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

Abstract

The present invention relates to the field of digital assessment technology, and specifically discloses an enterprise digital management maturity assessment method and system based on artificial intelligence. The method comprises receiving work scenarios fed back by digital devices, determining work content with time tags based on the work scenarios, and synchronously determining the digitization difficulty with time tags; querying the theoretical data volume of the work content, and calculating the standard data volume with time tags based on the digitization difficulty and the theoretical data volume; evaluating the actual data volume based on the standard data volume with time tags to determine maturity; the present invention determines the environmental conditions by a camera on each digital device, and then determines a data standard, evaluates the data locally stored in the digital device based on the data standard, determines the maturity of the digital device, and calculates the maturity of all digital devices to obtain the final enterprise maturity. This process does not require manual parameters and is highly objective.

Description

Technical Field

[0001] The present invention relates to the field of digital assessment technology, and in particular to an enterprise digital management maturity assessment method and system based on artificial intelligence. Background Art

[0002] With the development of computer equipment, many companies have gradually transformed into digital companies, and digital companies are also considered to be more standardized and efficient companies. The existing method of evaluating whether a company is a digital company is mostly a manual evaluation process, which involves manually setting some standards, and then using interviews and other forms to obtain different indicators. The obtained indicators are then discussed in meetings to finally obtain the evaluation results. 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 aims to solve. Summary of the Invention

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

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] An enterprise digital management maturity assessment method based on artificial intelligence, the method comprising:

[0006] obtaining a location of the digitizing device according to a locator in the digitizing device, generating an activation request according to the location, and sending the activation request to the digitizing device;

[0007] Receive a work scenario fed back by a digitizing device, determine a work content with a time tag based on the work scenario, and simultaneously determine a digitization difficulty with the time tag; the digitization difficulty is used to represent the difficulty of leaving a digital trace of a certain work;

[0008] Query the theoretical data volume of the work content and calculate the standard data volume with time tags based on the digitization difficulty and the theoretical data volume;

[0009] Obtain the actual amount of time-tagged data from each digital device within a preset time period, evaluate the actual amount of data based on the standard amount of time-tagged data, and determine maturity;

[0010] Among them, the process of determining the work content containing the time tag based on the work scenario includes applying an AI module.

[0011] As a further solution of the present invention, the step of obtaining the position of the digital device according to the locator in the digital device, generating an activation request according to the position, and sending the activation request to the digital device includes:

[0012] Receive the operation signal uploaded by the digital device and establish a transmission channel with the digital device;

[0013] Obtaining the position of the digitizing device according to a locator in the digitizing device, and updating the position representation map according to the position; wherein the updating process is to increment a preset value at the position, and the value at each position is decremented based on a preset rate;

[0014] determining the activation frequency at each position in real time according to the position representation map;

[0015] Query the activation frequency at the latest time, determine the activation period, take the latest time as the starting time, generate an activation request after the activation period, and send it to the digital device;

[0016] 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.

[0017] As a further solution of the present invention, the steps of receiving a work scenario fed back by a digitizing device, determining a work content with a time tag based on the work scenario, and simultaneously determining a digitization difficulty with the time tag include:

[0018] Receiving recorded video from a digitizing device and converting the recorded video into an image group; wherein the digitizing device is provided with a pre-process for obtaining user permissions when recording the video;

[0019] Reading scene features from a 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 work scene items;

[0020] determining a matching degree of each scene feature based on the number of times;

[0021] Count the scene features of each work scene, accumulate all the matching degrees, and obtain the total matching degree;

[0022] Determine a work scenario based on the total matching degree, query the work content corresponding to the work scenario, and simultaneously query the digitization difficulty of the work content;

[0023] Query the time period of the recorded video as a time label for the work content and its digitization difficulty.

[0024] As a further solution of the present invention, the steps of receiving a work scenario fed back by a digitizing device, determining a work content with a time tag based on the work scenario, and simultaneously determining a digitization difficulty with the time tag further include:

[0025] When the number of times that the similarities corresponding to all scene features in the scene feature library reach the preset threshold is less than the preset number threshold, the AI ​​module is activated;

[0026] Identify recorded videos based on the AI ​​module and determine the work scenario;

[0027] Extract scene features from the recorded video, obtain the working scene determined by the AI ​​module and the extracted scene features, and insert them into the scene feature library as new data items.

