CN116029287A

CN116029287A - Method, device and storage medium for determining working state based on self-correcting operation

Info

Publication number: CN116029287A
Application number: CN202310320112.4A
Authority: CN
Inventors: 赵峰; 董云龙
Original assignee: Beijing Wanli Software Development Co ltd
Current assignee: Beijing Wanli Software Development Co ltd
Priority date: 2023-03-29
Filing date: 2023-03-29
Publication date: 2023-04-28
Anticipated expiration: 2043-03-29
Also published as: CN116029287B

Abstract

The application discloses a method, a device and a storage medium for determining a working state based on self-correcting operation. The method for determining the working state based on the self-correcting operation comprises the following steps: during the working process, recording employee operation information corresponding to the operation of the employee in real time; according to the employee operation information, identifying self-correcting operation of the employee in the working process, wherein the self-correcting operation is an error which the employee finds and corrects in the working process; according to the identified self-correcting operation, determining the self-correcting operation frequency of the self-correcting operation of the staff in the working process; and determining the working state of the staff in the working process according to the determined self-correcting operation frequency. Therefore, fluctuation and problems of the working state of the staff can be found in time in the working process of the staff, the staff can be reminded of the adjustment state in time, errors in submitted documents are avoided or reduced, and adverse effects on the staff are avoided.

Description

Method, device and storage medium for determining working state based on self-correcting operation

Technical Field

The present invention relates to the field of information processing, and in particular, to a method, an apparatus, and a storage medium for determining a working state based on self-correcting operation.

Background

At present, a document error detection module is widely applied to office systems so as to help check errors in documents submitted by staff, and therefore performance assessment is carried out on the staff according to error conditions of the staff documents.

For example, the published patent invention (CN 115688705 a) discloses an intelligent document processing system based on natural semantics, comprising: the system comprises an intelligent auditing module, a document auxiliary writing module, a document intelligent typesetting module, a word stock and a semantic training module; the intelligent auditing module is used for checking and correcting text contents based on big data, artificial intelligence and natural language processing technology; the document auxiliary writing module is used for providing functions of document searching, information recommending, information referencing and document generating based on big data and semantic analysis technology; the document intelligent typesetting module is used for generating standard document files in various formats; the word library is used for converging various words, combining with a language model based on statistics and a semantic model based on rules, checking expression errors, prompting error reasons and providing modification suggestions; the semantic training module is used for continuously carrying out model training and parameter optimization according to the use condition of the user and the analysis result of the template.

For another example, the published patent application (CN 114564912 a) discloses a method and a system for intelligently checking and correcting document formats, the method comprises: template matching is carried out on the format and the content of the document to be detected, and correct format information and abnormal format information are determined; based on the Chinese text error correction model, identifying and correcting grammar errors of the abnormal format information; the Chinese text error correction model comprises a word embedding layer, an encoding end, a decoding end and an attention layer which are connected in sequence; the encoding end and the decoding end are both bidirectional LSTM structures. The invention solves the problem of no marked data by adopting the language model based on the LSTM, and can accurately check rule errors and partial common irregular errors at the same time, thereby improving the efficiency and the accuracy of document checking and correcting.

However, the prior art focuses on auditing documents that an employee has completed or submitted in order to assess the employee's work. In this case, even if an error is detected in a document of an employee, it is still too late for the employee. The best solution is to help staff avoid errors, so that adverse effects on staff can be avoided while improving the working quality.

However, in the prior art, there is no technical solution for helping staff avoid or reduce errors in submitted documents to avoid adverse effects on staff.

Disclosure of Invention

The embodiment of the disclosure provides a method, a device and a storage medium for determining a working state based on self-correcting operation, so that the working state of an employee is determined according to the frequency of the self-correcting operation of the employee in the working process of the employee, and the employee is timely reminded of adjusting the working state when the working state of the employee is unstable.

According to one aspect of an embodiment of the present disclosure, there is provided a method of determining an operating state based on an auto-correcting operation, including: during the working process, recording employee operation information corresponding to the operation of the employee in real time; according to the employee operation information, identifying self-correcting operation of the employee in the working process, wherein the self-correcting operation is the operation of the employee for finding and correcting errors in the working process; according to the identified self-correcting operation, determining the self-correcting operation frequency of the self-correcting operation of the staff in the working process; and determining the working state of the staff in the working process according to the determined self-correcting operation frequency.

According to another aspect of the embodiments of the present disclosure, there is also provided a storage medium including a stored program, wherein the method of any one of the above is performed by a processor when the program is run.

According to another aspect of the embodiments of the present disclosure, there is also provided an apparatus for determining an operating state based on an auto-correcting operation, including: the information recording module is used for recording employee operation information corresponding to the operation of the employee in real time in the working process; the operation identification module is used for identifying self-correcting operation of staff in the working process according to the staff operation information, wherein the self-correcting operation is an error which the staff finds and corrects in the working process; the first determining module is used for determining the self-correcting operation frequency of the self-correcting operation of the staff in the working process according to the identified self-correcting operation; and the second determining module is used for determining the working state of the staff in the working process according to the determined self-correcting operation frequency.

According to another aspect of the embodiments of the present disclosure, there is also provided an apparatus for determining an operating state based on an auto-correcting operation, including: a processor; and a memory, coupled to the processor, for providing instructions to the processor for processing the steps of: during the working process, recording employee operation information corresponding to the operation of the employee in real time; according to the employee operation information, identifying self-correcting operation of the employee in the working process, wherein the self-correcting operation is an error which the employee finds and corrects in the working process; according to the identified self-correcting operation, determining the self-correcting operation frequency of the self-correcting operation of the staff in the working process; and determining the working state of the staff in the working process according to the determined self-correcting operation frequency.

In the embodiment of the disclosure, the working state of the staff is determined according to the frequency of self-correcting operation of the staff in the working process of the staff, so that the staff can be timely reminded of adjusting the working state when the working state of the staff is unstable. Staff can correct own errors in the actual document writing and editing process. For example, when a typing error occurs during the process of writing a document, an employee may first discover the typing error and make a modification. This operation is defined as a "self-correcting operation", i.e. the employee corrects his own errors during the work. The working state of the staff can be truly reflected under the condition of self-correcting operation in the working process of the staff, so that if the working state of the staff can be estimated in the working process of the staff, the staff can be reminded of adjusting the working state before the staff has errors.

