Method and system for analyzing influence of artificial intelligence on working efficiency of enterprise

Abstract

The invention provides a method and system for analyzing the influence of artificial intelligence on the working efficiency of an enterprise, and relates to the field of artificial intelligence, and the method comprises the steps: constructing an artificial intelligence tool platform based on a large language model; constructing a work task completion condition statistical platform; constructing a work task; setting an excitation mechanism; according to the use condition of the artificial intelligence tool platform and an incentive mechanism, employees are grouped, and a plurality of employee groups are obtained; submitting a task completion result that the employees in each employee group complete the work task through a work task completion condition statistics platform; setting an evaluation mechanism; according to an evaluation mechanism and the task completion results corresponding to the employees in each employee group, evaluating the use frequency of the artificial intelligence tool platform and the task completion effects of the employees in each employee group; and analyzing the influence of the artificial intelligence tool platform and the incentive mechanism on the working efficiency of the enterprise according to the use frequency of the artificial intelligence tool platform and the task completion effect of the employees in each employee group.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence, and particularly to an analysis method and system for the impact of artificial intelligence on enterprise work efficiency. Background Art

[0002] With the rapid development of artificial intelligence technology, the demand for artificial intelligence tools by enterprises and individuals is increasing continuously. Especially in terms of improving work efficiency and productivity, artificial intelligence tools (such as large language models and ChatGPT, etc.) have been widely used. Many enterprises have introduced artificial intelligence technology, expecting to improve employees' work efficiency, optimize decision-making quality, and promote the improvement of overall productivity through automated and intelligent means.

[0003] In the prior art, traditional analysis methods for the impact of artificial intelligence on enterprise work efficiency lack systematic data support, cannot discover the potential impact of artificial intelligence applications on individual work patterns, cannot comprehensively evaluate the actual impact of artificial intelligence tools under different user groups and incentive conditions, and are difficult to provide a decision-making basis for how enterprises can better combine artificial intelligence tools with human resources in the future.

[0004] In addition, existing methods fail to explore the driving effect of incentive mechanisms on the use effect of artificial intelligence tools and ignore the potential for productivity improvement brought by incentive mechanisms. Summary of the Invention

[0005] In order to solve the technical problems that the existing analysis methods for the impact of artificial intelligence on enterprise work efficiency lack systematic data support, cannot discover the potential impact of artificial intelligence applications on individual work patterns, cannot comprehensively evaluate the actual impact of artificial intelligence tools under different user groups and incentive conditions, are difficult to provide a decision-making basis for how enterprises can better combine artificial intelligence tools with human resources in the future, and fail to explore the driving effect of incentive mechanisms on the use effect of artificial intelligence tools and ignore the potential for productivity improvement brought by incentive mechanisms, the present invention provides an analysis method and system for the impact of artificial intelligence on enterprise work efficiency.

[0006] The technical solutions provided by the embodiments of the present invention are as follows:

[0007] First aspect:

[0008] An analysis method for the impact of artificial intelligence on enterprise work efficiency provided by an embodiment of the present invention includes:

[0009] S1: Construct an artificial intelligence tool platform based on a large language model;

[0010] S2: Construct a statistical platform for the completion of work tasks;

[0011] S3: Construct work tasks based on employees' daily work content and the usage of the artificial intelligence tool platform.

[0012] S4: Set up an incentive mechanism.

[0013] S5: Group employees according to the usage of the artificial intelligence tool platform and the incentive mechanism to obtain multiple employee groups.

[0014] S6: Submit the task completion results of employees in each employee group for completing the work tasks through the work task completion statistics platform.

[0015] S7: Set up an evaluation mechanism.

[0016] S8: Evaluate the usage frequency of the artificial intelligence tool platform and the task completion effects of employees in each employee group according to the evaluation mechanism and the task completion results corresponding to employees in each employee group.

[0017] S9: Analyze the impact of the artificial intelligence tool platform and the incentive mechanism on the enterprise work efficiency according to the usage frequency of the artificial intelligence tool platform and the task completion effects of employees in each employee group.

[0018] Second aspect:

[0019] An analysis system for the impact of artificial intelligence on enterprise work efficiency provided by an embodiment of the present invention includes: a memory and one or more processors;

[0020] One or more application programs are stored in the memory, and the one or more application programs are adapted to be executed by the one or more processors to implement the above analysis method for the impact of artificial intelligence on enterprise work efficiency.

