Enterprise digital management maturity evaluation method and system based on artificial intelligence

Abstract

The invention relates to the technical field of digital evaluation, and particularly discloses an enterprise digital management maturity evaluation method and system based on artificial intelligence, and the method comprises the steps: receiving a working scene fed back by a digital device, determining the working content containing a time label according to the working scene, and carrying out the evaluation of the maturity of the enterprise digital management. Synchronously determining the digitization difficulty containing the time label; querying a theoretical data volume of the work content, and calculating a standard data volume containing a time label according to the digitization difficulty and the theoretical data volume; the actual data volume is evaluated according to the standard data volume containing the time label, and the maturity is determined; according to the method, the environment condition is determined by the camera on each piece of digital equipment, then one data standard is determined, the locally stored data of the digital equipment is evaluated according to the data standard, the maturity of the digital equipment is determined, the maturity of all the digital equipment is counted, and the final enterprise maturity is obtained. The process does not need manual parameters, and the objectivity is extremely high.

Description

Technical Field

[0001] The present invention relates to the technical field of digital evaluation, and specifically, to an artificial intelligence-based method and system for evaluating the maturity of enterprise digital management. Background Art

[0002] With the development of computer devices, many enterprises have gradually transformed into digital enterprises, and digital enterprises are also considered to be more standard and efficient. The existing methods for evaluating whether an enterprise is a digital enterprise are mostly manual evaluation processes. Some standards are set artificially, and interviews and other forms are used to obtain different indicators. The obtained indicators are discussed in meetings, and finally the evaluation results are obtained. This method is obviously too subjective. How to provide a more objective evaluation scheme is the technical problem that the technical solution of the present invention wants to solve. Summary of the Invention

[0003] The purpose of the present invention is to provide an artificial intelligence-based method and system for evaluating the maturity of enterprise digital management to solve the problems raised in the above background art.

[0004] To achieve the above purpose, the present invention provides the following technical solutions: An artificial intelligence-based method for evaluating the maturity of enterprise digital management, the method comprising: Obtaining the location of a digital device according to a locator in the digital device, generating an activation request according to the location, and sending the activation request to the digital device; Receiving a work scenario fed back by the digital device, determining work content with time tags according to the work scenario, and synchronously determining digital difficulty with time tags; the digital difficulty is used to characterize the difficulty of digital traceability of a certain work; Querying the theoretical data volume of the work content, and calculating the standard data volume with time tags according to the digital difficulty and the theoretical data volume; Obtaining the actual data volume with time tags of each digital device within a preset time period, evaluating the actual data volume according to the standard data volume with time tags, and determining the maturity; Wherein, the process of determining the work content with time tags according to the work scenario includes applying an AI module.

[0005] As a further solution of the present invention: the step of obtaining the location of the digital device according to the locator in the digital device, generating an activation request according to the location, and sending the activation request to the digital device includes: Receiving an operation signal uploaded by the digital device, and establishing a transmission channel with the digital device; Obtain the location of the digital device according to the locator in the digital device, and update the location representation map according to the location; wherein, the update process is to increment a preset value at the location, and the value at each location decreases based on a preset rate; Determine the activation frequency at each location in real time according to the location representation map; Query the activation frequency at the location at the latest moment, determine the activation period, generate an activation request starting from the latest moment after the activation period, and send it to the digital device; Among them, when determining the activation frequency at each location, determine the location acquisition frequency at each location in real time according to the location representation map.

[0006] As a further solution of the present invention: the steps of receiving the working scenario fed back by the digital device, determining the working content with time tags according to the working scenario, and synchronously determining the digital difficulty with time tags include: Receive the recorded video fed back by the digital device, and convert the recorded video into a group of images; wherein, when the digital device records the video, a pre-set permission acquisition process facing the user is provided; Read the scene features in the scene feature library, traverse the group of images according to the scene features, and extract the number of times the similarity reaches the preset threshold; the scene feature library includes scene feature items and corresponding working scenario items; Determine the matching degree of each scene feature according to the number of times; Count the scene features of each working scenario, accumulate all the matching degrees, and obtain the total matching degree; Determine the working scenario according to the total matching degree, query the working content corresponding to the working scenario, and synchronously query the digital difficulty of the working content; Query the time period of the recorded video, and use it as the time tag for the working content and its digital difficulty.

