A method, device and medium for automatically checking the content of a work report

Abstract

The present invention discloses a method, device, and medium for automatically checking the content of work reports. The method includes: defining standardized content for work reports of different employees, generating standardized report templates for different employees, wherein the standardized report templates include required fields, field format requirements, and specific task content; capturing work report data of different employees and converting the work report data into a predetermined format; extracting key indicators from the work report data using a pre-trained key indicator extraction algorithm to determine key indicator data in the work report data of different employees; and performing data verification on the key indicator data of different employees according to the standardized report templates to determine verification results for the work reports of different employees, wherein the verification results are used to improve the employees' work reports.

Description

Technical Field

[0001] The present invention relates to the technical field of work report checking, and more particularly, to a method, device and medium for automatically checking the content of a work report. Background Art

[0002] In order to make the weekly and daily reports of employees more standardized, the company will have a dedicated person to check everyone's weekly and daily reports on weekends; due to the large workload of manual inspection, the inspection takes a very long time, and manual inspection cannot find all problems, and some misjudgments will occur, resulting in waste of personnel costs and technical problems of low inspection efficiency. Summary of the Invention

[0003] In view of the deficiencies in the prior art, the present invention provides a method, device and medium for automatically checking the content of a work report.

[0004] According to one aspect of the present invention, there is provided a method for automatically checking the content of a work report, comprising:

[0005] Define the standardized content of work reports for different employees and generate standardized report templates for different employees. The standardized report templates include: required fields, field format requirements, and specific task content;

[0006] Capture work report data of different employees and convert the work report data into a predetermined format;

[0007] Use pre-trained key indicator extraction algorithms to extract key indicators from work report data and determine the key indicator data in the work report data of different employees;

[0008] Data verification is performed on key indicator data of different employees according to the report specification template to determine the verification results of different employees' work reports, where the verification results are used to improve the employees' work reports.

[0009] Optionally, a pre-trained key indicator extraction algorithm is used to extract key indicators from the work report data to determine key indicator data in the work report data of different employees, including:

[0010] Clean the work report data to determine the valid work report data;

[0011] Extract characteristic data related to key indicators from historical work report data;

[0012] Convert feature data into feature vectors;

[0013] Train the machine learning model based on the feature vectors to generate a key indicator extraction algorithm;

[0014] Key indicators are extracted from valid work report data according to the key indicator extraction algorithm to determine the key indicator data.

[0015] Optionally, feature data related to key indicators is extracted from historical work report data, including:

[0016] Use a pre-trained named entity recognition model to extract entity information from historical work report data;

[0017] Use natural language processing technology to conduct correlation analysis on entity information and determine the correlation relationship between entity information;

[0018] Identify sentiment information in historical work report data and extract sentiment impact in key indicators;

[0019] Construct feature data related to key indicators based on entity information, their associations, and emotional impact.

[0020] Optionally, perform data verification on key indicator data of different employees according to the report specification template to determine the verification results of the work reports of different employees, including:

[0021] Use regular expressions or string matching to verify the key indicator data of different employees according to the report specification template to determine the verification results of different employees' work reports. The verification results include: required fields are missing, field format errors, and specific task content is missing.

[0022] According to another aspect of the present invention, there is provided an apparatus for automatically checking the content of a work report, comprising:

[0023] The generation module is used to define the standard content of work reports for different employees and generate standard report templates for different employees. The standard report templates include: required fields, field format requirements, and specific task content;

[0024] The capture module is used to capture the work report data of different employees and convert the work report data into a predetermined format;

[0025] An extraction module is used to extract key indicators from work report data using a pre-trained key indicator extraction algorithm to determine key indicator data in the work report data of different employees;

[0026] The verification module is used to verify the key indicator data of different employees according to the report specification template, determine the verification results of the work reports of different employees, and use the verification results to improve the work reports of employees.

[0027] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and the computer program is used to execute the method according to any one of the above aspects of the present invention.

