CN117217196A - Data processing system, method and computer device - Google Patents
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Abstract
The application discloses a data processing system, a method and a computer device, wherein the data processing comprises the following steps: collecting data exported and/or imported to each manufacturer, and determining the data classification mode corresponding to each manufacturer; and automatically adjusting the preset data templates according to the data classification mode of each manufacturer and a preset template so as to individually match the data templates for each different manufacturer.
Description
Technical Field
The present application relates to the field of data processing, and in particular, to a data processing system, a data processing method, and a computer device.
Background
As electronic commerce becomes increasingly popular with the public, more and more users tend to shop online. Wherein the tremble e-commerce is also gradually a part of the main stream of the e-commerce. With the increase of the purchase of goods by users through the tremble electronic commerce, the data to be processed by the tremble electronic commerce is more and more, and especially for some electronic commerce enterprises selling more product types, the data volume to be processed is huge.
Traditionally, most trembling merchants have selected sophisticated software to manage such data, such as merchandise information, order information, and the like. When the jittering merchant checks with the manufacturer, the data needs to be intensively arranged to obtain a data form meeting the requirements of the manufacturer. While different vendors have different requirements for the data that is needed. Some manufacturers need to feed back data including the single-day sales number of the commodity, but not the monthly sales number, and some manufacturers need to feed back data including the single-day sales number of the commodity and also including the approximate sales number of the commodity.
When the existing software is used for solving the problems, a template for importing or exporting data is usually set, and a user can import or export data according to the preset template. However, these templates cannot be individually adapted to different vendors.
In addition, when the user sets up the template, data appearing in the template may be repeated, such as single day data and weekly data, wherein the weekly data includes single day data. However, when the user sets up the template, there is an error operation, which causes the repeated data to be required to appear in the template, and thus the efficiency of data import or export is greatly reduced.
Disclosure of Invention
One advantage of the present application is to provide a data processing system, method and computer device, wherein the data processing method can automatically form a personalized template according to different vendor requirements, so that a user can obtain data corresponding to vendor requirements.
Another advantage of the present application is to provide a data processing system, method, and computer apparatus, where the data processing method can automatically perform similarity determination on data attribution in the data template, so that redundant data in the exported data can be removed when the data is exported, and further, export or import of the data can be faster.
Another advantage of the present application is to provide a data processing system, method, and computer apparatus, by which data can be processed by asynchronous processing when the data is exported or imported, thereby effectively preventing data loss and repeated import or export of data.
To achieve at least one of the above advantages, the present application provides a data processing method including:
collecting data of each manufacturer exported and/or imported;
calculating the frequency and/or the frequency of occurrence of each data attribution in imported or exported data under the data of the same service type of each manufacturer, recording the data attributions with the frequency and/or the frequency lower than a preset threshold value as the data attributions not accounting for the data template, and recording the data attributions with the frequency and/or the frequency higher than a preset threshold value as the data attributions accounting for the data template;
determining all the data attributions which are counted into the data templates as a display strategy, determining all the data attributions which are not counted into the data templates as a non-display strategy, and defining the display strategy and the non-display strategy as a data classification mode of the manufacturer;
and automatically adjusting the preset templates according to the data classification mode of each manufacturer, a preset template, the display strategy and the non-display strategy.
According to one embodiment of the application, the collected data for each vendor is set to be exported and/or imported data for a predetermined period of time.
According to an embodiment of the present application, according to the acquired preset templates set by a user, a display template policy to which data to be displayed on the preset templates belongs and a non-display template policy to which data to be adjusted belongs are formed in response to an operation signal related to data attribution which the user sets the data to be displayed in the preset templates.
According to an embodiment of the present application, the data processing method further includes the steps of:
collecting all the data templates of each data type of each manufacturer;
judging the similarity between the attributions of the data in the data templates in each data type;
and shielding at least one of at least two data attributions with higher similarity of the data attributions in the data template, and displaying the data attributions with lower similarity of the data attributions in the data template to form the adjusted data template.
According to an embodiment of the present application, shielding at least one of at least two data attributions having a higher similarity of the data attributions in the data template, and displaying the data attributions having a lower similarity of the data attributions in the data template, to form the adjusted data template includes:
hiding at least one of at least two data attributions with higher similarity of the data attributions in the data template, and displaying the data attributions with lower similarity of the data attributions in the data template.
According to an embodiment of the present application, shielding at least one of at least two data attributions having a higher similarity of the data attributions in the data template, and displaying the data attributions having a lower similarity of the data attributions in the data template, to form the adjusted data template includes:
and deleting at least one of at least two data attributions with higher similarity of the data attributions in the data template, and displaying the data attributions with lower similarity of the data attributions in the data template.
According to an embodiment of the present application, determining similarity between attributions of the data in the data templates in each data type includes:
collecting data exported and/or imported via the data templates corresponding to the data types by the same manufacturer;
determining the similarity between the attributions of the data in the data templates corresponding to the data types of the same manufacturer in a semantic analysis mode;
judging the times and/or frequencies of at least two data attributions with high similarity between the data attributions in the data templates corresponding to the data types of the same manufacturer and simultaneously appearing in data exported and/or imported by the data templates corresponding to the data types of the same manufacturer in a semantic analysis mode, and determining that the similarity between the data attributions is lower when the times and/or frequencies of at least two data attributions with high similarity between the data attributions simultaneously appearing in data exported and/or imported by the data templates corresponding to the data types of the same manufacturer exceed a preset value; whereas the opposite is higher.
According to one aspect of the present application, there is provided a data processing system comprising:
the data acquisition module is used for acquiring data exported and/or imported to each manufacturer;
the data processing module is used for calculating the frequency and/or the frequency of occurrence of each data attribution in imported or exported data under the data of the same business type of each manufacturer, marking the data attribution with the frequency and/or the frequency lower than a preset threshold value as the data attribution of a non-counted data template, and marking the data attribution with the frequency and/or the frequency higher than a preset threshold value as the data attribution counted into the data template; determining all the data attributions which are counted into the data templates as a display strategy, determining all the data attributions which are not counted into the data templates as a non-display strategy, and defining the display strategy and the non-display strategy as a data classification mode of the manufacturer; and
the execution module is used for setting the preset templates according to the data classification mode of each manufacturer, a preset template, the display strategy and the non-display strategy.
According to an aspect of the present application, to achieve the above object, the present application provides a storage medium storing a computer program which, when executed, causes the computer program to perform any one of the data processing methods described above.
According to one aspect of the present application, to achieve the above object, the present application provides a computer apparatus comprising one or more processors and a storage medium, wherein the storage medium is communicatively connected to the processor, and the processor is configured to be able to perform any of the data processing methods described above.
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Fig. 1 shows a flow chart of a data processing method according to the application.
Figure 2 shows a schematic diagram of one embodiment of the data processing method of the application when performed.
FIG. 3 is a schematic diagram of an operation interface when the data processing method of the present application is executed.
Fig. 4 shows a schematic diagram of another embodiment of the data processing method according to the application when it is performed.
FIG. 5 is a block diagram illustrating the structure of a data processing system according to the present application.
Fig. 6 shows a block diagram of a computer device according to the application.
Detailed Description
The following description is presented to enable one of ordinary skill in the art to make and use the application. The preferred embodiments in the following description are by way of example only and other obvious variations will occur to those skilled in the art. The basic principles of the application defined in the following description may be applied to other embodiments, variations, modifications, equivalents, and other technical solutions without departing from the spirit and scope of the application.
Exemplary data processing method
Referring to fig. 1, a data processing method according to a preferred embodiment of the present application will be described in detail, wherein the data processing method comprises the steps of:
s1001, data exported and/or imported to each manufacturer is collected, and the data classification mode of each manufacturer is determined.
It will be appreciated by those skilled in the art that the collected data for each vendor may be provided as export and/or import data for a predetermined period of time, such as one month, last quarter, or last year export and/or import data. By the mode, the data quantity of the collected data can be ensured, and meanwhile, the pressure of subsequent data analysis can be effectively reduced.
Further, it is understood that each vendor is given a preset tag to associate the tag with the corresponding vendor when exporting or importing data.
Furthermore, the data department of each manufacturer is assigned to a different header, that is, the data assignment of the data can be implemented as determined from the header attributes of the table. For example, as shown in Table 1 below, the manner in which data from one vendor is categorized is shown.
Table 1: data classification mode of commodity files of certain manufacturer
| Commodity name | Delivery time | Number of sales per month | Monovalent unit price | Quantity of returned goods | Refund amount |
| A commodity | *** | **** | ***** | ******* | ********* |
| B commodity | *** | **** | ***** | ******* | ********* |
Further, the data processing method comprises the following steps:
s1002, automatically adjusting the preset templates according to the data classification mode and the preset templates of each manufacturer to form a data template which is individually matched with different manufacturers.
It should be noted that, in general, a default template is preset for the data imported and exported. The default templates are not necessarily applicable to each different manufacturer, mainly because the categorization manner of the data by the different manufacturers may have its own personalized requirements, and the data templates can be automatically personalized for the different manufacturers via the above step S1001 and the step S1002.
Further, in step S1001, data exported and/or imported to each manufacturer is collected, and a data classification mode corresponding to each manufacturer is determined, including:
s10011, collecting data of each manufacturer exported and/or imported;
s10012, calculating the frequency and/or the frequency of occurrence of each data attribution in imported or exported data under the data of the same service type of each manufacturer, recording the data attribution with the frequency and/or the frequency lower than a preset threshold value as the data attribution of a non-counted data template, and recording the data attribution with the frequency and/or the frequency higher than a preset threshold value as the data attribution counted into the data template;
s10013, determining all the data attributions which are counted into the data templates as a display strategy, determining all the data attributions which are not counted into the data templates as a non-display strategy, and defining the display strategy and the non-display strategy as a data classification mode of the manufacturer.
For example, referring to FIG. 2, in the embodiment shown in FIG. 2, the vendor's same-service data "commodity archive" occurred at a time in the last quarter with a data attribution "return scenario description" that was significantly below a set threshold, and therefore, would be referred to as a data attribution that does not count into the data template; and the number and/or frequency of occurrence for other data attributions is higher than the set threshold, so other data attributions will be counted as data attributions which are counted into the data templates.
Correspondingly, through the step S1002, the preset templates are automatically adjusted according to the display policy and the non-display policy, so that the formed data templates can be automatically matched with each manufacturer.
For example, in the example shown in fig. 2, the number and/or frequency of occurrences of the data attribution "return description" is relatively low, so that it may be defaulted that the manufacturer may not display the content in the data when extracting the data, and thus, by performing the step S10013, a policy of attribution of the data is formed which is not counted in the data template accordingly. The other data attribution "return situation description" occurs more frequently and/or more frequently, so that it may be defaulted that the manufacturer may wish to display the content in the data when extracting the data, and the policy of attribution of the data is formed by executing the step S10012, accordingly.
And by executing the step S1002, the preset template can be automatically adjusted according to the policy formed in the step S1003. For example, if there is a data attribution of the non-counted data template in the preset template, the data attribution of the non-counted data template may be automatically deleted and/or hidden by performing the step S1002, as in the "return situation description". Conversely, if the preset template does not have the data attribution counted in the data template, the step S1002 may be performed to automatically display and/or add the data attribution counted in the data template, such as other data attributions.
It will be appreciated by those skilled in the art that in the above manner, the user does not need to manually set templates during data import or export, especially in the case of more vendors. By the mode, the data collection efficiency of the user can be effectively improved.
Preferably, the data processing method further comprises the steps of:
s2001, according to the acquired preset templates set by the user, responding to the operation signals related to the attribution of the data which must be displayed in the preset templates set by the user, and forming a display template strategy of the attribution of the data which must be displayed on the preset templates and a non-display template strategy related to the attribution of the data which can be adjusted. Such as an operational cross-section as shown in fig. 3.
Correspondingly, when executing the step S1002, the step S1002 includes:
s10021, adjusting the preset templates according to the display template policy, the non-display template policy, and a policy related to attribution of data that must be displayed in the preset templates and attribution of data that can be adjusted, so as to individually match the data templates for each different manufacturer.
For example, in the example shown in fig. 2, the user may determine that the data attribution "commodity name", "shipment time", and the like are data attributions that must be displayed, and "unit price", "shipment number", and the like are data attributions that can be adjusted.
Then, by executing the steps S2001 and S10021, the data templates of all manufacturers can be assigned with data that must be displayed, such as "commodity name" and "delivery time", and the data that must be displayed, such as "commodity name" and "delivery time", can be assigned with data that must be displayed, such as "commodity name" and "delivery time", among the data that is exported or imported. Preferably, the unit price, the return amount, and the like are determined according to the steps S10011 to S10013 as the data attribution which can be adjusted.
It can be appreciated by those skilled in the art that by the above manner, not only the finally formed data template can be individually matched with a manufacturer, i.e. meets the requirements of the manufacturer, but also the data amount in the data processing process can be effectively reduced, so that the data processing efficiency is effectively improved.