[0007] As a further solution of the present invention: the steps of receiving the working scenario fed back by the digital device, determining the working content with time tags according to the working scenario, and synchronously determining the digital difficulty with time tags further include: When the number of times the similarity corresponding to all scene features in the scene feature library reaches the preset threshold is less than the preset number threshold, activate the AI module; Based on the AI module, identify the recorded video to determine the working scenario; Extract the scene features in the recorded video, obtain the working scenario determined by the AI module and the extracted scene features, and insert them into the scene feature library as new data items.

[0008] As a further solution of the present invention: The steps of calculating the standard data volume with time tags according to the digitalization difficulty and the theoretical data volume of the query work content include: The theoretical data volume of the query work content; Calculate the ratio of the digitalization difficulty to the preset minimum difficulty, and determine the correction coefficient according to the inverse ratio of the ratio; Calculate the product of the correction coefficient and the theoretical data volume to obtain the standard data volume; Read the time tag of the work content as the time tag of the standard data volume.

[0009] As a further solution of the present invention: The steps of obtaining the actual data volume with time tags of each digital device within a preset time period, evaluating the actual data volume according to the standard data volume with time tags, and determining the maturity include: Obtain the work records of the digital device; Determine the actual data volume of each time period according to the work record; Query the standard data volume with time tags, expand the standard data volume with time tags to obtain the actual data volume of each time period; Fit the actual data volume of each time period and the actual data volume of each time period into functions, calculate the integral difference between the two functions, and determine the maturity of the digital device according to the integral difference; Statistically analyze the maturity of all digital devices, calculate the average maturity, and use it as the maturity of the enterprise.

[0010] The technical solution of the present invention also provides an enterprise digital management maturity evaluation system based on artificial intelligence. The system includes: An activation request generation module, configured to obtain the location of the digital device according to the locator in the digital device, generate an activation request according to the location, and send it to the digital device; A work scenario recognition module, configured to receive the work scenario fed back by the digital device, determine the work content with time tags according to the work scenario, and synchronously determine the digitalization difficulty with time tags; the digitalization difficulty is used to characterize the difficulty of digital traceability of a certain work; A standard determination module, configured to query the theoretical data volume of the work content, and calculate the standard data volume with time tags according to the digitalization difficulty and the theoretical data volume; An actual determination module, configured to obtain the actual data volume with time tags of each digital device within a preset time period, evaluate the actual data volume according to the standard data volume with time tags, and determine the maturity; Among them, the process of determining the work content with time tags according to the work scenario includes applying the AI module.

[0011] As a further solution of the present invention: the activation request generation module includes: A channel establishment unit, configured to receive the operation signal uploaded by the digital device and establish a transmission channel with the digital device; A position map generation unit, configured to obtain the position of the digital device according to the locator in the digital device and update the position representation map according to the position; wherein, the update process is to increment a preset value at the position, and the value at each position decreases based on a preset rate; An activation frequency determination unit, configured to determine the activation frequency at each position in real time according to the position representation map; A request sending unit, configured to query the activation frequency at the position at the latest moment, determine the activation period, generate an activation request starting from the latest moment after passing through the activation period, and send it to the digital device; Wherein, when determining the activation frequency at each position, the position acquisition frequency at each position is determined in real time according to the position representation map.

[0012] As a further solution of the present invention: the working scenario recognition module includes: A video conversion unit, configured to receive the recorded video fed back by the digital device and convert the recorded video into an image group; wherein, when the digital device records the video, a pre-set permission acquisition process facing the user is provided; A frequency generation unit, configured to read the scenario features in the scenario feature library, traverse the image group according to the scenario features, and extract the number of times the similarity reaches a preset threshold; the scenario feature library includes scenario feature items and corresponding working scenario items; A matching degree determination unit, configured to determine the matching degree of each scenario feature according to the number of times; A matching degree accumulation unit, configured to count the scenario features of each working scenario, accumulate all the matching degrees, and obtain the total matching degree; A data query unit, configured to determine the working scenario according to the total matching degree, query the working content corresponding to the working scenario, and synchronously query the digitalization difficulty of the working content; A label insertion unit, configured to query the time period of the recorded video as the time label of the working content and its digitalization difficulty.