[0021] The beneficial effects brought by the technical solution provided by the embodiment of the present invention at least include:

[0022] In the present invention, by constructing an artificial intelligence tool platform and a work task completion statistics platform based on large language models, systematic data support is provided. Based on the daily work content of employees and the usage of the artificial intelligence tool platform, work tasks are constructed, and the potential impact of artificial intelligence applications on individual work patterns can be discovered. By setting up an incentive mechanism and grouping employees, the task completion results of employees in each employee group are submitted in the work task completion statistics platform, and the actual impact of artificial intelligence tools under different user groups and incentive conditions can be comprehensively evaluated. It is easy to provide a decision-making basis for how enterprises can better combine artificial intelligence tools with human resources in the future. According to the usage frequency of the artificial intelligence tool platform and the task completion effects of employees in each employee group, the impact of the artificial intelligence tool platform and the incentive mechanism on the work efficiency of the enterprise is analyzed, the driving effect of the incentive mechanism on the usage effect of artificial intelligence tools can be explored, and the potential for productivity improvement brought by the incentive mechanism is considered. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0024] Figure 1 It is a schematic flowchart of an analysis method for the impact of artificial intelligence on the work efficiency of an enterprise provided by an embodiment of the present invention;

[0025] Figure 2 It is a schematic structural diagram of an analysis system for the impact of artificial intelligence on the work efficiency of an enterprise provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] The following will describe the technical solutions in the present invention in conjunction with the accompanying drawings.

[0027] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations, or explanations. Any embodiment or design solution described as "exemplary" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, the use of the word "example" is intended to present concepts in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two can be selected.

[0028] In order to make the technical problems, technical solutions, and advantages to be solved by the present invention clearer, the following will be described in detail in conjunction with the accompanying drawings and specific embodiments.

[0029] Refer to the attached specificationFigure 1 , showing a schematic flowchart of an analysis method for the impact of artificial intelligence on enterprise work efficiency provided by an embodiment of the present invention.

[0030] An embodiment of the present invention provides an analysis method for the impact of artificial intelligence on enterprise work efficiency. This method can be implemented by an analysis device for the impact of artificial intelligence on enterprise work efficiency, and this analysis device can be a terminal or a server. The processing flow of the analysis method for the impact of artificial intelligence on enterprise work efficiency may include the following steps:

[0031] S1: Build an artificial intelligence tool platform based on a large language model.

[0032] Specifically, with a large language model capable of natural language conversations as the core, combined with a front-end user conversation interface, a usage record database, and a database management tool, an artificial intelligence platform tool is built. This platform is in the form of a Web and mobile application with intelligent conversation, information integration, and suggestion functions, providing an artificial intelligence application for assisting users in daily work tasks, and saving and processing the records of users' tool usage in real time. In addition, by recording the operation behaviors and interaction contents of users through the platform, it provides data support for subsequent data statistics and task analysis, and further optimizes task management and efficiency evaluation.

[0033] Optionally, the artificial intelligence tool platform specifically includes: a front-end part of a Web application based on the React framework, a backend part using the Node.js and Express frameworks, and a database part using PostgreSQL as the storage system.

[0034] It should be noted that React is a JavaScript library for building user interfaces, especially suitable for developing single-page applications (SPAs). It is developed and maintained by Facebook and uses a component-based structure, enabling developers to build reusable UI components and improve development efficiency. The core feature of React is the virtual DOM, which improves the rendering efficiency by minimizing the reflection of page changes to the real DOM.

[0035] It should be noted that the Web is a system composed of a global computer network that allows users to access and interact with web application programs through a browser. The Web uses technologies such as Hypertext Markup Language (HTML), Cascading Style Sheets (CSS), and JavaScript to present content and achieve interaction. Web application programs usually transfer data between the client (such as a browser) and the server through the HTTP protocol.

[0036] It should be noted that Node.js is a JavaScript runtime based on the Chrome V8 engine, allowing developers to run JavaScript on the server side. It is an open-source, event-driven, non-blocking I / O model runtime environment, especially suitable for building high-performance and scalable network applications. Node.js enables developers to use JavaScript for both front-end and back-end development, improving development unity and efficiency.

