, the electronic equipment determines a target working condition complexity level corresponding to the target employee from the at least one preset working condition complexity level according to the at least one evaluation parameter mean value corresponding to the target employee.

The evaluation parameter mean value corresponding to each preset working condition complexity level is determined according to the actual production data of the target staff, so that the quality of products produced by the target staff under each working condition complexity level can be reflected, and the target working condition complexity level which is in accordance with the actual production capacity of the target staff can be determined according to the evaluation parameter mean value corresponding to each preset working condition complexity level.

And step 204, the electronic equipment creates a work task event for the target staff according to the target working condition complexity level.

In a specific implementation, the electronic device may create the work task event for the target employee according to the target working condition complexity level, and may allocate the work task of the target working condition complexity level to the target employee.

Specifically, after the electronic device creates a work task event for a target employee, the created work task event can be uploaded to a block chain node point corresponding to the target employee in a block chain network, specifically, the block chain network can respectively create corresponding block chain node points for each employee, when a work task time acquisition request from an employee is received, a work event information acquisition permission of the employee can be determined according to equipment information or account information contained in the acquisition request, so that a block chain node point open to the employee can be determined from the block chain network according to the work event information acquisition permission, corresponding work event information is called from the open block chain node according to the work event acquisition request of the employee, and the work task event information is encrypted and then transmitted to the electronic device of the employee.

It can be seen that, in the embodiment of the application, the electronic device first obtains production data of a target employee, determines at least one evaluation parameter mean value corresponding to the target employee according to a quality evaluation parameter of each batch of output in the production data and an actual working condition complexity level during production of each batch of output, includes an evaluation parameter mean value corresponding to each preset working condition complexity level in at least one preset working condition complexity level, determines a target working condition complexity level corresponding to the target employee from at least one preset working condition complexity level according to the at least one evaluation parameter mean value corresponding to the target employee, and finally creates a work task event for the target employee according to the determined target working condition complexity level. Therefore, the electronic equipment automatically evaluates the evaluation parameter mean value of the staff under different actual working condition complexity levels according to the quality evaluation parameters of the output produced by the staff and the actual working condition complexity levels during production, further distributes work tasks for the staff according to the evaluation result, reduces the manual determination cost, and improves the accuracy and efficiency of distributing the work tasks for the staff.

In a possible example, the determining, according to the quality evaluation parameter of each batch of output and the actual working condition complexity level when each batch of output is produced, at least one evaluation parameter mean value corresponding to the target employee includes: determining an evaluation parameter mean value corresponding to the same actual working condition complexity grade according to the quality evaluation parameters corresponding to the same actual working condition complexity grade in the quality evaluation parameters of each batch of output; if the at least one preset working condition complexity level comprises: and determining the mean value of the evaluation parameters corresponding to the complexity grades of other working conditions as a first preset value, wherein the complexity grades of other working conditions are different from the complexity grade of the actual working condition during the production of each batch of the product.

In a specific implementation, the first preset value may be, for example, a value used for indicating that the target employee is not suitable for the other working condition complexity levels, so as to avoid allocating a work task exceeding its actual production capacity to the target employee, and improve accuracy of work task allocation. In addition, in other embodiments, according to different actual needs, the first preset value may also be a value used for indicating that the target employee is suitable for the complexity level of the other working conditions, and is not specifically limited herein.

The average evaluation parameter value of the same actual working condition complexity level may specifically be: average value of quality evaluation parameters of each batch of output corresponding to the same working condition complexity grade. For example, at least one batch of effluent comprises: the method comprises the following steps of obtaining a first batch of output, a second batch of output and a third batch of output, wherein the quality evaluation parameter of the first batch of output is 80, the quality evaluation parameter of the second batch of output is 40, and the quality evaluation parameter of the third batch of output is 70, wherein the actual working condition complexity grade corresponding to the first batch of output and the second batch of output is grade A, the actual working condition complexity grade corresponding to the third batch of output is grade B, the mean value of the evaluation parameters corresponding to the grade A is the average value 60 of 80 and 40, the mean value of the evaluation parameters corresponding to the grade C is 70, if the preset grade comprises the grade A, the grade B and the grade C, the grade C is the complexity grade of other working conditions, and the mean value of the evaluation parameters corresponding to the grade C can be determined to be a first preset value such as 0.

It can be seen that, in this example, the electronic device determines, according to the quality evaluation parameters corresponding to the same actual condition complexity level in the quality evaluation parameters of each batch of output, an evaluation parameter mean value corresponding to the same actual condition complexity level, and directly determines, as the first preset value, the evaluation parameter mean values corresponding to the other working condition complexity levels, which are different from the actual condition complexity level, in at least one preset working condition complexity level, so as to further improve the efficiency of work task allocation of the electronic device.

