CN116402596A - Data analysis method, device, computer equipment and readable storage medium - Google Patents
Data analysis method, device, computer equipment and readable storage medium Download PDFInfo
- Publication number
- CN116402596A CN116402596A CN202310150516.3A CN202310150516A CN116402596A CN 116402596 A CN116402596 A CN 116402596A CN 202310150516 A CN202310150516 A CN 202310150516A CN 116402596 A CN116402596 A CN 116402596A
- Authority
- CN
- China
- Prior art keywords
- data
- data analysis
- analyzed
- target data
- characteristic
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000007405 data analysis Methods 0.000 title claims abstract description 302
- 238000000034 method Methods 0.000 title claims abstract description 65
- 238000004458 analytical method Methods 0.000 claims abstract description 64
- 230000014509 gene expression Effects 0.000 claims description 21
- 238000004590 computer program Methods 0.000 claims description 12
- 230000005856 abnormality Effects 0.000 claims description 9
- 238000010276 construction Methods 0.000 claims description 7
- 230000008569 process Effects 0.000 abstract description 17
- 230000002159 abnormal effect Effects 0.000 description 18
- 238000010586 diagram Methods 0.000 description 15
- 238000012545 processing Methods 0.000 description 8
- 238000004891 communication Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 230000000007 visual effect Effects 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 239000000284 extract Substances 0.000 description 3
- 238000012790 confirmation Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 230000002045 lasting effect Effects 0.000 description 2
- 230000002093 peripheral effect Effects 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 238000011144 upstream manufacturing Methods 0.000 description 2
- 230000004931 aggregating effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000000802 evaporation-induced self-assembly Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000006386 memory function Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000011664 signaling Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/25—Integrating or interfacing systems involving database management systems
- G06F16/252—Integrating or interfacing systems involving database management systems between a Database Management System and a front-end application
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/283—Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Strategic Management (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Quality & Reliability (AREA)
- General Business, Economics & Management (AREA)
- Game Theory and Decision Science (AREA)
- Tourism & Hospitality (AREA)
- Operations Research (AREA)
- Marketing (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention provides a data analysis method, a device, computer equipment and a readable storage medium, which comprise the following steps: acquiring a plurality of data features corresponding to the data analysis object; determining a plurality of target data features associated with the index to be analyzed from the plurality of data features, generating a plurality of data analysis rules based on the target data features, and constructing a data analysis model corresponding to the index to be analyzed based on all the data analysis rules; the data analysis model is used for representing early warning triggering conditions; acquiring data to be analyzed corresponding to a data analysis object, and determining a characteristic value of each target data characteristic based on the data to be analyzed; and inputting the characteristic value of the target data characteristic into a data analysis model for analysis to obtain an analysis result of the index to be analyzed. The whole analysis process utilizes the data analysis model to complete data analysis once, reduces the pressure of a database, has simple logic, does not need excessive human intervention of professionals, and can improve the data analysis efficiency to a great extent.
Description
Technical Field
The present invention relates to the field of big data processing technologies, and in particular, to a data analysis method, a data analysis device, a computer device, and a readable storage medium.
Background
In various fields, with the increasing abundance of service types and the increasing complexity of data interaction environments, the requirements on data quality are also higher. For example, in the financial field, it is necessary to check data quality, check customer information accuracy, and initiate security management measures once abnormal data is found to make up for security vulnerabilities.
In the prior art, in order to improve timeliness and accuracy of abnormal data monitoring, professional developers are required to write SQL sentences according to specific data analysis, and abnormal data conforming to business logic can be determined after the system executes the SQL sentences.
However, the above data analysis method using SQL needs to be performed multiple times to determine the abnormal data, and the database resource consumption is relatively high.
Disclosure of Invention
One of the purposes of the present invention is to provide a data analysis method, apparatus, computer device and readable storage medium, which are used to improve the delivery efficiency and execution efficiency of early warning analysis and reduce the burden and pressure of a database.
In a first aspect, the present invention provides a data analysis method, the method comprising: acquiring a plurality of data features corresponding to the data analysis object; determining a plurality of target data features associated with the index to be analyzed from a plurality of data features, generating a plurality of data analysis rules based on the target data features, and constructing a data analysis model corresponding to the index to be analyzed based on all the data analysis rules; the data analysis model is used for representing early warning triggering conditions; acquiring data to be analyzed corresponding to the data analysis object, and determining a characteristic value of each target data characteristic based on the service data; inputting the characteristic value of the target data characteristic into the data analysis model for analysis to obtain an analysis result of the index to be analyzed.
In a second aspect, the present invention provides a data analysis device, including an acquisition module, a construction module, an acquisition module, and an analysis module; the acquisition module is used for acquiring a plurality of data characteristics corresponding to the data analysis object; the construction module is used for determining a plurality of target data features associated with the index to be analyzed from a plurality of data features, generating a plurality of data analysis rules based on the target data features, and constructing a data analysis model corresponding to the index to be analyzed based on all the data analysis rules; the data analysis model is used for representing early warning triggering conditions; the acquisition module is used for acquiring data to be analyzed corresponding to the data analysis object and determining the characteristic value of each target data characteristic based on the service data; the analysis module is used for inputting the characteristic value of the target data characteristic into the data analysis model for analysis to obtain an analysis result of the index to be analyzed.
In a third aspect, the present invention provides a computer device comprising a processor and a memory, the memory storing a computer program executable by the processor, the processor being executable to implement the data analysis method of the first aspect.
In a fourth aspect, the present invention provides a readable storage medium having stored thereon a computer program which, when executed by a processor, implements the data analysis method of the first aspect.