[0028] As a further solution of the present invention, the step of calculating the standard data volume containing time tags based on the digitization difficulty and the theoretical data volume of the query work content includes:

[0029] The theoretical data volume of the query work content;

[0030] Calculating the ratio of the digitization difficulty to the preset minimum difficulty, and determining the correction coefficient according to the inverse ratio of the ratio;

[0031] Calculate the product of the correction coefficient and the theoretical data volume to obtain the standard data volume;

[0032] Read the time stamp of the work content as the time stamp of the standard data volume.

[0033] As a further embodiment of the present invention, the step of obtaining the actual amount of time-tagged data of each digital device within a preset time period, evaluating the actual amount of data based on the standard amount of time-tagged data, and determining the maturity includes:

[0034] Obtain working records of digital equipment;

[0035] Determine the actual data volume for each period based on the work records;

[0036] Query the standard data volume with time tags, expand the standard data volume with time tags, and obtain the actual data volume of each time period;

[0037] Fit the actual data volume of each period and the actual data volume of each period into a function, calculate the integral difference of the two functions, and determine the maturity of the digital equipment based on the integral difference;

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

[0039] The technical solution of the present invention also provides an enterprise digital management maturity assessment system based on artificial intelligence, the system comprising:

[0040] an activation request generating module, configured to obtain a location of the digitizing device according to a locator in the digitizing device, generate an activation request according to the location, and send the activation request to the digitizing device;

[0041] A work scene recognition module is used to receive work scenes fed back by a digitizing device, determine the work content with a time tag based on the work scene, and simultaneously determine the digitization difficulty of the time tag; the digitization difficulty is used to represent the difficulty of leaving a digital trace of a certain work;

[0042] The standard determination module is used to query the theoretical data volume of the work content and calculate the standard data volume with time tags based on the digitization difficulty and the theoretical data volume;

[0043] An actual determination module is used to obtain the actual amount of data with time tags from each digital device within a preset time period, evaluate the actual amount of data based on the standard amount of data with time tags, and determine the maturity level;

[0044] Among them, the process of determining the work content containing the time tag based on the work scenario includes applying an AI module.

[0045] As a further solution of the present invention: the activation request generation module includes:

[0046] A channel establishing unit is used to receive the operation signal uploaded by the digital device and establish a transmission channel with the digital device;

[0047] a position map generating unit, configured to obtain a position of the digitized device based on a locator in the digitized device and update the position representation map based on the position; wherein the updating process is to increment a preset value at each position and decrement the value at each position based on a preset rate;

[0048] an activation frequency determination unit, configured to determine the activation frequency at each position in real time according to the position representation diagram;

[0049] A request sending unit is used to query the activation frequency at the location at the latest time, determine the activation period, take the latest time as the starting time, generate an activation request after the activation period has elapsed, and send it to the digital device;

[0050] 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.

[0051] As a further solution of the present invention: the work scene recognition module includes:

[0052] A video conversion unit is configured to receive a recorded video fed back by a digitizing device and convert the recorded video into an image group; wherein the digitizing device is provided with a pre-set permission acquisition process for the user when recording the video;

[0053] a number generation unit, configured to read scene features from a 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 scene items;

[0054] a matching degree determining unit, configured to determine a matching degree of each scene feature according to the number of times;

[0055] The matching degree accumulation unit is used to count the scene features of each work scene, accumulate all matching degrees, and obtain the total matching degree;

[0056] A data query unit, configured to determine a work scenario based on the total matching degree, query the work content corresponding to the work scenario, and simultaneously query the digitization difficulty of the work content;

[0057] The label insertion unit is used to query the time period of the recorded video as a time label for the work content and its digitization difficulty.

[0058] As a further solution of the present invention: the standard determination module includes:

[0059] Theoretical data query unit, used to query the theoretical data volume of work content;

[0060] a correction coefficient calculation unit, configured to calculate a ratio of the digitization difficulty to a preset minimum difficulty, and determine a correction coefficient according to an inverse ratio of the ratio;

[0061] A product calculation unit is used to calculate the product of the correction coefficient and the theoretical data volume to obtain the standard data volume;

[0062] The tag reading unit is used to read the time tag of the work content as the time tag of the standard data volume.

[0063] Compared with the existing technology, the beneficial effects of the present invention are: the present invention uses the locator and camera on each digital device to determine the environmental conditions, and then determines a data standard. According to the data standard, the data stored locally on the digital device is evaluated to determine the maturity of the digital device. The maturity of all digital devices is counted to obtain the final enterprise maturity. This process does not require manual parameters and is highly objective. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention.