[0028] According to another aspect of the present invention, an electronic device is provided, comprising: a processor; a memory for storing instructions executable by the processor; and the processor for reading the executable instructions from the memory and executing the instructions to implement the method described in any one of the above aspects of the present invention.

[0029] Therefore, the method for automatically checking the content of work reports provided by this application defines the standard content of work reports of different employees, generates standard report templates for different employees, captures the work report data of different employees, and converts the work report data into a predetermined format; uses a pre-trained key indicator extraction algorithm to extract key indicators from the work report data, and determines the key indicator data in the work report data of different employees; performs data verification on the key indicator data of different employees according to the report standard template, and determines the verification results of the work reports of different employees, wherein the verification results are used to improve the work reports of employees. Automating the inspection of weekly and daily reports can significantly improve work efficiency, reduce errors and loopholes, and provide comprehensive inspection and analysis functions. This will greatly improve the problems of manual inspection, improve management efficiency and data quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] A more complete understanding of exemplary embodiments of the present invention may be obtained by referring to the following drawings:

[0031] Figure 1 is a flowchart of a method for automatically checking the content of a work report provided by an exemplary embodiment of the present invention;

[0032] Figure 2 It is a structural diagram of an apparatus for automatically checking the content of a work report provided by an exemplary embodiment of the present invention;

[0033] Figure 3 This is a structure of an electronic device provided by an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0034] Below, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0035] It should be noted that the relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

[0036] Those skilled in the art will understand that the terms "first" and "second" in the embodiments of the present invention are only used to distinguish different steps, devices or modules, and neither represent any specific technical meaning nor indicate the necessary logical order between them.

[0037] It should also be understood that, in the embodiments of the present invention, “a plurality of” may refer to two or more than two, and “at least one” may refer to one, two or more than two.

[0038] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more, unless explicitly limited or otherwise indicated in the context.

[0039] In addition, the term "and / or" in this invention merely describes an association relationship between related objects, indicating that three possible relationships exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Furthermore, the character " / " in this invention generally indicates that the related objects are in an "or" relationship.

[0040] It should also be understood that the description of the various embodiments of the present invention focuses on the differences between the various embodiments, and the same or similar aspects thereof can be referenced with each other. For the sake of brevity, they will not be described one by one.

[0041] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0042] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0043] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, they should be considered part of the specification.

[0044] It should be noted that like reference numerals and letters refer to like items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0045] Embodiments of the present invention can be applied to electronic devices such as terminal devices, computer systems, and servers, and can operate in conjunction with numerous other general-purpose or specialized computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments, and / or configurations suitable for use with terminal devices, computer systems, servers, and other electronic devices include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, minicomputer systems, mainframe computer systems, and distributed cloud computing technology environments including any of the above.

[0046] Electronic devices such as terminal devices, computer systems, and servers can be described in the general context of computer system-executable instructions (such as program modules) executed by a computer system. Generally, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in a distributed cloud computing environment, where tasks are performed by remote processing devices linked via a communication network. In a distributed cloud computing environment, program modules can be located on local or remote computing system storage media, including storage devices.

[0047] Exemplary Methods

[0048] Figure 1 This is a flow chart of a method for automatically checking the content of a work report provided by an exemplary embodiment of the present invention. This embodiment can be applied to electronic devices, such as Figure 1 As shown, the method 100 for automatically checking the content of a work report includes the following steps:

[0049] Step 101: define the standard content of work reports for different employees and generate standard report templates for different employees, wherein the standard report templates include: required fields, field format requirements, and specific task content;

[0050] Step 102: Capture work report data of different employees and convert the work report data into a predetermined format;

[0051] Step 103: extract key indicators from the work report data using a pre-trained key indicator extraction algorithm to determine key indicator data in the work report data of different employees;

[0052] Step 104 , performing data verification on key indicator data of different employees according to the report specification template, and determining verification results of work reports of different employees, wherein the verification results are used to improve the work reports of the employees.