The invention provides a data analysis method, a device, computer equipment and a readable storage medium, wherein the method comprises the following steps: the method comprises the steps that a server firstly obtains data characteristics of a data analysis object, determines target data characteristics corresponding to the data analysis object, then generates a plurality of data analysis rules by the target data characteristics, builds a data analysis model of the data analysis object based on all the data analysis rules, obtains data to be analyzed corresponding to the data analysis object by the server in the process of utilizing the data analysis model to conduct data analysis, determines characteristic values of each target data characteristic based on the data to be analyzed, inputs the characteristic values of the target data characteristics into the data analysis model to conduct one-time analysis, obtains an analysis result of the data analysis object, reduces database pressure, and meanwhile, because the scheme is that the server firstly automatically generates the data analysis model and then utilizes the data to be analyzed to execute the data analysis model, the data analysis process does not need excessive human intervention of professionals, the requirement on service personnel is low, the whole data analysis process is disassembled into executing the plurality of data analysis rules, and the data analysis efficiency can be improved to a great extent.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic view of a scenario of a data analysis method provided in an embodiment of the present application;
FIG. 2 is a schematic flow chart of a data analysis method according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart of step S202 according to an embodiment of the present invention;
FIG. 4 is an exemplary diagram of a data analysis rule included in one data analysis model according to an embodiment of the present invention;
fig. 5 is a visual terminal interface provided in an embodiment of the present invention;
FIG. 6 is a schematic flow chart of another implementation of step S202 provided in an embodiment of the present application;
FIG. 7 is an exemplary diagram of a scoring card according to an embodiment of the present invention;
FIG. 8 is a functional block diagram of a data analysis device according to an embodiment of the present invention;
Fig. 9 is a block diagram of a computer device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
In the description of the present invention, it should be noted that, if the terms "upper", "lower", "inner", "outer", and the like indicate an azimuth or a positional relationship based on the azimuth or the positional relationship shown in the drawings, or the azimuth or the positional relationship in which the inventive product is conventionally put in use, it is merely for convenience of describing the present invention and simplifying the description, and it is not indicated or implied that the apparatus or element referred to must have a specific azimuth, be configured and operated in a specific azimuth, and thus it should not be construed as limiting the present invention.
Furthermore, the terms "first," "second," and the like, if any, are used merely for distinguishing between descriptions and not for indicating or implying a relative importance.
It should be noted that the features of the embodiments of the present invention may be combined with each other without conflict.
In the prior art, the data analysis mode is generally: and writing SQL sentences by professional developers according to specific business logic, then executing the SQL sentences by a data analysis system, determining abnormal data, and providing the abnormal data for the system or a third party system to do special workflow processing. However, this data analysis method has the following drawbacks:
1. The business logic is generated through SQL dynamic splicing, is not optimized, and can determine abnormal data only by multiple times of execution, so that more database resources are consumed.
2. The SQL mode is adopted to have stronger dependence on professional developers, so that the manual intervention is excessive, and if new requirements exist or the requirements are changed, the professional developers are required to be relied on to change the written business logic, the threshold is higher, and the efficiency of the whole data analysis flow is lower.
In order to solve the technical defects, the embodiment of the invention provides a data analysis method which is used for improving the efficiency of the whole data analysis flow, meeting the timeliness requirement of users and reducing the burden and pressure of a database.
Referring to fig. 1 first, fig. 1 is a schematic view of a scenario of a data analysis method provided in an embodiment of the present application, where, as shown in fig. 1, the scenario includes a client 102 and a server 104, and the server 104 may be in a server cluster or a cloud server form.
The client 102 may send data to be analyzed to the server 104 under the operation of the user, including but not limited to login data, registration data, payment data, transaction data, and the like. For example, the client 102 sends login data of the user to the server 104 according to login operation of the user, and for example, the client 102 sends transaction data of the user to the server 104 according to transaction operation of the user. The server 104 may first construct a data analysis model of the index to be analyzed, then the server 104 determines a characteristic value of a data feature corresponding to the index to be analyzed according to the data to be analyzed sent by the client 102, and then inputs the characteristic value into the data analysis model to determine whether the index to be analyzed is abnormal, if so, early warning is performed, so that abnormal data is rapidly determined. For example, server 104 analyzes the login data to determine the authenticity or validity of the user's customer identity.
The following describes the data analysis method provided by the embodiment of the present invention with the server 104 as an execution body, as shown in fig. 2, fig. 2 is a schematic flowchart of the data analysis method provided by the embodiment of the present invention, which may include the following steps:
s201, a plurality of data features corresponding to the data analysis objects are obtained.
In the embodiment of the present application, the data analysis object may include, but is not limited to, any one of the following and a combination thereof: customers, securities, companies, etc. Such as a single customer, a single security, a single customer single security, etc., the data characteristics refer to an abstract representation of the characteristics exhibited by the data analysis object at the business level, e.g., for the example of a single customer, the data characteristics may include, but are not limited to: single customer financing scale, single customer coupon scale, single customer financing balance, single customer coupon balance, etc.
The attribute information of the data feature includes: ID. Name, data analysis object, service source table, data type, numeric unit, dictionary number, field defaults, etc. The data type comprises a numerical value type and a dictionary type, and attribute information of the data features can provide basis for generating data analysis rules subsequently.
In a real scenario, there are two types of data features: attribute data features and aggregate data features. The attribute data features are common fields corresponding to dimension columns in the service table, namely, the inherent attributes of the data analysis objects, and can also be fields derived from one or more common fields (simply called derived fields), namely, new attributes calculated according to the inherent attributes of the data analysis objects, wherein the columns except the data analysis object columns in the service table are dimension columns.
For example, referring to table 1, table 1 is a service table for maintaining client information provided in the embodiment of the present invention, where the service table, that is, a table for storing service entity information and a table for storing service actions in an upstream service system, is called a service table, and service data in each dimension corresponding to a data analysis object is stored in the service table.
