CN112733195A - Data configuration method and device, readable storage medium and computing equipment - Google Patents
Data configuration method and device, readable storage medium and computing equipment Download PDFInfo
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- CN112733195A CN112733195A CN202110323161.4A CN202110323161A CN112733195A CN 112733195 A CN112733195 A CN 112733195A CN 202110323161 A CN202110323161 A CN 202110323161A CN 112733195 A CN112733195 A CN 112733195A
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Abstract
The invention discloses a data configuration method, a data configuration device, a readable storage medium and a computing device, wherein the data configuration method comprises the following steps: reading the operation authority of the current user and all basic data; carrying out authority classification on all the basic data, and determining the authority category of each basic data; determining the configurable class and the non-configurable class of the current user according to the operation authority of the current user; receiving an operation instruction of the current user, displaying the basic data in the configurable class and the non-configurable class to the current user, and selecting the basic data in the configurable class to form combined configuration data in a combined mode. The method and the device can enable a user to check all data information but only configure the data in the authority range of the user, enable the user to master the data in all directions, and are more favorable for the user to configure the data in the authority range of the user, so that the configuration result is more reasonable and reliable.
Description
Technical Field
The present invention relates to the field of internet technologies, and in particular, to a data configuration method and apparatus, a readable storage medium, and a computing device.
Background
In the management and configuration of data, because the levels of operators are different, the data that different operators can view, manage and configure are different, and therefore, the management and control of data are inconvenient. Meanwhile, data classification is not fine and accurate enough, and inconvenience is caused to the management and control of data again.
Disclosure of Invention
In order to solve at least one of the above technical problems, the present disclosure provides a data configuration method, an apparatus, a readable storage medium, and a computing device.
In a first aspect, an embodiment of the present invention provides a data configuration method, where the data configuration method includes:
reading the operation authority of the current user and all basic data;
carrying out authority classification on all the basic data, and determining the authority category of each basic data;
determining the configurable class and the non-configurable class of the current user according to the operation authority of the current user;
receiving an operation instruction of the current user, displaying the basic data in the configurable class and the non-configurable class to the current user according to the operation instruction, and selecting the basic data in the configurable class to form combined configuration data in a combined manner.
Optionally, the classifying the permissions of all the basic data to determine the permission category of each basic data includes:
calculating the risk-benefit ratio of each basic data in a first preset time period;
dividing the basic data corresponding to the risk-benefit ratio larger than a preset standard threshold into non-standard classes;
dividing the basic data corresponding to the risk-benefit ratio smaller than or equal to a preset standard threshold into standard classes;
the standard class and the non-standard class have different permission levels.
Optionally, the data configuration method further includes, for the basic data in the non-standard class:
determining influence factors of the basic data and influence factors of the influence factors;
inputting the influence factors into a hierarchical clustering model to obtain a correlation coefficient of each basic data and each residual other basic data;
and dividing the basic data of which the correlation coefficient is greater than a preset correlation threshold value into the same subclass.
Optionally, the data configuration method further includes, for the basic data in the standard class:
calculating a rolling risk-benefit ratio average value of each basic data in a second preset time period;
dividing threshold ranges of rolling risk-benefit ratio mean values, and dividing each basic data into corresponding different subclasses according to the rolling risk-benefit ratio mean values of each basic data, wherein one threshold range corresponds to one subclass.
In a second aspect, the present invention provides a data configuration apparatus, including: a data reading module, a data classifying module, a category corresponding module and a data configuring module, wherein,
the data reading module is used for reading the operation authority of the current user and all basic data;
the data classification module is used for carrying out authority classification on all the basic data and determining the authority category of each basic data;
the category corresponding module is used for determining the configurable category and the non-configurable category of the current user according to the operation authority of the current user;
the data configuration module is configured to receive an operation instruction of the current user, display the basic data in the configurable class and the non-configurable class to the current user, and select the basic data in the configurable class to combine to form combined configuration data.