[0013] As a further solution of the present invention: the standard determination module includes: A theoretical data query unit, configured to query the theoretical data volume of the working content; A correction coefficient calculation unit, configured to calculate the ratio of the digitalization difficulty to the preset minimum difficulty, and determine the correction coefficient according to the inverse ratio of the ratio; A product calculation unit for calculating the product of a correction coefficient and a theoretical data volume to obtain a standard data volume; A label reading unit for reading the time label of the work content as the time label of the standard data volume.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: In the present invention, the positioner and camera on each digital device are used to determine the environmental conditions, and then a data standard is determined. According to the data standard, the data stored locally on the digital device is evaluated to determine the maturity of the digital device. The maturities of all digital devices are statistically analyzed to obtain the final enterprise maturity. This process does not require manual parameters and is highly objective. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0016] Figure 1 It is a flowchart of a method for evaluating the maturity of enterprise digital management based on artificial intelligence.

[0017] Figure 2 It is a block diagram of the composition structure of a system for evaluating the maturity of enterprise digital management based on artificial intelligence. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention clearer, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0019] Figure 1 It is a flowchart of a method for evaluating the maturity of enterprise digital management based on artificial intelligence. In an embodiment of the present invention, a method for evaluating the maturity of enterprise digital management based on artificial intelligence includes: Step S100: Obtain the position of the digital device according to the positioner in the digital device, generate an activation request based on the position, and send it to the digital device; The technical solution of the present invention is applied to a digital enterprise. The staff are basically equipped with digital devices, some of which are computers and some are portable devices. Positioners are built into these digital devices. The position of the digital device is obtained according to the positioner in the digital device. Based on the change of the position, the type of the device and the change of the working scenario can be determined, and then some activation requests are generated at intervals and sent to the digital device.

[0020] Step S200: receiving a work scenario fed back by a digitizing device, determining a work content with a time tag according to the work scenario, and simultaneously determining a digitizing difficulty with the time tag; The activation request is used to activate the digital device to obtain the work scene. After the digital device obtains the work scene, it feeds back to the platform. The platform identifies the work scene, obtains the work content, and determines the digitization difficulty of the work content. The digitization difficulty is used to characterize the difficulty of leaving a digital trace of a certain work; the process of identifying the work scene can be assisted by the AI module, that is, the process of determining the work content with a time tag based on the work scene includes applying the AI module. The AI module has extremely strong recognition ability, a wide recognition range, and high recognition accuracy.

[0021] Step S300: querying the theoretical data volume of the work content, and calculating the standard data volume including the time tag according to the digitization difficulty and the theoretical data volume; The theoretical data volume of each work content is known and can be obtained in advance by statistics. The actual data volume can be evaluated by combining the digitization difficulty and the theoretical data volume. The evaluation method is to adjust the theoretical data volume according to the digitization difficulty. The theoretical data volume is essentially a standard. The greater the digitization difficulty, the smaller the standard. Since the work scene obtained in step S200 is the work scene for each period of time, the obtained standard data volume also has a time tag, and the time tag is the time period corresponding to the work scene.

[0022] Step S400: obtaining the actual amount of data with time tags from each digital device within a preset time period, evaluating the actual amount of data based on the standard amount of data with time tags, and determining maturity; When each digital device is running, it will actually generate local logs and record data. This data is the actual data at each moment, and the actual data volume at each moment can be calculated. Generally, the log period is daily, and the actual data volume with time tags of each digital device within the preset time period is obtained. It is determined whether the actual data volume with time tags reaches the preset standard data volume, thereby determining the maturity of the digital device. By counting the maturity of all digital devices, the maturity of the enterprise can be obtained.

[0023] Regarding step S100, the steps of obtaining the location of the digital device according to the locator in the digital device, generating an activation request according to the location, and sending the activation request to the digital device include: Receive the operation signal uploaded by the digital device and establish a transmission channel with the digital device; Obtain the location of the digital device according to the locator in the digital device, and update the location representation map according to the location; wherein, the update process is to increment a preset value at the location, and the value at each location decreases based on a preset rate; Determine the activation frequency at each location in real time according to the location representation map; Query the activation frequency at the location at the latest moment, determine the activation period, and generate an activation request after the activation period starting from the latest moment, and send it to the digital device.