Therefore, the method and the device can timely find out fluctuation and problems of the working state of the staff in the working process of the staff, and accordingly can timely remind the staff of adjusting the state, avoid or reduce errors in submitted documents, and avoid adverse effects on the staff.

Drawings

The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this application, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and do not constitute an undue limitation on the disclosure. In the drawings:

FIG. 1 is a block diagram of a hardware architecture of a computing device for implementing a method according to embodiment 1 of the present disclosure;

FIG. 2 is a schematic diagram of a system for determining an operational state based on self-correcting operations according to embodiment 1 of the present disclosure;

FIG. 3 is a block diagram of determining an operating state based on self-correcting operations according to a first aspect of embodiment 1 of the present disclosure;

FIG. 4 is a flow chart of a method of determining an operational state based on self-correcting operations according to a first aspect of embodiment 1 of the present disclosure;

FIG. 5 is a schematic view of document 1 according to the first aspect of embodiment 1 of the present disclosure;

FIG. 6A is a schematic illustration of document 1 corresponding to a first type of self-correcting operation according to the first aspect of embodiment 1 of the present disclosure;

FIG. 6B is another schematic diagram of document 1 corresponding to a first type of self-correcting operation according to the first aspect of embodiment 1 of the present disclosure;

FIG. 6C is yet another schematic illustration of document 1 corresponding to a first type of self-correcting operation according to the first aspect of embodiment 1 of the present disclosure;

FIG. 7A is a schematic illustration of document 1 corresponding to a second type of self-correcting operation according to the first aspect of embodiment 1 of the present disclosure;

FIG. 7B is another schematic illustration of document 1 corresponding to a second type of self-correcting operation according to the first aspect of embodiment 1 of the present disclosure;

FIG. 8 is a schematic illustration of document 1 corresponding to a third type of self-correcting operation according to the first aspect of embodiment 1 of the present disclosure;

FIG. 9A is a schematic illustration of a normal distribution curve corresponding to a first type of self-correcting operation according to the first aspect of embodiment 1 of the present disclosure;

FIG. 9B is another schematic illustration of a normal distribution curve corresponding to a second type of self-correcting operation according to the first aspect of embodiment 1 of the present disclosure;

FIG. 9C is yet another schematic illustration of a normal distribution curve corresponding to a third type of self-correcting operation according to the first aspect of embodiment 1 of the present disclosure;

FIG. 10 is a schematic diagram of an apparatus for determining an operating state based on self-correcting operation according to embodiment 2 of the present disclosure; and

fig. 11 is a schematic view of an apparatus for determining an operating state based on an auto-correcting operation according to embodiment 3 of the present disclosure.

Detailed Description

In order to better understand the technical solutions of the present disclosure, the following description will clearly and completely describe the technical solutions of the embodiments of the present disclosure with reference to the drawings in the embodiments of the present disclosure. It will be apparent that the described embodiments are merely embodiments of a portion, but not all, of the present disclosure. All other embodiments, which can be made by one of ordinary skill in the art without inventive effort, based on the embodiments in this disclosure, shall fall within the scope of the present disclosure.

It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the foregoing figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the disclosure described herein may be capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example 1

According to the present embodiment, there is provided a method embodiment of a method of determining an operating state based on self-correcting operations, it being noted that the steps illustrated in the flowchart of the figures may be performed in a computer system, such as a set of computer executable instructions, and that, although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order other than that illustrated herein.

The method embodiments provided by the present embodiments may be performed in a mobile terminal, a computer terminal, a server, or similar computing device. FIG. 1 illustrates a block diagram of a hardware architecture of a computing device for implementing a method of determining a working state based on self-correcting operations. As shown in fig. 1, the computing device may include one or more processors (which may include, but are not limited to, a microprocessor MCU, a processing device such as a programmable logic device FPGA), memory for storing data, transmission means for communication functions, and input/output interfaces. Wherein the memory, the transmission device and the input/output interface are connected with the processor through a bus. In addition, the method may further include: a display connected to the input/output interface, a keyboard, and a cursor control device. It will be appreciated by those of ordinary skill in the art that the configuration shown in fig. 1 is merely illustrative and is not intended to limit the configuration of the electronic device described above. For example, the computing device may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.

It should be noted that the one or more processors and/or other data processing circuits described above may be referred to herein generally as "data processing circuits. The data processing circuit may be embodied in whole or in part in software, hardware, firmware, or any other combination. Furthermore, the data processing circuitry may be a single stand-alone processing module, or incorporated in whole or in part into any of the other elements in the computing device. As referred to in the embodiments of the present disclosure, the data processing circuit acts as a processor control (e.g., selection of the variable resistance termination path to interface with).

The memory may be used to store software programs and modules of application software, such as a program instruction/data storage device corresponding to a method for determining an operating state based on self-correcting operation in the embodiments of the present disclosure, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, implements the method for determining an operating state based on self-correcting operation of an application program. The memory may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid state memory. In some examples, the memory may further include memory remotely located with respect to the processor, which may be connected to the computing device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The transmission means is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communications provider of the computing device. In one example, the transmission means comprises a network adapter (Network Interface Controller, NIC) connectable to other network devices via the base station to communicate with the internet. In one example, the transmission device may be a Radio Frequency (RF) module, which is used to communicate with the internet wirelessly.

The display may be, for example, a touch screen type Liquid Crystal Display (LCD) that may enable a user to interact with a user interface of the computing device.

It should be noted herein that in some alternative embodiments, the computing device shown in FIG. 1 described above may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that fig. 1 is only one example of a particular specific example and is intended to illustrate the types of components that may be present in the computing devices described above.