[0037] It should be noted that Express is a web application framework based on Node.js, which simplifies the development of server-side applications. It provides many functions for building web applications and APIs, such as route management, request handling, middleware support, etc. Express is easy to use and highly flexible, and is one of the most popular frameworks in the Node.js environment.

[0038] It should be noted that PostgreSQL is an open-source relational database management system that uses SQL (Structured Query Language) for data query and management. It supports complex queries, high-concurrency operations, and transaction control, and can handle various data types and index types. PostgreSQL emphasizes extensibility, data integrity, and support for multiple programming languages, and is widely used in various types of applications.

[0039] It should be noted that the front-end part is responsible for interacting with users.

[0040] Specifically, the front-end part uses a React framework-based web application to provide a user interface, enabling employees to have natural language conversations and task interactions with the artificial intelligence tool platform through a browser.

[0041] It should be noted that the back-end part implements the operation and task processing of the artificial intelligence tool platform.

[0042] Specifically, the back-end part uses the Node.js and Express frameworks, which are responsible for handling requests from the front-end part, calling the large language model interface, and providing functions such as intelligent conversation, task suggestions, and question answering.

[0043] It should be noted that the database part is used to store user interaction records and task execution data.

[0044] Specifically, the database part uses PostgreSQL as the storage system to record and manage unstructured data (such as chat records, user inputs, etc.) and structured data (such as user information, task execution time, task completion status, etc.) in the platform.

[0045] In the present invention, the artificial intelligence tool based on the large language model can conduct natural language conversations, and employees can interact with the platform in a way similar to human conversations, which improves the intuitiveness and ease of use of operations and reduces the learning curve. By integrating the large language model interface, the platform can provide functions such as intelligent conversations, task suggestions, and question answers, greatly enhancing the intelligence level of the platform, enabling it to not only handle basic tasks but also provide real-time and personalized assistance according to the needs of employees.

[0046] S2: Build a platform for statistics of work task completion.

[0047] Specifically, based on the daily work forms of enterprise employees (centered on the work content of enterprise employees and combined with the tools commonly used by employees), build a platform for summarizing and statistics of work task completion (the platform for summarizing and statistics of work task completion is a Web application platform), and the platform has functions such as user login, task publishing, and task viewing.

[0048] Furthermore, after employees log in to the platform, they fill in and submit specific work tasks and task completion results through the user interface. The platform uniformly records all the data submitted by employees, including employee ID, task ID, task description, task completion result, usage frequency of the artificial intelligence tool platform, task working hours, etc. All task data is stored in the task record database, and the data format is standardized for subsequent analysis and application.

[0049] In the present invention, the task data of all employees (such as task ID, task description, task completion result, etc.) are uniformly stored in the task record database. This enables centralized management of all task-related data, facilitating subsequent query and analysis. By automatically recording and analyzing task data (such as task completion time, tool usage frequency, etc.) through the platform, enterprises can obtain more accurate efficiency data, identify which tasks take a long time and which tasks have low efficiency due to low tool usage frequency, and thus carry out corresponding optimizations.

[0050] S3: Build work tasks based on the daily work content of employees and the usage of the artificial intelligence tool platform.

[0051] Specifically, build three different levels of difficulty of work tasks based on the daily work content of employees and whether they use the artificial intelligence tool platform.

[0052] In the present invention, by combining the actual work content of employees, tasks can be customized for employees to ensure the relevance and practical applicability of the tasks. Employees are faced with tasks closely related to their work, which helps to improve the efficiency and quality of task completion. Designing tasks of different difficulties can provide different challenges according to the abilities of employees and task requirements, avoiding tasks being too simple or too complex, thereby enhancing employees' work enthusiasm and task completion rate.

[0053] Optionally, the work tasks specifically include: the first work task, the second work task, and the third work task.

[0054] Among them, the first work task specifically is: when employees are not allowed to use the artificial intelligence tool platform, they are required to complete the first writing task within the first preset duration. When the first writing task progresses to the second preset duration, the first writing task is interrupted, and employees are required to perform the first calculation task. When employees complete the first calculation task and submit the calculation results, they continue to complete the first writing task.

