Method and system for carrying out digital level evaluation by utilizing AI (Artificial Intelligence) technology

Abstract

The invention discloses a method and a system for carrying out digital level evaluation by utilizing an AI technology, and relates to the technical field of data evaluation and analysis. The method comprises the following steps: performing data acquisition by utilizing a questionnaire; performing index grade division on the survey items; converting the text information into a digital sequence; assigning scoring coefficients to the dimensions and the index grades; giving a deviation adjustment score value to the filling deviation of each investigation item; calculating a final score value according to the second score value and the deviation adjustment score value; constructing to obtain a digital level evaluation model; and obtaining the score of each dimension and the comprehensive score of the user in the aspect of the digitization level by using the digitization level evaluation model. According to the method, the corresponding scoring coefficients are given to the index grades and dimensions respectively, the corresponding score values are given to the text information content, the deviation adjustment score values are given to the filling deviation, the digital level is evaluated from different angles and different emphasis points, and the evaluation result is more reasonable and accurate.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the technical field of data evaluation and analysis, and in particular, to a method, system, electronic device, and storage medium for performing digital level evaluation using AI (Artificial Intelligence) technology. Background Art

[0002] With the development of information digitization, more and more industries have begun to use digital systems to plan and analyze the internal operations and business conditions of enterprises. However, due to the large differences in the level of digital application among various industries or enterprises, users need to analyze their own digital level to evaluate whether they have a high level of digitalization and in which areas they need to be further improved.

[0003] In the related art, users are usually required to fill out a questionnaire about their digitalization level first, and then the results of the questionnaire are manually analyzed and interpreted.

[0004] Since the above methods are manual analysis and interpretation, the evaluation takes a long time, involves a lot of manpower and material resources, and is costly. In addition, the manual evaluation process often focuses on qualitative description rather than quantitative scoring, lacks consistency and comparability, and is also affected by subjective judgment, resulting in low accuracy of the evaluation results.

[0005] Therefore, it is necessary to improve one or more problems existing in the above-mentioned related technical solutions.

[0006] It should be noted that this section is intended to provide background or context for the technical solutions of the present disclosure stated in the claims. The description herein is not admitted to be prior art by virtue of being included in this section. Summary of the invention

[0007] The purpose of the embodiments of the present disclosure is to provide a method and system for digital level assessment using AI technology, thereby overcoming one or more problems caused by the limitations and defects of related technologies, at least to a certain extent.

[0008] According to a first aspect of an embodiment of the present disclosure, a method for evaluating a digitalization level using AI technology is provided, comprising:

[0009] Collecting data on the user's digital level using a questionnaire, wherein the questionnaire includes at least the following dimensions: digital infrastructure, data governance, digital skills, and digital culture, and each dimension includes multiple survey items;

[0010] Classify all survey items in the questionnaire into index levels;

[0011] Converting the text information filled in the questionnaire into a corresponding digital sequence, wherein the digital sequence includes a plurality of glyphs, and the numbers on each glyph respectively correspond to the dimension, survey item, indicator level and content of the text information;

[0012] Assigning corresponding scoring coefficients to the dimension and the indicator level respectively, converting the content of the text information into a first score value, and calculating a second score value of the text information according to the scoring coefficient and the first score value;

[0013] Assigning a deviation adjustment score value according to the filling deviation of each survey item by the user;

[0014] Calculate a final score value according to the second score value and the deviation adjustment score value;

[0015] Using a machine learning model or a deep learning model to learn and train the dimensions, survey items, indicator levels, text information, scoring coefficients, first score values, deviation adjustment score values, and final score values, and construct a digital level assessment model;

[0016] The digital sequence is input into the digital level evaluation model to obtain the scores of each dimension and the comprehensive score of the user in terms of the digital level, thereby performing classification evaluation and comprehensive evaluation on the digital level of the user.

[0017] In one embodiment of the present disclosure, after the step of collecting data on the user's digitalization level using a questionnaire, the following step is further performed:

[0018] The collected data is cleaned, and the cleaning process at least includes: removing invalid answers, filling missing values ​​and data type conversion.

[0019] In one embodiment of the present disclosure, the step of classifying all survey items in the questionnaire into index levels includes:

[0020] The survey items in the questionnaire are divided into primary indicators, secondary indicators and tertiary indicators, wherein the primary indicators, secondary indicators and tertiary indicators are of decreasing importance for the digital level assessment.