[0065] Figure 1 A flowchart of the AI-based enterprise digital management maturity assessment method.

[0066] Figure 2 This is a structural diagram of the enterprise digital management maturity assessment system based on artificial intelligence. DETAILED DESCRIPTION

[0067] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0068] Figure 1 This is a flowchart of a method for assessing enterprise digital management maturity based on artificial intelligence. In an embodiment of the present invention, a method for assessing enterprise digital management maturity based on artificial intelligence includes:

[0069] Step S100: obtaining the location of the 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;

[0070] The technical solution of the present invention is applied to digital enterprises. Basically, all staff members are equipped with digital devices, some of which are computers and some are portable devices. These digital devices have built-in locators. The position of the digital device is obtained according to the locator in the digital device. Based on the changes in the position, the type of device and the changes in the working scene can be determined, and then some activation requests are generated at intervals and sent to the digital device.

[0071] 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;

[0072] 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.

[0073] 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;

[0074] 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.

[0075] 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;

[0076] 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.

[0077] 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:

[0078] Receive the operation signal uploaded by the digital device and establish a transmission channel with the digital device;

[0079] Obtaining the position of the digitizing device according to a locator in the digitizing device, and updating the position representation map according to the position; wherein the updating process is to increment a preset value at the position, and the value at each position is decremented based on a preset rate;

[0080] determining the activation frequency at each position in real time according to the position representation map;

[0081] The activation frequency at the location of the latest moment is queried to determine the activation period. The activation request is generated after the activation period takes the latest moment as the starting point and sent to the digital device.

[0082] When each digital device is started, a startup program is 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 is established.

[0083] The position of the digitizing device is obtained according to the locator in the digitizing device, and the position representation map is updated according to the position. The meaning of the position representation map is as follows:

[0084] After obtaining the location, mark the corresponding point on the map and increment the value of the point by 1. Repeat this process continuously. The value of each point will continue to increase, and eventually a map reflecting the historical location of the digital device will be obtained, called a location representation map. The value at each location represents the length of time the digital device stayed at the corresponding location.

[0085] The activation frequency can be determined based on the numerical values ​​of each position in the position representation diagram. In one example of the technical solution of the present invention, the activation frequency is inversely proportional to the numerical value. The larger the numerical value, the more conventional the position is considered to be, and the smaller the activation frequency is. Conversely, the smaller the numerical value, the more unconventional the position is considered to be, and the greater the activation frequency is.

[0086] Query the activation frequency at the latest moment and determine the activation period. The relationship between period and frequency is very simple and will not be described here. Take the latest moment as the starting point and generate an activation request after the activation period and send it to the digital device.

[0087] It is worth mentioning that while determining the activation frequency, it is also possible to determine the adjustment of the position acquisition process itself. The position acquisition frequency at each position is determined in real time according to the position representation diagram. The position acquisition frequency is the same and is inversely proportional to the value. It can directly use the activation frequency or use different coefficients to determine different degrees of inverse proportion.

[0088] Regarding step S200, the steps of receiving the work scenario fed back by the digitizing device, determining the work content with the time tag according to the work scenario, and simultaneously determining the digitization difficulty with the time tag include:

[0089] Receiving recorded video from a digitizing device and converting the recorded video into an image group; wherein the digitizing device is provided with a pre-process for obtaining user permissions when recording the video;

[0090] Reading scene features from a 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 work scene items;

[0091] determining a matching degree of each scene feature based on the number of times;

[0092] Count the scene features of each work scene, accumulate all the matching degrees, and obtain the total matching degree;

[0093] Determine a work scenario based on the total matching degree, query the work content corresponding to the work scenario, and simultaneously query the digitization difficulty of the work content;

[0094] Query the time period of the recorded video as a time label for the work content and its digitization difficulty.

[0095] Before recording a video, the digital device needs to send a permission request to the user. After receiving the permission granted by the user, the digital device receives the recorded video feedback from the digital device, converts the recorded video into an image group, reads the scene features in the scene feature library, traverses the image group according to the scene features, extracts the number of times the similarity reaches a preset threshold, counts the scene features of each work scene, accumulates all the matching degrees, and obtains the total matching degree; selects the work scene whose total matching degree reaches the preset matching degree threshold, and then queries the corresponding work content in the preset database, and simultaneously queries the digitization difficulty of the work content, and queries the time period of the recorded video as a time label for the work content and its digitization difficulty.