The data processing method further comprises the following steps:
s3001, collecting all data templates of each data type of each manufacturer;
s3002, judging the similarity between the attributions of the data in the data templates in each data type;
s3003, shielding at least one of at least two data attributions with higher similarity in the data templates, and displaying the data attributions with lower similarity in the data templates to form the adjusted data templates.
Preferably, in one embodiment, the step S3003 is implemented as:
s30031, hiding at least one of at least two data attributions with higher similarity in the data template, and displaying the data attributions with lower similarity in the data template.
More preferably, in another embodiment, the step S3003 is implemented as:
s30032, deleting at least one of at least two data attributions with higher similarity in the data template, and displaying the data attributions with lower similarity in the data template.
It should be noted that, in this embodiment, since at least one of the at least two data attributions with higher similarity of the data attributions in the data templates is deleted, when the data is exported or imported through the adjusted data templates later, not only the finally formed data can meet the requirements of the user, but also the data processing amount can be reduced when the data is exported or imported, and the data processing efficiency can be improved.
Preferably, in one embodiment, the similarity determination between the data attributions may be determined by means of semantic analysis.
Preferably, the step S3002 of determining the similarity between the attributions of the data in the data templates in each data type includes:
s30021, collecting data exported and/or imported through the data templates corresponding to the data types by the same manufacturer;
s30022, determining the similarity between the attributions of the data in the data templates corresponding to the data types by the same manufacturer in a semantic analysis mode;
s30023, judging the times and/or frequencies of at least two data attributions with high similarity between the data attributions in the data exported and/or imported by the data templates corresponding to the data types of the same manufacturer at the same time, and determining that the similarity between the data attributions is lower when the times and/or frequencies of at least two data attributions with high similarity between the data attributions in the data exported and/or imported by the data templates corresponding to the data types of the same manufacturer at the same time exceed a preset value; whereas the opposite is higher.
For example: in the example shown in fig. 4, it is finally possible to determine that the data attribution "number of daily sales" and "number of monthly sales" are highly similar through semantic analysis. However, as can be seen by executing the step S30023, the data attribution "daily sales count" and "monthly sales count" include the "daily sales count" and "monthly sales count" in all of the 3n pieces of data, and at this time, the data attribution "daily sales count" and "monthly sales count" are also retained in the finally formed adjusted data template by executing the step S30023 and the step S3003.
It will be appreciated by those skilled in the art that by the above steps, the data templates that are ultimately formed can be effectively and accurately simplified, such that redundant data with respect to each vendor is effectively eliminated when data is imported or exported via the data templates.
Preferably, the data processing method further includes:
s4001, storing exported or imported data in a message queuing mode;
s4002, after storage is completed, exporting or importing data according to the formed data template.
It will be appreciated by those skilled in the art that by performing the step S4001, data can be stored first when being imported or imported, so as to prevent data loss. Then, by executing the step S4002, asynchronous processing of data can be realized.
Exemplary data processing System
Referring to FIG. 5, in accordance with another aspect of the present application, a data processing system is provided, wherein the data processing system includes a data acquisition module 100, a data processing module 200, and an execution module 300, wherein the data acquisition module 100 is configured to acquire data exported and/or imported to each vendor; wherein the data processing module 200 is configured to calculate the number of times and/or frequency of occurrence of each data attribution in the imported or exported data under the same service type data of each manufacturer, record the data attribution with the number of times and/or frequency lower than a preset threshold as the data attribution of the non-accounting data template, and record the data attribution with the number of times and/or frequency higher than a preset threshold as the data attribution accounting the data template; determining all the data attributions which are counted into the data templates as a display strategy, determining all the data attributions which are not counted into the data templates as a non-display strategy, and defining the display strategy and the non-display strategy as a data classification mode of the manufacturer; wherein the output module 300 is configured to automatically adjust the preset templates according to the data classification and a preset template and the display and non-display policies of each manufacturer.
In addition, the acquisition module 100, the data processing module 200 and the execution module 300 are further configured to execute other steps of the data processing method described above.
Exemplary computer device
FIG. 6 is a schematic structural diagram of an embodiment of a computer device according to the present application, as shown in FIG. 6, the computer device may include: one or more processors and memory; and one or more computer programs.