[0055] Among them, the second work task specifically is: when some employees are allowed to use the artificial intelligence tool platform, they are required to complete the second writing task within the third preset duration. When the second writing task progresses to the fourth preset duration, the second writing task is interrupted, and employees are required to perform the second calculation task. When employees complete the second calculation task and submit the calculation results, they continue to complete the second writing task.

[0056] Among them, the third work task specifically is: when some employees are allowed to use the artificial intelligence tool platform, they are required to complete the third writing task within the fifth preset duration.

[0057] It should be noted that the employees allowed to use artificial intelligence tools in the third writing task are the same as those allowed to use in the second writing task.

[0058] It should be noted that those skilled in the art can set the magnitudes of the first preset duration, the second preset duration, the third preset duration, the fourth preset duration, and the fifth preset duration according to actual needs, and the present invention does not make any limitations here.

[0059] In the present invention, by designing three different work tasks (the first task, the second task, and the third task), the role of artificial intelligence tools can be evaluated under different conditions. In Task 1, the use of artificial intelligence tools is completely prohibited, while in Task 2 and Task 3, some employees can use artificial intelligence tools. This can clearly demonstrate the role of artificial intelligence tools in different tasks and different working environments, and compare the task completion effects with and without tool assistance. By setting scenarios such as task interruption and task switching, complex situations that may be encountered in actual work (such as multitasking and task switching) are simulated. This design can help enterprises evaluate the role of artificial intelligence tools in a more realistic working scenario, thus obtaining more practically significant results.

[0060] S4: Set up an incentive mechanism.

[0061] Optionally, the incentive mechanism specifically includes: writing task incentive and calculation task incentive.

[0062] The writing task incentive is specifically: based on the quality score of the employee's completion of the writing task, when the quality score of the employee's completion of the writing task is within the preset ranking of the employee's group, the employee is rewarded according to the preset reward standard of the employee's group.

[0063] It should be noted that those skilled in the art can set the size of the preset ranking according to actual needs, and the present invention does not make a limitation here.

[0064] Optionally, the preset reward standard specifically includes: first-level reward (for example: 100 yuan in RMB) and second-level reward (for example: 200 yuan in RMB).

[0065] The calculation task incentive is specifically: in the first work task and the second work task, when the calculation result submitted by the employee is a wrong answer, according to the preset penalty mechanism, a part of the employee's reward is deducted.

[0066] Optionally, the preset penalty mechanism is specifically: deduct 5 yuan from the employee.

[0067] In the present invention, by setting up a clear incentive mechanism (including writing task incentive and calculation task incentive), the enterprise can ensure the task quality while improving the work efficiency and enthusiasm of employees. The reward and penalty mechanisms not only increase the sense of responsibility of employees but also promote the improvement of work efficiency.

[0068] S5: According to the usage situation of the artificial intelligence tool platform and the incentive mechanism, group employees to obtain multiple employee groups.

[0069] In the present invention, grouping employees enables the enterprise to more detailedly analyze the differences in work performance under different conditions (such as whether to use artificial intelligence tools and the amount of rewards), so as to draw more accurate conclusions and obtain data support. Through group comparison, the enterprise can clarify which combinations of incentive mechanisms and tool usages produce the best work effects.

[0070] Optionally, the employee groups specifically include: a control group, an experimental group, and an incentive group.

[0071] Among them, the control group does not use the artificial intelligence tool platform in the first writing task, the second writing task, and the third writing task.

[0072] Optionally, the reward standard for the control group is: first-level reward.

[0073] Specifically, if the quality scores of the employees in the control group for completing the writing tasks in the first writing task, the second writing task, and the third writing task are within the preset rankings of their respective employee groups, they can obtain a first-level reward.

[0074] The experimental group does not use the artificial intelligence tool platform in the first writing task, and uses the artificial intelligence tool platform in the second writing task and the third writing task.

[0075] Optionally, the reward standard for the experimental group is: first-level reward.

[0076] Specifically, if the quality scores of the employees in the experimental group for completing the writing tasks in the first writing task, the second writing task, and the third writing task are within the preset rankings of their respective employee groups, they can obtain a first-level reward.

[0077] The incentive group does not use the artificial intelligence tool platform in the first writing task, and uses the artificial intelligence tool platform in the second writing task and the third writing task.

[0078] Optionally, the reward standard for the incentive group is: second-level reward.