TABLE 1
For example, in the business table, the client is a data analysis object, the client name columns are data analysis object columns, the other columns are dimension columns, and each dimension column corresponds to a common field, such as a business part number, a client number, and the like, so the business part number and the client number are attribute data features corresponding to the client.
In addition, the attribute data features may be derived fields from common fields, that is, a new field is obtained by calculating a plurality of common fields, for example, a service table may further include an identification card number, and accordingly, other fields capable of characterizing the characteristics of the client may be derived according to the identification card number and gender, for example, derived fields such as whether the identification card number is legal, whether the gender of the identification card number matches the registered gender, and the like. Although not directly shown in the service table, these derived fields are still attribute data features that need to be considered in the embodiment of the present invention.
It can be seen that, in the data structure layer, the service table information includes a field for identifying the data analysis object and a field for measuring the attribute characteristics of the data analysis object, and in the actual implementation process, the server may perform field analysis based on the obtained service table, extract a common field, calculate the extracted characteristic field to obtain a derivative field, and use the common field and the derivative field as the data characteristics of the data analysis object.
The aggregate data feature is a new field value obtained by aggregating and integrating the data analysis object field or the common field in the service table. For example, the "accumulated transaction amount of the current day bill client" is an aggregate data feature obtained by summarizing client fields in a real-time transaction table of securities; for another example, the "number of clients with consistent client contact addresses" is an aggregate data feature obtained by summarizing the client contact address fields, and the data analysis object of the client information table is a client, and the client contact addresses are common fields. In this case, the data analysis object needs to derive the aggregate data feature based on the normal field disassociation, so the normal field must be the attribute data feature.
Therefore, in the implementation process, in the process of acquiring the data characteristics, the server may acquire a plurality of service tables with data analysis objects, and for each service table, the server may perform the following implementation modes: extracting a target common field as a data characteristic; summarizing and calculating the target common field and the data analysis object field to obtain data characteristics; performing joint calculation on at least two target common fields to obtain data characteristics; the data characteristics may also be obtained by combining common fields in multiple service tables, and the target common fields may be specified by related personnel or determined by a server based on data analysis requirements, which is not limited herein.
S202, determining a plurality of target data features associated with the index to be analyzed from the plurality of data features, generating a plurality of data analysis rules based on the target data features, and constructing a data analysis model corresponding to the index to be analyzed based on all the data analysis rules.
In the embodiment of the invention, the index to be analyzed is set by a user according to the actual analysis requirement or randomly selected by a server according to the data analysis task, for example, the index to be analyzed is the net capital proportion of the single customer financing coupon scale and the customer information quality. The server may pre-establish a correspondence between the index and at least one data feature in response to a user setting operation, then determine an index to be analyzed in response to a user selecting operation, and extract a target data feature corresponding to the index to be analyzed based on the pre-established correspondence.
In the embodiment of the present invention, the data analysis rule is a logic expression for judging abnormal data, that is, when the input value satisfies the logic expression, it indicates that the input value is abnormal, for example, the data analysis rule is as follows: whether the client risk level of the single client is in a preset risk level set or not, wherein the preset risk level set is an abnormal level, and when the client level is in the preset risk level set, the abnormality is indicated; for another example, if the quotient of the sum of the single customer financing size and the single customer coupon size and the net company capital is greater than or equal to a preset comparison threshold, the preset comparison threshold is an anomaly threshold, and when the input value is greater than or equal to the preset comparison threshold, an anomaly is indicated. The data analysis model is constructed based on logic operation among all data analysis rules, and represents early warning trigger conditions when the index to be analyzed is abnormal.
S203, obtaining data to be analyzed corresponding to the data analysis object, and determining the characteristic value of each target data characteristic based on the data to be analyzed.
In the embodiment of the invention, the server can acquire the data analysis corresponding service table from the upstream service system, and acquire the service data from the service table to the database of the server so as to perform data analysis based on the acquired service data.
S204, inputting the characteristic value of the target data characteristic into a data analysis model for analysis, and obtaining an analysis result of the index to be analyzed.
In the embodiment of the invention, a data analysis model is constructed based on target data characteristics of an index to be analyzed, after a characteristic value of the target data characteristics is obtained, the characteristic value is directly input into the data analysis model for data analysis, specifically, after the server obtains the characteristic value, each data analysis rule in the data analysis model is sequentially executed to obtain an analysis result corresponding to each analysis result, then all the analysis results execute logic operation, the result of the logic operation is a Boolean value, if the Boolean value is true, it is indicated that at least one characteristic value input by the data analysis rule is abnormal, at the moment, the index to be analyzed can be determined to be abnormal, otherwise, the index to be analyzed is indicated to be normal.
In the data analysis method, the server firstly acquires the data characteristics of the data analysis object, determines the target data characteristics corresponding to the data analysis object to be analyzed, then generates a plurality of data analysis rules by the target data characteristics, constructs a data analysis model of the data analysis object based on all the data analysis rules, acquires the data to be analyzed corresponding to the data analysis object by the server in the process of carrying out data analysis by using the data analysis model, determines the characteristic value of each target data characteristic based on the data to be analyzed, then inputs the characteristic value of the target data characteristics into the data analysis model for one-time analysis to obtain the analysis result of the data analysis object, reduces the pressure of a database, and meanwhile, because the scheme is that the server firstly automatically generates the data analysis model and then executes the data analysis model by using the data to be analyzed, the data analysis process does not need excessive human intervention of professionals, the requirement on service personnel is low, the whole data analysis process is disassembled into executing the plurality of data analysis rules, and the data analysis efficiency can be improved to a great extent.