[0024] When each digital device starts up, a boot-up program will be preset to upload an operation signal. For the platform, after receiving the operation signal uploaded by the digital device, a transmission channel with the digital device will be established. Obtain the location of the digital device according to the locator in the digital device, and update the location representation map according to the location. The meaning of the location representation map is as follows: After obtaining the location, mark the corresponding point in the map, increment the value of the point by 1, and repeat this process continuously. The values of each point will increase continuously, and finally a map reflecting the historical locations of the digital device is obtained, which is called the location representation map. The values at each location represent the residence duration of the digital device at the corresponding location.

[0025] According to the values at each location in the location representation map, the activation frequency can be determined. In an example of the technical solution of the present invention, the activation frequency is inversely proportional to the value. The larger the value, the more the location is considered a regular location, and the smaller the activation frequency. On the contrary, the smaller the value, the less regular the location is considered, and the larger the activation frequency.

[0026] Query the activation frequency at the location at the latest moment, determine the activation period. The relationship between the period and the frequency is very simple and will not be elaborated here; generate an activation request after the activation period starting from the latest moment, and send it to the digital device.

[0027] It is worth mentioning that while determining the activation frequency, it is also possible to determine the adjustment of the location acquisition process itself. Determine the location acquisition frequency at each location in real time according to the location representation map. The location acquisition frequency is also the same, inversely proportional to the value. It can directly adopt the activation frequency or use different coefficients to determine different degrees of inverse ratio.

[0028] Regarding step S200, the steps of receiving the working scenario feedback by the digital device, determining the working content with time tags according to the working scenario, and synchronously determining the digital difficulty with time tags include: Receive the recorded video fed back by the digital device and convert the recorded video into a group of images. When the digital device records the video, a permission acquisition process facing the user is set in advance. Read the scene features in the scene feature library, traverse the group of images according to the scene features, and extract the number of times the similarity reaches a preset threshold. The scene feature library includes scene feature items and corresponding work scene items. Determine the matching degree of each scene feature according to the number of times. Count the scene features of each work scene, accumulate all the matching degrees, and obtain the total matching degree. Determine the work scene according to the total matching degree, query the work content corresponding to the work scene, and synchronously query the digitalization difficulty of the work content. Query the time period of the recorded video and use it as the time label for the work content and its digitalization difficulty.

[0029] Before the digital device records the video, it needs to first send a permission acquisition request to the user. After receiving the permission granted by the user, receive the recorded video fed back by the digital device, convert the recorded video into a group of images, read the scene features in the scene feature library, traverse the group of images according to the scene features, extract the number of times the similarity reaches a preset threshold, count the scene features of each work scene, accumulate all the matching degrees, and obtain the total matching degree. Select the work scene whose total matching degree reaches the preset matching degree threshold, then query the corresponding work content in the preset database, synchronously query the digitalization difficulty of the work content, and query the time period of the recorded video and use it as the time label for the work content and its digitalization difficulty.

[0030] In addition, the process of comparing the scene features with the images is a simple image comparison process, which will not be elaborated here.

[0031] As a preferred embodiment of the technical solution of the present invention, the steps of receiving the work scene fed back by the digital device, determining the work content with a time label according to the work scene, and synchronously determining the digitalization difficulty with a time label further include: When the number of times the similarity corresponding to all scene features in the scene feature library reaches the preset threshold is less than the preset number threshold, activate the AI module. Based on the AI module, identify the recorded video to determine the work scene. Extract the scene features from the recorded video, obtain the work scene determined by the AI module and the extracted scene features, and insert them into the scene feature library as new data items.

[0032] [[ID=

[0033] Regarding step S300, the steps of calculating the standard data volume with time tags according to the digitalization difficulty and the theoretical data volume of querying the theoretical data volume of the work content include: Query the theoretical data volume of the work content; Calculate the ratio of the digitalization difficulty to the preset minimum difficulty, and determine the correction coefficient according to the inverse ratio of the ratio; Calculate the product of the correction coefficient and the theoretical data volume to obtain the standard data volume; Read the time tag of the work content as the time tag of the standard data volume.

[0034] In an example of the technical solution of the present invention, the adjustment process of the standard data volume is described. Query the theoretical data volume of the work content. The theoretical data volume of each work content is a known value and can be directly read. Calculate the ratio of the digitalization difficulty to the preset minimum difficulty, and determine the correction coefficient according to the inverse ratio of the ratio. Calculate the product of the correction coefficient and the theoretical data volume to obtain the standard data volume. Read the time tag of the work content as the time tag of the standard data volume.