The computer device may be a computer, a server, a mobile terminal (mobile phone), a cashing device, a computer, an intelligent screen, an unmanned aerial vehicle, an intelligent network vehicle (Intelligent Connected Vehicle; hereinafter abbreviated as ICV), an intelligent vehicle (smart/intelligent car) or a vehicle-mounted device.
Wherein the one or more computer programs are stored in the memory, the one or more computer programs comprising instructions that, when executed by the apparatus, cause the apparatus to perform the data processing method.
The computer device shown in fig. 6 may be a terminal device or a server, or may be a circuit device incorporated in the terminal device or the server. The device may be used to perform the data processing method provided by the embodiment of the application shown in fig. 1.
As shown in fig. 6, computer device 900 includes a processor 910 and a memory 920. Wherein the processor 910 and the memory 920 may communicate with each other via an internal connection, and transfer control and/or data signals, the memory 920 is configured to store a computer program, and the processor 910 is configured to call and run the computer program from the memory 920.
The memory 920 may be a read-only memory (ROM), other type of static storage device that can store static information and instructions, a random access memory (random access memory, RAM) or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable read-only memory (electrically erasable programmable read-only memory, EEPROM), a compact disc read-only memory (compact disc read-only memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, versatile discs, blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, etc.
The processor 910 and the memory 920 may be combined into a single processing device, more commonly referred to as separate components, and the processor 910 is configured to execute program code stored in the memory 920 to perform the functions described above. In particular, the memory 920 may also be integrated into the processor 910 or may be separate from the processor 910.
It should be appreciated that the computer device 900 shown in fig. 6 is capable of implementing the various processes of the method provided by the embodiment of fig. 1 of the present application. The operations and/or functions of the respective modules in the computer apparatus 900 are respectively for implementing the respective flows in the above-described method embodiments. Reference is made in particular to the description of the embodiment of the method according to the application shown in fig. 1, and a detailed description is omitted here as appropriate for avoiding repetition.
In addition, in order to further improve the functionality of the computer device 900, the computer device 900 may further include one or more of a power supply 940, an input unit 950, and the like.
Optionally, a power supply 950 is used to provide power to various devices or circuits in the computer apparatus.
It should be understood that the processor 910 in the computer apparatus 900 shown in fig. 6 may be a system on a chip SOC, and the processor 910 may include a central processing unit (Central Processing Unit; hereinafter referred to as a CPU) and may further include other types of processors.
In general, portions of the processors or processing units within the processor 910 may cooperate to implement the preceding method flows, and corresponding software programs for the portions of the processors or processing units may be stored in the memory 920.
The present application also provides a computer apparatus, which includes a storage medium, which may be a nonvolatile storage medium, in which a computer executable program is stored, and a central processor connected to the nonvolatile storage medium and executing the computer executable program to implement the method provided by the embodiment shown in fig. 1 of the present application.
In the above embodiments, the processor may include, for example, a CPU, DSP, microcontroller, or numerical signal processor, and may also include a GPU, an embedded Neural network processor (Neural-network Process Units); the processor may also include the necessary hardware accelerators or logic processing hardware circuitry, such as an ASIC, or one or more integrated circuits for controlling the execution of the program of the present application, etc. Further, the processor may have a function of operating one or more software programs, which may be stored in a storage medium.
Embodiments of the present application also provide a computer readable storage medium having a computer program stored therein, which when run on a computer causes the computer to perform the method provided by the embodiment of the present application shown in fig. 1.
Embodiments of the present application also provide a computer program product comprising a computer program which, when run on a computer, causes the computer to perform the method provided by the embodiment of the present application shown in fig. 1.
Those of ordinary skill in the art will appreciate that the various elements and algorithm steps described in the embodiments disclosed herein can be implemented as a combination of electronic hardware, computer software, and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In several embodiments provided by the present application, any of the functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (hereinafter referred to as ROM), a random access Memory (Random Access Memory) and various media capable of storing program codes such as a magnetic disk or an optical disk.
The foregoing is merely exemplary embodiments of the present application, and any person skilled in the art may easily conceive of changes or substitutions within the technical scope of the present application, which should be covered by the present application. The protection scope of the present application shall be subject to the protection scope of the claims.
It will be appreciated by persons skilled in the art that the embodiments of the application described above and shown in the drawings are by way of example only and are not limiting. The advantages of the present application have been fully and effectively realized. The functional and structural principles of the present application have been shown and described in the examples and embodiments of the application may be modified or practiced without departing from the principles described.
Claims (10)
1. A data processing method, characterized in that the data processing method comprises:
collecting data of each manufacturer exported and/or imported;
calculating the frequency and/or the frequency of occurrence of each data attribution in imported or exported data under the data of the same service type of each manufacturer, recording the data attributions with the frequency and/or the frequency lower than a preset threshold value as the data attributions not accounting for the data template, and recording the data attributions with the frequency and/or the frequency higher than a preset threshold value as the data attributions accounting for the data template;
determining all the data attributions which are counted into the data templates as a display strategy, determining all the data attributions which are not counted into the data templates as a non-display strategy, and defining the display strategy and the non-display strategy as a data classification mode of the manufacturer;
and automatically adjusting the preset templates according to the data classification mode of each manufacturer, a preset template, the display strategy and the non-display strategy.
2. A data processing method according to claim 1, wherein the collected data for each vendor is set as exported and/or imported data for a predetermined period of time.
3. A data processing method according to claim 1 or 2, wherein a display template policy to which data to be displayed on the preset template is attributed and a non-display template policy to which data to be adjusted is attributed are formed in response to a user setting of an operation signal to which data to be displayed on the preset template is attributed in accordance with the acquired preset template set by the user.
4. The data processing method according to claim 1 or 2, characterized in that the data processing method further comprises the steps of:
collecting all the data templates of each data type of each manufacturer;
judging the similarity between the attributions of the data in the data templates in each data type;
and shielding at least one of at least two data attributions with higher similarity of the data attributions in the data template, and displaying the data attributions with lower similarity of the data attributions in the data template to form the adjusted data template.
5. The data processing method of claim 4, wherein masking at least one of the at least two data attributes in the data template that have a higher similarity to the data attributes, and displaying the data attributes in the data template that have a lower similarity to the data attributes to form the adjusted data template comprises:
hiding at least one of at least two data attributions with higher similarity of the data attributions in the data template, and displaying the data attributions with lower similarity of the data attributions in the data template.
6. The data processing method of claim 4, wherein masking at least one of the at least two data attributes in the data template that have a higher similarity to the data attributes, and displaying the data attributes in the data template that have a lower similarity to the data attributes to form the adjusted data template comprises:
and deleting at least one of at least two data attributions with higher similarity of the data attributions in the data template, and displaying the data attributions with lower similarity of the data attributions in the data template.
7. The data processing method of claim 4, wherein determining the similarity between the attributions of the data in the data templates for each data type comprises:
collecting data exported and/or imported via the data templates corresponding to the data types by the same manufacturer;
determining the similarity between the attributions of the data in the data templates corresponding to the data types of the same manufacturer in a semantic analysis mode;
judging the times and/or frequencies of at least two data attributions with high similarity between the data attributions in the data templates corresponding to the data types of the same manufacturer and simultaneously appearing in data exported and/or imported by the data templates corresponding to the data types of the same manufacturer in a semantic analysis mode, and determining that the similarity between the data attributions is lower when the times and/or frequencies of at least two data attributions with high similarity between the data attributions simultaneously appearing in data exported and/or imported by the data templates corresponding to the data types of the same manufacturer exceed a preset value; whereas the opposite is higher.
8. A data processing system, the data processing system comprising:
the data acquisition module is used for acquiring data exported and/or imported to each manufacturer;
the data processing module is used for calculating the frequency and/or the frequency of occurrence of each data attribution in imported or exported data under the data of the same business type of each manufacturer, marking the data attribution with the frequency and/or the frequency lower than a preset threshold value as the data attribution of a non-counted data template, and marking the data attribution with the frequency and/or the frequency higher than a preset threshold value as the data attribution counted into the data template; determining all the data attributions which are counted into the data templates as a display strategy, determining all the data attributions which are not counted into the data templates as a non-display strategy, and defining the display strategy and the non-display strategy as a data classification mode of the manufacturer; and
the execution module is used for setting the preset templates according to the data classification mode of each manufacturer, a preset template, the display strategy and the non-display strategy.
9. A storage medium, characterized in that a computer program is stored which, when executed, causes it to carry out the data processing method of any one of the preceding claims 1 to 7.
10. Computer device, characterized in that it comprises one or more processors and a storage medium, wherein the storage medium is communicatively connected to the processor and the processor is arranged to be able to perform the data processing method of any of the preceding claims 1 to 7.
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