CN113032434A - Data processing method and device for risk model training - Google Patents
Data processing method and device for risk model training Download PDFInfo
- Publication number
- CN113032434A CN113032434A CN202110293473.5A CN202110293473A CN113032434A CN 113032434 A CN113032434 A CN 113032434A CN 202110293473 A CN202110293473 A CN 202110293473A CN 113032434 A CN113032434 A CN 113032434A
- Authority
- CN
- China
- Prior art keywords
- data
- service
- feature
- preset
- 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
- 238000012549 training Methods 0.000 title claims abstract description 28
- 238000003672 processing method Methods 0.000 title claims abstract description 24
- 238000011161 development Methods 0.000 claims abstract description 27
- 238000000034 method Methods 0.000 claims abstract description 24
- 238000009795 derivation Methods 0.000 claims description 18
- 238000012545 processing Methods 0.000 claims description 14
- 238000013507 mapping Methods 0.000 claims description 5
- 238000004590 computer program Methods 0.000 claims description 3
- 238000004458 analytical method Methods 0.000 claims description 2
- 230000000694 effects Effects 0.000 abstract description 3
- 230000006399 behavior Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
Images
Classifications
-
- 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
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2455—Query execution
- G06F16/24564—Applying rules; Deductive queries
-
- 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
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2457—Query processing with adaptation to user needs
Abstract
The application discloses a data processing method and device for model training. The method comprises the following steps: acquiring demand data of a developer, wherein the demand data is generated in the process of risk model development; matching service data corresponding to the demand data in a preset system service database based on the demand data; and identifying the service data based on a preset characteristic data generation rule to obtain characteristic data corresponding to the service data. According to the method and the device, the characteristic data of the business data are acquired through the preset characteristic data generation rule, the early-stage characteristic preparation period of risk model development is shortened, the technical effect of improving the development efficiency of the risk model is achieved, and the technical problem that the risk model development efficiency is low in the prior art is solved.
Description
Technical Field
The present application relates to the field of computers, and in particular, to a data processing method and apparatus for risk model training.
Background
In the prior art, user data are mainly processed through a risk model, different training data need to be acquired according to different model development requirements in the training process of the risk model, a wind control model is trained, training characteristic data of the risk model are mainly acquired through a manual processing method in the prior art, the manual processing efficiency is low, the early-stage characteristic preparation period of risk model development is prolonged, and the risk model development efficiency is low.
Therefore, the prior art has the technical problem of low risk model development efficiency.
Content of application
The main purpose of the present application is to provide a data processing method and apparatus for model training, so as to improve the efficiency of obtaining feature data in the process of model development, solve the technical problem of low risk model development efficiency in the prior art, and improve the development efficiency of risk models.
In order to achieve the above object, the present application proposes a data processing method for model training.
In a second aspect of the application, a data processing apparatus for model training is presented.
In a third aspect of the present application, a computer-readable storage medium is presented.
In view of the above, according to a first aspect of the present application, a data processing method for model training is provided, including: acquiring demand data of a developer, wherein the demand data is generated in the process of risk model development; matching service data corresponding to the demand data in a preset system service database based on the demand data; and identifying the service data based on a preset characteristic data generation rule to obtain characteristic data corresponding to the service data.
Further, based on the demand data, matching the service data corresponding to the demand data in a preset system service database, including: identifying the demand data to obtain service identification data in the demand data; and matching service data corresponding to the service identification data in a preset service database, wherein the preset service database stores the mapping relation between the service identification data and the service data.
Further, based on a preset feature data generation rule, performing identification processing on the service data to obtain feature data corresponding to the service data, including: identifying the service data based on a preset standard service data structure to obtain the first feature data, wherein the first feature data comprises basic level feature data; and identifying the service data by combining the first characteristic data based on a preset characteristic derivation rule to obtain second characteristic data, wherein the second characteristic data comprises derived characteristic data derived based on the first characteristic.
Further, based on a preset feature data generation rule, performing identification processing on the service data to obtain feature data corresponding to the service data, including: outputting feature data corresponding to the service data, wherein the feature data comprises first feature data and second feature data.
Further, based on a preset standard service data structure, identifying the service data to obtain the first feature data, including: based on a preset standard service data structure, identifying the service data to obtain first basic level feature data, wherein the first basic level feature data comprises: the type of the service data and the node data of the service data; identifying the service data based on the first basic level feature data to obtain second basic level feature data; outputting the first feature data, the first feature data comprising at least a first base-level feature data and a second base-level feature data.