In one possible example, the determining, according to the at least one evaluation parameter mean value corresponding to the target employee, a target working condition complexity level corresponding to the target employee from the at least one preset working condition complexity level includes: executing the following operations aiming at each preset working condition complexity level: determining whether the target evaluation parameter mean value corresponding to the complexity level of the current preset working condition meets a preset standard or not; and if so, determining the current preset working condition complexity level as the target working condition complexity level.

In the concrete implementation, the target evaluation parameter mean value corresponding to the preset working condition complexity level meets the preset standard, so that the target staff can be represented to be suitable for the preset working condition complexity level, the target staff can be determined as the target working condition complexity level, and the staff can be directly assigned with the working task of the target working condition complexity level when the staff are assigned with the working task subsequently. For example, taking H, M and L as examples in sequence from high to low in preset working condition complexity levels, if both working condition complexity levels H, M and L meet preset standards, a working task of any one of working condition complexity levels H, M and L may be subsequently allocated to the target employee, and if only M of the working condition complexity levels meets the preset standards, and neither level L nor level H meets the preset standards, a working task of the working condition complexity level M may be only allocated to the target employee when a working task is subsequently allocated to the target employee.

In addition, the electronic device, such as a server, may further prestore a mapping relationship between a target working condition complexity level and a production difficulty parameter, after the target working condition complexity level is determined, a production difficulty parameter corresponding to the target employee may be determined according to the target working condition complexity level, and the production difficulty parameter and identity information such as a number of the target employee may be stored in an associated manner.

For example, taking H, M and L as examples of preset working condition complexity levels from top to bottom, whether a target employee is suitable for a certain working condition complexity level may be represented by different parameter values, for example, whether the target employee is unsuitable for a current working condition complexity level may be represented by a parameter value 0, that is, a target evaluation parameter mean value corresponding to the target employee at the current working condition complexity level does not meet a preset standard, and correspondingly, whether the target employee is suitable for the current working condition complexity level may be represented by a parameter value 1, that is, a target evaluation transmission mean value corresponding to the target employee at the current working condition complexity level meets the preset standard (for example, H =1 indicates that the target employee is suitable for a working task with a working condition complexity level of H, and H =0 indicates that the target employee is unsuitable for a working task with a working condition complexity level of H); the production difficulty parameter G may be calculated as follows:

if H =1, and M =1, and L =1, then G = 3;

if H =0, and M =1, and L =1, then G = 2;

if H =0, and M =0, and L =1, then G = 1;

the higher the parameter value of the production difficulty parameter G is, the higher the complexity level of the working condition suitable for the target employee is. For example, in determining the specific parameter value of the production difficulty parameter G corresponding to the target employee, if the target employee is suitable for the grades H, M and L, G =3 may be determined. Correspondingly, when the target working condition complexity level is determined according to the production difficulty parameter G, according to the parameter value of G, all preset working condition complexity levels not higher than a certain level are determined as the target working condition complexity levels, for example, when G =3, all a plurality of working condition complexity levels (including levels H, M and L) not higher than H are determined as the target working condition complexity levels.

Specifically, if the target employee does not have an associated production difficulty parameter G, for example, the target employee H =0, and M =1, and L =0, the target employee does not have an associated production difficulty parameter value, and at this time, the target employee may be directly assigned with a work task of a suitable working condition complexity level, that is, the target employee may be assigned with a work task of a working condition complexity level M.

It should be noted that, in the embodiment of the present application, a parameter value representing whether a target employee is suitable for a certain complexity level may also be set to other values than 0 and 1 as needed, which is not specifically limited herein; similarly, the parameter value of G is also an exemplary illustration, and may be adjusted as needed in practical applications.

Therefore, in this example, the electronic device determines the preset working condition complexity meeting the preset standard as the target working condition complexity level, and can allocate the work tasks to the target employees according to the target working condition complexity level, which is beneficial to improving the accuracy and efficiency of work task allocation.

In addition, in other embodiments, the manner of determining the target working condition complexity level corresponding to the target employee from the at least one preset working condition complexity level may be: executing the following operations aiming at each preset working condition complexity level: determining whether the target evaluation parameter mean value corresponding to the complexity level of the current preset working condition meets a preset standard or not; if so, determining the current preset working condition complexity grade as a reference working condition complexity grade; determining the target working condition complexity grade according to a first reference working condition complexity grade with the highest grade in the reference working condition complexity grades, wherein the target working condition complexity grade comprises the following steps: and the level in the at least one preset working condition complexity level is not higher than the preset working condition complexity level of the first reference working condition complexity, wherein the higher the working condition complexity level is, the more complicated the working condition is during production.