[0021] In one embodiment of the present disclosure, the step of assigning a deviation adjustment score value to the filling deviation of each survey item by the user includes:

[0022] Compare the historical questionnaire filling results of the user with the actual digital level of the user to find out the survey items with deviations in the questionnaire filling of the user;

[0023] A deviation adjustment score value is assigned to the survey item with deviation, wherein the deviation adjustment score value includes a positive score value and a negative score value.

[0024] In one embodiment of the present disclosure, the method further includes:

[0025] The digital level assessment model is optimized using cross-validation and hyperparameter tuning methods;

[0026] The digital level assessment model is regularly updated and optimized.

[0027] In one embodiment of the present disclosure, the method further includes:

[0028] Formulate corresponding adjustment strategies for the user according to the classification evaluation results and the comprehensive evaluation results of the user;

[0029] The adjustment strategy is pushed to the user, and the adjustment strategy is updated according to the evaluation result of the user.

[0030] According to a second aspect of an embodiment of the present disclosure, a system for evaluating a digitalization level using AI technology is provided, the system comprising:

[0031] A data collection module, used to collect data on the user's digital level using a questionnaire, wherein the questionnaire includes at least the following dimensions: digital infrastructure, data governance, digital skills, and digital culture, and each dimension includes multiple survey items;

[0032] An indicator grading module is used to grade all survey items in the questionnaire;

[0033] A text information conversion module, used to convert the text information filled in the questionnaire into a corresponding digital sequence, wherein the digital sequence includes a plurality of glyphs, and the numbers on each glyph respectively correspond to the dimension, survey item, indicator level and content of the text information;

[0034] A coefficient and score calculation module, used to assign corresponding scoring coefficients to the dimension and the indicator level, convert the content of the text information into a first score value, and calculate a second score value of the text information according to the scoring coefficient and the first score value;

[0035] A deviation assigning module, used for assigning a deviation adjustment score value according to the filling deviation of each survey item by the user;

[0036] a final score value calculation module, configured to calculate a final score value according to the second score value and the deviation adjustment score value;

[0037] A model building module, used to learn and train the dimensions, survey items, indicator levels, text information, scoring coefficients, first score values, deviation adjustment score values, and final score values ​​using a machine learning model or a deep learning model, and to build a digital level assessment model;

[0038] The evaluation module is used to input the digital sequence into the digital level evaluation model to obtain the scores of each dimension and the comprehensive score of the user in terms of the digital level, so as to perform classification evaluation and comprehensive evaluation on the digital level of the user.

[0039] According to a third aspect of an embodiment of the present disclosure, there is provided an electronic device, including:

[0040] Processor; and

[0041] A memory, configured to store executable instructions of the processor;

[0042] The processor is configured to execute the steps of the method for evaluating the digitalization level using AI technology in any of the above embodiments by executing the executable instructions.

[0043] According to a fourth aspect of an embodiment of the present disclosure, a computer storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the steps of the method for evaluating the digitalization level using AI technology described in any of the above embodiments are implemented.

[0044] According to a fifth aspect of an embodiment of the present disclosure, a computer program product is provided, including a computer program or instructions. When the computer program or instructions are executed by a processor, the steps of the method for evaluating the digitalization level using AI technology described in any one of the above embodiments are implemented.

[0045] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects:

[0046] The method and system for evaluating the digitalization level using AI technology in the embodiments of the present disclosure accelerate the data analysis process through AI technology and reduce the cost required for manual evaluation; reduce subjective bias through machine learning models or deep learning models and provide more objective and more accurate evaluation results; assign corresponding scoring coefficients to indicator levels and dimensions, assign corresponding score values ​​to text information content, and assign deviation adjustment score values ​​to user filling deviations, so as to classify and comprehensively evaluate the user's digitalization level, and evaluate the user's digitalization level from different angles and with different focuses, and the evaluation results are more reasonable and accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0048] Figure 1 A flow chart showing a method for evaluating a digitalization level using AI technology in an exemplary embodiment of the present disclosure;

[0049] Figure 2 A schematic structural diagram of an electronic device in an exemplary embodiment of the present disclosure is shown;

[0050] Figure 3 A schematic diagram showing the structure of a program product for implementing a method for evaluating a digitalization level using AI technology in an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0051] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the disclosure will be more comprehensive and complete and to fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0052] In addition, the accompanying drawings are only schematic illustrations of the embodiments of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated descriptions will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities.