Further, based on a preset feature derivation rule, in combination with the first feature data, identifying the service data to obtain second feature data, including: based on a preset feature derivation rule, identifying the service data to obtain first derived feature data; based on a preset feature derivation rule, in combination with first derived feature data, identifying the service data to obtain second derived feature data; outputting the second feature data, the second feature data comprising at least the first derived feature data and the second feature derived data.
According to a second aspect of the application, a data processing apparatus for model training is proposed, comprising: the system comprises a demand data acquisition module, a risk model development module and a risk model analysis module, wherein the demand data acquisition module is used for acquiring demand data of developers, and the demand data is generated in the risk model development process; the service data acquisition module is used for matching service data corresponding to the demand data in a preset system service database based on the demand data; the characteristic identification module is used for identifying the service data based on a preset characteristic data generation rule and acquiring characteristic data corresponding to the service data; and the result output module is used for outputting the characteristic data.
Further, the service data obtaining module matches service data corresponding to the demand data in a preset system service database based on the demand data, and includes: identifying the demand data to obtain service identification data in the demand data; and matching service data corresponding to the service identification data in a preset service database, wherein the preset service database stores the mapping relation between the service identification data and the service data.
Further, a feature identification module, comprising: the first feature identification module is used for identifying the service data based on a preset standard service data structure to acquire first feature data, wherein the first feature data comprises basic level feature data; and the second feature identification module is used for identifying the service data by combining the first feature data based on a preset feature derivation rule to obtain second feature data, wherein the second feature data comprises derived feature data derived based on the first feature.
According to a third aspect of the present application, a computer-readable storage medium is proposed, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the data processing method for model training as described above.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
in this application, to developer's demand data, acquire the business data that developer's demand data corresponds, based on predetermineeing the characteristic data and generating the rule, it is right the business data carries out identification process, based on standard business data structure, discernment first characteristic data among the business data is based on the affairs characteristic data and the rule of predetermineeing the characteristic derivation of characteristic that discernment arrived, right the business data is discerned, obtains second characteristic data among the business data handles first characteristic data and second characteristic data, obtains the characteristic data that the business data corresponds, the characteristic data includes first characteristic data and second characteristic data. According to the method and the device, the characteristic data of the business data are acquired through the preset characteristic data generation rule, the early-stage characteristic preparation period of risk model development is shortened, the technical effect of improving the development efficiency of the risk model is achieved, and the technical problem that the risk model development efficiency is low in the prior art is solved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, serve to provide a further understanding of the application and to enable other features, objects, and advantages of the application to be more apparent. The drawings and their description illustrate the embodiments of the invention and do not limit it. In the drawings:
FIG. 1 is a schematic flow chart of a data processing method for model training provided in the present application;
FIG. 2 is a schematic flow chart of a data processing method for model training provided in the present application;
FIG. 3 is a schematic flow chart of a data processing method for model training provided in the present application;
FIG. 4 is a schematic flow chart of a data processing method for model training provided in the present application;
FIG. 5 is a schematic diagram of a data processing apparatus for model training according to the present application;
fig. 6 is a schematic structural diagram of another data processing apparatus for model training provided in the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In this application, the terms "upper", "lower", "left", "right", "front", "rear", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings. These terms are used primarily to better describe the present application and its embodiments, and are not used to limit the indicated devices, elements or components to a particular orientation or to be constructed and operated in a particular orientation.
Moreover, some of the above terms may be used to indicate other meanings besides the orientation or positional relationship, for example, the term "on" may also be used to indicate some kind of attachment or connection relationship in some cases. The specific meaning of these terms in this application will be understood by those of ordinary skill in the art as appropriate.
Furthermore, the terms "mounted," "disposed," "provided," "connected," and "sleeved" are to be construed broadly. For example, "connected" may be a fixed connection, a detachable connection, or a unitary construction; can be a mechanical connection, or an electrical connection; may be directly connected, or indirectly connected through intervening media, or may be in internal communication between two devices, elements or components. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art as appropriate.
Fig. 1 is a schematic flowchart of a data processing method for model training provided in the present application, and as shown in fig. 1, the method includes the following steps:
s101: acquiring demand data of a developer, wherein the demand data is generated in the process of risk model development;
in the development process of the risk model, feature data of the risk model needs to be acquired, feature preparation in the early stage of model development is carried out, and requirement data of developers is acquired.