[0096] In addition, the comparison process between scene features and images is a simple image comparison process and will not be described in detail here.

[0097] As a preferred embodiment of the technical solution of the present invention, the steps of receiving a work scenario fed back by a digitizing device, determining a work content with a time tag based on the work scenario, and simultaneously determining the digitization difficulty with the time tag further include:

[0098] When the number of times that the similarities corresponding to all scene features in the scene feature library reach the preset threshold is less than the preset number threshold, the AI ​​module is activated;

[0099] Identify recorded videos based on the AI ​​module and determine the work scenario;

[0100] Extract scene features from the recorded video, obtain the working scene determined by the AI ​​module and the extracted scene features, and insert them into the scene feature library as new data items.

[0101] The above content means that if there is no matching data item in the scene feature library, AI will be used for recognition. After the recognition is completed, the data item will be added to the scene feature library. Of course, the initial data items in the scene feature library are set manually.

[0102] Regarding step S300, the step of querying the theoretical data volume of the work content and calculating the standard data volume containing the time tag according to the digitization difficulty and the theoretical data volume includes:

[0103] The theoretical data volume of the query work content;

[0104] Calculating the ratio of the digitization difficulty to the preset minimum difficulty, and determining the correction coefficient according to the inverse ratio of the ratio;

[0105] Calculate the product of the correction coefficient and the theoretical data volume to obtain the standard data volume;

[0106] Read the time stamp of the work content as the time stamp of the standard data volume.

[0107] In an example of the technical solution of the present invention, the adjustment process of the standard data volume is explained, and the theoretical data volume of the work content is queried. The theoretical data volume of each work content is a known value and can be read directly. The ratio of the digitization difficulty to the preset minimum difficulty is calculated, and the correction coefficient is determined according to the inverse proportion of the ratio. The product of the correction coefficient and the theoretical data volume is calculated to obtain the standard data volume, and the time tag of the work content is read as the time tag of the standard data volume.

[0108] Regarding step S400, the steps of obtaining the actual amount of time-tagged data of each digital device within a preset time period, evaluating the actual amount of data based on the standard amount of time-tagged data, and determining the maturity include:

[0109] Obtain working records of digital equipment;

[0110] Determine the actual data volume for each period based on the work records;

[0111] Query the standard data volume with time tags, expand the standard data volume with time tags, and obtain the actual data volume of each time period;

[0112] Fit the actual data volume of each period and the actual data volume of each period into a function, calculate the integral difference of the two functions, and determine the maturity of the digital equipment based on the integral difference;

[0113] Count the maturity of all digital devices and calculate the average maturity value as the maturity of the enterprise.

[0114] In an example of the technical solution of the present invention, the evaluation stage is explained, and the working records of the digital equipment are obtained. The actual data volume of each time period is determined based on the working records. The actual data volume is obtained more frequently, such as once every few seconds, or recorded when there is behavior; the standard data volume with a time tag is queried, and the acquisition process of the standard data volume is more discrete. Each time it is activated, a working scene is obtained, and an identification is performed to obtain a standard data volume. These two types of data with different frequencies are difficult to compare together. Therefore, the technical solution of the present invention introduces an expansion process to expand the standard data volume with a time tag to obtain the actual data volume of each time period. The expansion process is very simple. For a time point without a standard data volume, the most recent existing standard data volume is queried as the standard data volume at that time point.

[0115] After the expansion is completed, the actual data volume of each time period and the actual data volume of each time period are fitted into functions. This is the functionalization process of discrete data, which is equivalent to further fitting. The theoretical data volume is simpler, and generally a piecewise function can be used. The integral difference of the two functions is calculated. The integral difference reflects the difference between the actual data volume and the theoretical data volume. The smaller the difference, the more sufficient the data on the digital device is, and it is more in line with the requirements of the digital standard and the higher the maturity.

[0116] Count the maturity of all digital devices and calculate the average maturity value as the maturity of the enterprise.