In an optional implementation manner, the data analysis model constructed in the embodiment of the present invention is composed of a logic relationship between a plurality of data analysis rules, so in the process of constructing the data analysis model, the server establishes different types of data analysis rules based on the data feature types, including establishing each data analysis rule by using each first type of target data feature and each data analysis rule by using at least two second types of target data features, and finally logically splicing all the data analysis rules to obtain the data analysis model, so the implementation manner of the step S202 is shown in fig. 3, and fig. 3 is a schematic flowchart of one of the steps S202 provided in the embodiment of the present invention, which may include the following steps:
S202-1a: determining a set operator and a preset threshold set corresponding to the first type of target data features to obtain a data analysis rule corresponding to each first type of target data features;
in the embodiment of the invention, the value type of the characteristic value of the first type of target data characteristic is a dictionary type; the set operator comprises two types of 'in' and 'not in', and is used for determining whether the characteristic value of the first type of target data characteristic is in a preset threshold value set or not so as to determine whether the first target data characteristic is abnormal or not, wherein the preset threshold value set is an abnormal value set of the first target data characteristic set by a user.
Such as: the first class of target data is characterized by a client risk level of a single client, a relation operator is in, a preset threshold value set is { medium risk and high risk }, if a server determines that the characteristic value of the client risk level is low risk, and judges that the characteristic value is not in the preset threshold value set, the condition that the client risk level is normal is indicated, and the result of the data analysis rule is false; if the risk is high, in the preset threshold value set, the result of the data analysis rule is true.
S202-2a: determining an operation relation between at least two second-class target data features to generate an early warning occurrence condition expression, determining a comparison operator and a preset comparison threshold corresponding to the early warning occurrence condition expression to obtain a data analysis rule, and/or determining a comparison operator and a preset comparison threshold corresponding to one second-class target data feature to obtain a data analysis rule corresponding to the second-class target data feature.
In the embodiment of the invention, the value type of the characteristic value of the second class target data feature is a value type, and when one data analysis rule comprises a plurality of second class target data features, the data analysis rule further comprises a relation operator, an operator and a preset comparison threshold. When a data analysis rule contains only one target data feature, no operators may be included.
The operator may be used to determine an operational relationship between the second class of target data feature correspondence to generate the early warning occurrence condition expression, and the operator may include, but is not limited to, an add, subtract, multiply, and divide operator, where the comparison operator includes greater than, equal to, greater than or equal to, less than or equal to, and not equal to, and the comparison operator is used to determine whether a result of the early warning occurrence condition expression matches a preset comparison threshold, where the preset comparison threshold is a value set according to an actual service scenario.
For example, the second class of target data features include a single customer financing scale, a single customer coupon scale and a company net capital, and the early warning occurrence condition expressions corresponding to the three are generated through operators: (Single customer financing Scale + Single customer coupon Scale)/company net capital, the comparison operator may be set to be greater than or equal to a comparison threshold of 0.3, then the result of the data analysis rule is true when the result corresponding to the pre-alarm occurrence expression is greater than or equal to 0.3, otherwise the result of the data analysis rule is false.
Therefore, in the implementation process, the server extracts the value type from the attribute information of each target data feature, determines the first type of target feature data and the second type of target feature data based on the value type, responds to the confirmation operation of the user on the first type of target data feature and the selection operation on the set operator and the preset threshold set, and generates the data analysis rule corresponding to the first type of target data feature; and responding to the confirmation operation of the user on at least one second type target data feature and the selection operation of a preset comparison threshold value, a comparison operator and/or at least one operator, and generating a data analysis rule corresponding to the second type target data feature.
Optionally, the user can bind an early warning label for each data analysis rule, namely, the identification data analysis object has the characteristic of the bound early warning label, so that the subsequent portrait analysis of the data analysis object is facilitated.
S202-3a: and establishing a logic relation among all data analysis rules to obtain a data analysis model.
In the embodiment of the invention, a plurality of data analysis rules can be generated for each index to be analyzed, and then all the data analysis rules are subjected to logic operation, so that one data analysis model corresponding to each index to be analyzed can be obtained. The logic operation may be a logical or operation or a logical and operation, which is set by the user based on the actual requirement, and is not limited herein. That is, after the server generates a plurality of data analysis rules, responding to the selection operation of the user for the logic operators, and performing logic splicing on all the data analysis rules based on the selected logic operators to obtain the data analysis model.
In order to facilitate understanding of the above data analysis model, please refer to fig. 4, fig. 4 is an exemplary diagram of a data analysis rule included in one data analysis model provided in the embodiment of the present invention, and as shown in fig. 4, taking an index that a single-client financing coupon scale accounts for a net capital proportion of a company as an example, two data analysis rules determined by the above embodiment are used, where one data analysis rule is (single-client financing scale+single-client financing coupon scale)/company net capital is greater than or equal to 0.3 (simply referred to as expression one), and the other data analysis rule is that a single-client risk level is in { risk and high risk }, and then the data analysis model that the single-client financing coupon scale accounts for the net capital proportion of the company is finally obtained is: expression one or expression two, that is, when one of the values of expression one and expression two is true, indicates that there is an anomaly in the single customer financing coupon size that is a net capital proportion of the company.
In an actual implementation scenario, in order to generate a data analysis model, an embodiment of the present invention further provides a visual terminal interface, as shown in fig. 5, where fig. 5 is a visual terminal interface provided by an embodiment of the present invention, including: the data analysis rule generation device comprises a data feature selection area, a rule selection area, an index setting area, a data analysis rule generation area and a data analysis model generation area.
After the user selects the data analysis object, the data characteristics related to the data analysis object are displayed in the data characteristic selection area. For example, a single client of the data analysis object is selected, all data features of the single client are exposed. The user fills in the name of the early warning wind control index model and the description remark information.