S102: matching service data corresponding to the demand data in a preset system service database based on the demand data;
identifying the demand data to obtain service identification data in the demand data; and matching service data corresponding to the service identification data in a preset service database, wherein the preset service database stores the mapping relation between the service identification data and the service data.
S103: and identifying the service data based on a preset characteristic data generation rule to obtain characteristic data corresponding to the service data.
Fig. 2 is a schematic flowchart of a data processing method for model training provided in the present application, and as shown in fig. 2, the method includes the following steps:
s201: identifying the service data based on a preset standard service data structure to obtain the first feature data, wherein the first feature data comprises basic level feature data;
fig. 3 is a schematic flowchart of a data processing method for model training provided in the present application, and as shown in fig. 3, the method includes the following steps:
s301: based on a preset standard service data structure, identifying the service data to obtain first basic level feature data, wherein the first basic level feature data comprises: the type of the service data and the node data of the service data;
identifying the service data, and identifying first basic level feature data in the service data, wherein the first basic level feature data comprises a service data type and the service data node data;
identifying the service data to obtain first base level feature data, including: judging the service data type of the service data, such as independent external service data;
identifying node data in the service data, wherein the standard service data comprises important nodes of the full life cycle of a customer: 1) a customer base representation; 2) a registration link; 3) applying for link actuation information; 4) applying for a link; 5) actuating information of the dynamic branch link; 6) a dynamic branch link; 7) moving the branch for the first time; 8) an active condition; 9) transaction information; 10) current transaction information; 11) recent transaction information; 12) historical repayment information; 13) urging to remember information; 14) future repayment pressure; 15) external data; 16) and borrowing information to identify the node data existing in the service data.
S302: identifying the service data based on the first basic level feature data to obtain second basic level feature data;
identifying second base-level feature data of the traffic data on the basis of the first base-level feature data.
If the first basic level characteristic data is the single external data, the second basic level characteristic data does not exist in the service data;
if the first basic level characteristic data is a node containing historical repayment information in the service data, identifying repayment habits and buried point information in the current node data, and obtaining second basic level characteristic data;
the payment habits are the returning, actual returning and overdue conditions related to each payment plan of the user history, the buried point information and the buried point behavior information related to the payment behaviors of the user.
Further, on the basis of the second base-level feature data, identifying third base-level feature data of the business data until the base-level feature data is difficult to split.
If the second basic level characteristic data is that the service data comprises a repayment habit, identifying 'historical repayment willingness', 'historical repayment capacity' and 'historical repayment pressure' which can be split by the repayment habit, and identifying third basic level characteristic data of the service data;
and if the third basic level characteristic data is that the business data contains historical repayment willingness, identifying a basic variable of historical overdue days in the historical repayment willingness, and identifying fourth basic level characteristic data of the business data.
S303: outputting the first feature data, the first feature data comprising at least a first base-level feature data and a second base-level feature data.
And outputting the first characteristic data according to the identification result of the service data, wherein the first characteristic data at least comprises first base-level characteristic data and the second base-level characteristic data, and also comprises third base-level characteristic data or fourth base-level characteristic data.
S202: based on a preset feature derivation rule, in combination with the first feature data, identifying the service data to obtain second feature data, wherein the second feature data comprises derived feature data derived based on the first feature;
fig. 4 is a schematic flowchart of a data processing method for model training provided in the present application, and as shown in fig. 4, the method includes the following steps:
s401: based on a preset feature derivation rule, identifying the service data to obtain first derived feature data;
identifying the service data based on a preset feature derivation rule by combining basic level feature data in the first feature data, and identifying whether the service data comprises first derived feature data derived based on the basic level feature data;
s402: based on a preset feature derivation rule, in combination with first derived feature data, identifying the service data to obtain second derived feature data;
and identifying the service data based on a preset feature derivation rule by combining the first derived feature data, and identifying whether the service data comprises second derived feature data derived based on the first derived feature data.
S403: outputting the second feature data, the second feature data comprising at least the first derived feature data and the second feature derived data.
S203: outputting feature data corresponding to the service data, wherein the feature data comprises first feature data and second feature data.
Fig. 5 is a data processing apparatus for model training according to the present application, as shown in fig. 5,
a requirement data obtaining module 51, configured to obtain requirement data of a developer, where the requirement data is requirement data generated in a risk model development process;
a service data obtaining module 52, configured to match service data corresponding to the demand data in a preset system service database based on the demand data;
the feature identification module 53 is configured to perform identification processing on the service data based on a preset feature data generation rule, and obtain feature data corresponding to the service data;
and a result output module 54 for outputting the characteristic data.