[0053] Optionally, a pre-trained key indicator extraction algorithm is used to extract key indicators from the work report data to determine key indicator data in the work report data of different employees, including:

[0054] Clean the work report data to determine the valid work report data;

[0055] Extract characteristic data related to key indicators from historical work report data;

[0056] Convert feature data into feature vectors;

[0057] Train the machine learning model based on the feature vectors to generate a key indicator extraction algorithm;

[0058] Key indicators are extracted from valid work report data according to the key indicator extraction algorithm to determine the key indicator data.

[0059] Optionally, feature data related to key indicators is extracted from historical work report data, including:

[0060] Use a pre-trained named entity recognition model to extract entity information from historical work report data;

[0061] Use natural language processing technology to conduct correlation analysis on entity information and determine the correlation relationship between entity information;

[0062] Identify sentiment information in historical work report data and extract sentiment impact in key indicators;

[0063] Construct feature data related to key indicators based on entity information, their associations, and emotional impact.

[0064] Optionally, perform data verification on key indicator data of different employees according to the report specification template to determine the verification results of the work reports of different employees, including:

[0065] Use regular expressions or string matching to verify the key indicator data of different employees according to the report specification template to determine the verification results of different employees' work reports. The verification results include: required fields are missing, field format errors, and specific task content is missing.

[0066] Specifically, in response to the technical problems existing in the prior art, the present invention can check employees' weekly and daily reports by extracting key indicators and using natural language processing technology to ensure that the filled-in content meets the standard requirements and export abnormal situations to improve inspection efficiency and accuracy.

[0067] 1. First, you need to define the specification requirements:

[0068] Identify required fields: e.g. date, project name, work content, etc.

[0069] Define the format requirements of the field: for example, the date format (such as YYYY-MM-DD), the character length limit of the work content, etc.

[0070] As needed, formulate other standard requirements, such as work content requirements for certain specific projects or tasks, working hour calculation rules, and formulate different weekly and daily report standard templates for employees in different positions.

[0071] 2. Secondly, the captured data needs to be organized:

[0072] Ensure that the captured employee weekly and daily report data can be processed by computer programs and organize the data into appropriate data structures, such as JSON, CSV, Excel or database.

[0073] Clean the data: remove blank lines, special characters, or other invalid data.

[0074] The present invention uses the key indicator extraction technology to organize the captured data and debug the optimal model:

[0075] a. Ensure that the captured employee weekly and daily report data can be processed by computer programs and organize the data into appropriate data structures, such as JSON, CSV, Excel, or database.

[0076] b. Data cleaning: Use advanced natural language processing technology to segment the daily report text, remove stop words, and delete blank lines, special characters, or other invalid data.

[0077] c Then, through feature selection methods, mutual information and information entropy techniques are used to screen out features closely related to key indicators to improve the accuracy and stability of key indicator extraction.

[0078] dThen, the filtered features are represented as vectors, using methods such as term frequency-inverse document frequency (TF-IDF) or word embedding, so that subsequent supervised learning algorithms can better process the features.

[0079] In the supervised learning stage, advanced algorithms such as support vector machines, random forests, or deep learning are used to train models from pre-labeled key indicator datasets to automatically extract key indicators from daily reports.

[0080] Finally, the trained model is evaluated and tuned, including selecting the best model using techniques such as cross-validation to further improve the accuracy and stability of key indicator extraction.

[0081] In addition, to make the subsequent verification and inspection process more accurate, innovative natural language processing technology is introduced:

[0082] In the named entity recognition and extraction stage, pre-trained named entity recognition models, such as BERT and LSTM-CRF, are used to identify and extract important entity information such as names of people, places, and organizations in daily newspaper texts.

[0083] b. In the semantic analysis and relevance evaluation stage, natural language processing techniques (such as word vector models, word co-occurrence or syntactic analysis, etc.) are used to analyze and evaluate the relevance between key indicators and other keywords in order to better understand the correlation between daily content and key indicators.

[0084] c. Sentiment analysis and emotion recognition stage: sentiment dictionaries or deep learning methods are used to identify and analyze the sentiment tendencies and emotional states in daily newspaper texts to gain a more comprehensive understanding of the content of the daily newspapers and consider the impact of sentiment factors on key indicators.

[0085] d. In the text summarization and generation stage, advanced text summarization technologies, such as the RNN-based Seq2Seq model and the Transformer model, are used to extract key information and indicators from large amounts of daily text, automatically generating concise and accurate summaries, reducing the workload of the management team in reading and understanding.

[0086] 3. Then, verify the report using the report template defined in the first step and the work report data compiled in the second step:

[0087] For example, write a verification function or use relevant libraries or tools to perform data verification.

[0088] Check if required fields are empty: for example, date, project name, job description, etc.

[0089] Verify the format of the field: for example, whether the date conforms to a fixed format, whether the work content meets the length limit.

[0090] Verify whether the weekly and daily reports of employees in different positions conform to the template and whether the modules included meet the specifications.

[0091] Verify the logical integrity of the data: for example, check whether the date range is reasonable, whether the project name exists in the project list, whether the working hours are calculated accurately, whether the weekly and daily working hours match, etc.

[0092] 4. Then, further matching and checking of the rules are performed:

[0093] Use appropriate techniques (such as regular expressions, string matching, etc.) to match and check the rules of weekly and daily reports in the data.

[0094] According to the defined specifications, check whether the format and content of the fields meet the requirements.

[0095] Matching keywords or phrases: For example, checking if work content exists or meets requirements for a specific project.

[0096] Match weekly and daily report templates: Check whether the weekly and daily reports of employees in different positions conform to the template.

[0097] 5. After the inspection is completed, you can set up abnormal detection and reminders:

[0098] Based on the rule matching results, detect whether there are any anomalies, such as missing required fields, format errors, etc.

[0099] If an abnormality is found, relevant personnel can be notified via email or a report can be generated to remind them to provide additional information or handle the abnormality.

[0100] Record abnormal situations and handling situations for subsequent tracking and analysis.

[0101] 6. After completing the above steps, you can configure a result display and notification interface:

[0102] The inspection results can be presented to relevant personnel through interface display, report generation, etc.

[0103] As needed, set up email or message notifications to promptly notify employees who fill out weekly and daily reports that do not comply with regulations.

[0104] 7. Finally, you need to configure the automated execution:

[0105] Write the above steps into an automated script or application and set it up to run regularly after data is acquired each week. Use the cron scheduling tool to automate the task.

[0106] Therefore, the method provided by this application for automatically checking the content of work reports and realizing automated weekly and daily inspections can significantly improve work efficiency, reduce errors and loopholes, and provide comprehensive inspection and analysis functions. This will greatly improve the problems of manual inspections and improve management efficiency and data quality.

[0107] Exemplary devices

[0108] Figure 2 FIG. 1 is a schematic diagram of a structure of an apparatus for automatically checking the content of a work report provided by an exemplary embodiment of the present invention. Figure 2 As shown, the apparatus 200 includes:

[0109] A generation module 210 is used to define the standard content of work reports for different employees and generate standard report templates for different employees, wherein the standard report templates include: required fields, field format requirements, and specific task content;

[0110] The capture module 220 is used to capture work report data of different employees and convert the work report data into a predetermined format;

[0111] An extraction module 230 is configured to extract key indicators from the work report data using a pre-trained key indicator extraction algorithm, and determine key indicator data in the work report data of different employees;

[0112] The verification module 240 is used to perform data verification on the key indicator data of different employees according to the report specification template, and determine the verification results of the work reports of different employees, wherein the verification results are used to improve the work reports of employees.

[0113] Optionally, the extraction module 220 includes:

[0114] A cleaning submodule, configured to clean the work report data to determine valid work report data;

[0115] An extraction submodule, configured to extract characteristic data related to the key indicators from historical work report data;

[0116] A conversion submodule, configured to convert the feature data into a feature vector;

[0117] A generation submodule, configured to train a machine learning model based on the feature vector to generate the key indicator extraction algorithm;

[0118] The determination submodule is used to extract key indicators from the effective work report data according to the key indicator extraction algorithm to determine the key indicator data.