Optionally, the data classification module is specifically configured to calculate a risk-benefit ratio of each piece of basic data in a first preset time period, classify the basic data corresponding to the risk-benefit ratio larger than a preset standard threshold into a non-standard class, and classify the basic data corresponding to the risk-benefit ratio smaller than or equal to the preset standard threshold into a standard class; the standard class and the non-standard class have different permission levels.
Optionally, the data classification module is specifically configured to, for the basic data in the non-standard class: determining influence factors of the basic data and influence factors of the influence factors, inputting the influence factors into a hierarchical clustering model to obtain a correlation coefficient of each basic data and each remaining other basic data, and dividing the basic data of which the correlation coefficient is greater than a preset correlation threshold value into the same subclass.
Optionally, the data classification module is specifically configured to, for the basic data in the standard class: calculating a rolling risk-benefit ratio mean value of each basic data in a second preset time period, dividing each basic data into corresponding different subclasses according to the rolling risk-benefit ratio mean value of each basic data by dividing a threshold range of the rolling risk-benefit ratio mean value, wherein one threshold range corresponds to one subclass.
In a third aspect, embodiments of the present invention provide a readable storage medium having executable instructions thereon, which when executed, cause a computer to perform the operations as included in the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computing device, including: one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors to perform the operations included in the first aspect.
Compared with the prior art, the invention has at least the following beneficial effects:
the method and the device can enable a user to check all data information but only configure the data in the authority range of the user, enable the user to master the data in all directions, and are more favorable for the user to configure the data in the authority range of the user, so that the configuration result is more reasonable and reliable.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a data configuration method according to an embodiment of the present invention;
fig. 2 is a block diagram of a data configuration apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer and more complete, the technical solutions in the embodiments of the present invention will be described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention, and based on the embodiments of the present invention, all other embodiments obtained by a person of ordinary skill in the art without creative efforts belong to the scope of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a data configuration method, where the data configuration method includes:
reading the operation authority of the current user and all basic data;
carrying out authority classification on all the basic data, and determining the authority category of each basic data;
determining the configurable class and the non-configurable class of the current user according to the operation authority of the current user;
receiving an operation instruction of the current user, displaying the basic data in the configurable class and the non-configurable class to the current user according to the operation instruction, and selecting the basic data in the configurable class to form combined configuration data in a combined manner.
In the embodiment, the user can check all the data of all the categories according to the requirement of the user but can only configure the data in the authority range of the user, so that the user can master the data in all directions, the user can configure the data in the authority range of the user more conveniently, and the configuration result is more reasonable and reliable. Different embodiments can be applied to different application scenarios, for example, in contract management for a certain company, the basic data is a contract, the contracts can be classified, and users with different permission levels can configure corresponding contracts. For example, in the hierarchical management of assets, the assets are used as basic data, and can be classified, and users with different permission levels can configure corresponding assets.
In an embodiment of the present invention, the classifying all the basic data to determine the authority category of each basic data includes:
calculating the risk-benefit ratio of each basic data in a first preset time period;
dividing the basic data corresponding to the risk-benefit ratio larger than a preset standard threshold into non-standard classes;
dividing the basic data corresponding to the risk-benefit ratio smaller than or equal to a preset standard threshold into standard classes;
the standard class and the non-standard class have different permission levels.
In this embodiment, the assets are used as basic data, different benefits are obtained for different assets, different risks exist, the benefit of some assets is high, and the risk is high, while the risk tolerance of each user is different, so that some assets can be invested and some assets are not suitable for being invested for users with different risk tolerance. Then, the risk-benefit ratio of the set time period is used as a classification standard, each asset is divided into two types, and the assets which can be operated are different in operation authority levels of different users, for example, the users are divided into a high risk level and a low risk level, and the high risk level users and the low risk level users can look up the assets in the non-standard type and the standard type. A high risk level user may configure both assets in the non-standard class and assets in the standard class, while a low risk level user may only configure assets in the standard class and may not configure assets in the non-standard class, but may view assets in the non-standard class. In order to make the classification more accurate and credible, the average value of the rolling risk-benefit ratio can be used to replace the risk-benefit ratio, for example, three months are used as a time window, the average value of the rolling risk-benefit ratio of each asset from the starting time to the ending time is calculated, and the length of the time window can be arbitrarily selected according to actual requirements.