Fig. 6 is a data processing apparatus for model training according to the present application, as shown in fig. 6,
the first feature identification module 61 is configured to identify the service data based on a preset standard service data structure to obtain first feature data, where the first feature data includes basic level feature data;
the second feature identification module 62 identifies the service data based on a preset feature derivation rule in combination with the first feature data to obtain second feature data, where the second feature data includes derived feature data derived based on the first feature.
The specific manner of executing the operations of the units in the above embodiments has been described in detail in the embodiments related to the method, and will not be elaborated herein.
In summary, in the present application, for requirement data of a developer, business data corresponding to the requirement data of the developer is obtained, a rule is generated based on preset feature data, it is right that the business data is identified based on a standard business data structure, first feature data in the business data is identified based on identified matter feature data and a preset feature derivation rule, the business data is identified, second feature data in the business data is obtained, the first feature data and the second feature data are processed, feature data corresponding to the business data is obtained, and the feature data includes the first feature data and the second feature data. According to the method and the device, the characteristic data of the business data are acquired through the preset characteristic data generation rule, the early-stage characteristic preparation period of risk model development is shortened, the technical effect of improving the development efficiency of the risk model is achieved, and the technical problem that the risk model development efficiency is low in the prior art is solved.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
It will be apparent to those skilled in the art that the various elements or steps of the present application described above may be implemented by a general purpose computing device, centralized on a single computing device or distributed across a network of multiple computing devices, or alternatively, may be implemented by program code executable by a computing device, such that the program code may be stored in a memory device and executed by a computing device, or may be implemented by individual integrated circuit modules, or by a plurality of modules or steps included in the program code as a single integrated circuit module. Thus, the present application is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A data processing method for model training, comprising:
acquiring demand data of a developer, wherein the demand data is generated in the process of risk model development;
matching service data corresponding to the demand data in a preset system service database based on the demand data;
and identifying the service data based on a preset characteristic data generation rule to obtain characteristic data corresponding to the service data.
2. The data processing method according to claim 1, wherein matching, based on the demand data, service data corresponding to the demand data in a preset system service database comprises:
identifying the demand data to obtain service identification data in the demand data;
and matching service data corresponding to the service identification data in a preset service database, wherein the preset service database stores the mapping relation between the service identification data and the service data.
3. The data processing method according to claim 1, wherein identifying and processing the service data based on a preset feature data generation rule to obtain feature data corresponding to the service data comprises:
identifying the service data based on a preset standard service data structure to obtain the first feature data, wherein the first feature data comprises basic level feature data;
and identifying the service data by combining the first characteristic data based on a preset characteristic derivation rule to obtain second characteristic data, wherein the second characteristic data comprises derived characteristic data derived based on the first characteristic.
4. The data processing method according to claim 1 or claim 3, wherein identifying the service data based on a preset feature data generation rule to obtain feature data corresponding to the service data comprises:
outputting feature data corresponding to the service data, wherein the feature data comprises first feature data and second feature data.
5. The data processing method according to claim 3, wherein identifying the service data based on a preset standard service data structure to obtain the first feature data comprises:
based on a preset standard service data structure, identifying the service data to obtain first basic level feature data, wherein the first basic level feature data comprises: the type of the service data and the node data of the service data;
identifying the service data based on the first basic level feature data to obtain second basic level feature data;
outputting the first feature data, the first feature data comprising at least a first base-level feature data and a second base-level feature data.
6. The data processing method of claim 3, wherein identifying the service data based on a preset feature derivation rule in combination with the first feature data to obtain second feature data comprises:
based on a preset feature derivation rule, identifying the service data to obtain first derived feature data;
based on a preset feature derivation rule, in combination with first derived feature data, identifying the service data to obtain second derived feature data;
outputting the second feature data, the second feature data comprising at least the first derived feature data and the second feature derived data.
7. A data processing apparatus for model training, comprising:
the system comprises a demand data acquisition module, a risk model development module and a risk model analysis module, wherein the demand data acquisition module is used for acquiring demand data of developers, and the demand data is generated in the risk model development process;
the service data acquisition module is used for matching service data corresponding to the demand data in a preset system service database based on the demand data;
the characteristic identification module is used for identifying the service data based on a preset characteristic data generation rule and acquiring characteristic data corresponding to the service data;
and the result output module is used for outputting the characteristic data.