In the concrete implementation, the target evaluation parameter mean value corresponding to the preset working condition complexity level meets the preset standard, so that the target staff can be represented to be suitable for the preset working condition complexity level, the first reference working condition level is the most complex working condition complexity level suitable for the target staff in the preset working condition complexity level, the preset working condition complexity level with the level not higher than the first reference working condition complexity level in the first preset working condition complexity level is determined as the target working condition complexity level, and when a working task is subsequently created for the target staff, the working task matched with the actual production capacity of the target staff can be accurately distributed.

For example, still taking H, M and L as examples of preset working condition complexity levels from high to low, if the working condition complexity level H and the working condition complexity level L meet the preset standard, the first reference working condition complexity level is the working condition complexity level H, and at this time, the target working condition complexity level includes the working condition complexity level H, the working condition complexity level M, and the working condition complexity level L.

That is, when the target employee is suitable for the working condition complexity level H (no matter what the values of M and L are), the working condition complexity level H is a first reference working condition complexity level, and the target working condition complexity level includes the working condition complexity level H, the working condition complexity level M, and the working condition complexity level L; when the target staff is not suitable for the working condition complexity level H but is suitable for the working condition complexity level M (no matter what value L is), the first reference working condition complexity level is the working condition complexity level M, and the target working condition complexity comprises the working condition complexity level M and the working condition complexity level L; the target staff is not suitable for the working condition complexity level H and the working condition complexity level M, but when the target staff is suitable for the working condition complexity level L, the first reference working condition complexity level is L, and the target working condition complexity level comprises the working condition complexity level L.

In the specific implementation, when the electronic device executes the preset operation for each preset working condition complexity level, the preset operation may be executed for each working condition complexity level in sequence according to the level, so as to determine whether the working condition complexity level is the reference working condition complexity level, that is, whether the target employee is suitable for the preset working condition complexity level, and determine the determined first reference working condition complexity level as the first reference working condition complexity level, and the preset operation is no longer executed for the preset working condition complexity level whose subsequent level is lower than the reference working condition complexity level, so as to reduce the resource consumption of the electronic device.

It can be seen that, in this example, since the higher the working condition complexity level is, the more complicated the working condition during production is, the most complicated first reference working condition complexity level suitable for the target employee is determined by the electronic device, so that the preset working condition complexity level not higher than the first reference working condition complexity level is determined as the target working condition complexity level, and when a work task is subsequently allocated to the target employee according to the target working condition complexity level, it is beneficial to improve the matching degree between the allocated work task and the actual production capacity of the target employee, and improve the accuracy of allocating the work task to the employee.

In one possible example, the determining whether the target evaluation parameter mean value corresponding to the current preset working condition complexity level meets a preset standard includes: under the condition that the complexity level of the current preset working condition is higher than a preset level, obtaining a plurality of evaluation parameter mean values corresponding to the complexity level of the current preset working condition, wherein the plurality of evaluation parameter mean values are determined according to production data of a plurality of employees in the preset time period, and the plurality of evaluation parameter mean values comprise the target evaluation parameter mean value; under the condition that the plurality of evaluation parameter mean values are sequenced from large to small according to the sequence of the parameter values, determining whether a preset number of mean values which are sequenced most in front in the plurality of evaluation parameter mean values comprise the target evaluation parameter mean value; if so, determining that the target evaluation parameter mean value meets the preset standard; determining whether the target evaluation parameter mean value is not less than a second preset value or not under the condition that the complexity level of the current preset working condition is not higher than the preset level; and if so, determining that the target evaluation parameters meet the preset standard.

In a specific implementation, the step 201 and the step 202 are respectively executed for a plurality of employees including a target employee, and the calculated evaluation parameter average value corresponding to each employee under each preset working condition complexity level is obtained. The obtained production data of each employee is production data within the same preset time period, for example, the production data of each employee within one month closest to the current time can be obtained, and the average value of the evaluation parameters corresponding to the complexity levels of different preset working conditions within one month closest to each employee is calculated.

In specific implementation, the larger the evaluation parameter mean value is, the stronger the production capacity of the staff under the working condition complexity level is represented, that is, the more suitable the staff is for the working condition complexity level. Under the condition that the working condition complexity level is higher than the preset level, considering that the working condition is more complex and whether the specific parameter index of the staff to be evaluated meets the standard is difficult to determine, whether the target evaluation parameter average value of the target staff meets the preset standard can be determined by comparing the evaluation parameter average values of each staff in the plurality of staff under the working condition complexity level. And under the condition that the working condition complexity is not higher than the preset level, whether the target staff is suitable for the working condition complexity level or not can be directly determined according to the set preset parameter value.