[0035] Regarding step S400, the steps of obtaining the actual data volume with time tags of each digital device within a preset time period, evaluating the actual data volume according to the standard data volume with time tags, and determining the maturity include: Obtain the work records of the digital device; Determine the actual data volume of each time period according to the work records; Query the standard data volume with time tags, and expand the standard data volume with time tags to obtain the actual data volume of each time period; Fit the actual data volume of each time period and the actual data volume of each time period into functions, calculate the integral difference between the two functions, and determine the maturity of the digital device according to the integral difference; Statistically analyze the maturity of all digital devices, calculate the average maturity, and use it as the maturity of the enterprise.

[0036] In an example of the technical solution of the present invention, the evaluation stage is described. The work records of the digital device are obtained, and the actual data volume of each time period is determined according to the work records. The acquisition time of the actual data volume should be more frequent, for example, obtained every few seconds, or recorded when there is an action; the standard data volume with time tags is queried. The acquisition process of the standard data volume should be more discrete. Each time it is activated, a work scenario is obtained, an identification is performed, and a standard data volume is obtained. It is very difficult to compare these two data with different frequencies. Therefore, the technical solution of the present invention introduces an expansion process to expand the standard data volume with time tags to obtain the actual data volume of each time period. The expansion process is very simple. For the time points without standard data volume, query the nearest existing standard data volume as the standard data volume of this time point.

[0037] After the expansion is completed, the actual data volume of each time period and the actual data volume of each time period are both fitted into functions. This is the functionalization process of discrete data, which is equivalent to further fitting. The theoretical data volume is simpler. Generally, a piecewise function can be used. Calculate the integral difference between the two functions. The integral difference reflects the difference between the actual data volume and the theoretical data volume. The smaller the difference, the more data exists on the digital device, which better meets the requirements of the digital standard and the higher the maturity.

[0038] Statistically calculate the maturity of all digital devices and calculate the average maturity as the maturity of the enterprise.

[0039] Figure 2 It is a block diagram of the composition structure of an enterprise digital management maturity evaluation system based on artificial intelligence. In an embodiment of the present invention, an enterprise digital management maturity evaluation system based on artificial intelligence, the system 10 includes: An activation request generation module 11, configured to obtain the location of the digital device according to the locator in the digital device, generate an activation request according to the location, and send it to the digital device; A work scenario recognition module 12, configured to receive the work scenario fed back by the digital device, determine the work content with time tags according to the work scenario, and synchronously determine the digital difficulty with time tags; the digital difficulty is used to characterize the difficulty of digital traceability of a certain work. A standard determination module 13, configured to query the theoretical data volume of the work content, and calculate the standard data volume with time tags according to the digital difficulty and the theoretical data volume; An actual determination module 14, configured to obtain the actual data volume with time tags of each digital device within a preset time period, evaluate the actual data volume according to the standard data volume with time tags, and determine the maturity; Among them, the process of determining the work content with time tags according to the work scenario includes applying the AI module.

[0040] Further, the activation request generation module 11 includes: A channel establishment unit, configured to receive the operation signals uploaded by the digital device and establish a transmission channel with the digital device; A position map generation unit, configured to obtain the position of the digital device according to the locator in the digital device and update the position representation map according to the position; wherein, the update process is to increment a preset value at the position, and the value at each position decreases based on a preset rate; An activation frequency determination unit, configured to determine the activation frequency at each position in real time according to the position representation map; A request sending unit, configured to query the activation frequency at the position at the latest moment, determine the activation period, generate an activation request starting from the latest moment, and send it to the digital device after passing through the activation period; Among them, when determining the activation frequency at each position, the position acquisition frequency at each position is determined in real time according to the position representation map.