8. The data processing apparatus according to claim 7, wherein the service data obtaining module matches service data corresponding to the demand data in a preset system service database based on the demand data, and includes:
identifying the demand data to obtain service identification data in the demand data;
and matching service data corresponding to the service identification data in a preset service database, wherein the preset service database stores the mapping relation between the service identification data and the service data.
9. The data processing apparatus of claim 7, wherein the feature identification module comprises:
the first feature identification module is used for identifying the service data based on a preset standard service data structure to acquire first feature data, wherein the first feature data comprises basic level feature data;
and the second characteristic identification module is used for identifying the service data by combining the first characteristic data based on a preset characteristic derivation rule to obtain second characteristic data, wherein the second characteristic data comprises derived characteristic data derived based on the first characteristic data.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the data processing method for model training according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110293473.5A CN113032434A (en) | 2021-03-18 | 2021-03-18 | Data processing method and device for risk model training |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110293473.5A CN113032434A (en) | 2021-03-18 | 2021-03-18 | Data processing method and device for risk model training |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113032434A true CN113032434A (en) | 2021-06-25 |
Family
ID=76471683
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110293473.5A Pending CN113032434A (en) | 2021-03-18 | 2021-03-18 | Data processing method and device for risk model training |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113032434A (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110137760A1 (en) * | 2009-12-03 | 2011-06-09 | Rudie Todd C | Method, system, and computer program product for customer linking and identification capability for institutions |
| CN110135693A (en) * | 2019-04-12 | 2019-08-16 | 北京中科闻歌科技股份有限公司 | A kind of Risk Identification Method, device, equipment and storage medium |
| CN110909984A (en) * | 2019-10-28 | 2020-03-24 | 苏宁金融科技(南京)有限公司 | Business data processing model training method, business data processing method and device |
-
2021
- 2021-03-18 CN CN202110293473.5A patent/CN113032434A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110137760A1 (en) * | 2009-12-03 | 2011-06-09 | Rudie Todd C | Method, system, and computer program product for customer linking and identification capability for institutions |
| CN110135693A (en) * | 2019-04-12 | 2019-08-16 | 北京中科闻歌科技股份有限公司 | A kind of Risk Identification Method, device, equipment and storage medium |
| CN110909984A (en) * | 2019-10-28 | 2020-03-24 | 苏宁金融科技(南京)有限公司 | Business data processing model training method, business data processing method and device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109902849B (en) | User behavior prediction method and device, and behavior prediction model training method and device | |
| CN112800095A (en) | Data processing method, device, equipment and storage medium | |
| CN110458646A (en) | Page display method, device, computer equipment and storage medium | |
| CN111415199A (en) | Customer prediction updating method and device based on big data and storage medium | |
| CN111159897B (en) | Target optimization method and device based on system modeling application | |
| CN113032440A (en) | Data processing method and device for training risk model | |
| CN111159344A (en) | Robot response method, device, equipment and storage medium | |
| CN103095784A (en) | Cloud user mapping system and method | |
| CN110837549B (en) | Information processing method, device and storage medium | |
| CN113032434A (en) | Data processing method and device for risk model training | |
| CN115860877A (en) | Product marketing method, device, equipment and medium | |
| CN113886086A (en) | Cloud platform computing resource allocation method, system, terminal and storage medium | |
| CN112260933B (en) | Communication method, communication device, electronic equipment and medium | |
| CN114282940A (en) | Method and apparatus for intention recognition, storage medium, and electronic device | |
| CN113900885A (en) | Data processing method and device for buried point | |
| CN104580498B (en) | A kind of adaptive cloud management platform | |
| US20210081600A1 (en) | Coaching system and coaching method | |
| CN113902576A (en) | Deep learning-based information pushing method and device, electronic equipment and medium | |
| CN113190587A (en) | Data processing method and device for realizing service data processing | |
| CN110825749A (en) | Behavior trajectory analysis and display method and device, electronic equipment and storage medium | |
| CN109559013A (en) | Method for testing risk and device, electronic equipment and readable storage medium storing program for executing based on trivial games | |
| CN113391923B (en) | System resource data allocation method and device | |
| CN112394816B (en) | Psychological service method based on man-machine interaction and robot | |
| CN114296721A (en) | Data processing method and device for page identification | |
| CN115439094A (en) | Third-party service management method, device, equipment and medium |
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 |