Or taking H, M and L as examples of preset working condition complexity levels in sequence from high to low, if the preset number is 30% of the top in the sequence and the preset level is L, when the value of H is determined, the average value of the evaluation parameters of each employee in the working condition complexity level H in the plurality of employees can be obtained, the average values of the evaluation parameters of each employee are sequentially ranked according to the parameter values from large to small, if the average value of the target evaluation parameters of the target employee is 30% of the top in the sequence, H =1 is determined, and otherwise, H =0 is determined. When the value of M is determined, ranking the evaluation parameter mean values corresponding to the working condition complexity level M of each employee from large to small according to the parameter values, if the target evaluation parameter mean value of the target employee is the most front ranked 30%, determining M =1, otherwise, determining M = 0; when the value of L is determined, assuming that the second preset value is 80, when the target evaluation parameter average value corresponding to the working condition complexity level L of the target employee is greater than or equal to 80, determining that L =1, otherwise, determining that L = 0.

In this example, when the preset working condition complexity level is higher than the preset level, that is, the working condition is more complex, the electronic device determines that the target employee is suitable for the preset working condition complexity level by comparing the evaluation parameter mean values of the plurality of employees at the working condition complexity level when the target evaluation parameter mean value of the target employee is ranked in the front preset number of the plurality of employees, and determines whether the target employee is suitable for the preset working condition complexity level by comparing the target evaluation parameter mean value of the target employee with the preset second preset value when the preset working condition complexity level is not higher than the preset level, which is beneficial to improving the flexibility of the electronic device in determining whether the current preset working condition complexity level meets the preset standard.

In a possible example, the actual working condition complexity level during production of each batch of products is determined according to the working condition type during production of each batch of products, and different working condition types correspond to different working condition complexity levels; wherein, the operating mode type includes normal operating mode and unusual operating mode, normal operating mode includes: the working condition complexity level corresponding to the abnormal working condition is higher than the working condition complexity level corresponding to the normal working condition, and the working condition complexity level corresponding to the marking rod working condition is higher than the working condition complexity level corresponding to the learning task working condition; the preset grade is a working condition complexity grade corresponding to the working condition of the marker post.

In the specific implementation, the obtained work order of the target staff can record the working condition type of each batch of produced products, and the electronic equipment can determine the actual working condition complexity level of each batch of produced products according to the mapping relation between the working condition type and the working condition complexity level.

Wherein, staff can produce according to the set standard process parameters (the standard process parameters are the optimal process parameters obtained by machine learning) under the working condition of the marker post, the complexity of the working condition can be set to be the lowest, the technological parameters of the production of the staff under the working condition of the learning task are the technological parameters which are not adjusted to reach the optimal state in the machine learning process, and the quality of the output of the staff depends on the production capacity of the staff when the staff carries out the production operation, therefore, the complexity that can set up this operating mode is higher than the sighting rod operating mode, and unusual operating mode is that the staff appears abnormal state (for example production facility breaks down or the input material of production facility is unusual etc.) and can't produce according to the technological parameter who sets for when producing, and the influence of staff's self productivity to output quality is bigger this moment, consequently can set up the complexity of this operating mode and be the highest.

Therefore, in this example, the electronic device determines the actual working condition complexity level corresponding to each batch of output according to the working condition type during production of each batch of output, which is beneficial to improving the accuracy of determining the working condition complexity level.

In one possible example, the quality evaluation parameter of each output of the at least one output is obtained by: under the condition that the type of the current batch of output is a product, acquiring a product quality evaluation parameter of the current batch of product as the quality evaluation parameter of the current batch of output; and under the condition that the type of the current batch of output is the intermediate material, acquiring the quality evaluation parameter of a target product belonging to the same production batch with the current batch of intermediate material as the quality evaluation parameter of the current batch of output.

In the concrete implementation, the target product and the intermediate material belong to the same production batch, namely the target product and the intermediate material are respectively a final product and an intermediate product of the same complete production flow, and the target product is processed from the intermediate material. Considering that the quality of the final product is affected by the production quality of the intermediate material, when the output is the intermediate material, the quality of the intermediate material can be evaluated by the quality evaluation parameter of the target product of the same production batch thereof. Specifically, the electronic device may obtain quality evaluation parameters (including product quality evaluation parameters of a current batch of products or quality evaluation parameters of a target product) corresponding to each batch of output in advance to generate a work order of the target employee, and may obtain corresponding quality evaluation parameter information by directly obtaining the work order of the target employee when obtaining production data of the target employee.

For example, taking P1-Pn production process devices that need to be processed continuously to produce a certain product as an example, if the current batch output is the final product produced by the Pn production process device, the quality evaluation parameter of the product is directly obtained as the quality evaluation parameter of the current batch output, and if the current batch output is the intermediate material produced by the P3 production process device, the quality evaluation parameter of the final product produced by the Pn production process device is obtained as the quality evaluation parameter of the current batch output.

In this example, when the type of the current batch of output is a product, the electronic device obtains the product quality evaluation parameter of the current batch of product as the quality evaluation parameter of the current batch of output, and when the type of the current batch of output is an intermediate material, the electronic device obtains the quality evaluation parameter of the target product belonging to the same production batch as the current batch of intermediate material as the quality evaluation parameter of the current batch of output, which is beneficial to improving the accuracy and efficiency of determining the quality evaluation parameter.

In one possible example, the quality evaluation parameter of each output of the at least one output is obtained by: under the condition that the type of the current batch of output is a product, acquiring a product quality evaluation parameter of the current batch of product as the quality evaluation parameter of the current batch of output; under the condition that the type of the current batch of output materials is intermediate materials, if the working condition type during the production of the current batch of intermediate materials is a normal working condition and the current batch of intermediate materials has a preset material quality evaluation index, acquiring a material quality evaluation parameter of the current batch of intermediate materials as the quality evaluation parameter of the current batch of output materials, wherein the material quality evaluation parameter of the current batch of intermediate materials is determined according to the material quality evaluation index; and under the condition that the type of the current batch of output is the intermediate material, if the working condition type during the production of the current batch of intermediate material is the normal working condition and the current batch of intermediate material has no material quality evaluation index, acquiring a quality evaluation parameter of a target product belonging to the same production batch as the current batch of intermediate material as the quality evaluation parameter of the current batch of output.

If the type of the current batch of output materials is the intermediate materials and the type of the working condition during the production of the current batch of intermediate materials is the abnormal working condition, the quality evaluation parameters of the current batch of output materials are not obtained, namely, under the abnormal working condition, the quality evaluation parameters of the output materials are obtained only when the output materials are products, and the production quality of the intermediate materials produced under the abnormal working condition is not evaluated.

In the concrete implementation, considering that the complete production flow of a product comprises a plurality of different production flow devices, namely, the production process of a product has the output of a plurality of different intermediate materials, under the condition that the number of the production flow devices of the product is large, the quality parameters of the final product have a better effect on evaluating the overall output quality of the plurality of intermediate materials, but the problem of lower accuracy probably exists in directly evaluating the output quality of a certain intermediate material by the quality of the final product. Therefore, the material quality evaluation index can be set for the intermediate material, when the current batch of output material is the intermediate material and the batch of intermediate material has no material quality evaluation parameter, the quality evaluation parameter of the target product is determined as the quality evaluation parameter of the current batch of output material, and when the batch of intermediate material has the material quality evaluation index, the quality evaluation parameter of the batch of intermediate material can be determined according to the material quality evaluation index, so that the quality evaluation parameter of the single batch of intermediate material is accurately determined.

Specifically, the material quality evaluation index may be a specification line of the material set under the working condition of the benchmark (for example, an output measurement specification line of each batch of intermediate material under the working condition of the benchmark), if the output of the current batch of intermediate material of the target employee reaches the set material specification line, the electronic device may determine that the quality evaluation parameter of the current batch of output is a third preset value, if the output of the current batch of intermediate material does not reach the material specification line, the electronic device may determine that the quality evaluation parameter of the current batch of output is a fourth preset value, wherein the third preset value is a value meeting the preset standard, the fourth preset value is a value not meeting the preset standard, and the specific value may be determined as required without specific limitation.

For example, taking the continuous P1-Pn production flow devices as an example, if the current batch of product is the final product produced by the Pn-th production flow device, directly acquiring the quality evaluation parameters of the product as the quality evaluation parameters of the current batch of the product, if the current batch of the product is an intermediate material produced by the P3 th production flow device, and the working condition is the working condition of the marker post when the batch of intermediate materials are produced by the P3 th production flow device, the material specification line corresponding to the batch of intermediate materials can be directly obtained, determining the quality evaluation parameters of the current batch of output according to the material specification line and the actual output quantity of the intermediate material, if the working condition is the learning task working condition when the batch of intermediate material is produced through the P3, and acquiring the quality evaluation parameter of the final product produced by the Pn-th production process device as the quality evaluation parameter of the current batch of product.

As can be seen, in this example, when the current batch of output is a product, the electronic device directly obtains the quality evaluation parameter of the current batch of product as the quality evaluation parameter of the current batch of output, and when the current batch of output is an intermediate material and the working condition for producing the current batch of product is a normal working condition, if the current batch of intermediate material has a material quality evaluation index, the material quality evaluation parameter of the current batch of intermediate material is used as the quality evaluation parameter of the current batch of output, and when the current batch of intermediate material does not have the material quality evaluation index, the quality evaluation parameter of the target product is obtained as the quality evaluation parameter of the current batch of output, which is beneficial to improving the accuracy of determining the quality evaluation parameter.

Referring to fig. 3A, fig. 3A is a block diagram of functional units of a data processing apparatus according to an embodiment of the present application, where the apparatus 30 includes:

an obtaining unit 301, configured to obtain production data of a target employee, where the production data includes: the quality evaluation parameters of each batch of output in at least one batch of output produced by the target staff, and the complexity level of the actual working condition during the production of each batch of output;

a first determining unit 302, configured to determine at least one evaluation parameter mean value corresponding to the target employee according to the quality evaluation parameter of each batch of output and the actual working condition complexity level during production of each batch of output, where the at least one evaluation parameter mean value includes: the evaluation parameter mean value corresponding to each preset working condition complexity grade in at least one preset working condition complexity grade, wherein the at least one preset working condition complexity grade comprises the actual working condition complexity grade during production of each batch of products;

a second determining unit 303, configured to determine, according to at least one evaluation parameter mean value corresponding to the target employee, a target working condition complexity level corresponding to the target employee from the at least one preset working condition complexity level;

a creating unit 304, configured to create a work task event for the target employee according to the target working condition complexity level.

In one possible example, the first determining unit 302 is specifically configured to: determining an evaluation parameter mean value corresponding to the same actual working condition complexity grade according to the quality evaluation parameters corresponding to the same actual working condition complexity grade in the quality evaluation parameters of each batch of output; if the at least one preset working condition complexity level comprises: and determining the mean value of the evaluation parameters corresponding to the complexity grades of other working conditions as a first preset value, wherein the complexity grades of other working conditions are different from the complexity grade of the actual working condition during the production of each batch of the product.

In a possible example, the second determining unit 303 is specifically configured to: executing the following operations aiming at each preset working condition complexity level: determining whether the target evaluation parameter mean value corresponding to the complexity level of the current preset working condition meets a preset standard or not; if so, determining the current preset working condition complexity grade as a reference working condition complexity grade; determining the target working condition complexity level according to a first reference working condition complexity with the highest level in the reference working condition complexity levels, wherein the target working condition complexity level comprises the following steps: and the level in the at least one preset working condition complexity level is not higher than the preset working condition complexity level of the first reference working condition complexity, wherein the higher the working condition complexity level is, the more complicated the working condition is during production.

In a possible example, the production data of the target employee includes production data of the target employee within a preset time period, and in the aspect of determining whether the target evaluation parameter mean value corresponding to the current preset working condition complexity level meets a preset standard, the second determining unit 303 is specifically configured to: under the condition that the complexity level of the current preset working condition is higher than a preset level, obtaining a plurality of evaluation parameter mean values corresponding to the complexity level of the current preset working condition, wherein the plurality of evaluation parameter mean values are determined according to production data of a plurality of employees in the preset time period, and the plurality of evaluation parameter mean values comprise the target evaluation parameter mean value; under the condition that the plurality of evaluation parameter mean values are sequenced from large to small according to the parameter values, determining whether a preset number of mean values sequenced most in front in the plurality of evaluation parameter mean values comprise the target evaluation parameter mean value; if yes, determining that the target evaluation parameter mean value meets the preset standard; under the condition that the complexity level of the current preset working condition is not higher than the preset level, determining whether the target evaluation parameter mean value is not less than a second preset value or not; and if so, determining that the target evaluation parameters meet the preset standard.

In one possible example, the actual working condition complexity level during production of each batch of the product is determined according to the working condition type during production of each batch of the product, and different working condition types correspond to different working condition complexity levels; the working condition types comprise normal working conditions and abnormal working conditions, and the normal working conditions comprise: the working condition complexity level corresponding to the abnormal working condition is higher than the working condition complexity level corresponding to the normal working condition, and the working condition complexity level corresponding to the marking rod working condition is higher than the working condition complexity level corresponding to the learning task working condition; the preset grade is a working condition complexity grade corresponding to the working condition of the marker post.

In one possible example, the apparatus 30 further comprises a first processing unit for obtaining a quality evaluation parameter of each of the at least one batch of output by: under the condition that the type of the current batch of output is a product, acquiring a product quality evaluation parameter of the current batch of product as the quality evaluation parameter of the current batch of output; and under the condition that the type of the current batch of output is the intermediate material, acquiring the quality evaluation parameter of a target product belonging to the same production batch as the current batch of intermediate material as the quality evaluation parameter of the current batch of output.