[0119] Optionally, extract submodules, including:

[0120] an extraction unit, configured to extract entity information from the historical work report data using a pre-trained named entity recognition model;

[0121] An analysis unit, configured to perform correlation analysis on the entity information using natural language processing technology to determine correlation relationships between the entity information;

[0122] an extraction unit, configured to identify sentiment information in the historical work report data and extract sentiment impact from the key indicators;

[0123] A construction unit is used to construct the feature data related to the key indicators based on the entity information and the association relationship between them and the emotional impact.

[0124] Optionally, the verification module 240 includes:

[0125] The verification submodule is used to use regular expressions or string matching to perform data verification on the key indicator data of different employees according to the report specification template, and determine the verification results of the work reports of different employees, wherein the verification results include: required fields are missing, field format errors, and specific task content is missing.

[0126] Exemplary electronic devices

[0127] Figure 3 This is the structure of an electronic device provided by an exemplary embodiment of the present invention. Figure 3 As shown, the electronic device 30 includes one or more processors 31 and a memory 32 .

[0128] The processor 31 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0129] The memory 32 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may, for example, include read-only memory (ROM), a hard disk, a flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 31 may execute the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above and / or other desired functions. In one example, the electronic device may further include: an input device 33 and an output device 34, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0130] In addition, the input device 33 may also include, for example, a keyboard, a mouse, and the like.

[0131] The output device 34 can output various information to the outside. The output device 34 can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto.

[0132] Of course, to simplify, Figure 3Only some of the components related to the present invention in the electronic device are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application scenarios.

[0133] Exemplary computer program products and computer-readable storage media

[0134] In addition to the above-mentioned methods and devices, an embodiment of the present invention may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to perform the steps of the method according to various embodiments of the present invention described in the above "Exemplary Method" section of this specification.

[0135] The computer program product may be written in any combination of one or more programming languages ​​to implement the operations of embodiments of the present invention, including object-oriented programming languages ​​such as Java, C++, and conventional procedural programming languages ​​such as C or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's computing device, as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0136] In addition, an embodiment of the present invention may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enables the processor to execute the steps of the method for information mining of historical change records according to various embodiments of the present invention described in the above "Exemplary Method" section of this specification.

[0137] The computer-readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can, for example, include but is not limited to a system, system or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0138] The basic principles of the present invention have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in the present invention are merely illustrative and non-limiting, and should not be construed as necessarily possessed by each embodiment of the present invention. Furthermore, the specific details disclosed above are provided for illustrative purposes and to facilitate understanding, and are not intended to be limiting. These details do not necessarily limit the present invention to being implemented using these specific details.

[0139] Each embodiment in this specification is described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. References to the same or similar parts between the various embodiments are sufficient. For system embodiments, since they largely correspond to method embodiments, their description is relatively simple. For relevant parts, references to the description of the method embodiments are sufficient.

[0140] The block diagrams of the devices, systems, equipment, and systems involved in the present invention are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, systems, equipment, and systems can be connected, arranged, or configured in any manner. Words such as "including," "comprising," "having," and the like are open-ended words, meaning "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0141] The method and system of the present invention may be implemented in many ways. For example, the method and system of the present invention may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above sequence of steps for the method is for illustration only, and the steps of the method of the present invention are not limited to the sequence specifically described above, unless otherwise specified. In addition, in some embodiments, the present invention may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present invention. Thus, the present invention also covers recording media that store programs for executing the method according to the present invention.

[0142] It should also be noted that, in the system, device and method of the present invention, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. The above description of the disclosed aspects is provided to enable any technician in this field to make or use the present invention. Various modifications to these aspects will be very obvious to those skilled in the art, and the general principles defined here can be applied to other aspects without departing from the scope of the present invention. Therefore, the present invention is not intended to be limited to the aspects shown here, but according to the widest scope consistent with the principles disclosed here and novel features.