[0053] This example implementation first provides a method for evaluating the digital level using AI technology. Figure 1 , may include: Step S101-Step S108. Specifically as follows:

[0054] Step S101, collecting data on the user's digital level using a questionnaire, wherein the questionnaire includes at least the following dimensions: digital infrastructure, data governance, digital skills, and digital culture, and each dimension includes multiple survey items;

[0055] Step S102, classifying all survey items in the questionnaire into index levels;

[0056] Step S103, converting the text information filled in the questionnaire into a corresponding digital sequence, wherein the digital sequence includes a plurality of glyphs, and the numbers on each glyph respectively correspond to the dimension, survey item, indicator level and content of the text information;

[0057] Step S104, assigning corresponding scoring coefficients to the dimensions and the indicator levels, converting the content of the text information into a first score value, and calculating a second score value of the text information according to the scoring coefficient and the first score value;

[0058] Step S105, assigning a deviation adjustment score value according to the filling deviation of each survey item by the user;

[0059] Step S106, calculating a final score value according to the second score value and the deviation adjustment score value;

[0060] Step S107, using a machine learning model or a deep learning model to learn and train the dimensions, survey items, indicator levels, text information, scoring coefficients, first score values, deviation adjustment score values, and final score values, and construct a digital level assessment model;

[0061] Step S108, inputting the digital sequence into the digital level evaluation model to obtain the scores of each dimension and the comprehensive score of the user in terms of the digital level, thereby performing classification evaluation and comprehensive evaluation on the digital level of the user.

[0062] In this embodiment, AI technology is used to accelerate the data analysis process and reduce the cost required for manual evaluation. Machine learning models or deep learning models are used to reduce subjective bias and provide more objective and accurate evaluation results. By assigning corresponding scoring coefficients to indicator levels and dimensions, assigning corresponding score values ​​to text information content, and assigning deviation adjustment score values ​​to user data deviations, the user's digital level can be classified and comprehensively evaluated, and the user's digital level can be evaluated from different angles and with different focuses, and the evaluation results are more reasonable and accurate.

[0063] The specific process of the above method of the present application is described in more detail below.

[0064] In step S101, the questionnaire includes dimensions such as digital infrastructure, data governance, digital skills and digital culture, which can cover various scenarios of users in digital applications and can be analyzed and evaluated more comprehensively. The questionnaire can be collected in the form of online or paper questionnaires. Each dimension includes multiple survey items. For example, digital culture includes survey items such as sales digital culture and production digital culture.

[0065] Based on the above steps, the collected data can also be cleaned. The cleaning process can include: removing invalid answers, filling missing values, and data type conversion to ensure data quality. Removing invalid answers and filling missing values ​​can also improve the fluency of subsequent data input. The text information content under the survey items corresponding to these invalid answers or missing values ​​can also be assigned 0 as its score value. The data in the questionnaire can be text, options or scores. The above multiple types of data can be uniformly converted, such as converted to numerical values, for subsequent processing.

[0066] In step S102, all survey items in the questionnaire are divided into indicator levels. For example, it is possible to distinguish between more important survey items (i.e., items that contribute more to the digital level assessment) and less important items, so that each dimension can be evaluated later. Specifically, when dividing the indicator levels, it can be divided into primary indicators, secondary indicators, and tertiary indicators, wherein the primary indicators, secondary indicators, and tertiary indicators are of decreasing importance to the digital level assessment.

[0067] In step S103, the text information filled in the questionnaire is converted into a corresponding digital sequence.

[0068] Specifically, word embedding or TF-IDF (Term Frequency-Inverse Document Frequency) encoding can be used to convert text information into numerical information, but it is not limited to this. Converting text information into numerical information can simplify the subsequent data input process and improve evaluation efficiency. The numerical information is then converted into a corresponding digital sequence, wherein the digital sequence includes multiple character positions, and the numbers on each character position correspond to the dimension, survey item, indicator level, and content of the text information. For example, the number on the first character position indicates the dimension where the text information is located, the number on the second character position indicates the survey item where the text information is located, the number on the third character position indicates its indicator level, and the number on the fourth character position indicates the content of the text information.

[0069] In step S104, the dimension and the indicator level are assigned corresponding scoring coefficients, the content of the text information is converted into a first score value, and a second score value of the text information is calculated according to the scoring coefficient and the first score value. For example, the scoring coefficient and the first score value may be multiplied to obtain the second score value.