[0041] Specifically, the work scenario recognition module 12 includes: A video conversion unit, configured to receive the recorded video fed back by the digital device and convert the recorded video into an image group; wherein, when the digital device records the video, a pre - set permission acquisition process facing the user is provided; A frequency generation unit, configured to read the scene features in the scene feature library, traverse the image group according to the scene features, and extract the number of times the similarity reaches a preset threshold; the scene feature library includes scene feature items and corresponding work scenario items; A matching degree determination unit, configured to determine the matching degree of each scene feature according to the number of times; A matching degree accumulation unit, configured to count the scene features of each work scenario, accumulate all the matching degrees, and obtain the total matching degree; A data query unit, configured to determine the work scenario according to the total matching degree, query the work content corresponding to the work scenario, and synchronously query the digitalization difficulty of the work content; A tag insertion unit, configured to query the time period of the recorded video as the time tag of the work content and its digitalization difficulty.

[0042] Even further, the standard determination module 13 includes: A theoretical data query unit, configured to query the theoretical data volume of the work content; A correction coefficient calculation unit, configured to calculate the ratio of the digitalization difficulty to the preset minimum difficulty, and determine the correction coefficient according to the inverse of the ratio; A product calculation unit for calculating the product of a correction coefficient and a theoretical data volume to obtain a standard data volume; A label reading unit for reading the time label of the work content as the time label of the standard data volume.

[0043] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. An artificial intelligence-based method for evaluating the maturity of enterprise digital management, characterized in that, The method includes: Obtaining the location of the digital device according to the locator in the digital device, generating an activation request based on the location, and sending it to the digital device; Receiving the working scenario fed back by the digital device, determining the work content with time tags according to the working scenario, and synchronously determining the digital difficulty with time tags; the digital difficulty is used to characterize the difficulty of digital trace retention for a certain work; Querying the theoretical data volume of the work content, and calculating the standard data volume with time tags according to the digital difficulty and the theoretical data volume; Obtaining the actual data volume with time tags of each digital device within a preset time period, evaluating the actual data volume according to the standard data volume with time tags, and determining the maturity; Among them, the process of determining the work content with time tags according to the working scenario includes applying an AI module.

2. The method for evaluating the maturity of enterprise digital management based on artificial intelligence according to claim 1, wherein The steps of obtaining the location of the digital device according to the locator in the digital device, generating an activation request based on the location, and sending it to the digital device include: Receiving the operation signal uploaded by the digital device and establishing a transmission channel with the digital device; Obtaining the location of the digital device according to the locator in the digital device, and updating the location representation map according to the location; where the update process is to increment a preset value at the location, and the value at each location decreases based on a preset rate; Determining the activation frequency at each location in real time according to the location representation map; Querying the activation frequency at the location at the latest moment, determining the activation period, generating an activation request starting from the latest moment after the activation period, and sending it to the digital device; Among them, while determining the activation frequency at each location, the location acquisition frequency at each location is determined in real time according to the location representation map.

3. The method for evaluating the maturity of enterprise digital management based on artificial intelligence according to claim 1, wherein The steps of receiving the working scenario fed back by the digital device, determining the work content with time tags according to the working scenario, and synchronously determining the digital difficulty with time tags include: Receiving the recorded video fed back by the digital device and converting the recorded video into an image group; where when the digital device records the video, there is a pre-set permission acquisition process facing the user; Reading the scene features in the scene feature library, traversing the image group according to the scene features, and extracting the number of times the similarity reaches a preset threshold; the scene feature library includes scene feature items and corresponding working scenario items; Determining the matching degree of each scene feature according to the number of times; Counting the scene features of each working scenario, accumulating all the matching degrees, and obtaining the total matching degree; Determining the working scenario according to the total matching degree, querying the work content corresponding to the working scenario, and synchronously querying the digital difficulty of the work content; Querying the time period of the recorded video as the time tag of the work content and its digital difficulty.

4. The method for evaluating the maturity of enterprise digital management based on artificial intelligence according to claim 3, wherein, The steps of receiving the working scenario fed back by the digital device, determining the work content with time tags according to the working scenario, and synchronously determining the digital difficulty with time tags further include: When the number of times the similarity corresponding to all scene features in the scene feature library reaches the preset threshold is less than the preset number threshold, activating the AI module; Based on the recognition of the recorded video by the AI module, determine the working scenario; Extract the scenario features from the recorded video, obtain the working scenario determined by the AI module and the extracted scenario features, and insert them as new data items into the scenario feature library.