[0143] The above description has been provided for the purpose of illustration and description. Furthermore, this description is not intended to limit the embodiments of the present invention to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A method for automatically checking the content of a work report, characterized in that: include: Define the standard content of work reports for different employees and generate standard report templates for different employees, where the standard report templates include: required fields, field format requirements, and specific task content; Capturing work report data of different employees and converting the work report data into a predetermined format; extracting key indicators from the work report data using a pre-trained key indicator extraction algorithm, and determining key indicator data in the work report data of different employees; Performing data verification on the key indicator data of different employees according to the report specification template to determine verification results of the work reports of different employees, wherein the verification results are used to improve the work reports of the employees; A pre-trained key indicator extraction algorithm is used to extract key indicators from the work report data to determine key indicator data in the work report data of different employees, including: Performing data cleaning on the work report data to determine valid work report data; Extracting characteristic data related to the key indicators from historical work report data; Converting the feature data into a feature vector; Training a machine learning model based on the feature vector to generate the key indicator extraction algorithm; Extracting key indicators from the valid work report data according to the key indicator extraction algorithm to determine the key indicator data; Extract characteristic data related to the key indicators from historical work report data, including: Extracting entity information from the historical work report data using a pre-trained named entity recognition model; Using natural language processing technology to perform correlation analysis on the entity information to determine the correlation relationship between the entity information; Identifying emotional information in the historical work report data and extracting emotional impact in the key indicators; The feature data related to the key indicators are constructed based on the entity information, the association relationships therebetween, and the emotional impact.

2. The method according to claim 1, characterized in that Performing data verification on the key indicator data of different employees according to the report specification template to determine verification results of the work reports of different employees, including: Regular expressions or string matching are used to perform data verification on the key indicator data of different employees according to the report specification template to determine the verification results of the work reports of different employees, wherein the verification results include: required fields are missing, field format errors, and specific task content is missing.

3. A device for automatically checking the content of a work report, characterized in that: include: A generation module is used to define the standard content of work reports for different employees and generate standard report templates for different employees, wherein the standard report templates include: required fields, field format requirements, and specific task content; A capture module, used to capture work report data of different employees and convert the work report data into a predetermined format; an extraction module, configured to extract key indicators from the work report data using a pre-trained key indicator extraction algorithm, and determine key indicator data in the work report data of different employees; a verification module, configured to verify the key indicator data of different employees according to the report specification template, and determine verification results of the work reports of different employees, wherein the verification results are used to improve the work reports of the employees; Extraction modules, including: A cleaning submodule, configured to clean the work report data to determine valid work report data; An extraction submodule, configured to extract characteristic data related to the key indicators from historical work report data; A conversion submodule, configured to convert the feature data into a feature vector; A generation submodule, configured to train a machine learning model based on the feature vector to generate the key indicator extraction algorithm; A determination submodule, configured to extract key indicators from the valid work report data according to the key indicator extraction algorithm, and determine the key indicator data; Extract submodules, including: an extraction unit, configured to extract entity information from the historical work report data using a pre-trained named entity recognition model; An analysis unit, configured to perform correlation analysis on the entity information using natural language processing technology to determine correlation relationships between the entity information; an extraction unit, configured to identify sentiment information in the historical work report data and extract sentiment impact from the key indicators; A construction unit is used to construct the feature data related to the key indicators based on the entity information and the association relationship between them and the emotional impact.

4. The device according to claim 3, characterized in that Verification module, including: The verification submodule is used to use regular expressions or string matching to perform data verification on the key indicator data of different employees according to the report specification template, and determine the verification results of the work reports of different employees, wherein the verification results include: required fields are missing, field format errors, and specific task content is missing.

5. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and the computer program is used to execute the method according to any one of claims 1-2.

6. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing instructions executable by the processor; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method according to any one of claims 1 to 2.

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