Fig. 2 is a schematic diagram of an operation state evaluation system based on the self-correcting operation according to the present embodiment. Referring to fig. 2, the system includes: a terminal device 100 as a work device of the employee 400; a server 200 communicatively connected to the terminal device 100 via a network; and a database 300 communicatively connected to the server 200 via a network. It should be noted that the above-described hardware configuration may be applied to both the terminal device 100 and the server 200 in the system.

In which fig. 3 shows a block diagram of the terminal device 100 and the server 200. Referring to fig. 3, the terminal device 100 is provided with an employee operation monitoring module 110, an automatic correction operation identification module 120, an automatic correction operation statistics module 130, and an employee status determination module 140. The employee operation monitoring module 110 is configured to record, in real time, employee operation information corresponding to each operation of the employee 400 in the office process, where the employee operation information includes an operation of the employee 400 and time information corresponding to the operation. The self-correcting operation identifying module 120 is configured to identify self-correcting operation from the operations of the employee 400 according to the employee operation information recorded by the employee operation monitoring module 110, and generate corresponding self-correcting operation information, where the self-correcting operation information includes the self-correcting operation of the employee 400, a type corresponding to the self-correcting operation, and time information. The self-correcting operation statistics module 130 is configured to perform statistics on the self-correcting operation information identified by the self-correcting operation identification module 120, specifically, for example, statistics on the frequency of the self-correcting operation that the employee 400 appears in a unit time. And an employee status determining module 140, configured to determine the working status of the employee 400 according to the historical statistics data queried by the query module 210 of the server 200 and the frequency of the self-correcting operation of the employee 400 in the working process counted by the self-correcting operation statistics module 130.

The self-correcting operation refers to the operation of automatically correcting errors by staff during the working process, especially before the work task is completed and submitted to the subsequent flow or post. Since the employee corrects errors by himself, the task that the employee completes and uploads appears to be free of these errors in subsequent processes or posts.

Further, as further shown in fig. 3, the server 200 includes: the query module 210 is configured to query, from the database 300, historical statistics of the self-correcting operation corresponding to the employee 400 according to the query request received from the terminal device 100, and send the queried historical statistics to the terminal device 100.

In the above-described operation environment, according to the first aspect of the present embodiment, there is provided a method of determining an operation state based on an auto-correction operation, which is implemented by the terminal device 100 and the server 200 shown in fig. 2, for example. Fig. 4 shows a schematic flow chart of the method, and referring to fig. 4, the method includes:

s402: during the working process of staff, staff operation information corresponding to the operation of the staff is recorded in real time;

s404: according to the employee operation information, identifying self-correcting operation of the employee in the working process, wherein the self-correcting operation is the operation of the employee for finding and correcting errors in the working process;

S406: according to the identified self-correcting operation, determining the self-correcting operation frequency of the self-correcting operation of the staff in the working process; and

s408: and according to the determined self-correcting operation frequency, determining the working state of the staff in the working process.

Specifically, referring to fig. 2 and 3, employee 400 works with terminal device 100. In the present embodiment, the employee 400 performs document editing work with the terminal device 100 as an example. For example, referring to fig. 5, an employee 400 creates a document 1 in the terminal device 100, and performs document writing and editing in the document 1. Of course, it should be apparent to those skilled in the art that document editing work is merely one example of a work process. The work process may also be other text work, such as form filling, etc. And will not be described in detail herein.

Thus, in the process of editing the document 1 by the employee 400, the employee operation monitoring module 110 monitors the operation of the employee 400 during the work, and records employee operation information related to the operation of the employee 400 (S402). For example, table 1 below shows the contents of employee operation information of employee 400:

TABLE 1

As shown in reference to table 1, the employee 400 opens the document 1 at time 1, so that the employee operation monitoring module 110 records in real time the employee operation "open document 1" and the time "time 1" corresponding to the operation; at time 2, employee 400 enters "present", so that employee operation monitoring module 110 records in real time employee operation "enter 'present'" and time "time 2" corresponding to the operation; at time 3, the employee 400 inputs ",", so that the employee operation monitoring module 110 records the employee operation "input ','" and the time "time 3" corresponding to the operation in real time; and so on, at time m ₁ The employee 400 inputs "error", so that the employee operation monitoring module 110 records the employee operation "input 'error'" and the time "moment m corresponding to the operation in real time ₁ ”。

Further, the operation of employee 400 is not limited to merely entering text in a document. As shown with reference to fig. 6A and table 1, at time m ₃ Employee 400 entered a text "map" of the typing error. The employee operation monitoring module 110 thus records in real time the operation "input 'image'" of the employee 400 and the time "instant m corresponding to the operation ₃ ”。

Then referring to FIG. 6B and Table 1, at time m ₃ +1, employee 400 deleted the text "map" of typing errors. The employee operation monitoring module 110 thus records in real time the operation "delete 'image'" of the employee 400 and the time "instant m corresponding to the operation ₃ +1”。

Further referring to FIG. 6C and Table 1, at time m ₃ +2, employee 400 enters the correct text "influence". The employee operation monitoring module 110 thus records in real time the operation "input 'influence'" of the employee 400 and the time "instant m corresponding to the operation ₃ +2”。

Thereby from time m ₃ By time m ₃ +2, employee 400 completed a self-corrective action. I.e. employee 400 from time m ₃ By time m ₃ +2, self-discovers and corrects self-errors. And the above operations to And the corresponding times are all recorded in the employee operation information shown in table 1 by the employee operation monitoring module 110.

Thus, the self-correcting operation recognition module 120 of the terminal device 100 recognizes the self-correcting operation of the employee 400 according to the employee operation information recorded in real time during the operation of the employee 400. For example, the self-correcting operation identifying module 120 of the terminal device 100 may operate the monitoring module 110 at the time m according to the staff ₃ By time m ₃ +2 recorded employee operations, identifying one self-corrective operation of employee 400. And similarly, the self-correcting operation identifying module 120 may identify the self-correcting operation of the employee 400 at other time according to the employee operation information recorded by the employee operation monitoring module 110.