[0079] Specifically, if the quality scores of the employees in the incentive group for completing the writing tasks in the first writing task, the second writing task, and the third writing task are within the preset rankings of their respective employee groups, they can obtain a second-level reward.

[0080] In the present invention, by comparing the performances of employees in different groups in the same tasks, the applicability of artificial intelligence tools in different tasks can be evaluated. By collecting the task quality scores and completion times of employees, the enterprise can quantitatively evaluate the performances of different groups. Analyzing these data, the enterprise can clarify the specific impacts of artificial intelligence tools and incentive mechanisms on employees' work efficiency and task quality, and thus make optimization decisions supported by data.

[0081] S6: Through the work task completion statistics platform, submit the task completion results of employees in each employee group.

[0082] In the present invention, the platform can centrally record the task completion situations of all employees (including task completion time, quality scores, tool usage frequencies, etc.), enabling all data to be uniformly managed, facilitating subsequent querying and analysis. The results of task completion can be submitted to the platform in real time, helping managers track the task execution situations of employees at any time, quickly identify problems, and take appropriate measures. The platform can automatically summarize and statistically analyze the task completion situations of each group of employees, saving the time and cumbersome process of manual statistics and improving the evaluation efficiency.

[0083] S7: Set up an evaluation mechanism.

[0084] Optionally, the evaluation mechanism specifically includes: writing task evaluation and calculation task evaluation.

[0085] The writing task evaluation is specifically: according to the preset scoring criteria and preset scoring range, conduct quality scoring on the first writing task, the second writing task, and the third writing task respectively, and evaluate the task completion effect of employees according to the quality scoring results of the first writing task, the second writing task, and the third writing task.

[0086] Optionally, the preset scoring criteria specifically include: content accuracy, professionalism, logic, and creativity.

[0087] Optionally, the preset scoring range is 1 - 10 points.

[0088] The calculation task evaluation is specifically: record and judge the correctness of the calculation results submitted by employees.

[0089] Furthermore, when the calculation results submitted by employees in the calculation task are wrong answers, handle them according to the preset penalty mechanism (deduct 5 yuan from the employees).

[0090] In the present invention, setting up the mechanisms of writing task evaluation and calculation task evaluation can achieve a comprehensive and objective evaluation of the task completion effect of employees. Through multi-dimensional scoring of writing tasks (such as content accuracy, professionalism, logic, and creativity), ensure a comprehensive evaluation of task quality. At the same time, the correctness of calculation tasks and the penalty mechanism can effectively promote employees' attention to details and improve the accuracy of tasks. This evaluation mechanism helps enterprises scientifically quantify employees' performance, provides reliable data support for subsequent task optimization, tool usage, and incentive mechanism adjustment, thereby improving work efficiency and quality.

[0091] S8: According to the evaluation mechanism and the task completion results corresponding to the employees in each employee group, evaluate the usage frequency of the artificial intelligence tool platform and the task completion effects of the employees in each employee group.

[0092] Optionally, the task completion effect specifically includes: task completion time, task quality score, and task completion result.

[0093] In the present invention, by evaluating the usage frequency of the artificial intelligence tool, the usage situation of the tool in different groups can be analyzed, which helps the enterprise to judge the actual impact of the tool on task completion efficiency and quality. Through the comprehensive analysis of the task completion time, task quality score, and task completion result, the actual effects of the artificial intelligence tool in improving work efficiency, quality, and employee performance can be accurately evaluated.

[0094] S9: According to the usage frequency of the artificial intelligence tool platform and the task completion effects of the employees in each employee group, analyze the impact of the artificial intelligence tool platform and the incentive mechanism on the work efficiency of the enterprise.

[0095] In the present invention, by understanding the frequency of employees using the artificial intelligence tool, the actual application situation and effects of the tool can be judged, and further analyze the promotion effect of the artificial intelligence tool on work efficiency and task completion quality. Combining the data of task completion effects, the actual contributions of the artificial intelligence tool in improving work efficiency, reducing errors, and improving task quality can be accurately evaluated, which helps the enterprise to evaluate its return on investment. By analyzing the performance of employees under different incentive conditions, the impacts of different reward criteria and incentive methods on employees' work enthusiasm, task completion quality, and efficiency can be evaluated.