The user can set the name, the module, the index type and the index description information of the index to be analyzed in the early warning index setting area, and the computer equipment generates the ID of the index to be analyzed according to the preset rule.
In the data analysis rule generation area, a user can select at least one data feature from the data feature selection area and drag the data feature to the data analysis rule editing area, then select operators from the rule selection area to generate expressions among the selected data features, drag each expression to the data analysis model generation area, then select logic operators from the rule selection area to generate a logic relation among each early warning occurrence condition expression, and finally obtain the data analysis model.
Then, based on the data analysis model, the embodiment of the present invention provides an implementation manner of determining the analysis result of the index to be analyzed based on the data analysis model, that is, one implementation manner of the step S204 may be:
Inputting the characteristic value of the target data characteristic into a data analysis model, determining the Boolean value of each data analysis rule, performing logical AND operation or logical OR operation on all the Boolean values, and outputting an analysis result of abnormality of the index to be analyzed if the Boolean value is true; otherwise, outputting the normal analysis result of the index to be analyzed.
For example, with continued reference to fig. 4, assuming that the characteristic value of (single customer financing size + single customer coupon size)/company net capital (abbreviated as rule one) is 0.4, satisfying greater than or equal to 0.3, then the boolean value of rule one is true, the characteristic value of the customer risk level of a single customer (abbreviated as rule two) is low risk, and is not within the threshold set, then the boolean value of rule two is false, assuming that the logical operation relationship of rule one and rule two is logical or, then the final output boolean value is true, indicating that there is an anomaly in the net capital proportion of the single customer financing coupon size to the company.
In the actual implementation process, when a data analysis task is received, all data analysis models under the data analysis task can be obtained, and data analysis results corresponding to data features on which the data analysis models depend are summarized and analyzed and counted into a memory; when a piece of data containing data analysis objects and all data features relied on by the wind control index model enters a memory, an Avaitor expression engine of Google can be used for calculating the Boolean values of all data analysis rules in the wind control index model, and the Boolean values of all data analysis rules are subjected to logic operation to obtain a Boolean value, if the Boolean value is true, analysis results are generated and pushed to an early warning storage module; if the Boolean value is false, the calculation of the next piece of data is carried out until all the data are traversed, finally, the wind control analysis result is received, and the analysis result is stored in the analysis result table in a lasting mode.
In another alternative implementation, the data analysis model in the embodiment of the present invention may be a scoring card. A scoring card may contain a plurality of data features, and a data feature may contain a plurality of rows of records, where there is no intersection between the respective ranges of values of the rows of records. The header of the grading card mainly comprises: the data analysis object name, the data feature name, the description information corresponding to each data feature, at least one early warning value interval and weight, and the score corresponding to each early warning value interval. In the process of constructing the scoring card, a server determines the weight corresponding to each target data characteristic, a plurality of early warning value intervals and the relation operator and the score corresponding to each early warning value interval, and constructs the scoring card based on the weight, the relation operator and the score of each early warning value interval to serve as a data analysis model.
Thus, referring to fig. 6 for the above step S202, fig. 6 is a schematic flowchart of another implementation of step S202 provided in the embodiment of the present application, which may include the following steps:
s202-1b: dividing a plurality of early warning value intervals for each target data feature, configuring a relation operator and a score for each early warning value interval, and configuring weights for the target data features;
S202-2b: aiming at each target data feature, creating a plurality of data analysis rules corresponding to each target data feature based on a plurality of early warning value intervals and a relation operator and a score corresponding to each early warning value interval;
s202-3b: and constructing a scoring card based on a plurality of data analysis rules and weights corresponding to each target data characteristic, and taking the scoring card as a data analysis model.
In the embodiment of the invention, a user can establish a scoring card, and a relation operator corresponding to each target data characteristic is determined by the data type of the scoring card, wherein the numerical value type and the monetary type are a comparison operator and a numerical value interval operator, and the comparison operator comprises more than, equal to, more than or equal to, less than or equal to and not equal to; the numerical section operator includes left open and right close, left open and right open, left close and right close. Dictionary types are in and not in operators.
For easy understanding, please refer to fig. 7, fig. 7 is an exemplary diagram of a scoring card provided in an embodiment of the present invention, and the target data features include: the method comprises the steps of estimating the end-of-period customer quantity ratio, estimating the end-of-period customer asset size ratio and newly establishing the customer quantity ratio of the business relationship through a non-face-to-face channel in the estimation period, wherein descriptive information is used for describing a characteristic value calculation mode of each target data characteristic, each target data characteristic comprises a plurality of rows of records, no intersection exists between the value ranges of the plurality of rows of records, and each row of records is recorded as a data analysis rule.
Then, based on the scoring card shown in fig. 7, another embodiment of the present invention provides an implementation manner of analyzing the analysis result of the index to be analyzed based on the data analysis model, that is, one of the implementation manners of the step S204 may be:
step 1: inputting the characteristic value of the target data characteristic into a data analysis model, determining a target early warning interval in which the characteristic value of the target data characteristic falls, and obtaining the score of the target data characteristic based on the score corresponding to the target early warning interval and the weight corresponding to the target data characteristic;
step 2: and if the score sum of all the target data features is determined to be greater than or equal to a preset score threshold, outputting an analysis result of abnormality of the index to be analyzed.
For example, with continued reference to fig. 7, assuming that the characteristic value of the end-of-evaluation customer count ratio is 0.4 and falls within the early warning value interval [0.3,0.5 ], the score of the end-of-evaluation customer count ratio is finally 2×2% =0.04, and assuming that the preset score threshold is 80 points, when the sum of the scores corresponding to all the target data characteristics is greater than or equal to 80, an abnormality is indicated.