[0117] Figure 2 The following is a structural block diagram of an enterprise digital management maturity assessment system based on artificial intelligence. In an embodiment of the present invention, an enterprise digital management maturity assessment system based on artificial intelligence is provided. The system 10 includes:

[0118] An activation request generating module 11 is configured to obtain a location of the digital device according to a locator in the digital device, generate an activation request according to the location, and send the activation request to the digital device;

[0119] A work scene recognition module 12 is configured to receive a work scene fed back by a digitizing device, determine a work content with a time tag based on the work scene, and simultaneously determine a digitization difficulty with the time tag; the digitization difficulty is used to represent the difficulty of leaving a digital trace of a certain work;

[0120] The standard determination module 13 is used to query the theoretical data volume of the work content and calculate the standard data volume containing the time tag based on the digitization difficulty and the theoretical data volume;

[0121] The actual determination module 14 is used to obtain the actual amount of data with time tags from each digital device within a preset time period, evaluate the actual amount of data based on the standard amount of data with time tags, and determine the maturity level;

[0122] Among them, the process of determining the work content containing the time tag based on the work scenario includes applying an AI module.

[0123] Furthermore, the activation request generating module 11 includes:

[0124] A channel establishing unit is used to receive the operation signal uploaded by the digital device and establish a transmission channel with the digital device;

[0125] a position map generating unit, configured to obtain a position of the digitized device based on a locator in the digitized device and update the position representation map based on the position; wherein the updating process is to increment a preset value at each position and decrement the value at each position based on a preset rate;

[0126] an activation frequency determination unit, configured to determine the activation frequency at each position in real time according to the position representation diagram;

[0127] A request sending unit is used to query the activation frequency at the location at the latest time, determine the activation period, take the latest time as the starting time, generate an activation request after the activation period has elapsed, and send it to the digital device;

[0128] 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.

[0129] Specifically, the work scene recognition module 12 includes:

[0130] A video conversion unit is configured to receive a recorded video fed back by a digitizing device and convert the recorded video into an image group; wherein the digitizing device is provided with a pre-set permission acquisition process for the user when recording the video;

[0131] a number generation unit, configured to read scene features from a 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 scene items;

[0132] a matching degree determining unit, configured to determine a matching degree of each scene feature according to the number of times;

[0133] The matching degree accumulation unit is used to count the scene features of each work scene, accumulate all matching degrees, and obtain the total matching degree;

[0134] A data query unit, configured to determine a work scenario based on the total matching degree, query the work content corresponding to the work scenario, and simultaneously query the digitization difficulty of the work content;

[0135] The label insertion unit is used to query the time period of the recorded video as a time label for the work content and its digitization difficulty.

[0136] Furthermore, the standard determination module 13 includes:

[0137] Theoretical data query unit, used to query the theoretical data volume of work content;

[0138] a correction coefficient calculation unit, configured to calculate a ratio of the digitization difficulty to a preset minimum difficulty, and determine a correction coefficient according to an inverse ratio of the ratio;

[0139] A product calculation unit is used to calculate the product of the correction coefficient and the theoretical data volume to obtain the standard data volume;

[0140] The tag reading unit is used to read the time tag of the work content as the time tag of the standard data volume.

[0141] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An enterprise digital management maturity assessment method based on artificial intelligence, characterized by: The method comprises: obtaining a location of the digitizing device according to a locator in the digitizing device, generating an activation request according to the location, and sending the activation request to the digitizing device; Receive a work scenario fed back by a digitizing device, determine a work content with a time tag based on the work scenario, and simultaneously determine a digitization difficulty with the time tag; the digitization difficulty is used to represent the difficulty of leaving a digital trace of a certain work; Query the theoretical data volume of the work content and calculate the standard data volume with time tags based on the digitization difficulty and the theoretical data volume; Obtain the actual amount of time-tagged data from each digital device within a preset time period, evaluate the actual amount of data based on the standard amount of time-tagged data, and determine maturity; Among them, the process of determining the work content containing the time tag based on the work scenario includes applying an AI module.

2. The enterprise digital management maturity assessment method based on artificial intelligence according to claim 1 is characterized in that: The step of obtaining the position of the digital device according to the locator in the digital device, generating an activation request according to the position, and sending the activation request to the digital device comprises: Receive the operation signal uploaded by the digital device and establish a transmission channel with the digital device; Obtaining the position of the digitizing device according to a locator in the digitizing device, and updating the position representation map according to the position; wherein the updating process is to increment a preset value at the position, and the value at each position is decremented based on a preset rate; determining the activation frequency at each position in real time according to the position representation map; Query the activation frequency at the latest time, determine the activation period, take the latest time as the starting time, generate an activation request after the activation period, and send it to the digital device; 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.