In an embodiment of the present invention, the data configuration method further includes, for the basic data in the non-standard class:
determining influence factors of the basic data and influence factors of the influence factors;
inputting the influence factors into a hierarchical clustering model to obtain a correlation coefficient of each basic data and each residual other basic data;
and dividing the basic data of which the correlation coefficient is greater than a preset correlation threshold value into the same subclass.
In this embodiment, the base data in the non-standard class is subdivided into a plurality of subclasses. And because the variation of the risk-benefit ratio in the non-standard class is severe, serious errors can be caused by directly subdividing the risk-benefit ratio according to the historical risk-benefit ratio, and therefore a hierarchical clustering model is adopted for subdividing. The assets in the non-standard category are stocks, and the A stock and the American stock are taken as examples for explanation. For the equity shares, a plurality of influence factors such as financial quality factors and the like are calculated by adopting a plurality of influence factors in multiple aspects such as net profits, sales volumes and the like; and for the stock A, calculating a plurality of influence factors by adopting a plurality of influence factors in various aspects such as fluctuation rate, market value and the like. And finally obtaining 10 basically irrelevant influence factors by combining the calculated 30 influence factors in total according to classification, and inputting the 10 basically irrelevant influence factors into the hierarchical clustering model as characteristics. The hierarchical clustering model is to input the rolling linear correlation coefficient of each asset, initialize all samples to be a cluster, calculate the distance between all inputs, and calculate the loss function of all current clusters. The rolling linear correlation coefficient refers to that, in a given interval, for each day starting point, the length of a time window from the starting point to the back is taken, and the correlation coefficient in the time window is calculated. Wherein the loss function is defined to ensure high correlation of assets within a layer and low correlation of assets outside the layer by defining the correlation coefficient of assets within each layer to be greater than a threshold value, such as 0.9, to complete the hierarchical operation of the equity assets. The basic data in the non-standard class is divided into a plurality of subclasses, and the operation authority level of the corresponding user can also be divided into a plurality of subdivision levels. If the assets in the non-standard classes are divided into 3 subclasses, namely, non-standard class 1, non-standard class 2 and non-standard class 3, the high-risk class users can also be divided into three classes, namely, high-risk class 1, high-risk class 2 and high-risk class 3, and if the corresponding relations are respectively high-risk class 1-non-standard class 1, high-risk class 2-non-standard class 2 and high-risk class 3-non-standard class 3, the configurable classes of the high-risk class 1 users comprise all subclasses in the non-standard classes and standard classes; the configurable classes of the high-risk class 2 users comprise non-standard class 2 and non-standard class 3 in the non-standard classes and standard classes, but the non-standard class 1 is a non-configurable class; the configurable classes of the high-risk class 2 users include a non-standard class 3 of the non-standard classes and a standard class, but the non-standard class 1 and the non-standard class 2 are non-configurable classes. Further refining the classification can further ensure the reasonable and reliable configuration result.
In an embodiment of the present invention, the data configuration method further includes, for the basic data in the standard class:
calculating a rolling risk-benefit ratio average value of each basic data in a second preset time period;
dividing threshold ranges of rolling risk-benefit ratio mean values, and dividing each basic data into corresponding different subclasses according to the rolling risk-benefit ratio mean values of each basic data, wherein one threshold range corresponds to one subclass.
In this embodiment, the base data in the standard class is subdivided into a plurality of subclasses. And the risk-benefit ratio in the standard class is not changed greatly, and can be directly subdivided according to the risk-benefit ratio from history. The risk-benefit ratio can also be replaced by the average value of the rolling risk-benefit ratio, for example, three months are taken as a time window, the average value of the rolling risk-benefit ratio of each asset from the starting time to the ending time is calculated, and the length of the time window can be arbitrarily selected according to actual requirements. Similarly, the basic data in the standard class is divided into a plurality of subclasses, and the operation authority level of the corresponding user can also be divided into a plurality of subdivision levels. Similar to the non-standard class, will not be expanded here.
As shown in fig. 2, the present invention provides a data configuration apparatus, including: a data reading module, a data classifying module, a category corresponding module and a data configuring module, wherein,
the data reading module is used for reading the operation authority level of the current user and all basic data;
the data classification module is used for carrying out authority classification on all the basic data and determining the authority category of each basic data;
the category corresponding module is used for determining the configurable category and the non-configurable category of the current user according to the operation authority of the current user;
the data configuration module is configured to receive an operation instruction of the current user, display the basic data in the configurable class and the non-configurable class to the current user, and select the basic data in the configurable class to combine to form combined configuration data.
In an embodiment of the present invention, the data classification module is specifically configured to calculate a risk-benefit ratio of each piece of basic data in a first preset time period, classify the basic data corresponding to the risk-benefit ratio larger than a preset standard threshold into a non-standard class, and classify the basic data corresponding to the risk-benefit ratio smaller than or equal to the preset standard threshold into a standard class; the standard class and the non-standard class have different permission levels.
In an embodiment of the present invention, the data classification module is specifically configured to, for basic data in a non-standard class: determining influence factors of the basic data and influence factors of the influence factors, inputting the influence factors into a hierarchical clustering model to obtain a correlation coefficient of each basic data and each remaining other basic data, and dividing the basic data of which the correlation coefficient is greater than a preset correlation threshold value into the same subclass.
In an embodiment of the present invention, the data classification module is specifically configured to, for the basic data in the standard class: calculating a rolling risk-benefit ratio mean value of each basic data in a second preset time period, dividing each basic data into corresponding different subclasses according to the rolling risk-benefit ratio mean value of each basic data by dividing a threshold range of the rolling risk-benefit ratio mean value, wherein one threshold range corresponds to one subclass.
Because the content of information interaction, execution process, and the like among the modules in the device is based on the same concept as the method embodiment of the present invention, specific content can be referred to the description in the method embodiment of the present invention, and is not described herein again.
It should be understood that the various techniques described herein may be implemented in connection with hardware or software or, alternatively, with a combination of both. Thus, the methods and apparatus of the present invention, or certain aspects or portions thereof, may take the form of program code (i.e., instructions) embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, or any other machine-readable storage medium, wherein, when the program is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the invention.
In the case of program code execution on programmable computers, the computing device will generally include a processor, a storage medium readable by the processor (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device. Wherein the memory is configured to store program code; the processor is configured to perform the various methods of the present invention according to instructions in the program code stored in the memory.
By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer-readable media includes both computer storage media and communication media. Computer storage media store information such as computer readable instructions, data structures, program modules or other data. Communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. Combinations of any of the above are also included within the scope of computer readable media.
It should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the invention and aiding in the understanding of one or more of the various inventive aspects. However, the method of the invention should not be construed to reflect the intent: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing inventive embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules or units or components of the apparatus in the examples invented herein may be arranged in an apparatus as described in this embodiment or alternatively may be located in one or more apparatuses different from the apparatus in this example. The modules in the foregoing examples may be combined into one module or may be further divided into multiple sub-modules.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features of the invention in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so invented, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature of the invention in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
Furthermore, some of the described embodiments are described herein as a method or combination of method elements that can be performed by a processor of a computer system or by other means of performing the described functions. A processor having the necessary instructions for carrying out the method or method elements thus forms a means for carrying out the method or method elements. Further, the elements of the apparatus embodiments described herein are examples of the following apparatus: the apparatus is used to implement the functions performed by the elements for the purpose of carrying out the invention.
As used herein, unless otherwise specified the use of the ordinal adjectives "first", "second", "third", etc., to describe a common object, merely indicate that different instances of like objects are being referred to, and are not intended to imply that the objects so described must be in a given sequence, either temporally, spatially, in ranking, or in any other manner.
While the invention has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of this description, will appreciate that other embodiments can be devised which do not depart from the scope of the invention as described herein. Furthermore, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter. Accordingly, many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the appended claims. The present invention is to be considered as illustrative and not restrictive in character, with the scope of the invention being indicated by the appended claims.
Claims (10)
1. A data configuration method is characterized by comprising the following steps:
reading the operation authority of the current user and all basic data;
carrying out authority classification on all the basic data, and determining the authority category of each basic data;
determining the configurable class and the non-configurable class of the current user according to the operation authority of the current user;
receiving an operation instruction of the current user, displaying the basic data in the configurable class and the non-configurable class to the current user according to the operation instruction, and selecting the basic data in the configurable class to form combined configuration data in a combined manner.
2. The data configuration method according to claim 1, wherein the classifying all the basic data according to the authority to determine the authority category of each basic data comprises:
calculating the risk-benefit ratio of each basic data in a first preset time period;
dividing the basic data corresponding to the risk-benefit ratio larger than a preset standard threshold into non-standard classes;
dividing the basic data corresponding to the risk-benefit ratio smaller than or equal to a preset standard threshold into standard classes;
the standard class and the non-standard class have different permission levels.
3. The data configuration method of claim 2, further comprising, for the base data in the non-canonical class:
determining influence factors of the basic data and influence factors of the influence factors;
inputting the influence factors into a hierarchical clustering model to obtain a correlation coefficient of each basic data and each residual other basic data;
and dividing the basic data of which the correlation coefficient is greater than a preset correlation threshold value into the same subclass.
4. The data configuration method of claim 2, further comprising, for the base data in the standard class:
calculating a rolling risk-benefit ratio average value of each basic data in a second preset time period;
dividing threshold ranges of rolling risk-benefit ratio mean values, and dividing each basic data into corresponding different subclasses according to the rolling risk-benefit ratio mean values of each basic data, wherein one threshold range corresponds to one subclass.
5. A data configuration apparatus, comprising: a data reading module, a data classifying module, a category corresponding module and a data configuring module, wherein,
the data reading module is used for reading the operation authority of the current user and all basic data;
the data classification module is used for carrying out authority classification on all the basic data and determining the authority category of each basic data;
the category corresponding module is used for determining the configurable category and the non-configurable category of the current user according to the operation authority of the current user;
the data configuration module is configured to receive an operation instruction of the current user, display the basic data in the configurable class and the non-configurable class to the current user according to the operation instruction, and select the basic data in the configurable class to combine to form combined configuration data.
6. The data configuration device according to claim 5, wherein the data classification module is specifically configured to calculate a risk-benefit ratio of each piece of the basic data in a first preset time period, classify the basic data corresponding to the risk-benefit ratio larger than a preset standard threshold into a non-standard class, and classify the basic data corresponding to the risk-benefit ratio smaller than or equal to the preset standard threshold into a standard class; the standard class and the non-standard class have different permission levels.
7. The data configuration apparatus according to claim 6, wherein the data classification module is specifically configured to, for the base data in the non-standard class: determining influence factors of the basic data and influence factors of the influence factors, inputting the influence factors into a hierarchical clustering model to obtain a correlation coefficient of each basic data and each remaining other basic data, and dividing the basic data of which the correlation coefficient is greater than a preset correlation threshold value into the same subclass.
8. The data configuration apparatus according to claim 6, wherein the data classification module is specifically configured to, for the base data in the standard class: calculating a rolling risk-benefit ratio mean value of each basic data in a second preset time period, dividing each basic data into corresponding different subclasses according to the rolling risk-benefit ratio mean value of each basic data by dividing a threshold range of the rolling risk-benefit ratio mean value, wherein one threshold range corresponds to one subclass.
9. A readable storage medium having executable instructions thereon that, when executed, cause a computer to perform the method as included in any one of claims 1-4.
10. A computing device, comprising: memory, one or more processors, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors to perform the method as recited in any of claims 1-4.
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