An identification statistical analysis method, device and medium based on industrial Internet

Abstract

The present application discloses an identification statistical analysis method, device and medium based on the industrial Internet, the method comprising: providing a plurality of preset dimensions to a user; receiving a statistical instruction sent by a user; analyzing a plurality of specified dimensions according to a statistical target to predict the level of realization of the statistical target by the plurality of specified dimensions; if the level of realization is lower than a preset level, selecting at least some dimensions as auxiliary dimensions from the plurality of dimensions other than the specified dimension; creating a data statistical method for the user according to the specified dimensions and the auxiliary dimensions; obtaining a corresponding specified identification in an identification database according to the dimensions included in the data statistical method, and analyzing the specified identification to obtain the statistical target required by the user. The user is provided with a plurality of preset dimensions, and the user can make a customized selection among the plurality of dimensions, thereby selecting the most suitable specified dimensions for his or her own statistical target, making the statistical analysis process more flexible.

Description

Technical Field

[0001] This application relates to the field of industrial Internet, and specifically to an identification statistical analysis method, equipment and medium based on the industrial Internet. Background Art

[0002] The Industrial Internet (IIoT) is a new type of infrastructure, application model, and industrial ecosystem that deeply integrates the next-generation information and communication technology with the industrial economy. By fully connecting people, machines, objects, and systems, it builds a new manufacturing and service system covering the entire industrial chain and the entire value chain, providing a way to achieve the digitalization, networking, and intelligent development of industry and even industries, and is an important cornerstone of the Fourth Industrial Revolution.

[0003] Typically, statistical analysis of data in the Industrial Internet is based on specific templates. This results in fixed statistical data, making it difficult for users to customize the analysis. Furthermore, when performing statistical analysis, users may choose inaccurate templates, resulting in inaccurate statistical analysis results. Summary of the Invention

[0004] In order to solve the above problems, the present application proposes an identification statistical analysis method based on the industrial Internet, which is applied to an identification statistical analysis system, and the identification statistical analysis system is at the same level as the secondary node in the industrial Internet. The method includes: the identification statistical analysis system provides a user with a preset multiple dimensions for representing each identification stored in the identification database of the industrial Internet; receives a statistical instruction sent by the user, and the statistical instruction includes at least the user's statistical target and several specified dimensions selected by the user from the multiple dimensions; according to the statistical target, analyzes the several specified dimensions to predict the realization level of the several specified dimensions for the statistical target; if the realization level is lower than the preset level, selects at least some dimensions from the multiple dimensions other than the specified dimensions as auxiliary dimensions; creates a data statistical method for the user based on the specified dimensions and the auxiliary dimensions; according to the dimensions included in the data statistical method, obtains the corresponding specified identification in the identification database, and analyzes the specified identification to obtain the statistical target required by the user.

[0005] In one example, the analyzing of the several specified dimensions according to the statistical target to predict the realization levels of the several specified dimensions for the statistical target specifically includes: extracting instances of the statistical target to obtain the instances contained in the statistical target; for each of the instances, determining the fit level between each of the specified dimensions and the instance according to a preset mapping relationship table, and accumulating the fit levels corresponding to each of the specified dimensions to obtain the total fit level corresponding to the instance; predicting the realization levels of the several specified dimensions for the statistical target according to the total fit levels corresponding to each of the instances and the weights corresponding to each of the instances.

[0006] In one example, before obtaining the corresponding designated identifier in the identifier database based on the dimensions included in the data statistical method, the method also includes: determining the statistical type corresponding to the data statistical method, the statistical type including at least one of streaming processing and timed processing; obtaining a new registration identifier; determining whether the registration identifier hits the data statistical method in a real-time or timed manner corresponding to the statistical type; if so, adding the new registration identifier to the designated identifier.

[0007] In one example, after receiving the statistical instruction sent by the user, the method further includes: determining the identity information of the user, the user information including at least one of a secondary node and an enterprise node under the secondary node; determining the user's authority level based on the identity information, and judging whether the statistical target complies with the authority level; if so, judging whether the specified dimension complies with the authority level; if not, selecting the dimension with the highest correlation with the specified dimension and complying with the authority level according to the mapping relationship table, and replacing the specified dimension with the selected dimension, wherein the correlation is positively correlated with the fit level.

[0008] In one example, obtaining a corresponding designated identifier in the identifier database according to the dimension included in the data statistics method specifically includes: obtaining a corresponding candidate identifier in the identifier database according to the designated dimension included in the data statistics method; filtering the candidate identifiers according to the auxiliary dimension included in the data statistics method, filtering out some identifiers that do not include the auxiliary dimension, and obtaining a corresponding designated identifier, wherein the proportion of the filtered-out identifiers in the candidate identifiers is lower than a preset proportion.