In the actual implementation process, when a data analysis task is received, a data feature list downloaded by the data analysis task is obtained. The data analysis results corresponding to the data features on which the grading card depends are fetched into a memory; after an analysis data containing data analysis objects and data features relied on by a scoring card model enters a memory, initializing a total score X, assigning 0 to the X, traversing all rows in the scoring card in sequence, and carrying out logic judgment on each row based on the value of a data feature factor, if the Boolean value corresponding to the row record is true, adding the weight of the row to the total score X to obtain a new total score; and (3) comparing the obtained total score with an early warning score threshold value until all the rows are traversed, recording early warning if the total score is greater than or equal to the early warning score value, pushing the record early warning to an analysis result storage module, and storing the early warning in a lasting mode.
That is, when the data analysis model is a scoring card, inputting the feature value of each target data feature into the scoring card to obtain the score sum of all the target data features, and when the score sum is determined to be greater than or equal to a preset score threshold value, determining that the index to be analyzed is abnormal; the data analysis model is formed by logically splicing a plurality of data analysis rules, the characteristic value of each target data characteristic is input into the data analysis model to calculate the Boolean value of each data analysis rule, and when the logical operation result of all the Boolean values is true, the index to be analyzed is determined to have abnormality.
In an optional implementation manner, the embodiment of the invention may further set a triggering frequency of the data analysis task, for example, once a day, once every 5 minutes, twice a day, etc., so that if a time difference between a previous early warning analysis time and a current system time meets a preset triggering frequency, an early warning analysis task is triggered, a data analysis model list under the triggering early warning analysis task is obtained, for each data analysis model in the data analysis model list, a step of executing to-be-analyzed data corresponding to the obtained data analysis object and determining a feature value of each target data feature based on the to-be-analyzed data is returned until an analysis result corresponding to each data analysis model is obtained.
In summary, the data analysis method provided by the embodiment of the invention abstracts elements such as a data analysis model, occurrence conditions, data characteristics, trigger conditions and the like, emphasizes the process of generating data analysis rules by splicing based on one or more data characteristics, logical symbols, operators and thresholds, and can form a logical judgment relationship between the data analysis rules, so that service personnel can be enabled to generate the data analysis model through visual dragging combination conveniently, wind control early warning analysis is realized based on the data analysis model, the delivery efficiency and execution efficiency of early warning analysis are improved, and meanwhile, the burden and pressure of a database are reduced.
Based on the same inventive concept, the embodiment of the present application further provides a data analysis device, as shown in fig. 8, fig. 8 is a functional block diagram of the data analysis device provided in the embodiment of the present invention, and the data analysis device 300 may include: an acquisition module 310, a construction module 320, an acquisition module 330, and an analysis module 340;
an obtaining module 310, configured to obtain a plurality of data features corresponding to the data analysis object;
the construction module 320 is configured to determine a plurality of target data features associated with the index to be analyzed from the plurality of data features, generate a plurality of data analysis rules based on the target data features, and construct a data analysis model corresponding to the index to be analyzed based on all the data analysis rules; the data analysis model is used for representing early warning triggering conditions;
The acquisition module 330 is configured to acquire data to be analyzed corresponding to the data analysis object, and determine a feature value of each target data feature based on the data to be analyzed;
the analysis module 340 is configured to input the feature value of the target data feature into the data analysis model for analysis, so as to obtain an analysis result of the index to be analyzed.
It is appreciated that the acquisition module 310, the construction module 320, the acquisition module 330, and the analysis module 340 may cooperatively perform the steps of fig. 2 to achieve corresponding technical effects.
In an alternative embodiment, the build module 320 may be configured to: determining a set operator and a preset threshold set corresponding to the first type of target data features to obtain a data analysis rule corresponding to each first type of target data features; the characteristic value numerical value type of the first type of target data characteristic is dictionary type; determining an operation relation between at least two second class target data features to generate an early warning occurrence condition expression, and determining a comparison operator and a preset comparison threshold corresponding to the early warning occurrence condition expression to obtain a data analysis rule; the characteristic value numerical value type of the second class of target data characteristics is a numerical value type; and establishing a logic relation among all data analysis rules to obtain a data analysis model.
In an alternative embodiment, the build module 320 may also be configured to: dividing a plurality of early warning value intervals for each target data feature, configuring a relation operator and a score for each early warning value interval, and configuring weights for the target data features; aiming at each target data feature, creating a plurality of data analysis rules corresponding to each target data feature based on a plurality of early warning value intervals and a relation operator and a score corresponding to each early warning value interval; and constructing a scoring card based on a plurality of data analysis rules and weights corresponding to each target data characteristic, and taking the scoring card as a data analysis model.
In an alternative embodiment, the analysis module 340 may be configured to: inputting the characteristic value of the target data characteristic into a data analysis model, determining a target early warning interval in which the characteristic value of the target data characteristic falls, and obtaining the score of the target data characteristic based on the score corresponding to the target early warning interval and the weight corresponding to the target data characteristic; and if the score sum of all the target data features is determined to be greater than or equal to a preset score threshold, outputting an analysis result of abnormality of the index to be analyzed.
In an alternative embodiment, the analysis module 340 may also be configured to: and inputting the characteristic value of the target data characteristic into a data analysis model, determining the Boolean value of each data analysis rule, performing logical AND operation or logical OR operation on all the Boolean values, and outputting an analysis result of abnormality of the index to be analyzed if the Boolean value is true.
In an alternative embodiment, the data analysis device 300 may further include a triggering module, configured to trigger a data analysis task and obtain a data analysis model list under the data analysis task if a time difference between a last analysis time and a current system time meets a preset triggering frequency; and returning to execute the step of acquiring the data to be analyzed corresponding to the data analysis object aiming at each data analysis model in the data analysis model list, and determining the characteristic value of each target data characteristic based on the data to be analyzed until the analysis result corresponding to each data analysis model is obtained.