[0096] In a possible implementation manner, S9 specifically includes sub-steps S901 to S904:

[0097] S901: Compare the quality scores and task completion times of the control group, experimental group, and incentive group in the first writing task, second writing task, and third writing task, evaluate the impact of the usage of the artificial intelligence tool platform and the incentive mechanism on the quality and efficiency of the writing task, and obtain the first comparison result.

[0098] S902: Compare the performance differences of the employees in each employee group in the first writing task, second writing task, and third writing task, evaluate the impact of the usage of the artificial intelligence tool platform on work efficiency, and obtain the second comparison result.

[0099] S903: Analyze the correctness and completion times of the employees in each employee group in the first calculation task, second calculation task, and third calculation task, evaluate the impact of the usage of the artificial intelligence tool platform and the incentive mechanism on the quality and efficiency of the calculation task, and obtain the third comparison result.

[0100] S904: Analyze the impact of the artificial intelligence tool platform and the incentive mechanism on the work efficiency of the enterprise based on the first comparison result, the second comparison result, and the third comparison result.

[0101] Specifically, analyze the impact of the artificial intelligence tool platform: Evaluate the improvement effect of the artificial intelligence tool platform on employees' work efficiency and task quality through the performance comparison of different groups and tasks.

[0102] Furthermore, analyze the impact of the incentive mechanism: Evaluate whether increasing incentives (such as raising the reward amount) can further improve employees' work enthusiasm and performance by comparing the performance of the experimental group and the incentive group.

[0103] Furthermore, analyze the interaction effect: Explore whether there is an interaction between the use of the artificial intelligence tool platform and the incentive mechanism, and analyze its comprehensive impact on employees' work efficiency.

[0104] Furthermore, based on the analysis results of the impact of the artificial intelligence tool platform and the incentive mechanism on the enterprise's work efficiency, propose suggestions for optimizing the use strategy of artificial intelligence tools and the incentive mechanism to help the enterprise improve the overall work efficiency and employees' performance.

[0105] In the present invention, by comparing the performance of different groups in tasks and analyzing the impact of the artificial intelligence tool platform and the incentive mechanism, the improvement effect of the artificial intelligence tool and the incentive mechanism on employees' work efficiency and task quality can be evaluated systematically and comprehensively. By analyzing the interaction effect, the interaction between the artificial intelligence tool and the incentive mechanism can be understood deeply, and then the combined use of them can be optimized. This data-driven analysis method provides scientific and accurate decision-making basis for the enterprise, helps the enterprise adjust the tool use strategy and the incentive mechanism according to the actual performance of employees, thereby improving the overall work efficiency, enhancing employees' performance, and ultimately promoting the sustainable development and competitiveness improvement of the enterprise.

[0106] The beneficial effects brought by the technical solution provided by the embodiment of the present invention at least include:

[0107] In the present invention, by constructing an artificial intelligence tool platform and a work task completion situation statistics platform based on a large language model, systematic data support is provided. Based on the daily work content of employees and the usage of the artificial intelligence tool platform, work tasks are constructed, and the potential impact of artificial intelligence applications on individual work patterns can be discovered. By setting up an incentive mechanism and grouping employees, the task completion results of employees in each employee group for completing work tasks are submitted in the work task completion situation statistics platform, and the actual impact of artificial intelligence tools under different user groups and incentive conditions can be comprehensively evaluated. It is easy to provide a decision-making basis for how enterprises can better combine artificial intelligence tools with human resources in the future. According to the usage frequency of the artificial intelligence tool platform and the task completion effects of employees in each employee group, the impact of the artificial intelligence tool platform and the incentive mechanism on the work efficiency of the enterprise is analyzed, the driving effect of the incentive mechanism on the usage effect of artificial intelligence tools can be explored, and the potential for productivity improvement brought by the incentive mechanism is considered.

[0108] Refer to the attached drawings of the specification Figure 2 , which shows a schematic structural diagram of an analysis system for the impact of artificial intelligence on the work efficiency of an enterprise provided by the present invention.

[0109] The present invention also provides an analysis system 30 for the impact of artificial intelligence on the work efficiency of an enterprise, including: a memory 303 and one or more processors 301.

[0110] One or more application programs are stored in the memory 303, and the one or more application programs are adapted to be executed by the one or more processors 301 to implement the analysis method for the impact of artificial intelligence on the work efficiency of an enterprise described in the method embodiment.