[0009] In one example, the designated identifier includes a registration identifier of a designated industry, and the statistical target includes the daily production volume of the designated industry; analyzing the designated identifier to obtain the statistical target required by the user specifically includes: determining the daily registration volume of the registration identifier of the designated industry; determining the production efficiency of the designated industry based on the industry type of the designated industry; and obtaining the daily production volume of the designated industry based on the daily registration volume and the production efficiency.

[0010] In one example, the method further includes: determining whether the data statistics method carries the auxiliary dimension; if so, constructing a display chart, and using the specified dimension and the auxiliary dimension as the X-axis of the display chart, using the statistical target as the Y-axis of the display chart, and highlighting the specified dimension in the X-axis; and displaying the display chart to the user.

[0011] In one example, the preset multiple dimensions include at least multiple items of prefix, handle, template, and registration time.

[0012] On the other hand, the present application also proposes an identification statistical analysis device based on the industrial Internet, which is applied to the identification statistical analysis system. The identification statistical analysis system is at the same level as the secondary node in the industrial Internet. The device includes: at least one processor; and a memory that is communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute: the identification statistical analysis system provides the user with preset multiple dimensions for representing each identification stored in the identification database of the industrial Internet; receives statistical instructions sent by the user, and the statistical instructions The system comprises at least the statistical target of the user and several designated dimensions selected by the user from the multiple dimensions; according to the statistical target, the several designated dimensions are analyzed to predict the realization level of the several designated dimensions to the statistical target; if the realization level is lower than the preset level, at least some dimensions other than the designated dimensions are selected as auxiliary dimensions from among the multiple dimensions; according to the designated dimensions and the auxiliary dimensions, a data statistical method is created for the user; according to the dimensions included in the data statistical method, the corresponding designated identifiers are obtained from the identifier database, and the designated identifiers are analyzed to obtain the statistical target required by the user.

[0013] On the other hand, the present application also proposes a non-volatile computer storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured as follows: the identification statistical analysis system provides a user with a preset plurality of dimensions for representing each identification stored in the identification database of the industrial Internet; receives a statistical instruction sent by the user, wherein the statistical instruction includes at least the user's statistical target and several specified dimensions selected by the user from the multiple dimensions; according to the statistical target, analyzes the several specified dimensions to predict the level of realization of the statistical target by the several specified dimensions; if the realization level is lower than the preset level, selects at least some dimensions from the multiple dimensions other than the specified dimensions as auxiliary dimensions; creates a data statistical method for the user based on the specified dimensions and the auxiliary dimensions; obtains the corresponding specified identification in the identification database based on the dimensions included in the data statistical method, and analyzes the specified identification to obtain the statistical target required by the user.

[0014] The identification statistical analysis method based on the Industrial Internet proposed in this application can bring the following beneficial effects:

[0015] The system provides users with multiple preset dimensions in advance. Users can make customized choices among these dimensions, thereby selecting the most suitable specified dimensions for their own statistical goals, making the statistical analysis process more flexible.

[0016] In addition, during the user's customization process, the specified dimensions selected by the user will be analyzed. If the user's selection is not accurate enough, the user will be assisted in selecting some dimensions to improve the accuracy of the final statistical analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0018] Figure 1 This is a flow chart of the identification statistical analysis method based on the Industrial Internet in an embodiment of the present application;

[0019] Figure 2 This is a schematic diagram of an identification statistics analysis device based on the Industrial Internet in an embodiment of the present application. DETAILED DESCRIPTION

[0020] To make the purpose, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the specific embodiments of this application and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0021] The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0022] The embodiment of the present application provides an identification statistical analysis method based on the Industrial Internet, which is applied to an identification statistical analysis system (hereinafter referred to as the identification system), and the identification system is at the same level as the secondary nodes in the Industrial Internet. In the Industrial Internet, the primary nodes are usually national nodes, and the secondary nodes are usually nodes of relevant competent departments, such as the Ministry of Health, the Ministry of Industry and Commerce, industry associations, etc., while enterprises in various industries serve as subordinate nodes of the secondary nodes. In order to ensure that the identification system can supervise and perform data statistical analysis on the nodes of each enterprise, it is set to be at the same level as the secondary node, and it is set to have read permission for the identification database. The identification system can adopt a microservice architecture to improve processing speed and can be horizontally expanded according to statistical needs.

[0023] like Figure 1 As shown, the method includes:

[0024] S101: The identification statistical analysis system provides a user with preset multiple dimensions for representing each identification stored in the identification database of the industrial Internet.

[0025] The identification system provides users with multiple pre-defined dimensions, which can be displayed to users based on their needs. The Industrial Internet's identification database stores registered identifiers, organized into fields with different dimensions. For example, these dimensions can include at least one of the following: prefix, handle, template, and registration time.

[0026] S102: Receive a statistics instruction sent by the user, where the statistics instruction at least includes the user's statistics target and several designated dimensions selected by the user from the multiple dimensions.

[0027] A corresponding client can be pre-configured to associate with the identification system. When a user initiates a corresponding instruction within the client, the statistical instruction is sent to the identification system through the client. The statistical instruction includes several specified dimensions selected by the user from the multiple dimensions displayed and a statistical target. The specified dimensions are the dimensions selected by the user, and the statistical target indicates the type of identification the user wants to obtain in the identification database using the selected specified dimensions, and the statistical target to be achieved based on the obtained identification.

[0028] S103: Analyze the plurality of designated dimensions according to the statistical target to predict the achievement levels of the plurality of designated dimensions with respect to the statistical target.

[0029] The achievement level indicates the degree to which the statistical target can be achieved through the identification obtained through several specified dimensions, which can be expressed in percentage or other forms.

[0030] Specifically, first, instance extraction is performed on the statistical target to obtain the instances contained within it. For example, if a user's statistical target is to calculate the daily production volume of Industry A, instance extraction is performed to obtain instances containing at least "Industry A" and "Daily Production Volume." A corresponding mapping table is then pre-set, containing the fit level between each specified dimension and the instance. Each user's historical records can be pre-collected to identify the instances used and generate fit levels for each dimension. If an instance extracted from the user's statistical target is not in the mapping table, the most similar instance in the mapping table can be selected as a replacement. The fit level indicates the fit relationship between the specified dimension and the instance. The fit levels of each specified dimension are summed to obtain the total fit level for the instance. Of course, if the total fit level exceeds 100%, it can be calculated as 100%. Based on the total fit level of each instance and its corresponding weight, the achievement level of the specified dimension with respect to the statistical target can be determined. The weight can be determined based on the pre-set priority relationship between the instances.

[0031] S104: If the implementation level is lower than a preset level, select at least some dimensions from among the multiple dimensions except the designated dimension as auxiliary dimensions.

[0032] When the implementation level is too low, lower than the preset level, it means that the specified dimension currently selected by the user is difficult to obtain a very accurate statistical target. Therefore, based on the specified dimension selected by the user, at least some dimensions are selected from other dimensions of multiple dimensions as auxiliary dimensions. The auxiliary dimensions are used to help users find more accurate and appropriate identifications in the identification database.

[0033] S105: Creating a data statistics method for the user according to the designated dimension and the auxiliary dimension.

[0034] Based on the specified dimensions and auxiliary dimensions, a data statistics method is created for the user in a customized form. If the end user approves the corresponding statistical target, the data statistics method can be used as a template to create a corresponding template in the client for subsequent use by other users.

[0035] S106: According to the dimensions included in the data statistics method, a corresponding designated identifier is obtained from the identifier database, and the designated identifier is analyzed to obtain the statistical target required by the user.

[0036] When obtaining a specified identifier, you can first perform a first round of screening based on the specified dimension in the data statistics method, obtain the identifier to be selected in the identifier database, and then perform a second round of screening on the identifier to be selected through the auxiliary dimension. Of course, the auxiliary dimension is automatically generated, and it may not be particularly closely related to the user's own needs. Therefore, in the second round of screening, some identifiers in the identifier to be selected that do not contain the auxiliary dimension will be screened out, and the proportion of the screened-out identifiers in the identifier to be selected will be lower than the preset proportion, so as to ensure that the specified dimension selected by the user can be used as the main factor, and then the specified identifier can be obtained.

[0037] After obtaining the specified identifier, the statistical target required by the user can be obtained by analyzing it.

[0038] For example, if the designated identifier is a registered identifier for a specific industry, the daily registration volume for that industry can be calculated based on the number of registered identifiers. Then, based on the industry type, the corresponding production efficiency can be determined. For example, the daily necessities industry has higher production efficiency, while large machinery has lower production efficiency. Production efficiency can be determined by calculating the average daily production volume within that industry type. Once the production efficiency and daily registration volume are determined, the daily production volume for that specific industry can be calculated, achieving the user's statistical objectives.

[0039] In one embodiment, the representation system has two modes: streaming processing and timed processing, which can be adapted to different statistical requirements.

[0040] Based on this, before determining the specified identifier, you can first determine the statistical type corresponding to the data statistical method. Then, after obtaining the new registration identifier, determine whether it hits the data statistical method based on the real-time or scheduled method corresponding to the statistical type. If it hits, the new registration identifier is added to the specified identifier. Among them, streaming processing corresponds to the real-time method. Each registration identifier pushes the necessary information of the identifier to the message queue. The consumer thread reads the message queue data and adds it to the results of different data statistical methods according to the specified dimensions defined by the user. Scheduled processing corresponds to the scheduled method. The scheduled task reads the new data in the identifier database and then analyzes it according to the specified dimensions defined by the user.

[0041] In one embodiment, the identification system has a user authority management function, and different users can count different information. For example, for a secondary node and its subordinate enterprise node, the secondary node administrator can count the information of all identifications in the secondary node and the enterprise node, while the enterprise node can only count the identifications registered by the current enterprise.

[0042] Based on this, after receiving the statistical instruction sent by the user, the user's identity information is first determined, and the identity information includes secondary nodes, enterprise nodes, etc. Then, according to the corresponding authority level, it is judged whether the statistical target meets the authority level. If not, the user can be informed that the statistical target cannot be obtained. If it meets, it can be further determined whether the specified dimension meets the authority level. If it meets, the next step of processing and analysis can be carried out. If it does not meet, the mapping relationship table can be used to find the dimension with the highest correlation with the specified dimension (for example, through the mapping relationship table, the similarity of the fit level between different dimensions, the fit level and the correlation are positively correlated) and the dimension that meets the authority level, and then the selected dimension is used to replace the specified dimension to help users perform statistical analysis.

[0043] In one embodiment, after obtaining the statistical target, it can be displayed to the user based on the user's needs. The display method can be common charts, such as bar charts, pie charts, line charts, etc. For bar charts and line charts, first build a display chart, then use the specified dimension and auxiliary dimension as the X-axis of the display chart, and display the statistical target as the Y-axis. In addition, in order to highlight the user's selection, the specified dimension can be rendered prominently (for example, by adjusting the contrast, hue, brightness, etc.) to distinguish the specified dimension from the auxiliary dimension, thereby improving the user's viewing experience.

[0044] like Figure 2 As shown, the embodiment of the present application further provides an identification statistical analysis device based on the Industrial Internet, which is applied to the identification statistical analysis system. The identification statistical analysis system is at the same level as the secondary node in the Industrial Internet. The device includes:

[0045] at least one processor; and,

[0046] a memory communicatively connected to the at least one processor; wherein,

[0047] The memory stores instructions executable by the at least one processor, wherein the instructions are executed by the at least one processor to enable the at least one processor to perform:

[0048] The identification statistical analysis system provides the user with a plurality of preset dimensions for representing each identification stored in the identification database of the industrial Internet;

[0049] receiving a statistical instruction sent by the user, wherein the statistical instruction includes at least a statistical target of the user and a plurality of designated dimensions selected by the user from the plurality of dimensions;

[0050] Analyzing the plurality of designated dimensions according to the statistical target to predict achievement levels of the plurality of designated dimensions with respect to the statistical target;

[0051] If the achievement level is lower than a preset level, selecting at least some dimensions from among the multiple dimensions except the designated dimension as auxiliary dimensions;

[0052] Creating a data statistics method for the user according to the specified dimension and the auxiliary dimension;

[0053] According to the dimensions included in the data statistics method, the corresponding designated identifier is obtained from the identifier database, and the designated identifier is analyzed to obtain the statistical target required by the user.

[0054] The embodiment of the present application further provides a non-volatile computer storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured to:

[0055] The identification statistical analysis system provides the user with a plurality of preset dimensions for representing each identification stored in the identification database of the industrial Internet;

[0056] receiving a statistical instruction sent by the user, wherein the statistical instruction includes at least a statistical target of the user and a plurality of designated dimensions selected by the user from the plurality of dimensions;

[0057] Analyzing the plurality of designated dimensions according to the statistical target to predict achievement levels of the plurality of designated dimensions with respect to the statistical target;

[0058] If the achievement level is lower than a preset level, selecting at least some dimensions from among the multiple dimensions except the designated dimension as auxiliary dimensions;

[0059] Creating a data statistics method for the user according to the specified dimension and the auxiliary dimension;

[0060] According to the dimensions included in the data statistics method, the corresponding designated identifier is obtained from the identifier database, and the designated identifier is analyzed to obtain the statistical target required by the user.

[0061] The various embodiments in this application are described in a progressive manner. Similar portions between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the device and medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simple. For relevant portions, refer to the descriptions of the method embodiments.

[0062] The devices and media provided in the embodiments of the present application correspond one-to-one to the methods. Therefore, the devices and media also have similar beneficial technical effects to their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0063] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0064] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0065] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0066] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0067] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0068] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0069] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0070] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0071] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A method for statistical analysis of identification based on the Industrial Internet, characterized in that: Applied to an identification statistical analysis system, the identification statistical analysis system is at the same level as a secondary node in the industrial Internet, the method includes: The identification statistical analysis system provides the user with a plurality of preset dimensions for representing each identification stored in the identification database of the industrial Internet; receiving a statistical instruction sent by the user, wherein the statistical instruction includes at least a statistical target of the user and a plurality of designated dimensions selected by the user from the plurality of dimensions; According to the statistical target, the plurality of specified dimensions are analyzed to predict the achievement levels of the plurality of specified dimensions with respect to the statistical target, specifically comprising: extracting instances of the statistical target to obtain instances contained in the statistical target; for each instance, determining, according to a preset mapping relationship table, a fit level between each specified dimension and the instance, and accumulating the fit levels corresponding to each specified dimension to obtain a total fit level corresponding to the instance; and predicting the achievement levels of the plurality of specified dimensions with respect to the statistical target based on the total fit levels corresponding to each instance and the weights corresponding to each instance; If the achievement level is lower than a preset level, selecting at least some dimensions from among the multiple dimensions except the designated dimension as auxiliary dimensions; Creating a data statistics method for the user according to the specified dimension and the auxiliary dimension; According to the dimensions included in the data statistics method, a corresponding designated identifier is obtained from the identifier database, and the designated identifier is analyzed to obtain the statistical target required by the user; Before obtaining the corresponding designated identifier in the identifier database according to the dimension included in the data statistics method, the method further includes: Determining a statistical type corresponding to the data statistical method, where the statistical type includes at least one of stream processing and timed processing; Obtain a new registration ID; Determining whether the registration identifier matches the data statistics method in a real-time or scheduled manner corresponding to the statistics type; If a match is found, the new registration identifier is added to the designated identifier; After receiving the statistics instruction sent by the user, the method further includes: Determine the identity information of the user, where the user information includes at least one of a secondary node and an enterprise node under the secondary node; Determining the user's authority level based on the identity information, and judging whether the statistical target meets the authority level; If yes, then determining whether the specified dimension meets the permission level; If not, the dimension with the highest correlation with the designated dimension and in compliance with the permission level is selected according to the mapping relationship table, and the designated dimension is replaced by the selected dimension, and the correlation is positively correlated with the compliance level.

2. The method according to claim 1, wherein The acquiring, in the identification database, a corresponding designated identification according to the dimensions included in the data statistics method specifically includes: According to the specified dimension included in the data statistics method, obtaining a corresponding to-be-selected identifier in the identifier database; According to the auxiliary dimension included in the data statistics method, the candidate identifiers are screened, and some identifiers that do not include the auxiliary dimension are screened out to obtain corresponding designated identifiers, and the proportion of the screened out identifiers in the candidate identifiers is lower than the preset proportion.

3. The method according to claim 1, wherein The designated identifier includes the registration identifier of the designated industry, and the statistical target includes the daily production volume of the designated industry; The analyzing the designated identifier to obtain the statistical target required by the user specifically includes: Determine the daily registration volume of the registered logo for the designated industry; determining the production efficiency of the designated industry according to the industry type of the designated industry; The daily production volume of the designated industry is obtained according to the daily registration volume and the production efficiency.

4. The method according to claim 1, wherein The method further comprises: Determining whether the data statistics method includes the auxiliary dimension; If so, construct a display chart, use the specified dimension and the auxiliary dimension as the X-axis of the display chart, use the statistical target as the Y-axis of the display chart, and highlight the specified dimension in the X-axis; The display chart is displayed to the user.

5. The method according to any one of claims 1 to 4, wherein The preset multiple dimensions include at least multiple items of prefix, handle, template, and registration time.

6. An identification statistical analysis device based on the Industrial Internet, characterized in that: Applied to the identification statistical analysis system, which is at the same level as the secondary nodes in the Industrial Internet, the device includes: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute: the industrial Internet-based identification statistical analysis method described in claim 1.

7. A non-volatile computer storage medium storing computer executable instructions, characterized in that: The computer executable instructions are set to: the identification statistical analysis method based on the industrial Internet as described in claim 1.

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