In a possible example, the apparatus 30 further comprises a second processing unit for obtaining a quality evaluation parameter of each of the at least one batch of output by: under the condition that the type of the current batch of output is a product, acquiring a product quality evaluation parameter of the current batch of product as the quality evaluation parameter of the current batch of output; under the condition that the type of the current batch of output materials is intermediate materials, if the working condition type during the production of the current batch of intermediate materials is a normal working condition and the current batch of intermediate materials has a preset material quality evaluation index, acquiring a material quality evaluation parameter of the current batch of intermediate materials as the quality evaluation parameter of the current batch of output materials, wherein the material quality evaluation parameter of the current batch of intermediate materials is determined according to the material quality evaluation index; and under the condition that the type of the current batch of output is the intermediate material, if the working condition type during the production of the current batch of intermediate material is the normal working condition and the current batch of intermediate material has no material quality evaluation index, acquiring a quality evaluation parameter of a target product belonging to the same production batch as the current batch of intermediate material as the quality evaluation parameter of the current batch of output.

In the case of using an integrated unit, the functional units of another data processing apparatus provided in the embodiment of the present application constitute a block diagram, as shown in fig. 3B. In fig. 3B, the data processing apparatus includes: a processing module 310 and a communication module 311. The processing module 310 is used for controlling and managing actions of the data processing apparatus, for example, steps performed by the obtaining unit 301, the first determining unit 302, the second determining unit 303, the creating unit 304, and/or other processes for performing the techniques described herein. The communication module 311 is used to support interaction between the data processing apparatus and other devices. As shown in fig. 3B, the data processing apparatus may further include a storage module 312, the storage module 312 being used for storing program codes and data of the data processing apparatus.

The Processing module 310 may be a Processor or a controller, and may be, for example, a Central Processing Unit (CPU), a general-purpose Processor, a Digital Signal Processor (DSP), an ASIC, an FPGA or other programmable logic device, a transistor logic device, a hardware component, or any combination thereof. Which may implement or perform the various illustrative logical blocks, modules, and circuits described in connection with the disclosure. The processor may also be a combination of computing functions, e.g., comprising one or more microprocessors, DSPs, and microprocessors, and the like. The communication module 311 may be a transceiver, an RF circuit or a communication interface, etc. The storage module 312 may be a memory.

All relevant contents of each scene related to the method embodiment may be referred to the functional description of the corresponding functional module, and are not described herein again. The data processing apparatus can perform the steps performed by the electronic device in the data processing method shown in fig. 2A.

Embodiments of the present application also provide a computer storage medium, where the computer storage medium stores a computer program for electronic data exchange, the computer program enabling a computer to execute part or all of the steps of any one of the methods described in the above method embodiments, and the computer includes an electronic device.

Embodiments of the present application also provide a computer program product, which includes a computer program operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments.

The computer program product may be a software installation package, the computer comprising an electronic device.

It should be understood that, in the various embodiments of the present application, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

In the several embodiments provided in the present application, it should be understood that the disclosed method, apparatus and system may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative; for example, the division of the unit is only a logic function division, and there may be another division manner in actual implementation; for example, various elements or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be physically included alone, or two or more units may be integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.

The integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, an electronic device, or a network device) to execute some steps of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

Although the present invention is disclosed above, the present invention is not limited thereto. Various changes and modifications can be easily made by those skilled in the art without departing from the spirit and scope of the present invention, and it is within the scope of the present invention to include different functions, combination of implementation steps, software and hardware implementations.

Claims

1. A data processing method, comprising:

obtaining production data of a target employee, the production data comprising: the quality evaluation parameters of each batch of output in at least one batch of output produced by the target staff, and the complexity level of the actual working condition during the production of each batch of output;

determining at least one evaluation parameter mean value corresponding to the target staff according to the quality evaluation parameter of each batch of output and the actual working condition complexity level during production of each batch of output, wherein the at least one evaluation parameter mean value comprises: the evaluation parameter mean value corresponding to each preset working condition complexity grade in at least one preset working condition complexity grade, wherein the at least one preset working condition complexity grade comprises the actual working condition complexity grade during production of each batch of products;

2. The method of claim 1, wherein the determining the mean value of the at least one evaluation parameter corresponding to the target employee according to the quality evaluation parameter of each batch of the output and the actual working condition complexity level of each batch of the output during production comprises:

determining an evaluation parameter mean value corresponding to the same actual working condition complexity grade according to the quality evaluation parameters corresponding to the same actual working condition complexity grade in the quality evaluation parameters of each batch of output;

if the at least one preset working condition complexity level comprises: and determining the mean value of the evaluation parameters corresponding to the complexity grades of other working conditions as a first preset value, wherein the complexity grades of other working conditions are different from the complexity grade of the actual working condition during the production of each batch of the product.

3. The method according to claim 1, wherein the determining a target working condition complexity level corresponding to the target employee from the at least one preset working condition complexity level according to the at least one evaluation parameter mean corresponding to the target employee comprises:

executing the following operations aiming at each preset working condition complexity level:

determining whether the target evaluation parameter mean value corresponding to the complexity level of the current preset working condition meets a preset standard or not;

and if so, determining the current preset working condition complexity level as the target working condition complexity level.

4. The method according to claim 3, wherein the production data of the target employee comprises production data of the target employee within a preset time period, and the determining whether the target evaluation parameter mean value corresponding to the current preset working condition complexity level meets a preset standard comprises:

under the condition that the complexity level of the current preset working condition is higher than a preset level, obtaining a plurality of evaluation parameter mean values corresponding to the complexity level of the current preset working condition, wherein the plurality of evaluation parameter mean values are determined according to production data of a plurality of employees in the preset time period, and the plurality of evaluation parameter mean values comprise the target evaluation parameter mean value;

under the condition that the plurality of evaluation parameter mean values are sequenced from large to small according to the sequence of the parameter values, determining whether a preset number of mean values which are sequenced most in front in the plurality of evaluation parameter mean values comprise the target evaluation parameter mean value;

if yes, determining that the target evaluation parameter mean value meets the preset standard;

determining whether the target evaluation parameter mean value is not less than a second preset value or not under the condition that the complexity level of the current preset working condition is not higher than the preset level;

and if so, determining that the target evaluation parameters meet the preset standard.

5. The method of claim 4, wherein said actual operating condition complexity level for each production run is determined based on said operating condition type for each production run, and wherein different operating condition types correspond to different operating condition complexity levels; wherein, the operating mode type includes normal operating mode and unusual operating mode, normal operating mode includes: the working condition complexity level corresponding to the abnormal working condition is higher than the working condition complexity level corresponding to the normal working condition, and the working condition complexity level corresponding to the marking post working condition is higher than the working condition complexity level corresponding to the learning task working condition;

the preset grade is a working condition complexity grade corresponding to the working condition of the marker post.

6. The method of any one of claims 1 to 5, wherein the quality assessment parameters for each of the at least one output are obtained by:

under the condition that the type of the current batch of output is a product, acquiring a product quality evaluation parameter of the current batch of product as the quality evaluation parameter of the current batch of output;

and under the condition that the type of the current batch of output is the intermediate material, acquiring the quality evaluation parameter of a target product belonging to the same production batch as the current batch of intermediate material as the quality evaluation parameter of the current batch of output.

7. The method of any one of claims 1 to 5, wherein the quality assessment parameters for each of the at least one output are obtained by:

under the condition that the type of the current batch of intermediate materials is intermediate materials, if the working condition type during the production of the current batch of intermediate materials is a normal working condition and the current batch of intermediate materials has a preset material quality evaluation index, acquiring a material quality evaluation parameter of the current batch of intermediate materials as the quality evaluation parameter of the current batch of intermediate materials, wherein the material quality evaluation parameter of the current batch of intermediate materials is determined according to the material quality evaluation index;

and under the condition that the type of the current batch of output is the intermediate material, if the working condition type during the production of the current batch of intermediate material is the normal working condition and the current batch of intermediate material has no material quality evaluation index, acquiring a quality evaluation parameter of a target product belonging to the same production batch as the current batch of intermediate material as the quality evaluation parameter of the current batch of output.

8. A data processing apparatus, characterized by comprising:

a first determining unit, configured to determine at least one evaluation parameter mean value corresponding to the target employee according to the quality evaluation parameter of each batch of output and the actual working condition complexity level during production of each batch of output, where the at least one evaluation parameter mean value includes: the evaluation parameter average value corresponding to each preset working condition complexity level in at least one preset working condition complexity level comprises the actual working condition complexity level during production of each batch of products;

the second determining unit is used for determining a target working condition complexity grade corresponding to the target staff from the at least one preset working condition complexity grade according to the at least one evaluation parameter mean value corresponding to the target staff;

9. An electronic device, characterized in that the electronic device comprises:

one or more processors;

one or more memories for storing programs,

the one or more memories and the program are configured to control the apparatus to perform the steps in the method of any one of claims 1-7 by the one or more processors.

10. A computer-readable storage medium, characterized in that a computer program for electronic data exchange is stored, wherein the computer program causes a computer to perform the method according to any one of claims 1-7.