[0111] The analysis system 30 for the impact of artificial intelligence on the work efficiency of an enterprise includes: a processor 301 and a memory 303. Among them, the processor 301 and the memory 303 are connected, such as connected through a bus 302.

[0112] The structure of the analysis system 30 for the impact of artificial intelligence on the work efficiency of an enterprise does not constitute a limitation to the embodiments of the present invention.

[0113] The processor 301 can be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can implement or execute various exemplary logical blocks, modules and circuits described in connection with the disclosure of the present invention. The processor 301 can also be a combination for implementing computing functions, such as a combination including one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0114] The bus 302 may include a path for transmitting information between the above components. The bus 302 can be a PCI bus, an EISA bus, or the like. The bus 302 can be divided into an address bus, a data bus, a control bus, etc. For the sake of simplicity of representation, only a thick line is shown in the figure, but it does not mean that there is only one bus or one type of bus.

[0115] The memory 303 can be a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, or an EEPROM, a CD-ROM, or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0116] It should be noted that the analysis system 30 of the impact of artificial intelligence on enterprise work efficiency can implement the above-mentioned analysis method of the impact of artificial intelligence on enterprise work efficiency and can achieve the same or similar technical effects. To avoid repetition, the present invention will not be described in detail herein.

[0117] The beneficial effects brought by the technical solution provided in the embodiments of the present invention at least include:

[0118] In the present invention, by constructing an artificial intelligence tool platform and a work task completion situation statistics platform based on a large language model, systematic data support is provided. Based on the daily work content of employees and the usage of the artificial intelligence tool platform, work tasks are constructed, and the potential impact of artificial intelligence applications on individual work patterns can be discovered. By setting up an incentive mechanism and grouping employees, the task completion results of employees in each employee group for completing work tasks are submitted in the work task completion situation statistics platform, and the actual impact of artificial intelligence tools under different user groups and incentive conditions can be comprehensively evaluated. It is easy to provide a decision-making basis for how enterprises can better combine artificial intelligence tools with human resources in the future. According to the usage frequency of the artificial intelligence tool platform and the task completion effects of employees in each employee group, the impact of the artificial intelligence tool platform and the incentive mechanism on enterprise work efficiency is analyzed, the driving effect of the incentive mechanism on the usage effect of artificial intelligence tools can be explored, and the potential for productivity improvement brought by the incentive mechanism is considered.

[0119] The present invention also provides a computer-readable storage medium, on which a computer program is stored, and the computer program can be loaded and executed by a processor to perform the analysis method of the impact of artificial intelligence on enterprise work efficiency described in the first aspect.

[0120] As described above, this is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

[0121] The following points need to be explained:

[0122] (1) The attached drawings of the embodiments of the present invention only relate to the structures involved in the embodiments of the present invention, and other structures can refer to the general design.

[0123] (2) For clarity, in the attached drawings used to describe the embodiments of the present invention, the thickness of the layer or region is enlarged or reduced, that is, these drawings are not drawn according to the actual scale. It can be understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element can be "directly" on or under the other element or there can be an intermediate element.

[0124] (3) Without conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other to obtain new embodiments.

[0125] As above, this is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for analyzing the impact of artificial intelligence on enterprise work efficiency, characterized in that: include: S1: Build an artificial intelligence tool platform based on a large language model; S2: Build a statistical platform for the completion of work tasks; S3: Construct work tasks based on employees’ daily work content and the use of the artificial intelligence tool platform; S4: Setting incentives; S5: Grouping employees according to the usage of the artificial intelligence tool platform and the incentive mechanism to obtain multiple employee groups; S6: Submitting the task completion results of the employees in each employee group who have completed the task through the task completion statistics platform; S7: Set up evaluation mechanism; S8: Evaluate the usage frequency of the artificial intelligence tool platform and the task completion effect of the employees in each employee group according to the evaluation mechanism and the task completion results of the employees in each employee group; S9: Analyze the impact of the artificial intelligence tool platform and the incentive mechanism on the work efficiency of the enterprise based on the frequency of use of the artificial intelligence tool platform and the task completion results of employees in each employee group.

2. The method for analyzing the impact of artificial intelligence on enterprise work efficiency according to claim 1 is characterized in that: The work tasks specifically include: a first work task, a second work task and a third work task.