[0070] In this process, the second score value is obtained by combining the first score value of the comprehensive dimension, the indicator level corresponding to the survey item and the content of the text information itself, making the obtained score value more reasonable.

[0071] In step S105, a deviation adjustment score is assigned to the filling deviation of each survey item by the user. When filling in the questionnaire, due to the user's own understanding or the questionnaire design, the filling result may be inaccurate and there may be a certain difference from the actual level of the user. Therefore, in view of this defect, the difference is assigned a deviation adjustment score to eliminate this defect. By assigning a deviation adjustment score to the filling deviation, the user deviation that is often ignored can be measured, and the error of the questionnaire filling result can be eliminated to the greatest extent, making the evaluation result more objective and consistent.

[0072] In step S106, a final score is calculated based on the second score and the deviation adjustment score. The accuracy and rationality of the final score obtained at this time are greatly improved, providing more reliable data for the user's level assessment. For example, the second score and the deviation adjustment score are added to obtain the final score.

[0073] In step S107, by learning and training the historical values ​​of multiple data information, a sufficient and detailed data basis is provided for the evaluation. In this embodiment, a decision tree, support vector machine, neural network, etc. can be used to learn and train the above contents based on historical data to obtain the digital level evaluation model.

[0074] Through step S108, the level evaluation results in each dimension can be obtained, which can be used by the user to analyze the digitalization level in various aspects.

[0075] Based on the above embodiment, the step of assigning a deviation adjustment score value to the filling deviation of each survey item by the user includes:

[0076] Compare the historical questionnaire filling results of the user with the actual digital level of the user to find out the survey items with deviations in the questionnaire filling of the user;

[0077] A deviation adjustment score value is assigned to the survey item with deviation, wherein the deviation adjustment score value includes a positive score value and a negative score value.

[0078] In this embodiment, the user's filling deviation is the filling deviation caused by the difference in user's personal feelings or sensitivity. The deviation of each survey item can be obtained by comparing with the user's actual situation, or by conducting a comprehensive analysis of multiple questionnaires of the user (for example, filled in by different people), for example, taking the average value of multiple questionnaires as the actual value, and then calculating the deviation. When the filled-in value is lower than the average value, a positive value is given to the deviation adjustment score value corresponding to the filled-in result, otherwise a negative value is given. Taking the deviation into consideration, the evaluation result can be made more accurate and objective.

[0079] It should be noted that, based on the above embodiments, the model can also be optimized by the following methods:

[0080] The digital level assessment model is optimized using cross-validation and hyperparameter tuning methods;

[0081] The digital level assessment model is regularly updated and optimized.

[0082] In this embodiment, by updating and optimizing the model, the prediction accuracy is continuously improved, making the model more complete and reasonable.

[0083] It should be noted that, based on the above embodiment, the method further includes:

[0084] Formulate corresponding adjustment strategies for the user according to the classification evaluation results and the comprehensive evaluation results of the user;

[0085] The adjustment strategy is pushed to the user, and the adjustment strategy is updated according to the evaluation result of the user.

[0086] In this embodiment, the classification evaluation results and comprehensive evaluation results of users can be divided into primary, intermediate, advanced and other digital levels, or presented in the form of scores (0-100). Users can accurately know their own digital level, find their own shortcomings from various dimensions and comprehensive perspectives, formulate corresponding adjustment strategies for the shortcomings based on big data, and push the strategy to customers, so that customers can improve their own digital level more conveniently and accurately. For example, personalized feedback and improvement suggestions can be provided based on the industry characteristics of each user and the scenarios in which the digital level is applied, helping customers understand their own strengths and weaknesses while continuously making targeted improvements. The model is updated regularly to reflect new technologies and market trends to ensure the timeliness and accuracy of the evaluation. Participants can also be encouraged to refill the questionnaire regularly through data iteration to monitor changes in the digital level and continuously optimize the model based on new data.