Then, the self-correcting operation statistics module 130 of the terminal device 100 counts the self-correcting operation frequency of the self-correcting operation that the employee 400 has occurred during the work in real time (S406). For example, employee 400 averages the number of self-corrective actions per 10 minutes, or averages the number of self-corrective actions per 30 minutes, or averages the number of self-corrective actions per hour. In this embodiment, the self-corrective action statistics module 130, for example, counts in real-time the number of self-corrective actions that the employee 400 averages per hour.

Finally, the terminal device 100 determines the working state of the employee 400 in the process of editing the document 1 according to the frequency of the self-correcting operation determined by the self-correcting operation statistics module 130 (S408). For example, when the self-correcting operation frequency of the employee 400 is too high, it is determined that the work status of the employee 400 is not ideal. So that the terminal device 100 can remind the employee 400 to pay attention to adjust the operation state.

As described in the background, the prior art focuses on auditing documents that an employee has completed or submitted in order to assess the employee's work. In this case, even if an error is detected in a document of an employee, it is still too late for the employee. The best solution is to help staff avoid errors, so that adverse effects on staff can be avoided while improving the working quality. However, in the prior art, there is no technical solution for helping staff avoid errors to avoid adverse effects on staff.

Therefore, the working state of the staff is determined according to the frequency of the self-correcting operation of the staff in the working process of the staff, so that the staff can be timely reminded of adjusting the working state when the working state of the staff is unstable. Staff can correct own errors in the actual document writing and editing process. For example, when a typing error occurs during the process of writing a document, an employee may first discover the typing error and make a modification. This operation is defined as a "self-correcting operation", i.e. the employee corrects his own errors during the work. The working state of the staff can be truly reflected under the condition of self-correcting operation in the working process of the staff, so that if the working state of the staff can be evaluated in the working process of the staff, the staff can be reminded of adjusting the working state before the staff has errors.

Optionally, the operation of identifying the self-correcting operation of the staff during the work process includes: detecting a first operation of inputting a first text by an employee in a working process; detecting a second operation of deleting the first text by the staff in the working process; detecting a third operation of inputting the second text by the staff after the second operation; determining whether the first operation to the third operation are self-correcting operations of the staff according to time intervals among the first operation, the second operation and the third operation and/or according to contents of the first text and the second text.

In particular, at instant m, for example as described above with reference to ₃ Employee 400 entered a text "map" of typing errors (i.e., a first operation, where "map" corresponds to the first text), at time m ₃ +1, employee 400 deleted the text "image" of the typing error (i.e., the second operation), at time m ₃ +2, employee 400 enters the correct textThe present "influence" (i.e., the third operation, wherein "influence" corresponds to the second text). Due to m ₃ By time m ₃ The time interval between +3 is small, e.g. less than a predetermined threshold (e.g. 1 second), so that the time interval between the operations of entering the wrong text "map", deleting the wrong text "map" and entering the correct text "influence" is short, almost immediately. And the deleted text is a word, phrase, or a small number of words (e.g., the number of deleted words is less than a predetermined threshold, such as 5 words), the self-correcting operation recognition module 120 may recognize the operation as a self-correcting operation in which the employee 400 corrects a typing error in real-time during editing of the document. This real-time corrective action to correct typing errors is a first type of corrective action. This first type of self-correcting operation, if too frequent, can reflect that employee 400 is now in an urgent or irritated work condition.

Further, as shown with reference to FIG. 7A and Table 1, at time m ₄ By time m ₅ The employee 400 inputs "may alert the employee to adjust the work status before the employee makes an error" (corresponding to the first operation, wherein "may alert the employee to adjust the work status before the employee makes an error" is the first text). Then referring to FIG. 7B and Table 1, at time m ₅ +1 to time m ₆ Employee 400 removes the "may alert the employee to adjust work status before an error occurs to the employee" (corresponding to the second operation). Then referring to FIG. 7B and Table 1, at time m ₆ +1 to time m ₇ The employee 400 inputs "to prompt the employee to adjust the work status when the work status of the employee is unstable" (i.e., the third operation). Wherein due to employee 400 at time m ₅ +1 to time m ₆ A large number of words (at least one sentence of text, even a piece of text, or the number of words deleted is greater than a predetermined threshold, for example 15 words) are deleted, so the self-correcting operation recognition module 120 can recognize that the operation is a self-correcting operation in which the employee 400 reorganizes the language due to unclear ideas during the document editing process. The self-correcting operation being of a second type, i.e. deleting a number of words greater than a predetermined thresholdAnd modified operations are performed. This second type of self-correcting operation, if too frequent, can often reflect that employee 400 is now in a mental confusion, or a state of comparative anxiety.

Referring also to fig. 8 and table 1, at time 4, employee 400 entered the text "steady" of the typographical error (i.e., the first operation, where "steady" corresponds to the first text), at time m ₈ Staff 400 checks for this typing error during the checking and checking of document 1, thereby deleting the text "steady" (i.e., second operation) of the typing error, at time m ₈ +1, employee 400 enters the correct text "document" (i.e., a third operation, where "document" corresponds to the second text). In this case, due to time 4 and time m ₈ The time interval between +1 is long, so the self-correcting operation recognition module 120 can recognize the operation as a self-correcting operation in which the employee 400 corrects an error in the self-checking or proofing process. The self-correcting operation is a third type of self-correcting operation. This third type of self-correcting operation, if too frequent, indicates that employee 400 has found a large number of errors during the inspection, indicating that employee 400 is not working properly during the previous document editing process.

Thus, in the above-described manner, the self-correcting operation recognition module 120 recognizes the self-correcting operation of the employee 400 based on the employee operation information of the employee 400, so that the self-correcting operation occurring during the work of the employee 400 can be recognized accurately in a relatively simple recognition manner.

And, further, the self-correcting operation statistics module 130 may respectively count the frequency of the first type of self-correcting operation, the frequency of the second type of self-correcting operation, and the frequency of the third type of self-correcting operation of the employee 400 for different types. So that the working state of the employee 400 can be more comprehensively analyzed.

Optionally, determining the working state of the staff in the working process according to the determined self-correcting operation frequency includes: acquiring historical information related to self-correcting operation of staff; and determining the working state of the staff in the working process according to the determined self-correcting operation frequency and the acquired historical information.