3. The method for analyzing the impact of artificial intelligence on enterprise work efficiency according to claim 2, characterized in that: The first work task is specifically: when the employee is not allowed to use the artificial intelligence tool platform, the employee is required to complete a first writing task within a first preset time period, when the first writing task reaches a second preset time period, the first writing task is interrupted and the employee is required to perform a first calculation task, and when the employee completes the first calculation task and submits the calculation result, the first writing task is continued to be completed; The second work task is specifically: in the case where some employees are allowed to use the artificial intelligence tool platform, employees are required to complete a second writing task within a third preset time period; when the second writing task reaches a fourth preset time period, the second writing task is interrupted and the employee is required to perform a second calculation task; when the employee completes the second calculation task and submits the calculation result, the second writing task is continued; The third work task is specifically: while allowing some employees to use the artificial intelligence tool platform, employees are required to complete the third writing task within the fifth preset time period.

4. The method for analyzing the impact of artificial intelligence on enterprise work efficiency according to claim 1, characterized in that: The incentive mechanism specifically includes: writing task incentive and computing task incentive; The writing task incentive is specifically: based on the quality score of the writing task completed by the employee, when the quality score of the writing task completed by the employee is within the preset ranking of the employee group to which the employee belongs, the employee is rewarded according to the preset reward standard of the employee group to which the employee belongs; The computing task incentive is specifically as follows: in the first work task and the second work task, when the calculation result submitted by the employee is an incorrect answer, part of the employee's reward will be deducted according to a preset penalty mechanism.

5. The method for analyzing the impact of artificial intelligence on enterprise work efficiency according to claim 1 is characterized in that: The employee groups specifically include: a control group, an experimental group and an incentive group.

6. The method for analyzing the impact of artificial intelligence on enterprise work efficiency according to claim 5, characterized in that: The control group does not use the artificial intelligence tool platform in the first writing task, the second writing task, and the third writing task; The experimental group did not use the artificial intelligence tool platform in the first writing task, but used the artificial intelligence tool platform in the second writing task and the third writing task; The incentive group does not use the artificial intelligence tool platform in the first writing task, but uses the artificial intelligence tool platform in the second writing task and the third writing task.

7. The method for analyzing the impact of artificial intelligence on enterprise work efficiency according to claim 1, characterized in that: The evaluation mechanism specifically includes: writing task evaluation and computing task evaluation; The writing task evaluation specifically includes: performing quality scoring on the first writing task, the second writing task, and the third writing task respectively according to a preset scoring standard and a preset scoring range, and evaluating the task completion effect of the employee according to the quality scoring results of the first writing task, the second writing task, and the third writing task; The computing task evaluation specifically includes: recording and judging the correctness of the computing results submitted by the employees.

8. The method for analyzing the impact of artificial intelligence on enterprise work efficiency according to claim 1, characterized in that: The task completion effect specifically includes: task completion time, task quality score and task completion result.

9. The method for analyzing the impact of artificial intelligence on enterprise work efficiency according to claim 1, characterized in that: The S9 specifically includes: S901: Compare the quality scores and task completion time of the control group, the experimental group, and the incentive group in the first writing task, the second writing task, and the third writing task, evaluate the use of the artificial intelligence tool platform and the influence of the incentive mechanism on the quality and efficiency of the writing tasks, and obtain a first comparison result; S902: Compare the performance differences of employees in each employee group in the first writing task, the second writing task, and the third writing task, evaluate the impact of the use of the artificial intelligence tool platform on work efficiency, and obtain a second comparison result; S903: Analyze the correctness and completion time of employees in each employee group in the first computing task, the second computing task, and the third computing task, evaluate the use of the artificial intelligence tool platform and the influence of the incentive mechanism on the quality and efficiency of computing tasks, and obtain a third comparison result; S904: Analyze the impact of the artificial intelligence tool platform and the incentive mechanism on the work efficiency of the enterprise based on the first comparison result, the second comparison result, and the third comparison result.

10. An analysis system for the impact of artificial intelligence on enterprise work efficiency, characterized in that: include: memory and one or more processors; One or more applications are stored in the memory, and the one or more applications are suitable for being executed by the one or more processors to implement the analysis method of the impact of artificial intelligence on enterprise work efficiency as described in any one of claims 1 to 9.

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