[0087] In this example implementation, a system for evaluating a digitalization level using AI technology is provided, the system comprising:

[0088] A data collection module, used to collect data on the user's digital level using a questionnaire, wherein the questionnaire includes at least the following dimensions: digital infrastructure, data governance, digital skills, and digital culture, and each dimension includes multiple survey items;

[0089] An indicator grading module is used to grade all survey items in the questionnaire;

[0090] A text information conversion module, used to convert the text information filled in the questionnaire into a corresponding digital sequence, wherein the digital sequence includes a plurality of glyphs, and the numbers on each glyph respectively correspond to the dimension, survey item, indicator level and content of the text information;

[0091] A coefficient and score calculation module, used to assign corresponding scoring coefficients to the dimension and the indicator level, convert the content of the text information into a first score value, and calculate a second score value of the text information according to the scoring coefficient and the first score value;

[0092] A deviation assigning module, used for assigning a deviation adjustment score value according to the filling deviation of each survey item by the user;

[0093] a final score value calculation module, configured to calculate a final score value according to the second score value and the deviation adjustment score value;

[0094] A model building module, used to learn and train the dimensions, survey items, indicator levels, text information, scoring coefficients, first score values, deviation adjustment score values, and final score values ​​using a machine learning model or a deep learning model, and to build a digital level assessment model;

[0095] The evaluation module is used to input the digital sequence into the digital level evaluation model to obtain the scores of each dimension and the comprehensive score of the user in terms of the digital level, so as to perform classification evaluation and comprehensive evaluation on the digital level of the user.

[0096] The beneficial effects of the present system are the same as those of the aforementioned method and will not be repeated here.

[0097] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0098] It should be noted that although several modules of the system for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to an embodiment of the present invention, the features and functions of two or more modules described above can be concretized in one module. Conversely, the features and functions of a module described above can be further divided into multiple modules for concretization. The components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of the present invention. Those of ordinary skill in the art can understand and implement it without paying creative work.

[0099] See also Figure 2 The embodiment of the present invention further provides an electronic device 300, which includes at least one memory 310, at least one processor 320, and a bus 330 for connecting different platform systems.

[0100] The memory 310 may include a readable medium in the form of a volatile memory, such as a random access memory (RAM) 311 and / or a cache memory 312 , and may further include a read-only memory (ROM) 313 .

[0101] Among them, the memory 310 also stores a computer program, which can be executed by the processor 320, so that the processor 320 executes the steps of the method for digital level assessment using AI technology in any embodiment of the present invention. The specific implementation method is consistent with the implementation method and the technical effect recorded in the above-mentioned embodiment of the method for digital level assessment using AI technology, and some contents will not be repeated here.

[0102] The memory 310 may also include a utility 314 having at least one program module 315, such program module 315 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0103] Accordingly, the processor 320 may execute the above-mentioned computer program, and may execute the utility 314 .

[0104] Bus 330 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures.

[0105] The electronic device 300 may also communicate with one or more external devices 340 such as keyboards, pointing devices, Bluetooth devices, etc., and may also communicate with one or more devices that can interact with the electronic device 300, and / or communicate with any device (such as a router, a modem, etc.) that enables the electronic device 300 to communicate with one or more other computing devices. Such communication may be performed through an input / output interface 350. In addition, the electronic device 300 may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 360. The network adapter 360 may communicate with other modules of the electronic device 300 through a bus 330. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 300, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0106] An embodiment of the present invention also provides a computer-readable storage medium, which is used to store a computer program. When the computer program is executed, it implements the steps of the method for using AI technology to evaluate the digitalization level in the embodiment of the present invention. The specific implementation method is consistent with the implementation method and the technical effect recorded in the above-mentioned embodiment of the method for using AI technology to evaluate the digitalization level, and some contents will not be repeated here.

[0107] Figure 3 The program product 400 provided in this embodiment for implementing the above-mentioned method of using AI technology for digital level assessment is shown, which can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product 400 of the present invention is not limited to this. In the present invention, the readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it. The program product 400 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 be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, device or device, or any combination of the above. More specific examples of readable storage media (non-exhaustive list) 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 of the above.