Specifically, due to the different character characteristics and habits of different employees, the frequency of self-correcting operations in daily work is also different for different employees. In view of this, the technical solution of the present disclosure proposes to evaluate the working state according to the specific situations of different employees by using respective criteria.

Specifically, referring to fig. 2, the operation state evaluation system based on the self-correcting operation according to the present embodiment further includes a database 300 communicatively connected to the server 200 via a network. Wherein database 300 stores, for example, historical information of the frequency of self-corrective actions corresponding to different employees 400.

For example, table 2 below shows an example of history information of the frequency of self-correcting operations corresponding to the employee 400:

TABLE 2

Referring to Table 2, database 300 records that employee 400 has a first type of self-correcting operation frequency of date 1 of F _1,1 The second type of self-correcting operation frequency isF _2,1 The third type of self-correcting operation frequency isF _3,1 The method comprises the steps of carrying out a first treatment on the surface of the Record employee 400's first type of self-correcting operation frequency at date 2 asF _1,2 The second type of self-correcting operation frequency isF _2,2 The third type of self-correcting operation frequency isF _3,2 The method comprises the steps of carrying out a first treatment on the surface of the Similarly, database 300 records employee 400 as having a first type of self-correcting operation frequency of date nF _1,n The second type of self-correcting operation frequency isF _2,n The third type of self-correcting operation frequency isF _3,n 。

Although table 2 shows the history information of the self-correcting operation frequency of the employee 400, the data form of table 2 may be referred to for the history information of the self-correcting operation frequency of other employees. And will not be described in detail herein.

In determining the working state of the employee 400, the employee state determining module 140 first sends a request for obtaining the history information of the self-correcting operation of the employee 400 to the server 200, so that the query module 210 of the server 200 queries the database 300 for obtaining the history information of the self-correcting operation of the employee 400 (such as the history information shown in table 2) according to the request, thereby determining the working state of the employee 400 according to the frequency of the self-correcting operation of the employee 400 in the working process of editing the document 1 and the history information of the employee 400. Thus, if the frequency of self-correcting operations of the staff 400 during the work of editing the document 1 is relatively consistent with the statistical information of the history information of the staff 400, it is indicated that the work state of the staff 400 during the work of editing the document 1 is normal. When the frequency of self-correcting operation of the staff 400 in the process of editing the document 1 is higher than the statistical information of the history information of the staff 400 and exceeds a certain range, the working state of the staff 400 is not ideal.

For example, when the frequency of the first type of self-correcting operation of the employee 400 during the work of editing the document 1 is higher than the statistical information of the history information of the first type of self-correcting operation of the employee 400 and exceeds a certain range, it is indicated that the employee 400 is in an urgent or irritated work state.

When the frequency of the second type of self-correcting operation of the employee 400 during the work of editing the document 1 is higher than the statistical information of the history information of the second type of self-correcting operation of the employee 400 and exceeds a certain range, it is indicated that the employee 400 is in a state of confusion or comparative anxiety.

When the frequency of the third type of self-correcting operation of the employee 400 during the work of editing the document 1 is higher than the statistical information of the history information of the third type of self-correcting operation of the employee 400 and exceeds a certain range, it is indicated that the work state of the employee 400 during the work of editing the document 1 is not ideal.

Optionally, the operation of determining the working state of the staff in the working process according to the determined self-correcting operation frequency and the acquired history information comprises the following steps: determining probability distribution values corresponding to the different types of self-correcting operation frequencies according to historical information related to the different types of self-correcting operation frequencies, wherein the types of the self-correcting operation frequencies comprise a first type, a second type and a third type; and determining the working state of the staff in the working process according to the probability distribution value.

Specifically, the database 300 records the first type of self-correcting operation frequency, the second type of self-correcting operation frequency, and the third type of self-correcting operation frequency.

The employee status determination module 140 obtains the first type of self-correcting operation frequency of the date 1 to the date n, and calculates the average value of the first type of self-correcting operation frequency according to the first type of self-correcting operation frequency of the date 1 to the date n. And the employee status determination module 140 calculates the mean and variance of the first type of self-correcting operation frequency according to the first type of self-correcting operation frequency of date 1 to date n.

Thereafter, the employee status determination module 140 determines the average (labeled "μ") of the first type of self-correcting operation frequency according to date 1-date n ₁ ") and variance (labeled" sigma ₁ ² ") to obtain a corresponding probability distribution value. Wherein the probability distribution values are represented by a normal distribution curve (corresponding to fig. 9A). Wherein the mean value of the first type of self-correcting operation frequency is identified in the overall distribution curve as "μ ₁ ”。

Further, the employee status determination module 140 sets a confidence region (identified as "z 1-z 2" in the overall distribution curve, where z1 and z2 are thresholds of the first type of self-correcting operation frequency) in the normal distribution curve, and then sets the average μ of the first type of self-correcting operation frequency and the first type of self-correcting operation frequency of the employee 400 during the work of editing the document 1 ₁ By comparison, if employee 400 has a frequency of first type of self-correcting operations during the editing of document 1 greater than average μ ₁ The first type of self-correcting operation frequency of the employee 400 during the work of editing the document 1 is compared with the confidence region, and whether the first type of self-correcting operation frequency of the employee 400 during the work of editing the document 1 is normal is judged. When employee 400 is in the process of editing document 1, of a first typeIf the frequency of the self-correcting operations of the first type is far higher than the frequency of the self-correcting operations of the first type in the process of editing the document 1, the working state of the staff 400 is not ideal. When the first type of self-correcting operation frequency of the employee 400 during the work of editing the document 1 is within the confidence region (e.g., z2 or less), it is determined that the first type of self-correcting operation frequency of the employee 400 during the work of editing the document 1 is within the normal range, which indicates that the work state of the employee 400 during the work is normal.

Further, the employee status determination module 140 obtains the second type of self-correcting operation frequency of the date 1 to the date n, and calculates the average value of the second type of self-correcting operation frequency according to the second type of self-correcting operation frequency of the date 1 to the date n. And the employee status determination module 140 calculates the mean and variance of the second type of self-correcting operation frequency according to the second type of self-correcting operation frequency of date 1 to date n.