5. The method for evaluating the maturity of enterprise digital management based on artificial intelligence according to claim 1, wherein, The steps of calculating the standard data volume with time tags according to the digitalization difficulty and the theoretical data volume for querying the theoretical data volume of the work content include: Query the theoretical data volume of the work content; Calculate the ratio of the digitalization difficulty to the preset minimum difficulty, and determine the correction coefficient according to the inverse ratio of the ratio; Calculate the product of the correction coefficient and the theoretical data volume to obtain the standard data volume; Read the time tag of the work content as the time tag of the standard data volume.

6. The method for evaluating the maturity of enterprise digital management based on artificial intelligence according to claim 3, wherein The steps of obtaining the actual data volume with time tags of each digital device within a preset time period, evaluating the actual data volume according to the standard data volume with time tags, and determining the maturity include: Obtain the work records of the digital device; Determine the actual data volume of each time period according to the work records; Query the standard data volume with time tags, expand the standard data volume with time tags to obtain the actual data volume of each time period; Fit the actual data volume of each time period and the actual data volume of each time period into functions respectively, calculate the integral difference between the two functions, and determine the maturity of the digital device according to the integral difference; Statistically calculate the maturity of all digital devices, calculate the average maturity as the maturity of the enterprise.

7. An enterprise digital management maturity assessment system based on artificial intelligence, characterized in that, The system includes: An activation request generation module, configured to obtain the location of the digital device according to the locator in the digital device, generate an activation request according to the location, and send it to the digital device; A working scenario recognition module, configured to receive the working scenario fed back by the digital device, determine the work content with time tags according to the working scenario, and simultaneously determine the digitalization difficulty with time tags; the digitalization difficulty is used to characterize the difficulty of digital traceability of a certain work; A standard determination module, configured to query the theoretical data volume of the work content, and calculate the standard data volume with time tags according to the digitalization difficulty and the theoretical data volume; An actual determination module, configured to obtain the actual data volume with time tags of each digital device within a preset time period, evaluate the actual data volume according to the standard data volume with time tags, and determine the maturity; Among them, the process of determining the work content with time tags according to the working scenario includes applying the AI module.

8. The artificial intelligence-based enterprise digital management maturity assessment system according to claim 7, wherein The activation request generation module includes: A channel establishment unit, configured to receive the operation signal uploaded by the digital device and establish a transmission channel with the digital device; A location map generation unit, configured to obtain the location of the digital device according to the locator in the digital device, and update the location representation map according to the location; among them, the update process is to increment a preset value at the location, and the value at each location decreases based on a preset rate; An activation frequency determination unit, configured to determine the activation frequency at each location in real time according to the location representation map; A request sending unit, configured to query the activation frequency at the position at the latest moment, determine the activation period, generate an activation request starting from the latest moment after passing through the activation period, and send it to the digital device; Among them, while determining the activation frequency at each position, the position acquisition frequency at each position is determined in real time according to the position representation diagram.

9. The enterprise digital management maturity evaluation system based on artificial intelligence according to claim 7, characterized in that The working scenario recognition module includes: A video conversion unit, configured to receive the recorded video fed back by the digital device and convert the recorded video into an image group; wherein, when the digital device records the video, a permission acquisition process facing the user is provided in the front; A frequency generation unit, configured to read the scene features in the scene feature library, traverse the image group according to the scene features, and extract the frequency of the similarity reaching the preset threshold; the scene feature library includes scene feature items and corresponding working scenario items; A matching degree determination unit, configured to determine the matching degree of each scene feature according to the frequency; A matching degree accumulation unit, configured to count the scene features of each working scenario, accumulate all the matching degrees, and obtain the total matching degree; A data query unit, configured to determine the working scenario according to the total matching degree, query the work content corresponding to the working scenario, and synchronously query the digitalization difficulty of the work content; A label insertion unit, configured to query the time period of the recorded video as the time label of the work content and its digitalization difficulty.

10. The enterprise digital management maturity assessment system based on artificial intelligence according to claim 7, characterized in that, The standard determination module includes: A theoretical data query unit, configured to query the theoretical data volume of the work content; A correction coefficient calculation unit, configured to calculate the ratio of the digitalization difficulty to the preset minimum difficulty, and determine the correction coefficient according to the inverse ratio of the ratio; A product calculation unit, configured to calculate the product of the correction coefficient and the theoretical data volume to obtain the standard data volume; A label reading unit, configured to read the time label of the work content as the time label of the standard data volume.

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