[0108] Computer-readable storage media may include data signals propagated in baseband or as part of a carrier wave, wherein readable program codes are carried. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium may also be any readable medium, which may send, propagate, or transmit a program for use by an instruction execution system, an apparatus, or a device or for use in combination with it. The program code contained on the readable storage medium may be transmitted by any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above. The program code for performing the operation of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and also conventional procedural programming languages ​​such as C language or similar programming languages. The program code may be executed entirely on a user computing device, partially on a user device, as an independent software package, partially on a user computing device, partially on a remote computing device, or entirely on a remote computing device or server. Where a remote computing device is involved, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0109] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A method for evaluating digitalization level using AI technology, characterized in that: include: Collecting data on the user's digital level using a questionnaire, wherein the questionnaire includes at least the following dimensions: digital infrastructure, data governance, digital skills, and digital culture, and each dimension includes multiple survey items; Classify all survey items in the questionnaire into index levels; Converting the text information filled in the questionnaire into a corresponding digital sequence, wherein the digital sequence includes a plurality of glyphs, and the numbers on each glyph respectively correspond to the dimension, survey item, indicator level and content of the text information; Assigning corresponding scoring coefficients to the dimension and the indicator level respectively, converting the content of the text information into a first score value, and calculating a second score value of the text information according to the scoring coefficient and the first score value; Assigning a deviation adjustment score value according to the filling deviation of each survey item by the user; Calculate a final score value according to the second score value and the deviation adjustment score value; Using a machine learning model or a deep learning model to learn and train the historical values ​​of the dimensions, survey items, indicator levels, text information, scoring coefficients, first score values, deviation adjustment score values, and final score values, and construct a digital level assessment model; The digital sequence is input into the digital level evaluation model to obtain the scores of each dimension and the comprehensive score of the user in terms of the digital level, thereby performing classification evaluation and comprehensive evaluation on the digital level of the user.

2. The method for evaluating digitalization level using AI technology according to claim 1, characterized in that: After the step of collecting data on the digitalization level of users by using a questionnaire, the following steps are further included: The collected data is cleaned. The cleaning process at least includes: removing invalid answers, filling missing values ​​and converting data types.

3. The method for evaluating digitalization level using AI technology according to claim 1, characterized in that: The step of classifying all the survey items in the questionnaire into index levels comprises: The survey items in the questionnaire are divided into primary indicators, secondary indicators and tertiary indicators, wherein the primary indicators, secondary indicators and tertiary indicators are of decreasing importance for the digital level assessment.

4. The method for evaluating digitalization level using AI technology according to claim 1, characterized in that: The step of assigning a deviation adjustment score value to the filling deviation of each survey item by the user comprises: Compare the historical questionnaire filling results of the user with the actual digital level of the user to find out the survey items with deviations in the questionnaire filling of the user; A deviation adjustment score value is assigned to the survey item with deviation, wherein the deviation adjustment score value includes a positive score value and a negative score value.

5. The method for evaluating digitalization level using AI technology according to claim 1, characterized in that: The method further comprises: The digital level assessment model is optimized using cross-validation and hyperparameter tuning methods; The digital level assessment model is regularly updated and optimized.

6. The method for evaluating digitalization level using AI technology according to claim 1, characterized in that: The method further comprises: Formulate corresponding adjustment strategies for the user according to the classification evaluation results and the comprehensive evaluation results of the user; The adjustment strategy is pushed to the user, and the adjustment strategy is updated according to the evaluation result of the user.

7. A system for evaluating digitalization level using AI technology, characterized in that: The system comprises: A data collection module, used to collect data on the user's digital level using a questionnaire, wherein the questionnaire includes at least the following dimensions: digital infrastructure, data governance, digital skills, and digital culture, and each dimension includes multiple survey items; An indicator grading module is used to grade all survey items in the questionnaire; A text information conversion module, used to convert the text information filled in the questionnaire into a corresponding digital sequence, wherein the digital sequence includes a plurality of glyphs, and the numbers on each glyph respectively correspond to the dimension, survey item, indicator level and content of the text information; A coefficient and score calculation module, used to assign corresponding scoring coefficients to the dimension and the indicator level, convert the content of the text information into a first score value, and calculate a second score value of the text information according to the scoring coefficient and the first score value; A deviation assigning module, used for assigning a deviation adjustment score value according to the filling deviation of each survey item by the user; a final score value calculation module, configured to calculate a final score value according to the second score value and the deviation adjustment score value; A model building module, used to learn and train the dimensions, survey items, indicator levels, text information, scoring coefficients, first score values, deviation adjustment score values, and final score values ​​using a machine learning model or a deep learning model, and to build a digital level assessment model; The evaluation module is used to input the digital sequence into the digital level evaluation model to obtain the scores of each dimension and the comprehensive score of the user in terms of the digital level, so as to perform classification evaluation and comprehensive evaluation on the digital level of the user.

8. An electronic device, characterized in that: include: processor; as well as A memory, configured to store executable instructions of the processor; Wherein, the processor is configured to execute the steps of the method for evaluating the digitalization level using AI technology as described in any one of claims 1 to 6 by executing the executable instructions.

9. A computer storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method for evaluating the digitalization level using AI technology as described in any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps of the method for evaluating the digitalization level using AI technology described in any one of claims 1 to 6 are implemented.

Images