Thereafter, the employee status determination module 140 determines the average (labeled "μ") of the second type of self-correcting operation frequency according to date 1-date n ₂ ") and variance (labeled" sigma ₂ ² ") to obtain a corresponding probability distribution value. Wherein the probability distribution values are represented by a normal distribution curve (corresponding to fig. 9B). Wherein the mean value of the second type of self-correcting operation frequency is identified in the overall distribution curve as "μ ₂ ”。

Further, the employee status determination module 140 sets a confidence region (identified as "z 3-z 4" in the overall distribution curve, where z3 and z4 are thresholds of the second type of self-correcting operation frequency) in the normal distribution curve, and then sets the average μ of the second type of self-correcting operation frequency and the second type of self-correcting operation frequency of the employee 400 during the work of editing document 1 ₂ By comparison, if the frequency of the second type of self-correcting operation of employee 400 during the editing document 1 is greater than average μ ₂ Then employee 400 is subjected to a second type of self-correcting operation during the work of editing document 1The frequency is compared with the confidence area to determine whether the frequency of the second type of self-correcting operation by employee 400 during the editing of document 1 is normal. When the second type of self-correcting operation frequency of the employee 400 during the work of editing the document 1 is outside the confidence area (for example, greater than z 4), it is determined that the second type of self-correcting operation frequency of the employee 400 during the work of editing the document 1 is far higher than the previous second type of self-correcting operation frequency, which indicates that the work state of the employee 400 is not ideal. When the second type of self-correcting operation frequency of the employee 400 during the work of editing the document 1 is within the confidence region (e.g., z4 or less), it is determined that the second type of self-correcting operation frequency of the employee 400 during the work of editing the document 1 is within the normal range, which indicates that the work state of the employee 400 during the work is normal.

Further, the employee status determination module 140 obtains the third type of self-correcting operation frequency from date 1 to date n, and calculates the average value of the third type of self-correcting operation frequency according to the third type of self-correcting operation frequency from date 1 to date n. And the employee status determination module 140 calculates the mean and variance of the third type of self-correcting operation frequency according to the third type of self-correcting operation frequency of date 1 to date n.

Thereafter, the employee status determination module 140 determines the average (labeled "μ") of the frequency of the third type of self-corrective actions according to dates 1-n ₃ ") and variance (labeled" sigma ₃ ² ") to obtain a corresponding probability distribution value. Wherein the probability distribution values are represented by a normal distribution curve (corresponding to fig. 9C). Wherein the mean value of the third type of self-correcting operation frequency is identified in the overall distribution curve as "μ ₃ ”。

Further, employee status determination module 140 sets a confidence region (identified as "z 5-z 6" in the overall distribution curve, where z5 and z6 are thresholds for the third type of frequency of self-correcting operation) in the normal distribution curve, and then averages μ the third type of frequency of self-correcting operation with the average μ of the third type of frequency of self-correcting operation during the work of employee 400 in editing document 1 ₃ Comparison is performedIf the frequency of the third type of self-correcting operation of employee 400 during the editing document 1 is greater than average μ ₃ Comparing the third type of self-correcting operation frequency of the staff 400 in the working process of editing the document 1 with the confidence area, and judging whether the third type of self-correcting operation frequency of the staff 400 in the working process of editing the document 1 is normal or not. When the frequency of the third type of self-correcting operation of the employee 400 during the work of editing the document 1 is outside the confidence area (for example, greater than z 6), it is determined that the frequency of the third type of self-correcting operation of the employee 400 during the work of editing the document 1 is far higher than the frequency of the previous third type of self-correcting operation, which indicates that the working state of the employee 400 is not ideal. When the frequency of the third type of self-correcting operation of the employee 400 during the work of editing the document 1 is within the confidence region (e.g., z6 or less), it is determined that the frequency of the third type of self-correcting operation of the employee 400 during the work of editing the document 1 is within the normal range, which indicates that the work status of the employee 400 during the work is normal.

Therefore, the technical scheme can determine whether the self-correcting operation frequency is normal or not through a reasonable range by comparing the self-correcting operation frequency with the historical mean value and the confidence region respectively, and improves the accuracy of judging whether the working state of staff is normal or not.

Optionally, the operation of determining probability distribution values corresponding to the different types of self-correcting operation frequencies according to the history information related to the different types of self-correcting operation frequencies includes: calculating a first mean and a first variance corresponding to the first type of self-correcting operation frequency according to the history information related to the first type of self-correcting operation frequency; and determining a first probability distribution value corresponding to the first type of self-correcting operation frequency according to the first mean value and the first variance.

Specifically, referring to Table 2, employee status determination module 140 obtains a first type of frequency of self-corrective actions at date 1 from database 300F _1,1 The first type of self-correcting operation frequency at date 2 isF _1,2 ,., a first type of self-correcting operation on date nFrequency of operationF _1,n . The employee status determination module 140 calculates a first average μ of the first type of frequency of self-correcting operations according to an average calculation formula ₁ ：

(equation 1)

The employee status determination module 140 then calculates a first variance σ of the frequency of the first type of self-correcting operations according to the average calculation formula ₁ ² ：

(equation 2)

Further, the employee status determination module 140 determines the employee status based on the first average μ ₁ And a first difference sigma ₁ ² A first probability distribution value corresponding to the first type of self-correcting operation frequency is determined, thereby determining a normal distribution curve (corresponding to fig. 9A).

Therefore, the technical scheme forms a normal distribution curve corresponding to the first type of self-correcting operation frequency by calculating the mean value and the variance of the first type of self-correcting operation frequency, so that whether the current first type of self-correcting operation frequency of the staff is normal or not can be intuitively observed.

Optionally, the operation of determining the probability distribution values corresponding to the different types of self-correcting operation frequencies according to the history information related to the different types of self-correcting operation frequencies further includes: calculating a second mean and a second variance corresponding to the second type of self-correcting operation frequency according to the history information related to the second type of self-correcting operation frequency; and determining a second probability distribution value corresponding to the second type of self-correcting operation frequency according to the second mean value and the second variance.

Specifically, referring to Table 2, employee status determination module 140 obtains from database 300 the frequency of the second type of self-corrective action at date 1F _2,1 The second type of self-correcting operation frequency at date 2 is F _2,2 ,., a second type of self-correction at date nPositive frequency of operationF _2,n . The employee status determination module 140 calculates a second average μ of the frequency of the second type of self-correcting operations according to the average calculation formula ₂ ：

(equation 3)

The employee status determination module 140 then calculates a second variance σ of the frequency of the second type of self-correcting operation according to the average calculation formula ₂ ² ：

(equation 4)

Further, the employee status determination module 140 determines the second average μ ₂ And a second variance sigma ₂ ² A second probability distribution value corresponding to the second type of self-correcting operation frequency is determined, thereby determining a normal distribution curve (corresponding to fig. 9B).

Therefore, the technical scheme forms a normal distribution curve corresponding to the second type of self-correcting operation frequency by calculating the mean value and the variance of the second type of self-correcting operation frequency, so that whether the current second type of self-correcting operation frequency of the staff is normal or not can be intuitively observed.

Optionally, the operation of determining the probability distribution values corresponding to the different types of self-correcting operation frequencies according to the history information related to the different types of self-correcting operation frequencies further includes: calculating a third mean and a third variance corresponding to the third type of self-correcting operation frequency according to the history information related to the third type of self-correcting operation frequency; and determining a third probability distribution value corresponding to the third type of self-correcting operation frequency according to the third mean value and the third variance.

Specifically, referring to Table 2, employee status determination module 140 obtains from database 300 a third type of frequency of self-corrective actions at date 1F _3,1 The third type of self-correcting operation frequency at date 2 isF _3,2 ,., third type on date nFrequency of self-correcting operationF _3,n . The employee status determination module 140 calculates a third average μ of the frequency of the third type of self-correcting operations according to the average calculation formula ₃ ：

(equation 5)

The employee status determination module 140 then calculates a third variance σ of the frequency of the third type of self-correcting operation according to the average calculation formula ₃ ² ：

(equation 6)

Further, the employee status determination module 140 determines the employee status based on the third average μ ₃ And a third difference sigma ₃ ² A third probability distribution value corresponding to the third type of self-correcting operation frequency is determined, thereby determining a normal distribution curve (corresponding to fig. 9C).

Therefore, the technical scheme forms the normal distribution curve corresponding to the third type of self-correcting operation frequency by calculating the mean value and the variance of the third type of self-correcting operation frequency, so that whether the current third type of self-correcting operation frequency of the staff is normal or not can be intuitively observed.

Specifically, since different employees have different work habits, the average frequency of the self-correcting operation frequency is different from employee to employee, and the fluctuation range of the self-correcting operation frequency is also different. Therefore, the invention can obtain the confidence interval adapted to the staff more accurately according to the specific situations of different staff by counting the normal distribution of the self-correcting operation frequency of different staff and determining the confidence interval of each staff based on the normal distribution. Thereby more accurately evaluating the working state of the staff.

Further, referring to fig. 1, according to a second aspect of the present embodiment, there is provided a storage medium. The storage medium includes a stored program, wherein the method of any one of the above is performed by a processor when the program is run.

Therefore, according to the embodiment, the working state of the staff is determined according to the frequency of self-correcting operation of the staff in the working process of the staff, so that the staff can be timely reminded of adjusting the working state when the working state of the staff is unstable. Staff can correct own errors in the actual document writing and editing process. For example, when a typing error occurs during the process of writing a document, an employee may first discover the typing error and make a modification. This operation is defined as a "self-correcting operation", i.e. the employee corrects his own errors during the work. The working state of the staff can be truly reflected under the condition of self-correcting operation in the working process of the staff, so that if the working state of the staff can be evaluated in the working process of the staff, the staff can be reminded of adjusting the working state before the staff has errors.

It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present invention is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present invention. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present invention.

From the description of the above embodiments, it will be clear to a person skilled in the art that the method according to the above embodiments may be implemented by means of software plus the necessary general hardware platform, but of course also by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.

Example 2

Fig. 10 shows an apparatus 1000 for determining an operating state based on an autocorrection operation according to the present embodiment, which apparatus 1000 corresponds to the method according to the first aspect of embodiment 1. Referring to fig. 10, the apparatus 1000 includes: the information recording module 1010 is used for recording employee operation information corresponding to the operation of the employee in real time in the working process; the operation identifying module 1020 is configured to identify, according to the employee operation information, an auto-correction operation of the employee during the working process, where the auto-correction operation is an error that the employee finds and corrects during the working process; a first determining module 1030, configured to determine, according to the identified self-correcting operation, a self-correcting operation frequency of the self-correcting operation that occurs during the employee's working process; and a second determining module 1040, configured to determine a working state of the employee in the working process according to the determined self-correcting operation frequency.

Optionally, the operation identification module 1020 includes: the first detection sub-module is used for detecting a first operation of inputting a first text by an employee in the working process; the second detection sub-module is used for detecting a second operation of deleting the first text by the staff in the working process; a third detection sub-module for detecting a third operation of the staff entering the second text after the second operation; the first determining sub-module is used for determining whether the first operation to the third operation are self-correcting operations of staff according to time intervals among the first operation, the second operation and the third operation and/or according to contents of the first text and the second text.

Optionally, the second determining module 1040 includes: the information acquisition sub-module is used for acquiring historical information related to self-correcting operation of staff; and the second determining submodule is used for determining the working state of the staff in the working process according to the determined self-correcting operation frequency and the acquired historical information.

Optionally, the second determining sub-module comprises: a first determining unit configured to determine probability distribution values corresponding to different types of self-correcting operation frequencies according to history information related to the different types of self-correcting operation frequencies, wherein the types of the self-correcting operation frequencies include a first type, a second type, and a third type; and the second determining unit is used for determining the working state of the staff in the working process according to the probability distribution value.

Optionally, the first determining unit includes: calculating a first mean and a first variance corresponding to the first type of self-correcting operation frequency according to the history information related to the first type of self-correcting operation frequency; and determining a first probability distribution value corresponding to the first type of self-correcting operation frequency according to the first mean value and the first variance.

Optionally, the first determining unit includes: the operation of determining probability distribution values corresponding to the different types of self-correcting operation frequencies from the history information related to the different types of self-correcting operation frequencies further includes: calculating a second mean and a second variance corresponding to the second type of self-correcting operation frequency according to the history information related to the second type of self-correcting operation frequency; and determining a second probability distribution value corresponding to the second type of self-correcting operation frequency according to the second mean value and the second variance.

Optionally, the first determining unit further includes: calculating a third mean and a third variance corresponding to the third type of self-correcting operation frequency according to the history information related to the third type of self-correcting operation frequency; and determining a third probability distribution value corresponding to the third type of self-correcting operation frequency according to the third mean value and the third variance.

Example 3

Fig. 11 shows an apparatus 1100 for determining an operating state based on an autocorrection operation according to the present embodiment, the apparatus 1100 corresponding to the method according to the first aspect of embodiment 1. Referring to fig. 11, the apparatus 1100 includes: a processor 1110; and a memory 1120, coupled to the processor 1110, for providing instructions to the processor for processing the steps of: during the working process, recording employee operation information corresponding to the operation of the employee in real time; according to the employee operation information, identifying self-correcting operation of the employee in the working process, wherein the self-correcting operation is an error which the employee finds and corrects in the working process; according to the identified self-correcting operation, determining the self-correcting operation frequency of the self-correcting operation of the staff in the working process; and determining the working state of the staff in the working process according to the determined self-correcting operation frequency.

Optionally, determining the working state of the staff during the working process according to the determined self-correcting operation frequency includes: acquiring historical information related to self-correcting operation of staff; and determining the working state of the staff in the working process according to the determined self-correcting operation frequency and the acquired historical information.

The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.

In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.

In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, such as the division of the units, is merely a logical function division, and may be implemented in another manner, for example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.

The units described as separate units may or may not be physically separate, and units shown 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 may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.

The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.

Claims

1. A method of determining an operational state based on self-correcting operations, comprising:

during the working process, recording employee operation information corresponding to the operation of the employee in real time;

according to the employee operation information, identifying self-correcting operation of the employee in the working process, wherein the self-correcting operation is the operation of the employee for finding and correcting errors in the working process;

determining the self-correcting operation frequency of the self-correcting operation of the staff in the working process according to the identified self-correcting operation; and

and according to the determined self-correcting operation frequency, determining the working state of the staff in the working process.

2. The method of claim 1, wherein identifying the operation of the employee's self-corrective action during the work procedure comprises:

detecting a first operation of inputting a first text by the staff in the working process;

Detecting a second operation of deleting the first text by the staff in the working process;

detecting a third operation of the employee to enter a second text after the second operation;

determining whether the first operation to the third operation are self-correcting operations of the staff according to the time interval between the first operation, the second operation and the third operation and/or according to the content of the first text and the second text.

3. The method of claim 1, wherein determining the employee's operational status during the work procedure based on the determined frequency of self-correcting operations comprises:

acquiring historical information related to self-correcting operation of the staff; and

and determining the working state of the staff in the working process according to the determined self-correcting operation frequency and the acquired historical information.

4. A method according to claim 3, wherein determining the staff work status during the work process based on the determined frequency of self-correcting operations and the acquired history information comprises:

determining probability distribution values corresponding to different types of self-correcting operation frequencies according to historical information related to the different types of self-correcting operation frequencies, wherein the types of the self-correcting operation frequencies comprise a first type, a second type and a third type; and

And determining the working state of the staff in the working process according to the probability distribution value.

5. The method of claim 4, wherein determining probability distribution values corresponding to different types of self-correcting operation frequencies based on historical information related to the different types of self-correcting operation frequencies comprises:

calculating a first mean and a first variance corresponding to the first type of self-correcting operation frequency according to historical information related to the first type of self-correcting operation frequency; and

and determining a first probability distribution value corresponding to the first type of self-correcting operation frequency according to the first mean value and the first variance.

6. The method of claim 4, wherein the operation of determining probability distribution values corresponding to different types of self-correcting operation frequencies based on historical information related to the different types of self-correcting operation frequencies further comprises:

calculating a second mean and a second variance corresponding to the second type of self-correcting operation frequency according to the history information related to the second type of self-correcting operation frequency; and

and determining a second probability distribution value corresponding to the second type of self-correcting operation frequency according to the second mean value and the second variance.

7. The method of claim 4, wherein the operation of determining probability distribution values corresponding to different types of self-correcting operation frequencies based on historical information related to the different types of self-correcting operation frequencies further comprises:

calculating a third mean and a third variance corresponding to the third type of self-correcting operation frequency according to the historical information related to the third type of self-correcting operation frequency; and

and determining a third probability distribution value corresponding to the third type of self-correcting operation frequency according to the third mean value and the third variance.

8. A storage medium comprising a stored program, wherein the method of any one of claims 1 to 7 is performed by a processor when the program is run.

9. An apparatus for determining an operational state based on self-correcting operations, comprising:

the information recording module is used for recording employee operation information corresponding to the operation of the employee in real time in the working process;

the operation identification module is used for identifying self-correcting operation of the staff in the working process according to the staff operation information, wherein the self-correcting operation is an error which the staff finds and corrects in the working process;

The first determining module is used for determining the self-correcting operation frequency of the self-correcting operation of the staff in the working process according to the identified self-correcting operation; and

and the second determining module is used for determining the working state of the staff in the working process according to the determined self-correcting operation frequency.

10. An apparatus for determining an operational state based on self-correcting operations, comprising:

a processor; and

a memory, coupled to the processor, for providing instructions to the processor to process the following processing steps:

according to the employee operation information, identifying self-correcting operation of the employee in the working process, wherein the self-correcting operation is an error which the employee finds and corrects in the working process;