In an alternative embodiment, the acquisition module 310 may be configured to: acquiring a plurality of data features corresponding to the data analysis object, including: acquiring a plurality of business tables with data analysis objects; and carrying out feature analysis on the plurality of service tables to obtain a plurality of data features.
It should be noted that, in the above embodiments of the present application, the division of the modules is merely schematic, and there may be another division manner in actual implementation, and in addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or may exist separately and physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in part or all or part of the technical solution contributing to the prior art or in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Based on the foregoing embodiments, the embodiments of the present application further provide a schematic diagram of a computer device, where the computer device is configured to implement the data analysis method in the foregoing embodiments. For example, the computer device may be the server 104 in fig. 1, referring to fig. 9, fig. 9 is a block diagram of a computer device provided in an embodiment of the present invention, where the computer device 400 includes: the memory 401, the processor 402, the communication interface 403, and the bus 404 are electrically connected directly or indirectly to each other, so as to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines.
Alternatively, bus 404 may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, only one thick line is shown in fig. 9, but not only one bus or one type of bus.
In the embodiments of the present application, the processor 402 may be a general purpose processor, a digital signal processor, an application specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component, where the methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. The general purpose processor may be a microprocessor or any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present application may be embodied directly in a hardware processor for execution, or in a combination of hardware and software modules in the processor for execution. The software module may be located in the memory 401 and the processor 402 reads the program instructions in the memory 401, in combination with its hardware, to perform the steps of the method described above.
In the embodiment of the present application, the memory 401 may be a nonvolatile memory, such as a hard disk (HDD) or a Solid State Drive (SSD), or may be a volatile memory (RAM). The memory may also be 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, but is not limited to such. The memory in the embodiments of the present application may also be a circuit or any other device capable of implementing a memory function, for storing instructions and/or data.
The memory 401 may be used to store software programs and modules, such as instructions/modules of the data analysis apparatus 300 provided in the embodiments of the present invention, may be stored in the memory 401 in the form of software or firmware (firmware) or be solidified in an Operating System (OS) of the computer device 400, and the processor 402 executes the software programs and modules stored in the memory 401, thereby performing various functional applications and data processing. The communication interface 403 may be used for communication of signaling or data with other node devices.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the apparatus and units described above may refer to corresponding procedures in the foregoing method embodiments, which are not described herein again.
It is to be understood that the configuration shown in fig. 9 is merely illustrative, and that the computer device 400 may also include more or fewer components than shown in fig. 9, or have a different configuration than shown in fig. 9. The components shown in fig. 9 may be implemented in hardware, software, or a combination thereof.
Based on the above embodiments, the present application also provides a storage medium in which a computer program is stored, which when executed by a computer, causes the computer to execute the data analysis method provided in the above embodiments.
Based on the above embodiments, the present application further provides a computer program, which when run on a computer causes the computer to perform the data analysis method provided in the above embodiments.
Based on the above embodiments, the present application further provides a chip, where the chip is configured to read a computer program stored in a memory, and is configured to perform the data analysis method provided in the above embodiments.
Also provided in embodiments of the present application is a computer program product comprising instructions which, when run on a computer, cause the computer to perform the data analysis method provided in the above embodiments.
Embodiments of the present application are described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by instructions. These instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention is not limited to the above embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (10)
1. A method of data analysis, the method comprising:
acquiring a plurality of data features corresponding to the data analysis object;
determining a plurality of target data features associated with the index to be analyzed from a plurality of data features, generating a plurality of data analysis rules based on the target data features, and constructing a data analysis model corresponding to the index to be analyzed based on all the data analysis rules; the data analysis model is used for representing early warning triggering conditions;
acquiring data to be analyzed corresponding to the data analysis object, and determining a characteristic value of each target data characteristic based on the data to be analyzed;
inputting the characteristic value of the target data characteristic into the data analysis model for analysis to obtain an analysis result of the index to be analyzed.
2. The method for analyzing data according to claim 1, wherein,
determining a plurality of target data features associated with the index to be analyzed from a plurality of data features, generating a plurality of data analysis rules based on the target data features, and constructing a data analysis model corresponding to the index to be analyzed based on all the data analysis rules, wherein the method comprises the following steps:
Determining a set operator corresponding to first type target data features and a preset threshold set to obtain a data analysis rule corresponding to each first type target data feature; the characteristic value numerical value type of the first type of target data characteristic is a dictionary type;
determining an operation relation between at least two second class target data features and an early warning occurrence condition expression, and determining a comparison operator and a preset comparison threshold corresponding to the early warning occurrence condition expression to obtain a data analysis rule; and/or the number of the groups of groups,
determining the comparison operator and the preset comparison threshold corresponding to the second class target data feature to obtain a data analysis rule corresponding to the second class target data feature; wherein the feature value numerical type of the second class of target data features is a numerical type;
and establishing a logic relation among all the data analysis rules to obtain the data analysis model.
3. The method for analyzing data according to claim 1, wherein,
generating a plurality of data analysis rules based on the target data characteristics, and constructing a data analysis model corresponding to the index to be analyzed based on all the data analysis rules, wherein the data analysis model comprises the following components:
Dividing a plurality of early warning value intervals for each target data feature, configuring a relation operator and a score for each early warning value interval, and configuring weights for the target data features;
for each target data feature, creating a plurality of data analysis rules corresponding to each target data feature based on a plurality of early warning value intervals and the relation operators and the scores corresponding to each early warning value interval;
and constructing a scoring card based on the plurality of data analysis rules and the weights corresponding to each target data characteristic, and taking the scoring card as the data analysis model.