3. The enterprise digital management maturity assessment method based on artificial intelligence according to claim 1 is characterized in that: The steps of 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 digitization difficulty with the time tag include: Receiving recorded video from a digitizing device and converting the recorded video into an image group; wherein the digitizing device is provided with a pre-process for obtaining user permissions when recording the video; Reading scene features from a 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 work scene items; determining a matching degree of each scene feature based on the number of times; Count the scene features of each work scene, accumulate all the matching degrees, and obtain the total matching degree; Determine a work scenario based on the total matching degree, query the work content corresponding to the work scenario, and simultaneously query the digitization difficulty of the work content; Query the time period of the recorded video as a time label for the work content and its digitization difficulty.

4. The enterprise digital management maturity assessment method based on artificial intelligence according to claim 3 is characterized in that: The steps of 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 digitization difficulty with the time tag further include: When the number of times that the similarities corresponding to all scene features in the scene feature library reach the preset threshold is less than the preset number threshold, the AI ​​module is activated; Identify recorded videos based on the AI ​​module and determine the work scenario; Extract scene features from the recorded video, obtain the working scene determined by the AI ​​module and the extracted scene features, and insert them into the scene feature library as new data items.

5. The enterprise digital management maturity assessment method based on artificial intelligence according to claim 1 is characterized in that: The step of calculating the standard data volume containing time tags based on the theoretical data volume of the query work content and the digitization difficulty and the theoretical data volume includes: The theoretical data volume of the query work content; Calculating the ratio of the digitization difficulty to the preset minimum difficulty, and determining 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 stamp of the work content as the time stamp of the standard data volume.

6. The enterprise digital management maturity assessment method based on artificial intelligence according to claim 3 is characterized in that: The steps of obtaining the actual amount of data with time tags for 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 the maturity include: Obtain working records of digital equipment; Determine the actual data volume for each period based on the work records; Query the standard data volume with time tags, expand the standard data volume with time tags, and obtain the actual data volume of each time period; Fit the actual data volume of each period and the actual data volume of each period into a function, calculate the integral difference of the two functions, and determine the maturity of the digital equipment based on the integral difference; Count the maturity of all digital devices and calculate the average maturity value as the maturity of the enterprise.

7. An enterprise digital management maturity assessment system based on artificial intelligence, characterized by: The system comprises: an activation request generating module, configured to obtain a location of the digitizing device according to a locator in the digitizing device, generate an activation request according to the location, and send the activation request to the digitizing device; A work scene recognition module is used to receive work scenes fed back by a digitizing device, determine the work content with a time tag based on the work scene, and simultaneously determine the digitization difficulty of the time tag; the digitization difficulty is used to represent the difficulty of leaving a digital trace of a certain work; The standard determination module is used to query the theoretical data volume of the work content and calculate the standard data volume with time tags based on the digitization difficulty and the theoretical data volume; An actual determination module is used to obtain the actual amount of data with time tags from each digital device within a preset time period, evaluate the actual amount of data based on the standard amount of data with time tags, and determine the maturity level; Among them, the process of determining the work content containing the time tag based on the work scenario includes applying an AI module.

8. The enterprise digital management maturity assessment system based on artificial intelligence according to claim 7 is characterized in that: The activation request generation module includes: A channel establishing unit is used to receive the operation signal uploaded by the digital device and establish a transmission channel with the digital device; a position map generating unit, configured to obtain a position of the digitized device based on a locator in the digitized device and update the position representation map based on the position; wherein the updating process is to increment a preset value at each position and decrement the value at each position 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 diagram; A request sending unit is used to query the activation frequency at the location at the latest time, determine the activation period, take the latest time as the starting time, generate an activation request after the activation period has elapsed, and send it to the digital device; 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 assessment system based on artificial intelligence according to claim 7 is characterized in that: The working scene recognition module includes: A video conversion unit is configured to receive a recorded video fed back by a digitizing device and convert the recorded video into an image group; wherein the digitizing device is provided with a pre-set permission acquisition process for the user when recording the video; a number generation unit, configured to read scene features from a 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 scene items; a matching degree determining unit, configured to determine a matching degree of each scene feature according to the number of times; The matching degree accumulation unit is used to count the scene features of each work scene, accumulate all matching degrees, and obtain the total matching degree; A data query unit, configured to determine a work scenario based on the total matching degree, query the work content corresponding to the work scenario, and simultaneously query the digitization difficulty of the work content; The label insertion unit is used to query the time period of the recorded video as a time label for the work content and its digitization difficulty.

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

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