4. The method for analyzing data according to claim 3, wherein,
inputting the characteristic value of the target data characteristic into the data analysis model for logic operation until the analysis result of the index to be analyzed is output, wherein the logic operation comprises the following steps:
inputting the characteristic value of the target data characteristic into the data analysis model, determining a target early warning interval in which the characteristic value of the target data characteristic falls, and obtaining the score of the target data characteristic based on the score corresponding to the target early warning interval and the weight corresponding to the target data characteristic;
And if the score sum of all the target data features is greater than or equal to a preset score threshold, outputting an analysis result of abnormality of the index to be analyzed.
5. The method for analyzing data according to claim 2, wherein,
inputting the characteristic value of the target data characteristic into the data analysis model for logic operation until the analysis result of the index to be analyzed is output, wherein the logic operation comprises the following steps:
inputting the characteristic value of the target data characteristic into the data analysis model, determining the Boolean value of each data analysis rule, carrying out logic operation on all the Boolean values, and outputting an analysis result of abnormality of the index to be analyzed if the Boolean value is true.
6. The method of data analysis according to claim 1, wherein the method further comprises:
if the time difference between the last analysis time and the current system time meets the preset trigger frequency, triggering a data analysis task and acquiring a data analysis model list under the data analysis task;
and returning to execute the step of acquiring data to be analyzed corresponding to the data analysis object for each data analysis model in the data analysis model list, and determining the characteristic value of each target data characteristic based on the data to be analyzed until the analysis result corresponding to each data analysis model is obtained.
7. The data analysis method according to claim 1, wherein acquiring a plurality of data features corresponding to the data analysis object includes:
acquiring a plurality of business tables with the data analysis objects;
and carrying out feature analysis on a plurality of service tables to obtain a plurality of data features.
8. A data analysis device, comprising: the device comprises an acquisition module, a construction module, an acquisition module and an analysis module;
the acquisition module is used for acquiring a plurality of data characteristics corresponding to the data analysis object;
the construction module is used for determining a plurality of target data features associated with the index to be analyzed from a plurality of data features, generating a plurality of data analysis rules based on the target data features, and constructing a data analysis model corresponding to the index to be analyzed based on all the data analysis rules; the data analysis model is used for representing early warning triggering conditions;
the acquisition module is used for acquiring data to be analyzed corresponding to the data analysis object and determining the characteristic value of each target data characteristic based on the data to be analyzed;
the analysis module is used for inputting the characteristic value of the target data characteristic into the data analysis model for analysis to obtain an analysis result of the index to be analyzed.
9. A computer device comprising a processor and a memory, the memory storing a computer program executable by the processor, the processor being executable to implement the data analysis method of any one of claims 1 to 7.
10. A readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the data analysis method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310150516.3A CN116402596A (en) | 2023-02-15 | 2023-02-15 | Data analysis method, device, computer equipment and readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310150516.3A CN116402596A (en) | 2023-02-15 | 2023-02-15 | Data analysis method, device, computer equipment and readable storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116402596A true CN116402596A (en) | 2023-07-07 |
Family
ID=87018676
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310150516.3A Pending CN116402596A (en) | 2023-02-15 | 2023-02-15 | Data analysis method, device, computer equipment and readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116402596A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117113929A (en) * | 2023-09-08 | 2023-11-24 | 中电金信数字科技集团有限公司 | Method and device for splitting field data, electronic equipment and storage medium |
-
2023
- 2023-02-15 CN CN202310150516.3A patent/CN116402596A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117113929A (en) * | 2023-09-08 | 2023-11-24 | 中电金信数字科技集团有限公司 | Method and device for splitting field data, electronic equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11436617B2 (en) | Behavior pattern search system and behavior pattern search method | |
| CN110046929B (en) | Fraudulent party identification method and device, readable storage medium and terminal equipment | |
| CN112669138B (en) | Data processing method and related equipment | |
| CN111062808B (en) | Credit card limit evaluation method, credit card limit evaluation device, computer equipment and storage medium | |
| CN110766275A (en) | Data verification method and device, computer equipment and storage medium | |
| CN112037038A (en) | Bank credit risk prediction method and device | |
| CN111695938B (en) | Product pushing method and system | |
| CN113553583A (en) | Information system asset security risk assessment method and device | |
| CN116402596A (en) | Data analysis method, device, computer equipment and readable storage medium | |
| CN116452329A (en) | Abnormal behavior monitoring method and device, electronic equipment and storage medium | |
| CN114579523A (en) | Double-recording file quality inspection method and device, computer equipment and storage medium | |
| CN106874286B (en) | Method and device for screening user characteristics | |
| CN110717653A (en) | Risk identification method and device and electronic equipment | |
| CN116051272A (en) | Enterprise risk analysis method and related equipment | |
| CN114757757A (en) | Wind control method | |
| CN114202250A (en) | Enterprise evaluation system and method and electronic equipment | |
| CN114693428A (en) | Data determination method and device, computer readable storage medium and electronic equipment | |
| CN113706258A (en) | Product recommendation method, device, equipment and storage medium based on combined model | |
| CN114092216A (en) | Enterprise credit rating method, apparatus, computer device and storage medium | |
| CN114170000A (en) | Credit card user risk category identification method, device, computer equipment and medium | |
| CN113269179A (en) | Data processing method, device, equipment and storage medium | |
| CN115719181B (en) | Data quality analysis method and device | |
| CN110827144A (en) | Application risk evaluation method and application risk evaluation device for user and electronic equipment | |
| CN112949963A (en) | Employee service quality evaluation method and device, storage medium and intelligent equipment | |
| US20220261666A1 (en) | Leveraging big data, statistical computation and artificial intelligence to determine a likelihood of object renunciation prior to a resource event |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |